Merge branch 'upstream' into concedo_experimental

# Conflicts:
#	ggml/CMakeLists.txt
#	ggml/src/CMakeLists.txt
#	ggml/src/ggml-cpu/CMakeLists.txt
#	ggml/src/ggml-vulkan/CMakeLists.txt
#	scripts/sync-ggml.last

convert_hf_to_gguf.py

@@ -556,9 +556,6 @@ class TextModel(ModelBase):
             logger.info(f"gguf: experts used count = {n_experts_used}")
 
         if (head_dim := self.hparams.get("head_dim")) is not None:
-            # Workaround for incorrect AutoConfig value for DeepSeekV3 (is set correctly in DeepSeekV2Model class)
-            # https://github.com/huggingface/transformers/blob/19224c3642705c5b6988c9f5f4251f83323d05ae/src/transformers/models/deepseek_v3/configuration_deepseek_v3.py#L210
-            if self.hparams.get("model_type") != "deepseek_v3":
             self.gguf_writer.add_key_length(head_dim)
             self.gguf_writer.add_value_length(head_dim)
 
@@ -1901,9 +1898,7 @@ class LlamaModel(TextModel):
         hparams = self.hparams
         self.gguf_writer.add_vocab_size(hparams["vocab_size"])
 
-        if "head_dim" in hparams:
+        if (rope_dim := hparams.get("head_dim")) is None:
-            rope_dim = hparams["head_dim"]
-        else:
             rope_dim = hparams["hidden_size"] // hparams["num_attention_heads"]
         self.gguf_writer.add_rope_dimension_count(rope_dim)
 
@@ -1985,7 +1980,8 @@ class LlamaModel(TextModel):
         if rope_scaling := self.find_hparam(["rope_scaling"], optional=True):
             if rope_scaling.get("rope_type", '').lower() == "llama3":
                 base = self.hparams.get("rope_theta", 10000.0)
-                dim = self.hparams.get("head_dim", self.hparams["hidden_size"] // self.hparams["num_attention_heads"])
+                if (dim := self.hparams.get("head_dim")) is None:
+                    dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
                 freqs = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim))
 
                 factor = rope_scaling.get("factor", 8.0)
@@ -2321,9 +2317,7 @@ class DeciModel(TextModel):
         hparams = self.hparams
         self.gguf_writer.add_vocab_size(hparams["vocab_size"])
 
-        if "head_dim" in hparams:
+        if (rope_dim := hparams.get("head_dim")) is None:
-            rope_dim = hparams["head_dim"]
-        else:
             rope_dim = hparams["hidden_size"] // hparams["num_attention_heads"]
         self.gguf_writer.add_rope_dimension_count(rope_dim)
 
@@ -2363,7 +2357,8 @@ class DeciModel(TextModel):
         if rope_scaling := self.find_hparam(["rope_scaling"], optional=True):
             if rope_scaling.get("rope_type", '').lower() == "llama3":
                 base = self.hparams.get("rope_theta", 10000.0)
-                dim = self.hparams.get("head_dim", self.hparams["hidden_size"] // self.hparams["num_attention_heads"])
+                if (dim := self.hparams.get("head_dim")) is None:
+                    dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
                 freqs = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim))
 
                 factor = rope_scaling.get("factor", 8.0)
@@ -3681,9 +3676,7 @@ class InternLM3Model(TextModel):
         hparams = self.hparams
         self.gguf_writer.add_vocab_size(hparams["vocab_size"])
 
-        if "head_dim" in hparams:
+        if (rope_dim := hparams.get("head_dim")) is None:
-            rope_dim = hparams["head_dim"]
-        else:
             rope_dim = hparams["hidden_size"] // hparams["num_attention_heads"]
         self.gguf_writer.add_rope_dimension_count(rope_dim)
 
@@ -5098,9 +5091,7 @@ class DeepseekModel(TextModel):
     def set_gguf_parameters(self):
         super().set_gguf_parameters()
         hparams = self.hparams
-        if "head_dim" in hparams:
+        if (rope_dim := hparams.get("head_dim")) is None:
-            rope_dim = hparams["head_dim"]
-        else:
             rope_dim = hparams["hidden_size"] // hparams["num_attention_heads"]
 
         self.gguf_writer.add_rope_dimension_count(rope_dim)
@@ -5990,7 +5981,8 @@ class ExaoneModel(TextModel):
         if rope_scaling := self.find_hparam(["rope_scaling"], optional=True):
             if rope_scaling.get("rope_type", '').lower() == "llama3":
                 base = self.hparams.get("rope_theta", 10000.0)
-                dim = self.hparams.get("head_dim", self.hparams["hidden_size"] // self.hparams["num_attention_heads"])
+                if (dim := self.hparams.get("head_dim")) is None:
+                    dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
                 freqs = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim))
 
                 factor = rope_scaling.get("factor", 8.0)
@@ -6102,7 +6094,8 @@ class BailingMoeModel(TextModel):
     def set_gguf_parameters(self):
         super().set_gguf_parameters()
         hparams = self.hparams
-        rope_dim = hparams.get("head_dim") or hparams["hidden_size"] // hparams["num_attention_heads"]
+        if (rope_dim := hparams.get("head_dim")) is None:
+            rope_dim = hparams["hidden_size"] // hparams["num_attention_heads"]
 
         self.gguf_writer.add_rope_dimension_count(rope_dim)
         rope_scaling = self.hparams.get("rope_scaling") or {}
@@ -6134,7 +6127,8 @@ class BailingMoeModel(TextModel):
         n_head = self.hparams["num_attention_heads"]
         n_kv_head = self.hparams.get("num_key_value_heads")
         n_embd = self.hparams["hidden_size"]
-        head_dim = self.hparams.get("head_dim") or n_embd // n_head
+        if (head_dim := self.hparams.get("head_dim")) is None:
+            head_dim = n_embd // n_head
 
         output_name = self.format_tensor_name(gguf.MODEL_TENSOR.OUTPUT)
 

ggml/src/ggml.c

@@ -901,12 +901,6 @@ struct ggml_context {
     struct ggml_object * objects_end;
 };
 
-struct ggml_context_container {
-    bool used;
-
-    struct ggml_context context;
-};
-
 //
 // data types
 //

src/llama-chat.cpp

@@ -333,7 +333,7 @@ int32_t llm_chat_apply_template(
             std::string role(message->role);
             if (role == "system") {
                 // there is no system message for gemma, but we will merge it with user prompt, so nothing is broken
-                system_prompt = trim(message->content);
+                system_prompt += trim(message->content);
                 continue;
             }
             // in gemma, "assistant" is "model"
@@ -355,7 +355,7 @@ int32_t llm_chat_apply_template(
             std::string role(message->role);
             if (role == "system") {
                 // there is no system message support, we will merge it with user prompt
-                system_prompt = message->content;
+                system_prompt += message->content;
                 continue;
             } else if (role == "user") {
                 ss << "Human: ";

tools/mtmd/clip.cpp

@@ -205,7 +205,7 @@ struct clip_hparams {
     float eps = 1e-6;
     float rope_theta = 0.0;
 
-    std::vector<int32_t> image_grid_pinpoints;
+    std::vector<clip_image_size> image_res_candidates; // for llava-uhd style models
     int32_t image_crop_resolution;
     std::unordered_set<int32_t> vision_feature_layer;
     int32_t attn_window_size = 0;
@@ -2150,7 +2150,6 @@ struct clip_model_loader {
                 get_u32(KEY_IMAGE_SIZE, hparams.image_size);
                 get_u32(KEY_PATCH_SIZE, hparams.patch_size);
                 get_u32(KEY_IMAGE_CROP_RESOLUTION, hparams.image_crop_resolution, false);
-                get_arr_int(KEY_IMAGE_GRID_PINPOINTS, hparams.image_grid_pinpoints, false);
                 get_i32(KEY_MINICPMV_VERSION, hparams.minicpmv_version, false); // legacy
 
             } else if (is_audio) {
@@ -2160,6 +2159,20 @@ struct clip_model_loader {
                 GGML_ASSERT(false && "unknown modality");
             }
 
+            // for pinpoints, we need to convert it into a list of resolution candidates
+            {
+                std::vector<int> pinpoints;
+                get_arr_int(KEY_IMAGE_GRID_PINPOINTS, pinpoints, false);
+                if (!pinpoints.empty()) {
+                    for (size_t i = 0; i < pinpoints.size(); i += 2) {
+                        hparams.image_res_candidates.push_back({
+                            pinpoints[i],
+                            pinpoints[i+1],
+                        });
+                    }
+                }
+            }
+
             // default warmup value
             hparams.warmup_image_size = hparams.image_size;
 
@@ -2282,16 +2295,7 @@ struct clip_model_loader {
                     {
                         hparams.rope_theta = 10000.0f;
                         get_u32(KEY_PROJ_SCALE_FACTOR, hparams.proj_scale_factor);
-
+                        set_llava_uhd_res_candidates(model, 3);
-                        // borrowed from llava-1.6
-                        const int isize = hparams.image_size;
-                        hparams.image_grid_pinpoints = {
-                            isize,   isize*2, // 336, 672
-                            isize*2, isize,   // 672, 336
-                            isize*2, isize*2, // 672, 672
-                            isize*3, isize,   // 1008, 336
-                            isize,   isize*3, // 336, 1008
-                        };
                     } break;
                 case PROJECTOR_TYPE_ULTRAVOX:
                 case PROJECTOR_TYPE_QWEN2A:
@@ -2730,6 +2734,21 @@ struct clip_model_loader {
             output[i] = values[i];
         }
     }
+
+    void set_llava_uhd_res_candidates(clip_model & model, const int max_patches_per_side) {
+        auto & hparams = model.hparams;
+        for (int x = 1; x <= max_patches_per_side; x++) {
+            for (int y = 1; y <= max_patches_per_side; y++) {
+                if (x == 1 && y == 1) {
+                    continue; // skip the first point
+                }
+                hparams.image_res_candidates.push_back(clip_image_size{
+                    x*hparams.image_size,
+                    y*hparams.image_size,
+                });
+            }
+        }
+    }
 };
 
 struct clip_init_result clip_init(const char * fname, struct clip_context_params ctx_params) {
@@ -3195,36 +3214,41 @@ struct llava_uhd {
         bool padding_refined = false;  // if true, refine image will be padded to the grid size (e.g. llava-1.6)
     };
 
-    static int get_max_slices(struct clip_ctx * ctx) {
-        if (clip_is_minicpmv(ctx)) {
-            return 9;
-        }
-        return 0;
-    }
-
     static slice_instructions get_slice_instructions(struct clip_ctx * ctx, const clip_image_size & original_size) {
         slice_instructions res;
         const int patch_size      = clip_get_patch_size(ctx);
         const int slice_size      = clip_get_image_size(ctx);
-        const int max_slice_nums  = get_max_slices(ctx);
         const int original_width  = original_size.width;
         const int original_height = original_size.height;
-        const float log_ratio = log((float)original_width / original_height);
+
-        const float ratio = (float)original_width * original_height / (slice_size * slice_size);
+        const bool has_slices    = original_size.width > slice_size || original_size.height > slice_size;
-        const int multiple = fmin(ceil(ratio), max_slice_nums);
+        const bool has_pinpoints = !ctx->model.hparams.image_res_candidates.empty();
-        const bool has_slices = (multiple > 1);
+
-        const bool has_pinpoints = !ctx->model.hparams.image_grid_pinpoints.empty();
+        if (!has_slices) {
+            // skip slicing logic
+            res.overview_size = clip_image_size{slice_size, slice_size};
+            res.refined_size  = clip_image_size{0, 0};
+            res.grid_size     = clip_image_size{0, 0};
+
+            return res;
+        }
 
         if (has_pinpoints) {
             // has pinpoints, use them to calculate the grid size (e.g. llava-1.6)
             auto refine_size = llava_uhd::select_best_resolution(
-                ctx->model.hparams.image_grid_pinpoints,
+                original_size,
-                original_size);
+                ctx->model.hparams.image_res_candidates);
             res.overview_size   = clip_image_size{slice_size, slice_size};
             res.refined_size    = refine_size;
             res.grid_size       = clip_image_size{0, 0};
             res.padding_refined = true;
 
+            LOG_DBG("%s: using pinpoints for slicing\n", __func__);
+            LOG_DBG("%s: original size: %d x %d, overview size: %d x %d, refined size: %d x %d\n",
+                    __func__, original_width, original_height,
+                    res.overview_size.width, res.overview_size.height,
+                    res.refined_size.width,  res.refined_size.height);
+
             for (int y = 0; y < refine_size.height; y += slice_size) {
                 for (int x = 0; x < refine_size.width; x += slice_size) {
                     slice_coordinates slice;
@@ -3233,12 +3257,15 @@ struct llava_uhd {
                     slice.size.width  = std::min(slice_size, refine_size.width  - x);
                     slice.size.height = std::min(slice_size, refine_size.height - y);
                     res.slices.push_back(slice);
-                    if (x == 0) {
+                    LOG_DBG("%s: slice %d: x=%d, y=%d, size=%dx%d\n",
-                        res.grid_size.width++;
+                            __func__, (int)res.slices.size() - 1,
+                            slice.x, slice.y, slice.size.width, slice.size.height);
                 }
             }
-                res.grid_size.height++;
+
-            }
+            res.grid_size.height = refine_size.height / slice_size;
+            res.grid_size.width  = refine_size.width  / slice_size;
+            LOG_DBG("%s: grid size: %d x %d\n", __func__, res.grid_size.width, res.grid_size.height);
 
             return res;
         }
@@ -3248,17 +3275,23 @@ struct llava_uhd {
         auto best_size    = get_best_resize(original_size, slice_size, patch_size, !has_slices);
         res.overview_size = best_size;
 
-        if (!has_slices) {
+        {
-            // skip slicing logic
+            const int max_slice_nums = 9; // TODO: this is only used by minicpmv, maybe remove it
-            res.refined_size = clip_image_size{0, 0};
+            const float log_ratio = log((float)original_width / original_height);
-            res.grid_size    = clip_image_size{0, 0};
+            const float ratio = (float)original_width * original_height / (slice_size * slice_size);
+            const int multiple = fmin(ceil(ratio), max_slice_nums);
 
-        } else {
             auto best_grid   = get_best_grid(max_slice_nums, multiple, log_ratio);
             auto refine_size = get_refine_size(original_size, best_grid, slice_size, patch_size, true);
             res.grid_size    = best_grid;
             res.refined_size = refine_size;
 
+            LOG_DBG("%s: original size: %d x %d, overview size: %d x %d, refined size: %d x %d, grid size: %d x %d\n",
+                    __func__, original_width, original_height,
+                    res.overview_size.width, res.overview_size.height,
+                    res.refined_size.width, res.refined_size.height,
+                    res.grid_size.width, res.grid_size.height);
+
             int width  = refine_size.width;
             int height = refine_size.height;
             int grid_x = int(width  / best_grid.width);
@@ -3275,7 +3308,9 @@ struct llava_uhd {
                     slice.size.width  = grid_x;
                     slice.size.height = grid_y;
                     res.slices.push_back(slice);
-                    // LOG_INF("slice %d: %d %d %d %d\n", ic, patches_i, patches_j, grid_x, grid_y);
+                    LOG_DBG("%s: slice %d: x=%d, y=%d, size=%dx%d\n",
+                            __func__, (int)res.slices.size() - 1,
+                            slice.x, slice.y, slice.size.width, slice.size.height);
                 }
             }
         }
@@ -3333,48 +3368,55 @@ private:
         return res;
     }
 
+    static clip_image_size resize_maintain_aspect_ratio(const clip_image_size & orig, const clip_image_size & target_max) {
+        float scale_width  = static_cast<float>(target_max.width)  / orig.width;
+        float scale_height = static_cast<float>(target_max.height) / orig.height;
+        float scale = std::min(scale_width, scale_height);
+        return clip_image_size{
+            static_cast<int>(orig.width  * scale),
+            static_cast<int>(orig.height * scale),
+        };
+    }
+
     /**
      * Selects the best resolution from a list of possible resolutions based on the original size.
      *
+     * For example, when given a list of resolutions:
+     *  - 100x100
+     *  - 200x100
+     *  - 100x200
+     *  - 200x200
+     *
+     * And an input image of size 111x200, then 100x200 is the best fit (least wasted resolution).
+     *
      * @param original_size The original size of the image
      * @param possible_resolutions A list of possible resolutions
      * @return The best fit resolution
      */
     static clip_image_size select_best_resolution(const clip_image_size & original_size, const std::vector<clip_image_size> & possible_resolutions) {
-        int original_width = original_size.width;
-        int original_height = original_size.height;
         clip_image_size best_fit;
+        int min_wasted_area = std::numeric_limits<int>::max();
         int max_effective_resolution = 0;
-        int min_wasted_resolution = std::numeric_limits<int>::max();
 
-        for (const auto & resolution : possible_resolutions) {
+        for (const clip_image_size & candidate : possible_resolutions) {
-            int width  = resolution.width;
+            auto target_size = resize_maintain_aspect_ratio(original_size, candidate);
-            int height = resolution.height;
+            int effective_resolution = std::min(
-            float scale = std::min(static_cast<float>(width) / original_width, static_cast<float>(height) / original_height);
+                target_size.width * target_size.height,
-            int downscaled_width  = static_cast<int>(original_width * scale);
+                original_size.width * original_size.height);
-            int downscaled_height = static_cast<int>(original_height * scale);
+            int wasted_area = (candidate.width * candidate.height) - effective_resolution;
-            int effective_resolution = std::min(downscaled_width * downscaled_height, original_width * original_height);
+
-            int wasted_resolution = (width * height) - effective_resolution;
+            if (effective_resolution > max_effective_resolution || (effective_resolution == max_effective_resolution && wasted_area < min_wasted_area)) {
-            // LOG_INF("resolution: %d %d, scale: %f, downscaled: %d %d, effective: %d, wasted: %d\n", width, height, scale, downscaled_width, downscaled_height, effective_resolution, wasted_resolution);
-            if (effective_resolution > max_effective_resolution || (effective_resolution == max_effective_resolution && wasted_resolution < min_wasted_resolution)) {
                 max_effective_resolution = effective_resolution;
-                min_wasted_resolution = wasted_resolution;
+                min_wasted_area = wasted_area;
-                best_fit = resolution;
+                best_fit = candidate;
             }
+
+            LOG_DBG("%s: candidate: %d x %d, target: %d x %d, wasted: %d, effective: %d\n", __func__, candidate.width, candidate.height, target_size.width, target_size.height, wasted_area, effective_resolution);
         }
 
         return best_fit;
     }
 
-    // used by llava 1.6 with custom list of pinpoints
-    static clip_image_size select_best_resolution(const std::vector<int32_t> & pinpoints, const clip_image_size & original_size) {
-        std::vector<clip_image_size> possible_resolutions; // TODO @ngxson : construct this inside hparams, not here
-        for (size_t i = 0; i < pinpoints.size(); i += 2) {
-            possible_resolutions.push_back(clip_image_size{pinpoints[i], pinpoints[i+1]});
-        }
-        return select_best_resolution(original_size, possible_resolutions);
-    }
-
     static int ensure_divide(int length, int patch_size) {
         return std::max(static_cast<int>(std::round(static_cast<float>(length) / patch_size) * patch_size), patch_size);
     }
@@ -3498,7 +3540,7 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, str
         return true;
 
     } else if (ctx->proj_type() == PROJECTOR_TYPE_LLAMA4) {
-        GGML_ASSERT(!params.image_grid_pinpoints.empty());
+        GGML_ASSERT(!params.image_res_candidates.empty());
         auto const inst = llava_uhd::get_slice_instructions(ctx, original_size);
         std::vector<clip_image_u8_ptr> imgs = llava_uhd::slice_image(img, inst);
 
@@ -3538,7 +3580,7 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, str
         res_imgs->entries.push_back(std::move(res));
         return true;
 
-    } else if (!params.image_grid_pinpoints.empty()) {
+    } else if (!params.image_res_candidates.empty()) {
         // "spatial_unpad" with "anyres" processing for llava-1.6
         auto const inst = llava_uhd::get_slice_instructions(ctx, original_size);
         std::vector<clip_image_u8_ptr> imgs = llava_uhd::slice_image(img, inst);
@@ -3598,17 +3640,6 @@ const char * clip_patch_merge_type(const struct clip_ctx * ctx) {
     return ctx->model.hparams.mm_patch_merge_type == PATCH_MERGE_SPATIAL_UNPAD ? "spatial_unpad" : "flat";
 }
 
-const int32_t * clip_image_grid(const struct clip_ctx * ctx) {
-    if (ctx->model.hparams.image_grid_pinpoints.size()) {
-        return &ctx->model.hparams.image_grid_pinpoints.front();
-    }
-    return nullptr;
-}
-
-size_t get_clip_image_grid_size(const struct clip_ctx * ctx) {
-    return ctx->model.hparams.image_grid_pinpoints.size();
-}
-
 int clip_n_output_tokens_x(const struct clip_ctx * ctx, struct clip_image_f32 * img) {
     const auto & params = ctx->model.hparams;
     const int n_total = clip_n_output_tokens(ctx, img);

tools/mtmd/clip.h

@@ -46,9 +46,6 @@ int32_t clip_get_hidden_size(const struct clip_ctx * ctx);
 // TODO: should be enum, not string
 const char * clip_patch_merge_type(const struct clip_ctx * ctx);
 
-const int32_t * clip_image_grid(const struct clip_ctx * ctx);
-size_t get_clip_image_grid_size(const struct clip_ctx * ctx);
-
 int clip_n_output_tokens(const struct clip_ctx * ctx, struct clip_image_f32 * img);
 
 // for M-RoPE, this will be the number of token positions in X and Y directions

tools/mtmd/mtmd.cpp

@@ -501,7 +501,10 @@ struct mtmd_tokenizer {
                 || ctx->slice_tmpl == MTMD_SLICE_TMPL_MINICPMV_2_6
                 || ctx->slice_tmpl == MTMD_SLICE_TMPL_LLAMA4
             ) {
+                const int n_col = batch_f32.grid_x;
+                const int n_row = batch_f32.grid_y;
                 // split batch into chunks of single images
+                // NOTE: batch_f32 will be invalidated after this call
                 auto chunks = split_batch_to_chunk(std::move(batch_f32), bitmap->id);
                 GGML_ASSERT(chunks.size() > 0);
 
@@ -521,8 +524,7 @@ struct mtmd_tokenizer {
 
                 // add slices (or tiles)
                 if (!chunks.empty()) {
-                    const int n_col = batch_f32.grid_x;
+                    GGML_ASSERT((int)chunks.size() == n_row * n_col);
-                    const int n_row = batch_f32.grid_y;
                     if (ctx->tok_slices_start != LLAMA_TOKEN_NULL) {
                         add_text({ctx->tok_slices_start});
                     }