Merge branch 'upstream' into concedo_experimental

# Conflicts:
#	.devops/musa.Dockerfile
#	.github/workflows/build.yml
#	README.md
#	ci/README.md
#	docs/docker.md
#	examples/lookahead/lookahead.cpp
#	examples/lookup/lookup.cpp
#	examples/parallel/parallel.cpp
#	ggml/src/ggml-musa/CMakeLists.txt
#	ggml/src/ggml-sycl/ggml-sycl.cpp
#	tests/test-arg-parser.cpp

common/arg.cpp

@@ -1453,7 +1453,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         [](common_params & params) {
             params.swa_full = true;
         }
-    ));
+    ).set_env("LLAMA_ARG_SWA_FULL"));
     add_opt(common_arg(
         {"--no-context-shift"},
         string_format("disables context shift on infinite text generation (default: %s)", params.ctx_shift ? "disabled" : "enabled"),
@@ -2066,13 +2066,6 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.grp_attn_w = value;
         }
     ).set_env("LLAMA_ARG_GRP_ATTN_W").set_examples({LLAMA_EXAMPLE_MAIN}));
-    add_opt(common_arg(
-        {"-dkvc", "--dump-kv-cache"},
-        "verbose print of the KV cache",
-        [](common_params & params) {
-            params.dump_kv_cache = true;
-        }
-    ));
     add_opt(common_arg(
         {"-nkvo", "--no-kv-offload"},
         "disable KV offload",

common/common.cpp

@@ -1337,81 +1337,6 @@ std::string common_detokenize(const struct llama_vocab * vocab, const std::vecto
     return text;
 }
 
-//
-// KV cache utils
-//
-
-void common_kv_cache_dump_view(const llama_kv_cache_view & view, int row_size) {
-    static const char slot_chars[] = ".123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz+";
-
-    printf("=== Dumping KV cache. total cells %d, max sequences per cell %d, populated cells %d, total tokens in cache %d, largest empty slot=%d @ %d",
-        view.n_cells, view.n_seq_max, view.used_cells, view.token_count, view.max_contiguous, view.max_contiguous_idx);
-
-    llama_kv_cache_view_cell * c_curr = view.cells;
-    llama_seq_id * cs_curr = view.cells_sequences;
-
-    for (int i = 0; i < view.n_cells; i++, c_curr++, cs_curr += view.n_seq_max) {
-        if (i % row_size == 0) {
-            printf("\n%5d: ", i);
-        }
-        int seq_count = 0;
-        for (int j = 0; j < view.n_seq_max; j++) {
-            if (cs_curr[j] >= 0) { seq_count++; }
-        }
-        putchar(slot_chars[std::min(sizeof(slot_chars) - 2, size_t(seq_count))]);
-    }
-
-    printf("\n=== Done dumping\n");
-}
-
-void common_kv_cache_dump_view_seqs(const llama_kv_cache_view & view, int row_size) {
-    static const char slot_chars[] = "0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz";
-
-    printf("=== Dumping KV cache. total cells %d, max sequences per cell %d, populated cells %d, total tokens in cache %d, largest empty slot=%d @ %d\n",
-        view.n_cells, view.n_seq_max, view.used_cells, view.token_count, view.max_contiguous, view.max_contiguous_idx);
-
-    std::unordered_map<llama_seq_id, size_t> seqs;
-    llama_kv_cache_view_cell * c_curr = view.cells;
-    llama_seq_id * cs_curr = view.cells_sequences;
-
-    for (int i = 0; i < view.n_cells; i++, c_curr++, cs_curr += view.n_seq_max) {
-        for (int j = 0; j < view.n_seq_max; j++) {
-            if (cs_curr[j] < 0) { continue; }
-            if (seqs.find(cs_curr[j]) == seqs.end()) {
-                if (seqs.size() + 1 >= sizeof(slot_chars)) { break; }
-                const size_t sz = seqs.size();
-                seqs[cs_curr[j]] = sz;
-            }
-        }
-        if (seqs.size() + 1 >= sizeof(slot_chars)) { break; }
-    }
-
-    printf("=== Sequence legend: ");
-    for (const auto & it : seqs) {
-        printf("%zu=%d, ", it.second, it.first);
-    }
-    printf("'+'=other sequence ids");
-
-    c_curr = view.cells;
-    cs_curr = view.cells_sequences;
-    for (int i = 0; i < view.n_cells; i++, c_curr++, cs_curr += view.n_seq_max) {
-        if (i % row_size == 0) {
-            printf("\n%5d: ", i);
-        }
-        for (int j = 0; j < view.n_seq_max; j++) {
-            if (cs_curr[j] >= 0) {
-                const auto & it = seqs.find(cs_curr[j]);
-                putchar(it != seqs.end() ? int(slot_chars[it->second]) : '+');
-            } else {
-                putchar('.');
-            }
-        }
-        putchar(' ');
-    }
-
-    printf("\n=== Done dumping\n");
-}
-
 //
 // Embedding utils
 //

common/common.h

@@ -326,7 +326,6 @@ struct common_params {
     bool use_mlock         = false; // use mlock to keep model in memory
     bool verbose_prompt    = false; // print prompt tokens before generation
     bool display_prompt    = true;  // print prompt before generation
-    bool dump_kv_cache     = false; // dump the KV cache contents for debugging purposes
     bool no_kv_offload     = false; // disable KV offloading
     bool warmup            = true;  // warmup run
     bool check_tensors     = false; // validate tensor data
@@ -618,16 +617,6 @@ std::string common_detokenize(
         const std::vector<llama_token> & tokens,
                                   bool   special = true);
 
-//
-// KV cache utils
-//
-
-// Dump the KV cache view with the number of sequences per cell.
-void common_kv_cache_dump_view(const llama_kv_cache_view & view, int row_size = 80);
-
-// Dump the KV cache view showing individual sequences in each cell (long output).
-void common_kv_cache_dump_view_seqs(const llama_kv_cache_view & view, int row_size = 40);
-
 //
 // Embedding utils
 //

ggml/src/ggml-cuda/cpy.cu

@@ -1,5 +1,8 @@
 #include "cpy.cuh"
 #include "dequantize.cuh"
+#ifdef GGML_USE_MUSA
+#include "ggml-musa/mudnn.cuh"
+#endif // GGML_USE_MUSA
 
 typedef void (*cpy_kernel_t)(const char * cx, char * cdst);
 
@@ -597,7 +600,14 @@ void ggml_cuda_cpy(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, gg
 #endif
     if (src0->type == src1->type && ggml_is_contiguous(src0) && ggml_is_contiguous(src1)) {
         GGML_ASSERT(ggml_nbytes(src0) == ggml_nbytes(src1));
-        CUDA_CHECK(cudaMemcpyAsync(src1_ddc, src0_ddc, ggml_nbytes(src0), cudaMemcpyDeviceToDevice, main_stream));
+#ifdef GGML_USE_MUSA
+        if (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16) {
+            CUDA_CHECK(mudnnMemcpyAsync(ctx, src1, src0));
+        } else
+#endif // GGML_USE_MUSA
+        {
+            CUDA_CHECK(cudaMemcpyAsync(src1_ddc, src0_ddc, ggml_nbytes(src0), cudaMemcpyDeviceToDevice, main_stream));
+        }
     } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32) {
         ggml_cpy_f32_f32_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream, dest_ptrs_d, graph_cpynode_index);
     } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_BF16) {

ggml/src/ggml-cuda/fattn-mma-f16.cuh

@@ -772,7 +772,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_iter(
     GGML_UNUSED(stride_mask); GGML_UNUSED(jt); GGML_UNUSED(tile_K);
     GGML_UNUSED(tile_V); GGML_UNUSED(tile_mask); GGML_UNUSED(Q_B);
     GGML_UNUSED(VKQ_C); GGML_UNUSED(KQ_max); GGML_UNUSED(KQ_rowsum);
-    GGML_UNUSED(kb0);
+    GGML_UNUSED(kb0); GGML_UNUSED(tile_Q);
     NO_DEVICE_CODE;
 #endif // NEW_MMA_AVAILABLE
 }

ggml/src/ggml-cuda/fattn-vec-f16.cuh

@@ -2,9 +2,9 @@
 #include "fattn-common.cuh"
 
 template<int D, int ncols, ggml_type type_K, ggml_type type_V, bool use_logit_softcap> // D == head size
-#if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__))
+#ifndef GGML_USE_HIP
 __launch_bounds__(D, 1)
-#endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__))
+#endif // GGML_USE_HIP
 static __global__ void flash_attn_vec_ext_f16(
         const char * __restrict__ Q,
         const char * __restrict__ K,
@@ -48,6 +48,12 @@ static __global__ void flash_attn_vec_ext_f16(
         NO_DEVICE_CODE;
         return;
     }
+#if !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)
+    if (ncols > 1) {
+        NO_DEVICE_CODE;
+        return;
+    }
+#endif // !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)
 
     //In this kernel Q, K, V are matrices while i, j, k are matrix indices.
 
@@ -91,6 +97,13 @@ static __global__ void flash_attn_vec_ext_f16(
             kqsum_shared[j][threadIdx.x] = 0.0f;
         }
     }
+
+    __shared__ half maskh_shared[ncols*D];
+#pragma unroll
+    for (int j = 0; j < ncols; ++j) {
+        maskh_shared[j*D + tid] = 0.0f;
+    }
+
     __syncthreads();
 
     // Convert Q to half2 (f16 K) or q8_1 (quantized K) and store in registers:
@@ -175,6 +188,35 @@ static __global__ void flash_attn_vec_ext_f16(
     for (int k_VKQ_0 = blockIdx.y*D; k_VKQ_0 < ne11; k_VKQ_0 += gridDim.y*D) {
         // Calculate KQ tile and keep track of new maximum KQ values:
 
+        if (mask) {
+#pragma unroll
+            for (int j = 0; j < ncols; ++j) {
+                maskh_shared[j*D + tid] = slopeh*maskh[j*ne11 + k_VKQ_0 + tid];
+            }
+
+            __syncthreads();
+
+            // When using multiple parallel sequences in llama.cpp, some KV slices can be fully masked out.
+            // In such cases, skip the KV slice.
+            // On AMD __all_sync would not work correctly because it assumes a warp size of 64.
+#ifndef GGML_USE_HIP
+            bool skip = true;
+#pragma unroll
+            for (int j = 0; j < ncols; ++j) {
+#pragma unroll
+                for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) {
+                    const int i = i0 + threadIdx.x;
+
+                    const float2 tmp = __half22float2(((const half2 *) maskh_shared)[j*(D/2) + i]);
+                    skip = skip && isinf(tmp.x) && isinf(tmp.y);
+                }
+            }
+            if (__all_sync(0xFFFFFFFF, skip)) {
+                continue;
+            }
+#endif // GGML_USE_HIP
+        }
+
         // For unknown reasons using a half array of size 1 for kqmax_new causes a performance regression,
         // see https://github.com/ggerganov/llama.cpp/pull/7061 .
         // Therefore this variable is defined twice but only used once (so that the compiler can optimize out the unused variable).
@@ -202,7 +244,7 @@ static __global__ void flash_attn_vec_ext_f16(
                     sum = logit_softcap*tanhf(sum);
                 }
 
-                sum += mask ? slopeh*maskh[j*ne11 + k_VKQ_0 + i_KQ] : __float2half(0.0f);
+                sum += maskh_shared[j*D + i_KQ];
 
                 if (ncols == 1) {
                     kqmax_new        = ggml_cuda_hmax(kqmax_new,        sum);
@@ -335,7 +377,9 @@ void ggml_cuda_flash_attn_ext_vec_f16_case(ggml_backend_cuda_context & ctx, ggml
     float logit_softcap;
     memcpy(&logit_softcap, (const float *) KQV->op_params + 2, sizeof(float));
 
-    if (Q->ne[1] == 1) {
+    const int cc = ggml_cuda_info().devices[ggml_cuda_get_device()].cc;
+
+    if (Q->ne[1] == 1 || GGML_CUDA_CC_IS_NVIDIA(cc)) {
         constexpr int cols_per_block = 1;
         if (logit_softcap == 0.0f) {
             constexpr bool use_logit_softcap = false;

ggml/src/ggml-cuda/fattn-vec-f32.cuh

@@ -2,9 +2,9 @@
 #include "fattn-common.cuh"
 
 template<int D, int ncols, ggml_type type_K, ggml_type type_V, bool use_logit_softcap> // D == head size
-#if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__))
+#ifndef GGML_USE_HIP
 __launch_bounds__(D, 1)
-#endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__))
+#endif // GGML_USE_HIP
 static __global__ void flash_attn_vec_ext_f32(
         const char * __restrict__ Q,
         const char * __restrict__ K,
@@ -60,6 +60,12 @@ static __global__ void flash_attn_vec_ext_f32(
         NO_DEVICE_CODE;
         return;
     }
+#if !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)
+    if (ncols > 1) {
+        NO_DEVICE_CODE;
+        return;
+    }
+#endif // !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)
 
     //In this kernel Q, K, V are matrices while i, j, k are matrix indices.
 
@@ -104,6 +110,13 @@ static __global__ void flash_attn_vec_ext_f32(
             kqsum_shared[j][threadIdx.x] = 0.0f;
         }
     }
+
+    __shared__ float maskf_shared[ncols*D];
+#pragma unroll
+    for (int j = 0; j < ncols; ++j) {
+        maskf_shared[j*D + tid] = 0.0f;
+    }
+
     __syncthreads();
 
     // Convert Q to float2 (f16 K) or q8_1 (quantized K) and store in registers:
@@ -181,6 +194,34 @@ static __global__ void flash_attn_vec_ext_f32(
     for (int k_VKQ_0 = blockIdx.y*D; k_VKQ_0 < ne11; k_VKQ_0 += gridDim.y*D) {
         // Calculate KQ tile and keep track of new maximum KQ values:
 
+        if (mask) {
+#pragma unroll
+            for (int j = 0; j < ncols; ++j) {
+                maskf_shared[j*D + tid] = slope*__half2float(maskh[j*ne11 + k_VKQ_0 + tid]);
+            }
+
+            __syncthreads();
+
+            // When using multiple parallel sequences in llama.cpp, some KV slices can be fully masked out.
+            // In such cases, skip the KV slice.
+            // On AMD __all_sync would not work correctly because it assumes a warp size of 64.
+#ifndef GGML_USE_HIP
+            bool skip = true;
+#pragma unroll
+            for (int j = 0; j < ncols; ++j) {
+#pragma unroll
+                for (int i0 = 0; i0 < D; i0 += WARP_SIZE) {
+                    const int i = i0 + threadIdx.x;
+
+                    skip = skip && isinf(maskf_shared[j*D + i]);
+                }
+            }
+            if (__all_sync(0xFFFFFFFF, skip)) {
+                continue;
+            }
+#endif // GGML_USE_HIP
+        }
+
         float kqmax_new_arr[ncols];
 #pragma unroll
         for (int j = 0; j < ncols; ++j) {
@@ -204,7 +245,7 @@ static __global__ void flash_attn_vec_ext_f32(
                     sum = logit_softcap*tanhf(sum);
                 }
 
-                sum += mask ? slope*__half2float(maskh[j*ne11 + k_VKQ_0 + i_KQ]) : 0.0f;
+                sum += maskf_shared[j*D + i_KQ];
 
                 kqmax_new_arr[j] = fmaxf(kqmax_new_arr[j], sum);
 
@@ -326,7 +367,9 @@ void ggml_cuda_flash_attn_ext_vec_f32_case(ggml_backend_cuda_context & ctx, ggml
     float logit_softcap;
     memcpy(&logit_softcap, (const float *) KQV->op_params + 2, sizeof(float));
 
-    if (Q->ne[1] == 1) {
+    const int cc = ggml_cuda_info().devices[ggml_cuda_get_device()].cc;
+
+    if (Q->ne[1] == 1 || GGML_CUDA_CC_IS_NVIDIA(cc)) {
         constexpr int cols_per_block = 1;
         if (logit_softcap == 0.0f) {
             constexpr bool use_logit_softcap = false;

ggml/src/ggml-metal/ggml-metal.metal

@@ -3255,7 +3255,7 @@ template<
     typename kd4x4_t, // key type in device memory
     short nl_k,
     void (*deq_k)(device const kd4x4_t *, short, thread k4x4_t &),
-    typename vd4x4_t, // key type in device memory
+    typename vd4x4_t, // value type in device memory
     short nl_v,
     void (*deq_v)(device const vd4x4_t *, short, thread v4x4_t &),
     short DK,        // K head size
@@ -3776,7 +3776,7 @@ template<
     typename kd4_t, // key type in device memory
     short nl_k,
     void (*deq_k_t4)(device const kd4_t *, short, thread k4_t &),
-    typename vd4_t, // key type in device memory
+    typename vd4_t, // value type in device memory
     short nl_v,
     void (*deq_v_t4)(device const vd4_t *, short, thread v4_t &),
     short DK,       // K head size

ggml/src/ggml-musa/mudnn.cu

@@ -0,0 +1,112 @@
+#include <mutex>
+#include <mudnn.h>
+
+#include "mudnn.cuh"
+
+namespace mudnn = musa::dnn;
+
+// Returns a human-readable error string for mudnn::Status
+const char* mudnnGetErrorString(mudnn::Status err) {
+    switch (err) {
+        case mudnn::Status::SUCCESS:
+            return "Success";
+        case mudnn::Status::INVALID_PARAMETER:
+            return "Invalid parameter";
+        case mudnn::Status::NOT_INITIALIZED:
+            return "Not initialized";
+        case mudnn::Status::ALLOC_FAILED:
+            return "Allocation failed";
+        case mudnn::Status::NOT_SUPPORTED:
+            return "Not supported";
+        case mudnn::Status::INTERNAL_ERROR:
+            return "Internal error";
+        case mudnn::Status::ARCH_MISMATCH:
+            return "Architecture mismatch";
+        case mudnn::Status::EXECUTION_FAILED:
+            return "Execution failed";
+        default:
+            return "Unknown mudnn status";
+    }
+}
+
+// Error checking macro for MUDNN calls
+#define MUDNN_CHECK(err) CUDA_CHECK_GEN(err, mudnn::Status::SUCCESS, mudnnGetErrorString)
+
+namespace {
+    // Thread-safe cache for mudnn::Handle objects per device
+    std::unordered_map<int, std::unique_ptr<mudnn::Handle>> handle_cache;
+    std::mutex handle_cache_mutex;
+
+    mudnn::Handle* get_cached_handle(int device_id) {
+        std::lock_guard<std::mutex> lock(handle_cache_mutex);
+        auto it = handle_cache.find(device_id);
+        if (it != handle_cache.end()) {
+            return it->second.get();
+        }
+        auto handle = std::make_unique<mudnn::Handle>(device_id);
+        mudnn::Handle* handle_ptr = handle.get();
+        handle_cache[device_id] = std::move(handle);
+        return handle_ptr;
+    }
+}
+
+// Extracts dimensions and strides from a ggml_tensor
+int get_ggml_dims_and_strides(const ggml_tensor* tensor,
+                              std::vector<int64_t>& dims,
+                              std::vector<int64_t>& strides) {
+    const int ndims = ggml_n_dims(tensor);
+    const size_t element_size = ggml_element_size(tensor);
+
+    dims.resize(ndims);
+    strides.resize(ndims);
+
+    for (int i = 0; i < ndims; ++i) {
+        dims[i] = tensor->ne[i];
+        strides[i] = tensor->nb[i] / static_cast<int64_t>(element_size);
+    }
+    return ndims;
+}
+
+// Converts ggml_type to mudnn::Tensor::Type
+mudnn::Tensor::Type ggml_type_to_mudnn_type(ggml_type type) {
+    switch (type) {
+        case GGML_TYPE_F32:
+            return mudnn::Tensor::Type::FLOAT;
+        case GGML_TYPE_F16:
+            return mudnn::Tensor::Type::HALF;
+
+        // TODO: Add support for other types
+
+        default:
+            MUDNN_CHECK(mudnn::Status::NOT_SUPPORTED);
+    }
+
+    return mudnn::Tensor::Type::FLOAT; // Default fallback
+}
+
+// Asynchronous memory copy using mudnn::Unary::IDENTITY
+musaError_t mudnnMemcpyAsync(ggml_backend_cuda_context& ctx, const ggml_tensor* dst, const ggml_tensor* src) {
+    mudnn::Tensor tensor_dst, tensor_src;
+
+    MUDNN_CHECK(tensor_dst.SetType(ggml_type_to_mudnn_type(dst->type)));
+    MUDNN_CHECK(tensor_src.SetType(ggml_type_to_mudnn_type(src->type)));
+
+    std::vector<int64_t> dims, strides;
+    const int ndims = get_ggml_dims_and_strides(src, dims, strides);
+
+    MUDNN_CHECK(tensor_dst.SetNdInfo(ndims, dims.data(), strides.data()));
+    MUDNN_CHECK(tensor_src.SetNdInfo(ndims, dims.data(), strides.data()));
+    MUDNN_CHECK(tensor_dst.SetAddr(dst->data));
+    MUDNN_CHECK(tensor_src.SetAddr(src->data));
+
+    mudnn::Unary op;
+    MUDNN_CHECK(op.SetMode(mudnn::Unary::Mode::IDENTITY));
+    MUDNN_CHECK(op.SetAlpha(0.0f));
+    MUDNN_CHECK(op.SetBeta(0.0f));
+
+    mudnn::Handle* handle = get_cached_handle(ctx.device);
+    MUDNN_CHECK(handle->SetStream(ctx.stream()));
+    MUDNN_CHECK(op.Run(*handle, tensor_dst, tensor_src));
+
+    return musaSuccess;
+}

ggml/src/ggml-musa/mudnn.cuh

@@ -0,0 +1,12 @@
+#pragma once
+
+#include "../include/ggml.h"
+#include "../ggml-cuda/common.cuh"
+
+// Asynchronously copies data from src tensor to dst tensor using the provided context.
+// Returns a musaError_t indicating success or failure.
+musaError_t mudnnMemcpyAsync(
+    ggml_backend_cuda_context &ctx,
+    const ggml_tensor *dst,
+    const ggml_tensor *src
+);

ggml/src/ggml-vulkan/ggml-vulkan.cpp

@@ -4537,6 +4537,8 @@ static vk_pipeline ggml_vk_guess_matmul_pipeline(ggml_backend_vk_context * ctx,
         return aligned ? mmp->a_m : mmp->m;
     }
     return aligned ? mmp->a_l : mmp->l;
+
+    GGML_UNUSED(src1_type);
 }
 
 static uint32_t ggml_vk_guess_matmul_pipeline_align(ggml_backend_vk_context * ctx, vk_matmul_pipeline& mmp, int m, int n, ggml_type src0_type, ggml_type src1_type) {

ggml/src/ggml-vulkan/vulkan-shaders/dequant_iq1_m.comp

@@ -1,6 +1,6 @@
 #version 450
 
-#extension GL_EXT_shader_explicit_arithmetic_types_float16 : require
+#extension GL_EXT_shader_explicit_arithmetic_types_int16 : require
 
 #include "dequant_head.comp"
 

ggml/src/ggml-vulkan/vulkan-shaders/mul_mm.comp

@@ -7,7 +7,7 @@
 #extension GL_EXT_shader_explicit_arithmetic_types_float16 : require
 #endif
 #if defined(DATA_A_IQ1_M)
-#extension GL_EXT_shader_explicit_arithmetic_types_float16 : require
+#extension GL_EXT_shader_explicit_arithmetic_types_int16 : require
 #endif
 
 #if defined(DATA_A_BF16) && defined(COOPMAT)

include/llama.h

@@ -610,72 +610,13 @@ extern "C" {
     // KV cache
     //
 
-    // TODO: start using struct llama_kv_cache
-
-    // Information associated with an individual cell in the KV cache view.
-    struct llama_kv_cache_view_cell {
-        // The position for this cell. Takes KV cache shifts into account.
-        // May be negative if the cell is not populated.
-        llama_pos pos;
-    };
-
-    // An updateable view of the KV cache.
-    struct llama_kv_cache_view {
-        // Number of KV cache cells. This will be the same as the context size.
-        int32_t n_cells;
-
-        // Maximum number of sequences that can exist in a cell. It's not an error
-        // if there are more sequences in a cell than this value, however they will
-        // not be visible in the view cells_sequences.
-        int32_t n_seq_max;
-
-        // Number of tokens in the cache. For example, if there are two populated
-        // cells, the first with 1 sequence id in it and the second with 2 sequence
-        // ids then you'll have 3 tokens.
-        int32_t token_count;
-
-        // Number of populated cache cells.
-        int32_t used_cells;
-
-        // Maximum contiguous empty slots in the cache.
-        int32_t max_contiguous;
-
-        // Index to the start of the max_contiguous slot range. Can be negative
-        // when cache is full.
-        int32_t max_contiguous_idx;
-
-        // Information for an individual cell.
-        struct llama_kv_cache_view_cell * cells;
-
-        // The sequences for each cell. There will be n_seq_max items per cell.
-        llama_seq_id * cells_sequences;
-    };
-
-    // Create an empty KV cache view. (use only for debugging purposes)
-    LLAMA_API struct llama_kv_cache_view llama_kv_cache_view_init(const struct llama_context * ctx, int32_t n_seq_max);
-
-    // Free a KV cache view. (use only for debugging purposes)
-    LLAMA_API void llama_kv_cache_view_free(struct llama_kv_cache_view * view);
-
-    // Update the KV cache view structure with the current state of the KV cache. (use only for debugging purposes)
-    // TODO: change signature to llama_kv_cache_view_update(struct llama_kv_cache_view * view, const struct llama_context * ctx)
-    LLAMA_API void llama_kv_cache_view_update(const struct llama_context * ctx, struct llama_kv_cache_view * view);
-
-    ///
-
     // Returns the number of tokens in the KV cache (slow, use only for debug)
     // If a KV cell has multiple sequences assigned to it, it will be counted multiple times
     LLAMA_API int32_t llama_kv_self_n_tokens(const struct llama_context * ctx);
 
-    DEPRECATED(LLAMA_API int32_t llama_get_kv_cache_token_count(const struct llama_context * ctx),
-            "use llama_kv_self_n_tokens instead");
-
     // Returns the number of used KV cells (i.e. have at least one sequence assigned to them)
     LLAMA_API int32_t llama_kv_self_used_cells(const struct llama_context * ctx);
 
-    DEPRECATED(LLAMA_API int32_t llama_get_kv_cache_used_cells(const struct llama_context * ctx),
-            "use llama_kv_self_used_cells instead");
-
     // Clear the KV cache - both cell info is erased and KV data is zeroed
     LLAMA_API void llama_kv_self_clear(
             struct llama_context * ctx);
@@ -758,61 +699,6 @@ extern "C" {
     // Apply the KV cache updates (such as K-shifts, defragmentation, etc.)
     LLAMA_API void llama_kv_self_update(struct llama_context * ctx);
 
-    DEPRECATED(LLAMA_API void llama_kv_cache_clear(
-            struct llama_context * ctx),
-            "use llama_kv_self_clear instead");
-
-    DEPRECATED(LLAMA_API bool llama_kv_cache_seq_rm(
-            struct llama_context * ctx,
-                    llama_seq_id   seq_id,
-                       llama_pos   p0,
-                       llama_pos   p1),
-            "use llama_kv_self_seq_rm instead");
-
-    DEPRECATED(LLAMA_API void llama_kv_cache_seq_cp(
-            struct llama_context * ctx,
-                    llama_seq_id   seq_id_src,
-                    llama_seq_id   seq_id_dst,
-                       llama_pos   p0,
-                       llama_pos   p1),
-            "use llama_kv_self_seq_cp instead");
-
-    DEPRECATED(LLAMA_API void llama_kv_cache_seq_keep(
-            struct llama_context * ctx,
-                    llama_seq_id   seq_id),
-            "use llama_kv_self_seq_keep instead");
-
-    DEPRECATED(LLAMA_API void llama_kv_cache_seq_add(
-            struct llama_context * ctx,
-                    llama_seq_id   seq_id,
-                       llama_pos   p0,
-                       llama_pos   p1,
-                       llama_pos   delta),
-            "use llama_kv_self_seq_add instead");
-
-    DEPRECATED(LLAMA_API void llama_kv_cache_seq_div(
-            struct llama_context * ctx,
-                    llama_seq_id   seq_id,
-                       llama_pos   p0,
-                       llama_pos   p1,
-                             int   d),
-            "use llama_kv_self_seq_div instead");
-
-    DEPRECATED(LLAMA_API llama_pos llama_kv_cache_seq_pos_max(
-            struct llama_context * ctx,
-                    llama_seq_id   seq_id),
-            "use llama_kv_self_seq_pos_max instead");
-
-    DEPRECATED(LLAMA_API void llama_kv_cache_defrag(struct llama_context * ctx),
-            "use llama_kv_self_defrag instead");
-
-    DEPRECATED(LLAMA_API bool llama_kv_cache_can_shift(const struct llama_context * ctx),
-            "use llama_kv_self_can_shift instead");
-
-    DEPRECATED(LLAMA_API void llama_kv_cache_update(struct llama_context * ctx),
-            "use llama_kv_self_update instead");
-
-
     //
     // State / sessions
     //

otherarch/embeddings_adapter.cpp

@@ -149,7 +149,7 @@ bool embeddingstype_load_model(const embeddings_load_model_inputs inputs)
     }
 
     std::vector<int> tmp = {1, 2, 3, 4};
-    llama_kv_cache_clear(embeddings_ctx);
+    llama_kv_self_clear(embeddings_ctx);
     auto er = llama_decode(embeddings_ctx, llama_batch_get_one(tmp.data(), tmp.size()));
     if(er!=0)
     {
@@ -190,7 +190,7 @@ embeddings_generation_outputs embeddingstype_generate(const embeddings_generatio
     double timetaken = 0;
     timer_start();
 
-    llama_kv_cache_clear(embeddings_ctx);
+    llama_kv_self_clear(embeddings_ctx);
     std::string prompt = inputs.prompt;
 
     // max batch size

otherarch/tts_adapter.cpp

@@ -559,7 +559,7 @@ bool ttstype_load_model(const tts_load_model_inputs inputs)
     }
 
     std::vector<int> tmp = {1, 2, 3, 4};
-    llama_kv_cache_clear(ttc_ctx);
+    llama_kv_self_clear(ttc_ctx);
     auto er = llama_decode(ttc_ctx, llama_batch_get_one(tmp.data(), tmp.size()));
     if(er!=0)
     {
@@ -618,8 +618,8 @@ tts_generation_outputs ttstype_generate(const tts_generation_inputs inputs)
     const std::string sampletext = (custom_speaker_text=="")?process_text("but that is what it is",ttsver):process_text(custom_speaker_text,ttsver);
 
     // process prompt and generate voice codes
-    llama_kv_cache_clear(ttc_ctx);
+    llama_kv_self_clear(ttc_ctx);
-    llama_kv_cache_clear(cts_ctx);
+    llama_kv_self_clear(cts_ctx);
     std::vector<llama_token> prompt_inp;
     prompt_init(prompt_inp, ttcvocab);
 
@@ -817,7 +817,7 @@ tts_generation_outputs ttstype_generate(const tts_generation_inputs inputs)
             }
         }
         guide_tokens.clear();
-        llama_kv_cache_clear(ttc_ctx);
+        llama_kv_self_clear(ttc_ctx);
         prompt_init(prompt_inp, ttcvocab);
         next_token_uses_guide_token = true;
     }

src/llama-context.cpp

@@ -2288,39 +2288,10 @@ int32_t llama_apply_adapter_cvec(
     return res ? 0 : -1;
 }
 
-//
-// kv cache view
-//
-
-llama_kv_cache_view llama_kv_cache_view_init(const llama_context * ctx, int32_t n_seq_max) {
-    const auto * kv = ctx->get_kv_self();
-    if (kv == nullptr) {
-        LLAMA_LOG_WARN("%s: the context does not have a KV cache\n", __func__);
-        return {};
-    }
-
-    return llama_kv_cache_view_init(*kv, n_seq_max);
-}
-
-void llama_kv_cache_view_update(const llama_context * ctx, llama_kv_cache_view * view) {
-    const auto * kv = ctx->get_kv_self();
-    if (kv == nullptr) {
-        LLAMA_LOG_WARN("%s: the context does not have a KV cache\n", __func__);
-        return;
-    }
-
-    llama_kv_cache_view_update(view, kv);
-}
-
 //
 // kv cache
 //
 
-// deprecated
-int32_t llama_get_kv_cache_token_count(const llama_context * ctx) {
-    return llama_kv_self_n_tokens(ctx);
-}
-
 int32_t llama_kv_self_n_tokens(const llama_context * ctx) {
     const auto * kv = ctx->get_kv_self();
     if (!kv) {
@@ -2330,11 +2301,6 @@ int32_t llama_kv_self_n_tokens(const llama_context * ctx) {
     return kv->get_n_tokens();
 }
 
-// deprecated
-int32_t llama_get_kv_cache_used_cells(const llama_context * ctx) {
-    return llama_kv_self_used_cells(ctx);
-}
-
 int32_t llama_kv_self_used_cells(const llama_context * ctx) {
     const auto * kv = ctx->get_kv_self();
     if (!kv) {
@@ -2344,11 +2310,6 @@ int32_t llama_kv_self_used_cells(const llama_context * ctx) {
     return kv->get_used_cells();
 }
 
-// deprecated
-void llama_kv_cache_clear(llama_context * ctx) {
-    llama_kv_self_clear(ctx);
-}
-
 void llama_kv_self_clear(llama_context * ctx) {
     auto * kv = ctx->get_kv_self();
     if (!kv) {
@@ -2358,15 +2319,6 @@ void llama_kv_self_clear(llama_context * ctx) {
     kv->clear();
 }
 
-// deprecated
-bool llama_kv_cache_seq_rm(
-        llama_context * ctx,
-         llama_seq_id   seq_id,
-            llama_pos   p0,
-            llama_pos   p1) {
-    return llama_kv_self_seq_rm(ctx, seq_id, p0, p1);
-}
-
 bool llama_kv_self_seq_rm(
         llama_context * ctx,
          llama_seq_id   seq_id,
@@ -2380,16 +2332,6 @@ bool llama_kv_self_seq_rm(
     return kv->seq_rm(seq_id, p0, p1);
 }
 
-// deprecated
-void llama_kv_cache_seq_cp(
-        llama_context * ctx,
-         llama_seq_id   seq_id_src,
-         llama_seq_id   seq_id_dst,
-            llama_pos   p0,
-            llama_pos   p1) {
-    llama_kv_self_seq_cp(ctx, seq_id_src, seq_id_dst, p0, p1);
-}
-
 void llama_kv_self_seq_cp(
         llama_context * ctx,
          llama_seq_id   seq_id_src,
@@ -2404,13 +2346,6 @@ void llama_kv_self_seq_cp(
     kv->seq_cp(seq_id_src, seq_id_dst, p0, p1);
 }
 
-// deprecated
-void llama_kv_cache_seq_keep(
-        llama_context * ctx,
-         llama_seq_id   seq_id) {
-    llama_kv_self_seq_keep(ctx, seq_id);
-}
-
 void llama_kv_self_seq_keep(llama_context * ctx, llama_seq_id seq_id) {
     auto * kv = ctx->get_kv_self();
     if (!kv) {
@@ -2420,16 +2355,6 @@ void llama_kv_self_seq_keep(llama_context * ctx, llama_seq_id seq_id) {
     kv->seq_keep(seq_id);
 }
 
-// deprecated
-void llama_kv_cache_seq_add(
-        llama_context * ctx,
-         llama_seq_id   seq_id,
-            llama_pos   p0,
-            llama_pos   p1,
-            llama_pos   delta) {
-    llama_kv_self_seq_add(ctx, seq_id, p0, p1, delta);
-}
-
 void llama_kv_self_seq_add(
         llama_context * ctx,
          llama_seq_id   seq_id,
@@ -2444,16 +2369,6 @@ void llama_kv_self_seq_add(
     kv->seq_add(seq_id, p0, p1, delta);
 }
 
-// deprecated
-void llama_kv_cache_seq_div(
-        llama_context * ctx,
-         llama_seq_id   seq_id,
-            llama_pos   p0,
-            llama_pos   p1,
-                  int   d) {
-    llama_kv_self_seq_div(ctx, seq_id, p0, p1, d);
-}
-
 void llama_kv_self_seq_div(
         llama_context * ctx,
          llama_seq_id   seq_id,
@@ -2477,11 +2392,6 @@ llama_pos llama_kv_self_seq_pos_min(llama_context * ctx, llama_seq_id seq_id) {
     return kv->seq_pos_min(seq_id);
 }
 
-// deprecated
-llama_pos llama_kv_cache_seq_pos_max(llama_context * ctx, llama_seq_id seq_id) {
-    return llama_kv_self_seq_pos_max(ctx, seq_id);
-}
-
 llama_pos llama_kv_self_seq_pos_max(llama_context * ctx, llama_seq_id seq_id) {
     const auto * kv = ctx->get_kv_self();
     if (!kv) {
@@ -2491,11 +2401,6 @@ llama_pos llama_kv_self_seq_pos_max(llama_context * ctx, llama_seq_id seq_id) {
     return kv->seq_pos_max(seq_id);
 }
 
-// deprecated
-void llama_kv_cache_defrag(llama_context * ctx) {
-    llama_kv_self_defrag(ctx);
-}
-
 void llama_kv_self_defrag(llama_context * ctx) {
     auto * kv = ctx->get_kv_self();
     if (!kv) {
@@ -2506,11 +2411,6 @@ void llama_kv_self_defrag(llama_context * ctx) {
     kv->defrag_sched(-1.0f);
 }
 
-// deprecated
-bool llama_kv_cache_can_shift(const llama_context * ctx) {
-    return llama_kv_self_can_shift(ctx);
-}
-
 bool llama_kv_self_can_shift(const llama_context * ctx) {
     const auto * kv = ctx->get_kv_self();
     if (!kv) {
@@ -2520,11 +2420,6 @@ bool llama_kv_self_can_shift(const llama_context * ctx) {
     return kv->get_can_shift();
 }
 
-// deprecated
-void llama_kv_cache_update(llama_context * ctx) {
-    llama_kv_self_update(ctx);
-}
-
 // llama state API
 
 // deprecated

src/llama-graph.cpp

@@ -1368,6 +1368,10 @@ ggml_tensor * llm_graph_context::build_attn(
 
     if (wo) {
         cur = build_lora_mm(wo, cur);
+        if (arch == LLM_ARCH_GLM4) {
+            // GLM4 seems to have numerical issues with half-precision accumulators
+            ggml_mul_mat_set_prec(cur, GGML_PREC_F32);
+        }
     }
 
     if (wo_b) {

src/llama-kv-cache.cpp

@@ -2888,38 +2888,3 @@ bool llama_kv_cache_recurrent::state_read_data(llama_io_read_i & io, uint32_t ce
 
     return true;
 }
-
-//
-// kv cache view
-//
-
-llama_kv_cache_view llama_kv_cache_view_init(const llama_kv_cache & kv, int32_t n_seq_max) {
-    llama_kv_cache_view result = {
-        /*.n_cells            = */ 0,
-        /*.n_seq_max          = */ n_seq_max,
-        /*.token_count        = */ 0,
-        /*.used_cells         = */ kv.get_used_cells(),
-        /*.max_contiguous     = */ 0,
-        /*.max_contiguous_idx = */ -1,
-        /*.cells              = */ nullptr,
-        /*.cells_sequences    = */ nullptr,
-    };
-
-    return result;
-}
-
-void llama_kv_cache_view_free(llama_kv_cache_view * view) {
-    if (view->cells != nullptr) {
-        free(view->cells);
-        view->cells = nullptr;
-    }
-    if (view->cells_sequences != nullptr) {
-        free(view->cells_sequences);
-        view->cells_sequences = nullptr;
-    }
-}
-
-void llama_kv_cache_view_update(llama_kv_cache_view * , const llama_kv_cache * ) {
-    // TODO: will be removed soon, keep this for now to avoid too many changes in
-    //       https://github.com/ggml-org/llama.cpp/pull/13194
-}

src/llama-kv-cache.h

@@ -534,12 +534,3 @@ private:
     bool state_read_meta(llama_io_read_i & io, uint32_t cell_count, llama_seq_id dest_seq_id = -1);
     bool state_read_data(llama_io_read_i & io, uint32_t cell_count);
 };
-
-
-//
-// kv cache view
-//
-
-llama_kv_cache_view llama_kv_cache_view_init(const llama_kv_cache & kv, int32_t n_seq_max);
-
-void llama_kv_cache_view_update(llama_kv_cache_view * view, const llama_kv_cache * kv);

src/llama-model.cpp

@@ -4903,8 +4903,21 @@ struct llm_build_llama_iswa : public llm_graph_context {
             ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
             cb(ffn_inp, "ffn_inp", il);
 
-            {
+            // feed-forward network (non-MoE)
-                // llama4 MoE
+            if (model.layers[il].ffn_gate_inp == nullptr) {
+                cur = build_norm(ffn_inp,
+                        model.layers[il].ffn_norm, NULL,
+                        LLM_NORM_RMS, il);
+                cb(cur, "ffn_norm", il);
+
+                cur = build_ffn(cur,
+                        model.layers[il].ffn_up,   model.layers[il].ffn_up_b,   NULL,
+                        model.layers[il].ffn_gate, model.layers[il].ffn_gate_b, NULL,
+                        model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
+                        NULL,
+                        LLM_FFN_SILU, LLM_FFN_PAR, il);
+                cb(cur, "ffn_out", il);
+            } else {
                 ggml_tensor * ffn_inp_normed = build_norm(ffn_inp,
                         model.layers[il].ffn_norm, NULL,
                         LLM_NORM_RMS, il);

tools/mtmd/mtmd-helper.cpp

@@ -231,12 +231,14 @@ int32_t mtmd_helper_eval_chunk_single(mtmd_context * ctx,
         while (i < n_tokens) { // split into batches
             text_batch.n_tokens = 0; // clear the batch
             for (; i < n_tokens && text_batch.n_tokens < n_batch; i++) {
+                int32_t j = text_batch.n_tokens;
+                text_batch.token   [j]    = tokens[i];
+                text_batch.pos     [j]    = n_past++;
+                text_batch.n_seq_id[j]    = 1;
+                text_batch.seq_id  [j][0] = seq_id;
+                text_batch.logits  [j]    = false;
+
                 text_batch.n_tokens++;
-                text_batch.token   [i]    = tokens[i];
-                text_batch.pos     [i]    = n_past++;
-                text_batch.n_seq_id[i]    = 1;
-                text_batch.seq_id  [i][0] = seq_id;
-                text_batch.logits  [i]    = false;
             }
             bool is_last_token = (i == n_tokens);
             if (logits_last && is_last_token) {