From 1d88cb05f1a257f90741538ca3aca6c1b015c6d3 Mon Sep 17 00:00:00 2001 From: aiqubits Date: Tue, 20 Oct 2026 11:58:38 +0800 Subject: [PATCH 01/69] ui: hide the native clear button in SearchInput (#30719) SearchInput already renders its own clear button. Hide the browser's native search cancel button to avoid showing both controls. Fixes #28627 Signed-off-by: aiqubits --- tools/ui/src/lib/components/app/forms/SearchInput.svelte | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/tools/ui/src/lib/components/app/forms/SearchInput.svelte b/tools/ui/src/lib/components/app/forms/SearchInput.svelte index 0280a801d6a0..054d477b1658 100644 --- a/tools/ui/src/lib/components/app/forms/SearchInput.svelte +++ b/tools/ui/src/lib/components/app/forms/SearchInput.svelte @@ -61,7 +61,9 @@ bind:ref bind:value {autofocus} - class="max-md:h-10 pl-9 {showClearButton ? 'pr-9' : ''} {size === 'sm' ? 'h-8' : ''}" + class="max-md:h-10 pl-9 {showClearButton ? 'pr-9' : ''} {size === 'sm' + ? 'h-8' + : ''} [&::-webkit-search-cancel-button]:hidden" {id} oninput={handleInput} onkeydown={onKeyDown} From dd002b91f583af0b80140f9a140dc04856bfc2e0 Mon Sep 17 00:00:00 2001 From: Michael Taylor <162068037+mctylr-gh@users.noreply.github.com> Date: Tue, 20 Oct 2026 01:47:25 -0300 Subject: [PATCH 02/69] sycl: fix compiler warnings - missing prototypes, dead code, extra semicolons (#30271) --- cmake/common.cmake | 2 +- ggml/cmake/common.cmake | 8 ++++++- ggml/src/ggml-sycl/CMakeLists.txt | 2 +- ggml/src/ggml-sycl/concat.cpp | 1 - ggml/src/ggml-sycl/convert.cpp | 3 --- ggml/src/ggml-sycl/count-equal.cpp | 1 - ggml/src/ggml-sycl/dpct/helper.hpp | 2 +- ggml/src/ggml-sycl/element_wise.cpp | 2 +- ggml/src/ggml-sycl/fattn-common.hpp | 34 +++++++++++++++++++++-------- ggml/src/ggml-sycl/fattn-tile.cpp | 2 +- ggml/src/ggml-sycl/getrows.cpp | 1 - ggml/src/ggml-sycl/ggml-sycl.cpp | 10 ++++----- ggml/src/ggml-sycl/gla.cpp | 1 + ggml/src/ggml-sycl/mem.cpp | 4 ++-- ggml/src/ggml-sycl/topk-moe.cpp | 1 - ggml/src/ggml-sycl/upscale.cpp | 8 +++---- ggml/src/ggml-sycl/vecdotq.hpp | 2 +- 17 files changed, 49 insertions(+), 35 deletions(-) diff --git a/cmake/common.cmake b/cmake/common.cmake index bcf403e0ee37..ee8d5bf7b1f2 100644 --- a/cmake/common.cmake +++ b/cmake/common.cmake @@ -2,7 +2,7 @@ include("ggml/cmake/common.cmake") function(llama_add_compile_flags) if (LLAMA_FATAL_WARNINGS) - if (CMAKE_CXX_COMPILER_ID MATCHES "GNU" OR CMAKE_CXX_COMPILER_ID MATCHES "Clang") + if (CMAKE_CXX_COMPILER_ID MATCHES "GNU" OR CMAKE_CXX_COMPILER_ID MATCHES "Clang" OR CMAKE_CXX_COMPILER_ID MATCHES "IntelLLVM") list(APPEND C_FLAGS -Werror) list(APPEND CXX_FLAGS -Werror) elseif (CMAKE_CXX_COMPILER_ID STREQUAL "MSVC") diff --git a/ggml/cmake/common.cmake b/ggml/cmake/common.cmake index f3610298ffb8..42b65a58051c 100644 --- a/ggml/cmake/common.cmake +++ b/ggml/cmake/common.cmake @@ -2,10 +2,16 @@ function(ggml_get_flags CCID CCVER) set(C_FLAGS "") set(CXX_FLAGS "") - if (CCID MATCHES "Clang") + if (CCID MATCHES "Clang" OR CCID MATCHES "IntelLLVM") set(C_FLAGS -Wunreachable-code-break -Wunreachable-code-return) set(CXX_FLAGS -Wunreachable-code-break -Wunreachable-code-return -Wmissing-prototypes -Wextra-semi) + if (CCID STREQUAL "IntelLLVM") + # for Intel DPC++ and LLVM SYCL, using debug does not disable optimization + list(APPEND C_FLAGS -Rno-debug-disables-optimization) + list(APPEND CXX_FLAGS -Rno-debug-disables-optimization) + endif() + if ( (CCID STREQUAL "Clang" AND CCVER VERSION_GREATER_EQUAL 3.8.0) OR (CCID STREQUAL "AppleClang" AND CCVER VERSION_GREATER_EQUAL 7.3.0) diff --git a/ggml/src/ggml-sycl/CMakeLists.txt b/ggml/src/ggml-sycl/CMakeLists.txt index dfdb4746d061..71c76737f413 100644 --- a/ggml/src/ggml-sycl/CMakeLists.txt +++ b/ggml/src/ggml-sycl/CMakeLists.txt @@ -44,7 +44,7 @@ if (WIN32) if(DEFINED ENV{LEVEL_ZERO_V1_SDK_PATH}) set(LEVEL_ZERO_V1_SDK_PATH $ENV{LEVEL_ZERO_V1_SDK_PATH}) if(EXISTS "${LEVEL_ZERO_V1_SDK_PATH}") - target_include_directories(ggml-sycl PRIVATE "${LEVEL_ZERO_V1_SDK_PATH}/include") + target_include_directories(ggml-sycl SYSTEM PRIVATE "${LEVEL_ZERO_V1_SDK_PATH}/include") set(LEVEL_ZERO_V1_SDK_LIB_PATH "${LEVEL_ZERO_V1_SDK_PATH}/lib") else() message(WARNING "LEVEL_ZERO_V1_SDK_PATH set but folder not found: ${LEVEL_ZERO_V1_SDK_PATH}") diff --git a/ggml/src/ggml-sycl/concat.cpp b/ggml/src/ggml-sycl/concat.cpp index bd5f3b2ceb30..d4c257cf78f5 100644 --- a/ggml/src/ggml-sycl/concat.cpp +++ b/ggml/src/ggml-sycl/concat.cpp @@ -504,6 +504,5 @@ void ggml_sycl_op_concat(ggml_backend_sycl_context & ctx, ggml_tensor *dst) { default: fprintf(stderr, "%s: unsupported types: dst: %s\n", __func__, ggml_type_name(dst->type)); GGML_ASSERT(false); - break; } } diff --git a/ggml/src/ggml-sycl/convert.cpp b/ggml/src/ggml-sycl/convert.cpp index 45c60e8e5cf1..092967b6f62e 100644 --- a/ggml/src/ggml-sycl/convert.cpp +++ b/ggml/src/ggml-sycl/convert.cpp @@ -757,7 +757,6 @@ to_fp16_sycl_t ggml_get_to_fp16_sycl(ggml_type type, ggml_tensor * dst) { #endif default: GGML_ABORT("fatal error: unsupport data type=%s\n", ggml_type_name(type)); - return nullptr; } } @@ -861,7 +860,6 @@ to_fp32_sycl_t ggml_get_to_fp32_sycl(ggml_type type, ggml_tensor *dst) { #endif default: GGML_ABORT("fatal error: unsupport data type=%s\n", ggml_type_name(type)); - return nullptr; } } @@ -877,7 +875,6 @@ to_bf16_sycl_t ggml_get_to_bf16_sycl(ggml_type type, ggml_tensor * /*dst*/) { return convert_unary_sycl; default: GGML_ABORT("fatal error: unsupport data type=%s\n", ggml_type_name(type)); - return nullptr; } } #endif diff --git a/ggml/src/ggml-sycl/count-equal.cpp b/ggml/src/ggml-sycl/count-equal.cpp index 4580354cd9d0..20126652baa1 100644 --- a/ggml/src/ggml-sycl/count-equal.cpp +++ b/ggml/src/ggml-sycl/count-equal.cpp @@ -74,6 +74,5 @@ void ggml_sycl_count_equal(ggml_backend_sycl_context &ctx, ggml_tensor *dst) { } break; default: GGML_ASSERT(false); - break; } } diff --git a/ggml/src/ggml-sycl/dpct/helper.hpp b/ggml/src/ggml-sycl/dpct/helper.hpp index 85af4cab6810..fd3b9655ad1c 100644 --- a/ggml/src/ggml-sycl/dpct/helper.hpp +++ b/ggml/src/ggml-sycl/dpct/helper.hpp @@ -3159,7 +3159,7 @@ namespace dpct _local_mem_size = local_mem_size; - }; + } static inline std::mutex kernel_function_ptr_map_mutex; public: diff --git a/ggml/src/ggml-sycl/element_wise.cpp b/ggml/src/ggml-sycl/element_wise.cpp index 0d263aa76897..c7fe70ac33db 100644 --- a/ggml/src/ggml-sycl/element_wise.cpp +++ b/ggml/src/ggml-sycl/element_wise.cpp @@ -1169,7 +1169,7 @@ static void swiglu_oai_sycl(const T * x, }); } -void ggml_sycl_op_swiglu_oai(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { +static void ggml_sycl_op_swiglu_oai(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { const ggml_tensor * src0 = dst->src[0]; const ggml_tensor * src1 = dst->src[1]; void * src0_d = src0->data; diff --git a/ggml/src/ggml-sycl/fattn-common.hpp b/ggml/src/ggml-sycl/fattn-common.hpp index 2e8ac6af3fc8..c37326336db1 100644 --- a/ggml/src/ggml-sycl/fattn-common.hpp +++ b/ggml/src/ggml-sycl/fattn-common.hpp @@ -832,6 +832,29 @@ static void flash_attn_combine_results(const float * __restrict__ VKQ_parts, dst[tid] = VKQ_numerator / VKQ_denominator; } +// Carries the sub-group size (and grf_size_automatic when use_large_grf) as +// kernel properties via get(properties_tag), in place of the +// reqd_sub_group_size attribute and the deprecated parallel_for overload. +template +struct props_kernel { + KernelFunc kernel; + + void operator()(sycl::nd_item<3> item) const { + kernel(item); + } + + auto get(sycl::ext::oneapi::experimental::properties_tag) const { + if constexpr (UseGrf) { + return sycl::ext::oneapi::experimental::properties{ + sycl::ext::intel::experimental::grf_size_automatic, + sycl::ext::oneapi::experimental::sub_group_size }; + } else { + return sycl::ext::oneapi::experimental::properties{ + sycl::ext::oneapi::experimental::sub_group_size }; + } + } +}; + template static void lauch_kernel( dpct::dim3 group_range, @@ -881,7 +904,7 @@ static void lauch_kernel( static_cast>(group_range * local_range), static_cast>(local_range)); - const auto kernel = [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(warp_size)]] { + const auto kernel = [=](sycl::nd_item<3> item_ct1) { GGML_UNUSED(item_ct1); fattn_kernel(Q, K, V, mask, sinks, KV_max, dst, dst_meta, scale, max_bias, m0, m1, n_head_log2, logit_softcap, ne00, @@ -892,14 +915,7 @@ static void lauch_kernel( q->submit([&](sycl::handler &cgh) { // grf_size_automatic lets the compiler use the large register file when the kernel needs it. grf_size<256> would say the same but is undefined on devices that do not have it. - if constexpr (use_large_grf) { - cgh.parallel_for( - rng, - sycl::ext::oneapi::experimental::properties{ sycl::ext::intel::experimental::grf_size_automatic }, - kernel); - } else { - cgh.parallel_for(rng, kernel); - } + cgh.parallel_for(rng, props_kernel{kernel}); }); } diff --git a/ggml/src/ggml-sycl/fattn-tile.cpp b/ggml/src/ggml-sycl/fattn-tile.cpp index 9449d75784d0..db030268e0e3 100644 --- a/ggml/src/ggml-sycl/fattn-tile.cpp +++ b/ggml/src/ggml-sycl/fattn-tile.cpp @@ -54,6 +54,6 @@ void ggml_sycl_flash_attn_ext_tile(ggml_backend_sycl_context & ctx, ggml_tensor } break; default: { GGML_ABORT("Unsupported head size"); - } break; + } } } diff --git a/ggml/src/ggml-sycl/getrows.cpp b/ggml/src/ggml-sycl/getrows.cpp index 9c17daf86a73..a3a32090070a 100644 --- a/ggml/src/ggml-sycl/getrows.cpp +++ b/ggml/src/ggml-sycl/getrows.cpp @@ -652,6 +652,5 @@ void ggml_sycl_op_get_rows_back(ggml_backend_sycl_context & ctx, ggml_tensor * d break; default: GGML_ABORT("%s: unsupported src0 type: %s\n", __func__, ggml_type_name(src0->type)); - break; } } diff --git a/ggml/src/ggml-sycl/ggml-sycl.cpp b/ggml/src/ggml-sycl/ggml-sycl.cpp index 3cc565d6baed..cbb2188c5c52 100644 --- a/ggml/src/ggml-sycl/ggml-sycl.cpp +++ b/ggml/src/ggml-sycl/ggml-sycl.cpp @@ -388,14 +388,14 @@ static ze_result_t init_zes() { return res; } -ze_result_t get_zes_init_res() { +static ze_result_t get_zes_init_res() { static ze_result_t zes_init_res = init_zes(); GGML_SYCL_DEBUG("[SYCL] call %s: zesInit result: %d\n", __func__, (int) zes_init_res); return zes_init_res; } #endif -void initialize_sycl_begining() { +static void initialize_sycl_begining() { #ifdef GGML_SYCL_SUPPORT_LEVEL_ZERO_API //must be called in initialization stage, before any other Level Zero API calls GGML_SYCL_DEBUG("[SYCL] call %s\n", __func__); @@ -1173,7 +1173,7 @@ static size_t ggml_backend_sycl_buffer_type_get_alignment(ggml_backend_buffer_ty GGML_UNUSED(buft); } -bool is_bmg_g31_arch(int device) { +static bool is_bmg_g31_arch(int device) { return ggml_sycl_info().devices[device].hw_info.arch == gpu_arch::intel_gpu_bmg_g31; } @@ -6326,7 +6326,7 @@ catch (sycl::exception const &exc) { std::exit(1); } -bool sycl_get_mem_info(int device, size_t * free, size_t * total) { +static bool sycl_get_mem_info(int device, size_t * free, size_t * total) { GGML_SYCL_DEBUG("[SYCL] [%s] g_ggml_sycl_get_mem_api=%d\n", __func__, g_ggml_sycl_get_mem_api); @@ -6952,7 +6952,6 @@ static bool do_ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, cons default: return false; } - break; case GGML_OP_MUL_MAT: case GGML_OP_MUL_MAT_ID: { @@ -7045,7 +7044,6 @@ static bool do_ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, cons (op->src[1]->type == GGML_TYPE_I64 || op->src[1]->type == GGML_TYPE_I32); return res; } - break; case GGML_OP_DSV4_HC_PRE: return op->src[0]->type == GGML_TYPE_F32 && op->src[1]->type == GGML_TYPE_F32 && op->type == GGML_TYPE_F32; diff --git a/ggml/src/ggml-sycl/gla.cpp b/ggml/src/ggml-sycl/gla.cpp index 879184fdd311..379483427046 100644 --- a/ggml/src/ggml-sycl/gla.cpp +++ b/ggml/src/ggml-sycl/gla.cpp @@ -1,6 +1,7 @@ #include #include "common.hpp" +#include "gla.hpp" template static void gated_linear_attn_f32_kernel(const dpct::queue_ptr stream, u_int B, u_int T, u_int C, u_int H, float scale, diff --git a/ggml/src/ggml-sycl/mem.cpp b/ggml/src/ggml-sycl/mem.cpp index ad5bfe0ff1eb..8a1c3262cc11 100644 --- a/ggml/src/ggml-sycl/mem.cpp +++ b/ggml/src/ggml-sycl/mem.cpp @@ -27,7 +27,7 @@ const char * mem_api_int2str(int mem_api) { /* * Depend on to call zesInit(0) before any other Level Zero API calls, otherwise the Level Zero API calls may fail. */ -bool query_free_memory_by_ze(sycl::device dev, size_t & free_bytes, size_t & total_bytes) { +static bool query_free_memory_by_ze(sycl::device dev, size_t & free_bytes, size_t & total_bytes) { GGML_SYCL_DEBUG("[SYCL] call %s: Querying free memory using Level Zero API.\n", __func__); free_bytes = 0; @@ -94,7 +94,7 @@ bool query_free_memory_by_ze(sycl::device dev, size_t & free_bytes, size_t & tot } #endif -bool get_memory_size_by_sycl_api(sycl::device dev, size_t & free_bytes, size_t & total_bytes) { +static bool get_memory_size_by_sycl_api(sycl::device dev, size_t & free_bytes, size_t & total_bytes) { GGML_SYCL_DEBUG("[SYCL] call %s: Querying free memory using SYCL API.\n", __func__); total_bytes = dev.get_info(); diff --git a/ggml/src/ggml-sycl/topk-moe.cpp b/ggml/src/ggml-sycl/topk-moe.cpp index 78574c4b5d0a..3dd92d2613eb 100644 --- a/ggml/src/ggml-sycl/topk-moe.cpp +++ b/ggml/src/ggml-sycl/topk-moe.cpp @@ -296,7 +296,6 @@ static void ggml_sycl_op_topk_moe(ggml_backend_sycl_context & ctx, break; default: GGML_ASSERT(false && "fatal error"); - break; } } diff --git a/ggml/src/ggml-sycl/upscale.cpp b/ggml/src/ggml-sycl/upscale.cpp index e42cb419d838..5f1c16be2b12 100644 --- a/ggml/src/ggml-sycl/upscale.cpp +++ b/ggml/src/ggml-sycl/upscale.cpp @@ -183,8 +183,8 @@ static void upscale_f32_bilinear_antialias(const float * src0, } namespace bicubic_interpolation { -static float weight1(float x, const float &a) { return ((a + 2) * x - (a + 3)) * x * x + 1; }; -static float weight2(float x, const float &a) { return ((a * x - 5 * a) * x + 8 * a) * x - 4 * a; }; +static float weight1(float x, const float &a) { return ((a + 2) * x - (a + 3)) * x * x + 1; } +static float weight2(float x, const float &a) { return ((a * x - 5 * a) * x + 8 * a) * x - 4 * a; } static float bicubic(float p0, float p1, float p2, float p3, float x, float a) { const float w0 = weight2(x + 1, a); @@ -192,7 +192,7 @@ static float bicubic(float p0, float p1, float p2, float p3, float x, float a) { const float w2 = weight1(1 - x, a); const float w3 = weight2(2 - x, a); return p0 * w0 + p1 * w1 + p2 * w2 + p3 * w3; -}; +} } @@ -358,7 +358,7 @@ static void upscale_f32_bicubic_sycl(const float * x, } } -void ggml_sycl_op_upscale(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { +static void ggml_sycl_op_upscale(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { const ggml_tensor * src0 = dst->src[0]; const float * src0_d = (const float *)src0->data; float * dst_d = (float *)dst->data; diff --git a/ggml/src/ggml-sycl/vecdotq.hpp b/ggml/src/ggml-sycl/vecdotq.hpp index 57360a004b64..f547b20e687f 100644 --- a/ggml/src/ggml-sycl/vecdotq.hpp +++ b/ggml/src/ggml-sycl/vecdotq.hpp @@ -442,7 +442,7 @@ template <> struct reorder_vec_dot_q_sycl { } return vec_dot_q4_0_q8_1_impl(v, u, d, *q8_1_ds); - }; + } }; // Load four contiguous dwords per operand instead of loading each value separately. From 84663953535d4323e92b4bc90971aa022c305d06 Mon Sep 17 00:00:00 2001 From: Max Krasnyansky Date: Mon, 19 Oct 2026 23:49:59 -0700 Subject: [PATCH 03/69] hexagon: update to snapdragon-toolchain v0.8 and enable hexagon v85 (#30854) * hex-build: bump toolchain, enable v85 * hex-build: bump toolchain to v0.8 in the CI and build scripts * hex-build: add a little python script for swiv needed on some iot devices * hex-build: fix flake8 warnings * hex-arch: update version cap to v85 * hex-inspect: flexible TOOLS_ROOT * hex-build: update to snake_case naming * hex-build: update leftover old toolchain refs * hex-target: update build and run scripts to support ubuntu with adb transport --- .../workflows/build-and-test-snapdragon.yml | 4 +- .github/workflows/release.yml | 4 +- docs/backend/snapdragon/README.md | 8 +- docs/backend/snapdragon/linux.md | 4 +- docs/backend/snapdragon/windows.md | 12 +- ggml/src/ggml-hexagon/CMakeLists.txt | 5 +- ggml/src/ggml-hexagon/ggml-hexagon.cpp | 6 +- ggml/src/ggml-hexagon/htp/CMakeLists.txt | 10 +- ggml/src/ggml-hexagon/htp/htp-swiv.py | 512 ++++++++++++++++++ ggml/src/ggml-hexagon/libggml-htp.inf | 2 + scripts/snapdragon/build.py | 146 ++++- scripts/snapdragon/ggml-hexagon-inspect.py | 19 +- scripts/snapdragon/run.py | 109 +++- scripts/snapdragon/sdk.py | 2 +- 14 files changed, 763 insertions(+), 80 deletions(-) create mode 100644 ggml/src/ggml-hexagon/htp/htp-swiv.py diff --git a/.github/workflows/build-and-test-snapdragon.yml b/.github/workflows/build-and-test-snapdragon.yml index 29be95c8be02..f7f94d7ee996 100644 --- a/.github/workflows/build-and-test-snapdragon.yml +++ b/.github/workflows/build-and-test-snapdragon.yml @@ -35,7 +35,7 @@ jobs: android-ndk-snapdragon: runs-on: ubuntu-24.04 # previously ubuntu-latest container: - image: 'ghcr.io/snapdragon-toolchain/arm64-android:v0.7' + image: 'ghcr.io/snapdragon-toolchain/arm64-android:v0.8' defaults: run: shell: bash @@ -69,7 +69,7 @@ jobs: linux-iot-snapdragon: runs-on: ubuntu-24.04 # previously ubuntu-latest container: - image: 'ghcr.io/snapdragon-toolchain/arm64-linux:v0.7' + image: 'ghcr.io/snapdragon-toolchain/arm64-linux:v0.8' defaults: run: shell: bash diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 6f828b94bec2..c43c609ad2df 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -547,7 +547,7 @@ jobs: if: ${{ needs.check-release.outputs.should_release == 'true' }} runs-on: ubuntu-latest - container: 'ghcr.io/snapdragon-toolchain/arm64-android:v0.7' + container: 'ghcr.io/snapdragon-toolchain/arm64-android:v0.8' defaults: run: @@ -600,7 +600,7 @@ jobs: if: ${{ needs.check-release.outputs.should_release == 'true' }} runs-on: ubuntu-latest - container: 'ghcr.io/snapdragon-toolchain/arm64-linux:v0.7' + container: 'ghcr.io/snapdragon-toolchain/arm64-linux:v0.8' defaults: run: diff --git a/docs/backend/snapdragon/README.md b/docs/backend/snapdragon/README.md index b8347a224aec..f0682ce7abcc 100644 --- a/docs/backend/snapdragon/README.md +++ b/docs/backend/snapdragon/README.md @@ -6,8 +6,8 @@ The cross-compilation toolchain images are provided by the [Qualcomm Snapdragon Toolchain registry](https://github.com/snapdragon-toolchain). These Docker images include the Android NDK, OpenCL SDK, Hexagon SDK, CMake, and the necessary cross-compilers: -* **Android toolchain**: `ghcr.io/snapdragon-toolchain/arm64-android:v0.7` -* **Linux toolchain**: `ghcr.io/snapdragon-toolchain/arm64-linux:v0.7` +* **Android toolchain**: `ghcr.io/snapdragon-toolchain/arm64-android:v0.8` +* **Linux toolchain**: `ghcr.io/snapdragon-toolchain/arm64-linux:v0.8` The unified build utility (`scripts/snapdragon/build.py`) automatically pulls and orchestrates these containers to perform target compilation. @@ -40,7 +40,7 @@ Alternatively, you can build llama.cpp manually by entering the cross-compilatio ```bash # Start the cross-compilation container manually: -~/src/llama.cpp$ docker run -it --rm -u $(id -u):$(id -g) --volume $(pwd):/workspace --platform linux/amd64 ghcr.io/snapdragon-toolchain/arm64-android:v0.7 +~/src/llama.cpp$ docker run -it --rm -u $(id -u):$(id -g) --volume $(pwd):/workspace --platform linux/amd64 ghcr.io/snapdragon-toolchain/arm64-android:v0.8 # Inside the container, build the project using presets: [d]/workspace> cp docs/backend/snapdragon/CMakeUserPresets.json . @@ -53,7 +53,7 @@ Preset CMake variables: GGML_HEXAGON="ON" GGML_OPENCL="ON" GGML_OPENMP="OFF" - HEXAGON_SDK_ROOT="/opt/hexagon/6.6.0.0" + HEXAGON_SDK_ROOT="/opt/hexagon/6.7.0.0" ... -- Including OpenCL backend -- Including Hexagon backend diff --git a/docs/backend/snapdragon/linux.md b/docs/backend/snapdragon/linux.md index d4ecd9b1b167..20cc4817a465 100644 --- a/docs/backend/snapdragon/linux.md +++ b/docs/backend/snapdragon/linux.md @@ -3,7 +3,7 @@ The cross-compilation is performed using the Snapdragon Linux Docker toolchain image (see [github.com/snapdragon-toolchain](https://github.com/snapdragon-toolchain)): -* **Linux toolchain**: `ghcr.io/snapdragon-toolchain/arm64-linux:v0.7` +* **Linux toolchain**: `ghcr.io/snapdragon-toolchain/arm64-linux:v0.8` The unified build utility (`scripts/snapdragon/build.py`) automatically pulls and orchestrates this container to perform target compilation. You only need to @@ -29,7 +29,7 @@ Alternatively, you can build llama.cpp manually by entering the cross-compilatio ```bash # Start the cross-compilation container manually: -~/src/llama.cpp$ docker run -it --rm -u $(id -u):$(id -g) --volume $(pwd):/workspace --platform linux/amd64 ghcr.io/snapdragon-toolchain/arm64-linux:v0.7 +~/src/llama.cpp$ docker run -it --rm -u $(id -u):$(id -g) --volume $(pwd):/workspace --platform linux/amd64 ghcr.io/snapdragon-toolchain/arm64-linux:v0.8 # Inside the container, build the project using presets: [d]/workspace> cp docs/backend/snapdragon/CMakeUserPresets.json . diff --git a/docs/backend/snapdragon/windows.md b/docs/backend/snapdragon/windows.md index 886cfda3f653..8e768491a5d4 100644 --- a/docs/backend/snapdragon/windows.md +++ b/docs/backend/snapdragon/windows.md @@ -8,7 +8,7 @@ Native Windows 11 arm64 builds have the following tool dependencies: - UCRT and Driver Kit - LLVM core libraries and Clang compiler (winget) - CMake, Git, Python (winget) -- Hexagon SDK Community Edition 6.6 or later (see below) +- Hexagon SDK Community Edition 6.7 or later (see below) - OpenCL SDK 2.3 or later (see below) Note: The rest of the **Windows** build process assumes that you're running natively in Powershell. @@ -54,15 +54,15 @@ c:\Qualcomm\OpenCL_SDK\2.3.2 Either use the trimmed down version (optimized for CI) from - https://github.com/snapdragon-toolchain/hexagon-sdk/releases/download/v6.6.0.0/hexagon-sdk-v6.6.0.0-arm64-wos.tar.xz + https://github.com/snapdragon-toolchain/hexagon-sdk/releases/download/v6.7.0.0/hexagon-sdk-v6.7.0.0-arm64-wos.tar.xz Or download the complete official version from - https://softwarecenter.qualcomm.com/catalog/item/Hexagon_SDK?version=6.6.0.0 + https://softwarecenter.qualcomm.com/catalog/item/Hexagon_SDK?version=6.7.0.0 Unzip/untar the archive into ``` -c:\Qualcomm\Hexagon_SDK\6.6.0.0 +c:\Qualcomm\Hexagon_SDK\6.7.0.0 ``` ## Install the latest Adreno GPU driver @@ -150,8 +150,8 @@ The overall Hexagon backend build procedure for Windows on Snapdragon is the sam However, additional settings are required for generating and signing HTP Ops libraries. ``` > $env:OPENCL_SDK_ROOT="C:\Qualcomm\OpenCL_SDK\2.3.2" -> $env:HEXAGON_SDK_ROOT="C:\Qualcomm\Hexagon_SDK\6.6.0.0" -> $env:HEXAGON_TOOLS_ROOT="C:\Qualcomm\Hexagon_SDK\6.6.0.0\tools\HEXAGON_Tools\19.0.07" +> $env:HEXAGON_SDK_ROOT="C:\Qualcomm\Hexagon_SDK\6.7.0.0" +> $env:HEXAGON_TOOLS_ROOT="C:\Qualcomm\Hexagon_SDK\6.7.0.0\tools\HEXAGON_Tools\21.0.04" > $env:HEXAGON_HTP_CERT="c:\Users\MyUsers\Certs\ggml-htp-v1.pfx" > $env:WINDOWS_SDK_BIN="C:\Program Files (x86)\Windows Kits\10\bin\10.0.26100.0" diff --git a/ggml/src/ggml-hexagon/CMakeLists.txt b/ggml/src/ggml-hexagon/CMakeLists.txt index 277a4c863ad0..6ab520175b89 100644 --- a/ggml/src/ggml-hexagon/CMakeLists.txt +++ b/ggml/src/ggml-hexagon/CMakeLists.txt @@ -21,6 +21,7 @@ message(STATUS "hexagon: using ${HEXAGON_SDK_ROOT} and ${HEXAGON_TOOLS_ROOT} for include(${HEXAGON_SDK_ROOT}/build/cmake/hexagon_fun.cmake) include(ExternalProject) +find_package(Python3 REQUIRED COMPONENTS Interpreter) option(GGML_HEXAGON_HTP_DEBUG "ggml-hexagon: enable HTP debug output" OFF) set(GGML_HEXAGON_HTP_BUILD_TYPE "Release" CACHE STRING "ggml-hexagon: HTP skel build type (Release, RelWithDebInfo, Debug)") @@ -74,7 +75,8 @@ function(build_htp_skel V) -DHEXAGON_TOOLS_ROOT=${HEXAGON_TOOLS_ROOT} -DHEXAGON_HTP_DEBUG=${GGML_HEXAGON_HTP_DEBUG} -DDSP_VERSION=${V} - -DPREBUILT_LIB_DIR="toolv19_${V}") + -DPython3_EXECUTABLE=${Python3_EXECUTABLE} + -DPREBUILT_LIB_DIR="toolv21_${V}") list(APPEND HTP_SKELS ${CMAKE_CURRENT_BINARY_DIR}/libggml-htp-${V}.so) list(APPEND HTP_PROJECTS htp-${V}) set(HTP_SKELS ${HTP_SKELS} PARENT_SCOPE) @@ -85,6 +87,7 @@ build_htp_skel(v73) build_htp_skel(v75) build_htp_skel(v79) build_htp_skel(v81) +build_htp_skel(v85) # Install Hexagon skels required at runtime install(FILES ${HTP_SKELS} TYPE LIB) diff --git a/ggml/src/ggml-hexagon/ggml-hexagon.cpp b/ggml/src/ggml-hexagon/ggml-hexagon.cpp index 2d69ac47e74c..da6dba85e8d3 100644 --- a/ggml/src/ggml-hexagon/ggml-hexagon.cpp +++ b/ggml/src/ggml-hexagon/ggml-hexagon.cpp @@ -8399,9 +8399,9 @@ static void ggml_hexagon_init(ggml_backend_reg * reg) { if (opt_arch < 73) { GGML_LOG_WARN("ggml-hex: Hexagon arch v%d is under supported range, capping at v73\n", opt_arch); opt_arch = 73; - } else if (opt_arch > 81) { - GGML_LOG_WARN("ggml-hex: Hexagon arch v%d is over supported range, capping at v81\n", opt_arch); - opt_arch = 81; + } else if (opt_arch > 85) { + GGML_LOG_WARN("ggml-hex: Hexagon arch v%d is over supported range, capping at v85\n", opt_arch); + opt_arch = 85; } } } else { diff --git a/ggml/src/ggml-hexagon/htp/CMakeLists.txt b/ggml/src/ggml-hexagon/htp/CMakeLists.txt index b1131317f5e0..28fae4fe83a5 100644 --- a/ggml/src/ggml-hexagon/htp/CMakeLists.txt +++ b/ggml/src/ggml-hexagon/htp/CMakeLists.txt @@ -53,9 +53,17 @@ target_compile_definitions(${HTP_LIB} PRIVATE $,HTP_DEBUG=1,NDEBUG=1> $,FARF_HIGH=1,>) - build_idl(htp_iface.idl ${HTP_LIB}) set_target_properties(${HTP_LIB} PROPERTIES EXPORT_COMPILE_COMMANDS ON) +if (NOT Python3_EXECUTABLE) + find_package(Python3 REQUIRED COMPONENTS Interpreter) +endif() + +add_custom_command(TARGET ${HTP_LIB} POST_BUILD + COMMAND ${CMAKE_COMMAND} -E echo "Adding SWIV section $" + COMMAND ${Python3_EXECUTABLE} ${CMAKE_CURRENT_SOURCE_DIR}/htp-swiv.py -i $ -o $ + VERBATIM) + install(TARGETS ${HTP_LIB}) diff --git a/ggml/src/ggml-hexagon/htp/htp-swiv.py b/ggml/src/ggml-hexagon/htp/htp-swiv.py new file mode 100644 index 000000000000..15d7bef274d9 --- /dev/null +++ b/ggml/src/ggml-hexagon/htp/htp-swiv.py @@ -0,0 +1,512 @@ +#!/usr/bin/env python3 + +import argparse +import logging +import os +import shutil +import struct +import sys +import tempfile +import zlib + +ELF_MAGIC = b'\x7fELF' +ELF_CLASS_INDEX = 4 +ELFCLASS32 = 1 +ELFCLASS64 = 2 +ELF_VERSION_INDEX = 6 +EV_CURRENT = 1 +MAX_PHDR_COUNT = 100 + +LOAD_TYPE = 0x1 + +SHT_NOBITS = 8 + +SWIV_MAGIC = b'SWIV' +SWIV_SEG_SIZE_MIN = 16 +# SHT_LOUSER plus ASCII "SWIV". +SWIV_SECTION_TYPE = 0xD3574956 + +logger = logging.getLogger("htp-swiv") + + +class Elf_Ehdr_common: + s = struct.Struct('16sHHI') + + def __init__(self, data): + unpacked_data = (Elf_Ehdr_common.s).unpack(data) + self.e_ident = unpacked_data[0] + self.e_type = unpacked_data[1] + self.e_machine = unpacked_data[2] + self.e_version = unpacked_data[3] + + +class Elf32_Ehdr: + s = struct.Struct('16sHHIIIIIHHHHHH') + + def __init__(self, data): + unpacked_data = (Elf32_Ehdr.s).unpack(data) + self.e_ident = unpacked_data[0] + self.e_type = unpacked_data[1] + self.e_machine = unpacked_data[2] + self.e_version = unpacked_data[3] + self.e_entry = unpacked_data[4] + self.e_phoff = unpacked_data[5] + self.e_shoff = unpacked_data[6] + self.e_flags = unpacked_data[7] + self.e_ehsize = unpacked_data[8] + self.e_phentsize = unpacked_data[9] + self.e_phnum = unpacked_data[10] + self.e_shentsize = unpacked_data[11] + self.e_shnum = unpacked_data[12] + self.e_shstrndx = unpacked_data[13] + + def get_packed_data(self): + values = [self.e_ident, + self.e_type, + self.e_machine, + self.e_version, + self.e_entry, + self.e_phoff, + self.e_shoff, + self.e_flags, + self.e_ehsize, + self.e_phentsize, + self.e_phnum, + self.e_shentsize, + self.e_shnum, + self.e_shstrndx + ] + + return (Elf32_Ehdr.s).pack(*values) + + +class Elf32_Phdr: + s = struct.Struct('I' * 8) + + def __init__(self, data): + unpacked_data = (Elf32_Phdr.s).unpack(data) + self.p_type = unpacked_data[0] + self.p_offset = unpacked_data[1] + self.p_vaddr = unpacked_data[2] + self.p_paddr = unpacked_data[3] + self.p_filesz = unpacked_data[4] + self.p_memsz = unpacked_data[5] + self.p_flags = unpacked_data[6] + self.p_align = unpacked_data[7] + + def get_packed_data(self): + values = [self.p_type, + self.p_offset, + self.p_vaddr, + self.p_paddr, + self.p_filesz, + self.p_memsz, + self.p_flags, + self.p_align + ] + + return (Elf32_Phdr.s).pack(*values) + + +class Elf32_Shdr: + s = struct.Struct('I' * 10) + + def __init__(self, data): + unpacked_data = (Elf32_Shdr.s).unpack(data) + self.sh_name = unpacked_data[0] + self.sh_type = unpacked_data[1] + self.sh_flags = unpacked_data[2] + self.sh_addr = unpacked_data[3] + self.sh_offset = unpacked_data[4] + self.sh_size = unpacked_data[5] + self.sh_link = unpacked_data[6] + self.sh_info = unpacked_data[7] + self.sh_addralign = unpacked_data[8] + self.sh_entsize = unpacked_data[9] + self.sh_name_str = "" + + def get_packed_data(self): + values = [self.sh_name, + self.sh_type, + self.sh_flags, + self.sh_addr, + self.sh_offset, + self.sh_size, + self.sh_link, + self.sh_info, + self.sh_addralign, + self.sh_entsize] + + return (Elf32_Shdr.s).pack(*values) + + +class Elf64_Ehdr: + s = struct.Struct('16sHHIQQQIHHHHHH') + + def __init__(self, data): + unpacked_data = (Elf64_Ehdr.s).unpack(data) + self.e_ident = unpacked_data[0] + self.e_type = unpacked_data[1] + self.e_machine = unpacked_data[2] + self.e_version = unpacked_data[3] + self.e_entry = unpacked_data[4] + self.e_phoff = unpacked_data[5] + self.e_shoff = unpacked_data[6] + self.e_flags = unpacked_data[7] + self.e_ehsize = unpacked_data[8] + self.e_phentsize = unpacked_data[9] + self.e_phnum = unpacked_data[10] + self.e_shentsize = unpacked_data[11] + self.e_shnum = unpacked_data[12] + self.e_shstrndx = unpacked_data[13] + + def get_packed_data(self): + values = [self.e_ident, + self.e_type, + self.e_machine, + self.e_version, + self.e_entry, + self.e_phoff, + self.e_shoff, + self.e_flags, + self.e_ehsize, + self.e_phentsize, + self.e_phnum, + self.e_shentsize, + self.e_shnum, + self.e_shstrndx + ] + + return (Elf64_Ehdr.s).pack(*values) + + +class Elf64_Phdr: + s = struct.Struct('IIQQQQQQ') + + def __init__(self, data): + unpacked_data = (Elf64_Phdr.s).unpack(data) + self.p_type = unpacked_data[0] + self.p_flags = unpacked_data[1] + self.p_offset = unpacked_data[2] + self.p_vaddr = unpacked_data[3] + self.p_paddr = unpacked_data[4] + self.p_filesz = unpacked_data[5] + self.p_memsz = unpacked_data[6] + self.p_align = unpacked_data[7] + + def get_packed_data(self): + values = [self.p_type, + self.p_flags, + self.p_offset, + self.p_vaddr, + self.p_paddr, + self.p_filesz, + self.p_memsz, + self.p_align + ] + + return (Elf64_Phdr.s).pack(*values) + + +class Elf64_Shdr: + s = struct.Struct('IIQQQQIIQQ') + + def __init__(self, data): + unpacked_data = (Elf64_Shdr.s).unpack(data) + self.sh_name = unpacked_data[0] + self.sh_type = unpacked_data[1] + self.sh_flags = unpacked_data[2] + self.sh_addr = unpacked_data[3] + self.sh_offset = unpacked_data[4] + self.sh_size = unpacked_data[5] + self.sh_link = unpacked_data[6] + self.sh_info = unpacked_data[7] + self.sh_addralign = unpacked_data[8] + self.sh_entsize = unpacked_data[9] + self.sh_name_str = "" + + def get_packed_data(self): + values = [self.sh_name, + self.sh_type, + self.sh_flags, + self.sh_addr, + self.sh_offset, + self.sh_size, + self.sh_link, + self.sh_info, + self.sh_addralign, + self.sh_entsize] + + return (Elf64_Shdr.s).pack(*values) + + +class SWIV_Seg: + s = struct.Struct('4sIQ') + + def __init__(self): + self.magic = SWIV_MAGIC + self.crc = 0 + self.padding = 0 + + def get_packed_data(self): + values = [self.magic, + self.crc, + self.padding + ] + + return (SWIV_Seg.s).pack(*values) + + +def is_elf64(elf_header): + return elf_header.e_ident[ELF_CLASS_INDEX] == ELFCLASS64 + + +def verify_elf_header(elf_header): + ident = elf_header.e_ident + return (ident.startswith(ELF_MAGIC) + and ident[ELF_CLASS_INDEX] in (ELFCLASS32, ELFCLASS64) + and ident[ELF_VERSION_INDEX] == EV_CURRENT) + + +def file_copy_offset(in_fp, in_off, out_fp, out_off, num_bytes): + in_fp.seek(in_off) + read_in = in_fp.read(num_bytes) + out_fp.seek(out_off) + out_fp.write(read_in) + + return num_bytes + + +def preprocess_elf_file(elf_file_name): + with open(elf_file_name, 'rb') as elf_fp: + elf_header = Elf_Ehdr_common(elf_fp.read(Elf_Ehdr_common.s.size)) + + if verify_elf_header(elf_header) is False: + raise RuntimeError("Input ELF verification failed : " + elf_file_name) + + elf_fp.seek(0) + + if is_elf64(elf_header): + elf_header = Elf64_Ehdr(elf_fp.read(Elf64_Ehdr.s.size)) + else: + elf_header = Elf32_Ehdr(elf_fp.read(Elf32_Ehdr.s.size)) + + phdr_table = [] + + phdr_size = elf_header.e_phentsize + + file_offset = elf_header.e_phoff + elf_fp.seek(file_offset) + + for _ in range(elf_header.e_phnum): + if is_elf64(elf_header): + phdr_table.append(Elf64_Phdr(elf_fp.read(phdr_size))) + else: + phdr_table.append(Elf32_Phdr(elf_fp.read(phdr_size))) + + return [elf_header, phdr_table] + + +def process_section_header(elf_file_name): + with open(elf_file_name, 'rb') as elf_fp: + elf_header = Elf_Ehdr_common(elf_fp.read(Elf_Ehdr_common.s.size)) + + if verify_elf_header(elf_header) is False: + raise RuntimeError("Input ELF verification failed : " + elf_file_name) + + elf_fp.seek(0) + + if is_elf64(elf_header): + elf_header = Elf64_Ehdr(elf_fp.read(Elf64_Ehdr.s.size)) + else: + elf_header = Elf32_Ehdr(elf_fp.read(Elf32_Ehdr.s.size)) + + shdr_table = [] + + shdr_size = elf_header.e_shentsize + elf_fp.seek(elf_header.e_shoff) + + for _ in range(elf_header.e_shnum): + if is_elf64(elf_header): + shdr_table.append(Elf64_Shdr(elf_fp.read(shdr_size))) + else: + shdr_table.append(Elf32_Shdr(elf_fp.read(shdr_size))) + + return shdr_table + + +def crc32_update_file(elf_fp, offset, size, crc): + elf_fp.seek(offset) + remaining = size + while remaining > 0: + chunk = elf_fp.read(min(1024 * 1024, remaining)) + if not chunk: + raise RuntimeError("Unexpected EOF while calculating CRC") + crc = zlib.crc32(chunk, crc) + remaining -= len(chunk) + return crc + + +def crc32_update_zeros(size, crc): + zero_chunk = b'\x00' * min(1024 * 1024, size) + remaining = size + while remaining > 0: + chunk = zero_chunk[:min(len(zero_chunk), remaining)] + crc = zlib.crc32(chunk, crc) + remaining -= len(chunk) + return crc + + +def crc32_calculate(elf_fp, phdr_table): + crc = 0 + + # SWIV validates loadable segment contents, including BSS as zeros. + for curr_phdr in phdr_table: + if curr_phdr.p_type != LOAD_TYPE: + continue + + crc = crc32_update_file(elf_fp, curr_phdr.p_offset, curr_phdr.p_filesz, crc) + + zero_size = curr_phdr.p_memsz - curr_phdr.p_filesz + if zero_size > 0: + crc = crc32_update_zeros(zero_size, crc) + + crc &= 0xffffffff + logger.info("calculated CRC: %#x", crc) + + return crc + + +def verify_swiv_seg(elf_in_fp, offset): + + elf_in_fp.seek(offset) + swiv_seg = SWIV_Seg() + unpacked_data = (SWIV_Seg.s).unpack(elf_in_fp.read(SWIV_SEG_SIZE_MIN)) + swiv_seg.magic = unpacked_data[0] + + return swiv_seg.magic == SWIV_MAGIC + + +def same_output_file(input_elf, output_elf): + try: + return os.path.exists(output_elf) and os.path.samefile(input_elf, output_elf) + except FileNotFoundError: + return False + + +def make_output_target(input_elf, output_elf): + if not same_output_file(input_elf, output_elf): + return output_elf, None + + output_dir = os.path.dirname(os.path.abspath(output_elf)) + output_base = os.path.basename(output_elf) + fd, temp_output = tempfile.mkstemp(prefix=output_base + ".", suffix=".tmp", dir=output_dir) + os.close(fd) + shutil.copymode(input_elf, temp_output) + return temp_output, temp_output + + +def swiv_generate(input_elf, output_elf): + output_target, temp_output = make_output_target(input_elf, output_elf) + + try: + with open(input_elf, "rb") as elf_in_fp, open(output_target, "wb+") as elf_out_fp: + [elf_header, phdr_table] = preprocess_elf_file(input_elf) + + num_phdrs = elf_header.e_phnum + num_shdrs = elf_header.e_shnum + phdr_size = elf_header.e_phentsize + + if num_phdrs == 0 or num_phdrs > MAX_PHDR_COUNT: + raise RuntimeError("Input ELF has invalid number of program headers.") + + if elf_header.e_shoff == 0 or num_shdrs == 0: + raise RuntimeError("Input ELF does not contain a section header table.") + + shdr_table_input = process_section_header(input_elf) + shdr_table_output = process_section_header(input_elf) + + last_phdr = phdr_table[num_phdrs - 1] + if last_phdr.p_memsz == SWIV_SEG_SIZE_MIN and verify_swiv_seg(elf_in_fp, last_phdr.p_offset): + raise RuntimeError("A SWIV segment is detected in the input ELF.") + + last_shdr = shdr_table_input[elf_header.e_shnum - 1] + if last_shdr.sh_size == SWIV_SEG_SIZE_MIN and verify_swiv_seg(elf_in_fp, last_shdr.sh_offset): + raise RuntimeError("A SWIV section is detected in the input ELF.") + + if is_elf64(elf_header): + swiv_shdr = Elf64_Shdr(b'\0' * Elf64_Shdr.s.size) + else: + swiv_shdr = Elf32_Shdr(b'\0' * Elf32_Shdr.s.size) + + swiv_shdr.sh_name = 0 + swiv_shdr.sh_type = SWIV_SECTION_TYPE + swiv_shdr.sh_flags = 0 + swiv_shdr.sh_addr = 0 + swiv_shdr.sh_size = SWIV_SEG_SIZE_MIN + swiv_shdr.sh_offset = elf_header.e_shoff + (elf_header.e_shnum + 1) * elf_header.e_shentsize + shdr_table_output.append(swiv_shdr) + elf_header.e_shnum += 1 + + if elf_header.e_phoff != 0: + for phdr in phdr_table: + file_copy_offset(elf_in_fp, phdr.p_offset, elf_out_fp, phdr.p_offset, phdr.p_filesz) + + elf_out_fp.seek(0) + elf_out_fp.write(elf_header.get_packed_data()) + + phdr_start = elf_header.e_phoff + for phdr in phdr_table: + elf_out_fp.seek(phdr_start) + elf_out_fp.write(phdr.get_packed_data()) + phdr_start += phdr_size + + for shdr in shdr_table_input[:num_shdrs]: + if shdr.sh_type == SHT_NOBITS or shdr.sh_size == 0: + continue + file_copy_offset(elf_in_fp, shdr.sh_offset, elf_out_fp, shdr.sh_offset, shdr.sh_size) + + shdr_start = elf_header.e_shoff + for shdr in shdr_table_output: + elf_out_fp.seek(shdr_start) + elf_out_fp.write(shdr.get_packed_data()) + shdr_start += elf_header.e_shentsize + + elf_out_fp.flush() + + swiv_seg = SWIV_Seg() + swiv_seg.crc = crc32_calculate(elf_out_fp, phdr_table) + elf_out_fp.seek(swiv_shdr.sh_offset) + elf_out_fp.write(swiv_seg.get_packed_data()) + + if temp_output is not None: + os.replace(temp_output, output_elf) + temp_output = None + + return 0 + finally: + if temp_output is not None and os.path.exists(temp_output): + os.unlink(temp_output) + + +def main(argv): + parser = argparse.ArgumentParser(description="Add a Software Image Validation (SWIV) section to an HTP ELF file.") + parser.add_argument("-i", "--input", dest="input_file", metavar="INPUT", required=True) + parser.add_argument("-o", "--output", dest="output_file", metavar="OUTPUT", required=True) + parser.add_argument("-v", "--verbose", action="store_true", help="show calculated CRC") + args = parser.parse_args(argv) + + logging.basicConfig( + level=logging.INFO if args.verbose else logging.WARNING, + format="%(message)s", + ) + + try: + return swiv_generate(args.input_file, args.output_file) + except RuntimeError as exc: + logger.error("ERROR: %s", exc) + return 2 + + +if __name__ == "__main__": + sys.exit(main(sys.argv[1:])) diff --git a/ggml/src/ggml-hexagon/libggml-htp.inf b/ggml/src/ggml-hexagon/libggml-htp.inf index 874dde1b8878..ff6ac27cdbeb 100644 --- a/ggml/src/ggml-hexagon/libggml-htp.inf +++ b/ggml/src/ggml-hexagon/libggml-htp.inf @@ -18,6 +18,7 @@ libggml-htp-v73.so = 1 libggml-htp-v75.so = 1 libggml-htp-v79.so = 1 libggml-htp-v81.so = 1 +libggml-htp-v85.so = 1 [ControlFlags] ExcludeFromSelect = * @@ -30,6 +31,7 @@ libggml-htp-v73.so,,,0x10 ;COPYFLG_NO_OVERWRITE libggml-htp-v75.so,,,0x10 ;COPYFLG_NO_OVERWRITE libggml-htp-v79.so,,,0x10 ;COPYFLG_NO_OVERWRITE libggml-htp-v81.so,,,0x10 ;COPYFLG_NO_OVERWRITE +libggml-htp-v85.so,,,0x10 ;COPYFLG_NO_OVERWRITE [Strings] GGML = 'GGML' diff --git a/scripts/snapdragon/build.py b/scripts/snapdragon/build.py index 62a114e04c0c..05e51516742d 100755 --- a/scripts/snapdragon/build.py +++ b/scripts/snapdragon/build.py @@ -11,6 +11,9 @@ import shutil import logging import glob +import shlex +import tarfile +import tempfile from sdk import validate_windows_sdks @@ -19,19 +22,70 @@ def parse_target(target_str): if not target_str: - return None, None - if target_str.startswith("adb") or target_str.startswith("android"): - parts = target_str.split(":", 1) - serial = parts[1] if len(parts) > 1 else None - return "android", serial - elif target_str.startswith("lnx") or target_str.startswith("linux") or target_str.startswith("ubuntu"): - parts = target_str.split(":", 1) - host = parts[1] if len(parts) > 1 else None - return "linux", host + return None, None, None + + parts = target_str.split(":") + target_name = parts[0] + + if target_name in ("adb", "android"): + serial = ":".join(parts[1:]) if len(parts) > 1 else None + return "android", serial, "adb" + elif target_name in ("lnx", "linux", "ubuntu"): + if len(parts) > 1 and parts[1] == "adb": + serial = ":".join(parts[2:]) if len(parts) > 2 else None + return "linux", serial, "adb" + host = ":".join(parts[1:]) if len(parts) > 1 else None + return "linux", host, "ssh" elif target_str in ("wos", "windows"): - return "windows", None + return "windows", None, None else: - return None, None + return None, None, None + + +def default_target_dir(target_type, target_transport, home_dir): + if target_type == "android": + return "/data/local/tmp/llama.cpp" + if target_type == "linux": + if home_dir: + return home_dir.rstrip("/") + "/llama.cpp" + if target_transport == "adb": + return "/home/llama.cpp" + return "~/llama.cpp" + return "~/llama.cpp" + + +def normalize_target_dir(target_dir): + target_dir = target_dir.rstrip("/") + if not target_dir or target_dir == "/": + raise RuntimeError("target directory must not be the filesystem root") + return target_dir + + +def adb_root(adb_cmd): + root_cmd = adb_cmd + ["root"] + logger.info(f"+ {' '.join(root_cmd)}") + res = subprocess.run(root_cmd) + if res.returncode != 0: + logger.error("ADB root failed.") + sys.exit(res.returncode) + + wait_cmd = adb_cmd + ["wait-for-device"] + logger.info(f"+ {' '.join(wait_cmd)}") + res = subprocess.run(wait_cmd) + if res.returncode != 0: + logger.error("ADB wait-for-device failed.") + sys.exit(res.returncode) + + +def make_package_archive(src_path): + fd, archive_path = tempfile.mkstemp(prefix="llama-cpp-", suffix=".tar.gz") + os.close(fd) + with tarfile.open(archive_path, "w:gz", dereference=False) as tar: + for item in os.listdir(src_path): + if item.startswith("."): + continue + tar.add(os.path.join(src_path, item), arcname=item, recursive=True) + return archive_path def get_uid_gid(): @@ -45,7 +99,7 @@ def main(): parser = argparse.ArgumentParser( description="Build llama.cpp for Snapdragon using cross-compilation docker containers or natively." ) - parser.add_argument("--target", default="android", help="Compilation target and deployment definition (e.g. android[:serial]/adb[:serial], linux:[user@]host/lnx:[user@]host/ubuntu:[user@]host, windows/wos) (default: android)") + parser.add_argument("--target", default="android", help="Compilation target and deployment definition (e.g. android[:serial]/adb[:serial], ubuntu:adb[:serial], linux:[user@]host/lnx:[user@]host/ubuntu:[user@]host, windows/wos) (default: android)") parser.add_argument("--build-dir", help="Build directory name (defaults to build-TARGET[-dbg], e.g. build-android)") parser.add_argument("--install-dir", help="Install directory name (defaults to pkg-TARGET[-dbg], e.g. pkg-android)") parser.add_argument("--jobs", "-j", type=int, help="Number of build jobs (defaults to CPU thread count)") @@ -53,21 +107,22 @@ def main(): parser.add_argument("--preset", help="Override the CMake preset to use") parser.add_argument("--debug", action="store_true", help="Build in debug mode (uses -debug presets instead of -release)") parser.add_argument("--hex-only", "--hexagon-only", action="store_true", help="Only build and push ggml-hexagon and libggml-htp libraries") - parser.add_argument("--hex-arch", choices=["v73", "v75", "v79", "v81", "73", "75", "79", "81"], help="Specific Hexagon DSP architecture to build with --hex-only (default: all)") + parser.add_argument("--hex-arch", choices=["v73", "v75", "v79", "v81", "v85", "73", "75", "79", "81", "85"], help="Specific Hexagon DSP architecture to build with --hex-only (default: all)") # Push options parser.add_argument("--push", action="store_true", help="Push built package to the target device via ADB or SSH/SCP") - parser.add_argument("--target-dir", help="Target directory on the device (default: /data/local/tmp/llama.cpp for Android, ~/llama.cpp for Linux)") + parser.add_argument("--target-dir", help="Target directory on the device (default: /data/local/tmp/llama.cpp for Android, ~/llama.cpp for Linux, /home/llama.cpp for Linux via ADB)") + parser.add_argument("--home-dir", help="Home directory for Linux targets when deriving the default target directory") # Toolchain options - parser.add_argument("--toolchain-version", default="v0.7", help="Docker toolchain image version/tag (default: v0.7)") + parser.add_argument("--toolchain-version", default="v0.8", help="Docker toolchain image version/tag (default: v0.8)") parser.add_argument("--toolchain-url", default="ghcr.io/snapdragon-toolchain", help="Docker toolchain registry URL/namespace (default: ghcr.io/snapdragon-toolchain)") args = parser.parse_args() - target_type, target_val = parse_target(args.target) + target_type, target_val, target_transport = parse_target(args.target) if not target_type: - logger.error(f"Error: Invalid target format '{args.target}'. Must be android[:serial]/adb[:serial], linux:[user@]host/lnx:[user@]host/ubuntu:[user@]host, or windows/wos.") + logger.error(f"Error: Invalid target format '{args.target}'. Must be android[:serial]/adb[:serial], ubuntu:adb[:serial], linux:[user@]host/lnx:[user@]host/ubuntu:[user@]host, or windows/wos.") sys.exit(1) if target_type == "windows": @@ -140,7 +195,7 @@ def main(): if hex_arch: hex_targets = ["ggml-hexagon", f"htp-{hex_arch}"] else: - hex_targets = ["ggml-hexagon", "htp-v73", "htp-v75", "htp-v79", "htp-v81"] + hex_targets = ["ggml-hexagon", "htp-v73", "htp-v75", "htp-v79", "htp-v81", "htp-v85"] if args.no_docker: # Native/local host build @@ -218,8 +273,8 @@ def main(): # Resolve target directory on device target_dir = args.target_dir if not target_dir: - target_dir = "/data/local/tmp/llama.cpp" if target_type == "android" else "~/llama.cpp" - target_dir = target_dir.rstrip("/") + target_dir = default_target_dir(target_type, target_transport, args.home_dir) + target_dir = normalize_target_dir(target_dir) sub_items = [item for item in os.listdir(src_path) if not item.startswith(".")] @@ -235,14 +290,23 @@ def main(): logger.error(f"Error: no hexagon libraries found in {lib_dir} to push.") sys.exit(1) - if target_type == "android": + if target_transport == "adb": adb_cmd = ["adb"] if target_val: # serial adb_cmd += ["-s", target_val] + if target_type == "linux": + adb_root(adb_cmd) + if args.hex_only: - logger.info("\nPushing Hexagon libraries to Android device via ADB...") + logger.info("\nPushing Hexagon libraries to target device via ADB...") target_lib_dir = f"{target_dir}/lib" + mkdir_cmd = adb_cmd + ["shell", f"mkdir -p {shlex.quote(target_lib_dir)}"] + logger.info(f"+ {' '.join(mkdir_cmd)}") + res = subprocess.run(mkdir_cmd) + if res.returncode != 0: + logger.error("ADB mkdir failed.") + sys.exit(res.returncode) for lib in hex_libs: push_cmd = adb_cmd + ["push", lib, f"{target_lib_dir}/"] logger.info(f"+ {' '.join(push_cmd)}") @@ -251,6 +315,44 @@ def main(): logger.error("ADB push failed.") sys.exit(res.returncode) logger.info("Hexagon libraries deployed successfully!") + elif target_type == "linux": + logger.info("\nDeploying Linux package to target device via ADB tar...") + remote_archive = f"/tmp/llama.cpp-package-{os.getpid()}.tar.gz" + archive_path = make_package_archive(src_path) + try: + mkdir_cmd = adb_cmd + ["shell", f"mkdir -p {shlex.quote(target_dir)}"] + logger.info(f"+ {' '.join(mkdir_cmd)}") + res = subprocess.run(mkdir_cmd) + if res.returncode != 0: + logger.error("ADB mkdir failed.") + sys.exit(res.returncode) + + if sub_items: + clean_paths = " ".join(shlex.quote(f"{target_dir}/{item}") for item in sub_items) + clean_cmd = adb_cmd + ["shell", f"rm -rf {clean_paths}"] + logger.info(f"+ {' '.join(clean_cmd)}") + subprocess.run(clean_cmd) + + push_cmd = adb_cmd + ["push", archive_path, remote_archive] + logger.info(f"+ {' '.join(push_cmd)}") + res = subprocess.run(push_cmd) + if res.returncode != 0: + logger.error("ADB push failed.") + sys.exit(res.returncode) + + extract_cmd = adb_cmd + ["shell", f"tar -xzf {shlex.quote(remote_archive)} -C {shlex.quote(target_dir)} && rm -f {shlex.quote(remote_archive)}"] + logger.info(f"+ {' '.join(extract_cmd)}") + res = subprocess.run(extract_cmd) + if res.returncode != 0: + logger.error("ADB untar failed.") + sys.exit(res.returncode) + + chmod_cmd = adb_cmd + ["shell", f"chmod -R 755 {shlex.quote(target_dir)}/bin 2>/dev/null || true"] + logger.info(f"+ {' '.join(chmod_cmd)}") + subprocess.run(chmod_cmd) + finally: + os.unlink(archive_path) + logger.info("ADB tar deploy completed successfully!") else: # Clean stale package files on device if sub_items: diff --git a/scripts/snapdragon/ggml-hexagon-inspect.py b/scripts/snapdragon/ggml-hexagon-inspect.py index 7b0e2aefa37d..a3572b3e87b7 100755 --- a/scripts/snapdragon/ggml-hexagon-inspect.py +++ b/scripts/snapdragon/ggml-hexagon-inspect.py @@ -255,7 +255,7 @@ def __init__( use_docker: bool = False, image_url: str = "ghcr.io/snapdragon-toolchain", image_name: str = "arm64-android", - image_ver: str = "v0.7", + image_ver: str = "v0.8", ): self.repo_root = repo_root self.image = f"{image_url}/{image_name}:{image_ver}" @@ -322,15 +322,14 @@ def run_tool(self, tool_name: str, args: List[str], stdin_data: Optional[str] = if not self.docker_bin: raise RuntimeError("Docker is required but not installed or found on PATH.") - container_tools_dir = "/opt/hexagon/6.6.0.0/tools/HEXAGON_Tools/19.0.07/Tools/bin" if "objdump" in tool_name: - container_tool = f"{container_tools_dir}/hexagon-llvm-objdump" + tool_bin = "hexagon-llvm-objdump" elif "addr2line" in tool_name: - container_tool = f"{container_tools_dir}/hexagon-addr2line" + tool_bin = "hexagon-addr2line" elif "nm" in tool_name: - container_tool = f"{container_tools_dir}/llvm-nm" + tool_bin = "llvm-nm" else: - container_tool = f"{container_tools_dir}/{tool_name}" + tool_bin = tool_name # Translate file paths from host to /workspace translated_args = [translate_container_arg(arg, self.repo_root) for arg in args] @@ -350,7 +349,9 @@ def run_tool(self, tool_name: str, args: List[str], stdin_data: Optional[str] = if platform.system() != "Windows": docker_cmd += ["-u", f"{os.getuid()}:{os.getgid()}"] - docker_cmd += [self.image, container_tool] + translated_args + default_tools_root = "/opt/hexagon/6.7.0.0/tools/HEXAGON_Tools/21.0.04" + sh_cmd = f'exec "${{HEXAGON_TOOLS_ROOT:-{default_tools_root}}}/Tools/bin/{tool_bin}" "$@"' + docker_cmd += [self.image, "sh", "-c", sh_cmd, "--"] + translated_args res = subprocess.run(docker_cmd, capture_output=True, text=True, input=stdin_data) if res.returncode != 0: @@ -1457,8 +1458,8 @@ def main(): ) parser.add_argument( "--toolchain-version", - default="v0.7", - help="Docker toolchain tag (default: v0.7).", + default="v0.8", + help="Docker toolchain tag (default: v0.8).", ) parser.add_argument( "--toolchain-url", diff --git a/scripts/snapdragon/run.py b/scripts/snapdragon/run.py index 7e11d90de842..99b43bb2b347 100755 --- a/scripts/snapdragon/run.py +++ b/scripts/snapdragon/run.py @@ -49,27 +49,46 @@ "GGML_OPENCL_ADRENO_XMEM_GEMM", "GGML_OPENCL_ADRENO_USE_LARGE_BUFFER", "GGML_SCHED_DEBUG", + "ADSP_LIBRARY_PATH", + "CDSP_LIBRARY_PATH", + "CDSP1_LIBRARY_PATH", + "CDSP2_LIBRARY_PATH", + "CDSP3_LIBRARY_PATH", "MTMD_BACKEND_DEVICE", "D", "DEVICE", ) +DSP_LIBRARY_ENV_NAMES = ( + "ADSP_LIBRARY_PATH", + "CDSP_LIBRARY_PATH", + "CDSP1_LIBRARY_PATH", + "CDSP2_LIBRARY_PATH", + "CDSP3_LIBRARY_PATH", +) + + def parse_target(target_str): if not target_str: - return None, None - if target_str.startswith("adb") or target_str.startswith("android"): - parts = target_str.split(":", 1) - serial = parts[1] if len(parts) > 1 else None - return "android", serial - elif target_str.startswith("lnx") or target_str.startswith("linux") or target_str.startswith("ubuntu"): - parts = target_str.split(":", 1) - host = parts[1] if len(parts) > 1 else None - return "linux", host + return None, None, None + + parts = target_str.split(":") + target_name = parts[0] + + if target_name in ("adb", "android"): + serial = ":".join(parts[1:]) if len(parts) > 1 else None + return "android", serial, "adb" + elif target_name in ("lnx", "linux", "ubuntu"): + if len(parts) > 1 and parts[1] == "adb": + serial = ":".join(parts[2:]) if len(parts) > 2 else None + return "linux", serial, "adb" + host = ":".join(parts[1:]) if len(parts) > 1 else None + return "linux", host, "ssh" elif target_str in ("wos", "windows"): - return "windows", None + return "windows", None, None else: - return None, None + return None, None, None def shlex_join(args_list): @@ -79,6 +98,41 @@ def shlex_join(args_list): return " ".join(pipes.quote(x) for x in args_list) +def default_target_dir(target_type, target_transport, home_dir): + if target_type == "android": + return "/data/local/tmp/llama.cpp" + if target_type == "linux": + if home_dir: + return home_dir.rstrip("/") + "/llama.cpp" + if target_transport == "adb": + return "/home/llama.cpp" + return "~/llama.cpp" + return "~/llama.cpp" + + +def library_env_parts(lib_dir): + env_parts = [f"LD_LIBRARY_PATH={lib_dir}"] + for name in DSP_LIBRARY_ENV_NAMES: + env_parts.append(f"{name}={lib_dir}") + return env_parts + + +def adb_root(adb_base): + root_cmd = adb_base + ["root"] + logger.info(f"+ {' '.join(root_cmd)}") + res = subprocess.run(root_cmd) + if res.returncode != 0: + logger.error("ADB root failed.") + sys.exit(res.returncode) + + wait_cmd = adb_base + ["wait-for-device"] + logger.info(f"+ {' '.join(wait_cmd)}") + res = subprocess.run(wait_cmd) + if res.returncode != 0: + logger.error("ADB wait-for-device failed.") + sys.exit(res.returncode) + + def split_device_list(devices): parts = [] curr = [] @@ -144,8 +198,9 @@ def main(): parser = argparse.ArgumentParser( description="Unified runner for llama.cpp tools on Snapdragon (natively, via ADB, or via SSH)." ) - parser.add_argument("--target", help="Execution target (e.g. android[:serial]/adb[:serial], linux:[user@]host/lnx:[user@]host/ubuntu:[user@]host, windows/wos) (default: local run)") - parser.add_argument("--target-dir", help="Target directory on the device (default: /data/local/tmp/llama.cpp for Android, ~/llama.cpp for Linux)") + parser.add_argument("--target", help="Execution target (e.g. android[:serial]/adb[:serial], ubuntu:adb[:serial], linux:[user@]host/lnx:[user@]host/ubuntu:[user@]host, windows/wos) (default: local run)") + parser.add_argument("--target-dir", help="Target directory on the device (default: /data/local/tmp/llama.cpp for Android, ~/llama.cpp for Linux, /home/llama.cpp for Linux via ADB)") + parser.add_argument("--home-dir", help="Home directory for Linux targets when deriving the default target directory") parser.add_argument("--install-dir", help="Install directory name (defaults to pkg-TARGET or pkg-TARGET-dbg prefix based on target)") parser.add_argument("--debug", action="store_true", help="Use debug build (defaults to pkg-TARGET-dbg folder)") parser.add_argument("--devices", "--device", "-d", help="Select execution devices (split into NPU and OpenCL GPUs automatically, default: HTP0)") @@ -201,11 +256,12 @@ def main(): target_type = None target_val = None + target_transport = None target_prefix = None if args.target: - target_type, target_val = parse_target(args.target) + target_type, target_val, target_transport = parse_target(args.target) if not target_type: - logger.error(f"Error: Invalid target format '{args.target}'. Must be android[:serial]/adb[:serial], linux:[user@]host/lnx:[user@]host/ubuntu:[user@]host, or windows/wos.") + logger.error(f"Error: Invalid target format '{args.target}'. Must be android[:serial]/adb[:serial], ubuntu:adb[:serial], linux:[user@]host/lnx:[user@]host/ubuntu:[user@]host, or windows/wos.") sys.exit(1) target_prefix = args.target.split(":", 1)[0] @@ -435,18 +491,19 @@ def set_env(env_name, opt_val): # Resolve target directory on device target_dir = args.target_dir if not target_dir: - target_dir = "/data/local/tmp/llama.cpp" if target_type == "android" else "~/llama.cpp" + target_dir = default_target_dir(target_type, target_transport, args.home_dir) - if target_type == "android": - # Run via ADB + if target_transport == "adb": adb_base = ["adb"] if target_val: # serial adb_base += ["-s", target_val] - env_parts = [ - "LD_LIBRARY_PATH=./lib", - "ADSP_LIBRARY_PATH=./lib" - ] + if target_type == "linux": + adb_root(adb_base) + + env_parts = library_env_parts("./lib") + if target_type == "linux": + env_parts.insert(0, f"HOME={args.home_dir or '/home'}") for k, v in env_vars.items(): env_parts.append(f"{k}={v}") env_str = " ".join(env_parts) @@ -466,10 +523,7 @@ def set_env(env_name, opt_val): sys.exit(1) # Linux remote run via SSH - env_parts = [ - "LD_LIBRARY_PATH=./lib", - "ADSP_LIBRARY_PATH=./lib" - ] + env_parts = library_env_parts("./lib") for k, v in env_vars.items(): env_parts.append(f"{k}={v}") env_str = " ".join(env_parts) @@ -490,7 +544,6 @@ def set_env(env_name, opt_val): # Run locally local_env = os.environ.copy() lib_dir = os.path.normpath(os.path.join(package_path, "lib")) - local_env["ADSP_LIBRARY_PATH"] = lib_dir if platform.system() == "Windows": local_env["PATH"] = lib_dir + os.path.pathsep + local_env.get("PATH", "") else: @@ -498,6 +551,8 @@ def set_env(env_name, opt_val): for k in MANAGED_ENV_NAMES: local_env.pop(k, None) + for name in DSP_LIBRARY_ENV_NAMES: + local_env[name] = lib_dir for k, v in env_vars.items(): local_env[k] = v diff --git a/scripts/snapdragon/sdk.py b/scripts/snapdragon/sdk.py index bb3cb77b2ad4..72b019346ebe 100644 --- a/scripts/snapdragon/sdk.py +++ b/scripts/snapdragon/sdk.py @@ -6,7 +6,7 @@ { "name": "Hexagon SDK", "repo": "snapdragon-toolchain/hexagon-sdk", - "default_version": "6.6.0.0", + "default_version": "6.7.0.0", "parent_dir": "Hexagon_SDK", "archive_prefix": "hexagon-sdk-v", "markers": ("hexagon_sdk.json",), From 7879491cf4c1cfb25acbd78fae34a9e7a8e3a062 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Tue, 20 Oct 2026 10:40:15 +0300 Subject: [PATCH 04/69] tests : make MoE router selection independent of the backend noise (#30818) * tests : make MoE router selection independent of the backend noise The router scores are in the range [0, 1), so a random draw can leave the last expert (or expert group) still selected and the first one dropped tied within the noise between the backends. They then route a token to different experts and the logits differ by 4e-3 instead of the usual 5e-7. Beam is the only arch here with a real top-k (8 experts, 2 used) and a group top-k (4 groups, 2 used), so it is the only one that hits this: 2 of 20 seeds failed on an M2 Ultra, the other MoE archs select 2 of 2 and cannot tie. Fill the selection bias with a ramp of step 2 to give every expert a score range of its own, which turns the top-k into a function of the expert index alone. The bias affects only the selection, the weights still come from the unbiased probs, so the MoE output keeps its magnitude. Beam now matches the CPU reference within 4e-7 for all of 160 seeds. Assisted-by: pi:llama.cpp/ggml-org/Qwen3.8-Flash-Next-GGUF (MacBook, M5 Max, 128GB) * tests : tell the CPU and the GPU of an Apple SoC apart in the device table Both devices report the same description ("Apple M2 Ultra" here), so the device column of the test table had two indistinguishable rows and a failing row did not say whether the Metal device or the host CPU produced it. Add the device name, which is also what -b takes, unless the description already is the name. Assisted-by: pi:llama.cpp/ggml-org/Qwen3.8-Flash-Next-GGUF (MacBook, M5 Max, 128GB) --- tests/test-llama-archs.cpp | 26 +++++++++++++++++++++++--- 1 file changed, 23 insertions(+), 3 deletions(-) diff --git a/tests/test-llama-archs.cpp b/tests/test-llama-archs.cpp index 94a22ff2f57f..21d27e17d930 100644 --- a/tests/test-llama-archs.cpp +++ b/tests/test-llama-archs.cpp @@ -85,10 +85,18 @@ static void fill_resid_gate(std::vector & dst, const char * name, const t } } +// a random router bias can tie within the noise between the backends, a ramp keeps the top-k selection the same +static void fill_exp_probs_b(std::vector & dst, const char *, const tensor_data_params &) { + for (size_t i = 0; i < dst.size(); i++) { + dst[i] = 2.0f*i; + } +} + // a tensor whose values hold a property of their own, matched on a substring of its name, first match wins static const struct { const char * match; tensor_filler fill; } TENSOR_FILLERS[] = { - { "ssm_a", fill_ssm_a }, - { "_resid_", fill_resid_gate }, + { "ssm_a", fill_ssm_a }, + { "_resid_", fill_resid_gate }, + { "exp_probs_b", fill_exp_probs_b }, }; static void set_tensor_data(struct ggml_tensor * tensor, void * userdata) { @@ -934,6 +942,18 @@ static int save_models(const std::string & arch_filter, const size_t seed, const return 0; } +// the CPU and the GPU of an Apple SoC carry the same description, the name tells them apart (CPU, MTL0) +static std::string get_device_label(ggml_backend_dev_t dev) { + const char * desc = ggml_backend_dev_description(dev); + const char * name = ggml_backend_dev_name(dev); + + if (strcmp(desc, name) == 0) { + return desc; + } + + return std::string(desc) + " (" + name + ")"; +} + static int test_backends(const std::string & arch_filter, const size_t seed, const float stdev, const int verbosity, const char * target_backend) { struct user_data_t { struct { @@ -986,7 +1006,7 @@ static int test_backends(const std::string & arch_filter, const size_t seed, con if (target_backend != nullptr && strcmp(target_backend, ggml_backend_dev_name(dev)) != 0) { continue; } - dev_configs.emplace_back(std::vector{dev}, ggml_backend_dev_description(dev), LLAMA_SPLIT_MODE_LAYER); + dev_configs.emplace_back(std::vector{dev}, get_device_label(dev), LLAMA_SPLIT_MODE_LAYER); max_device_label_length = std::max(max_device_label_length, dev_configs.back().label.length()); // cpu-based devices cannot be used in tensor split mode From d8d92973c8c21bb4dd5d592f0fec26fe2cd1e654 Mon Sep 17 00:00:00 2001 From: tkran2 <157059462+tkran2@users.noreply.github.com> Date: Tue, 20 Oct 2026 03:37:46 -0500 Subject: [PATCH 05/69] vocab : add \p{M} to lookaheads in Tekken and GPT-4o pre-tokenizer regexes (#30845) --- src/llama-vocab.cpp | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/llama-vocab.cpp b/src/llama-vocab.cpp index a237a4b044e1..84f50d782681 100644 --- a/src/llama-vocab.cpp +++ b/src/llama-vocab.cpp @@ -418,7 +418,7 @@ struct llm_tokenizer_bpe : llm_tokenizer { // original regex from tokenizer.json // "[^\\r\\n\\p{L}\\p{N}]?[\\p{Lu}\\p{Lt}\\p{Lm}\\p{Lo}\\p{M}]*[\\p{Ll}\\p{Lm}\\p{Lo}\\p{M}]+|[^\\r\\n\\p{L}\\p{N}]?[\\p{Lu}\\p{Lt}\\p{Lm}\\p{Lo}\\p{M}]+[\\p{Ll}\\p{Lm}\\p{Lo}\\p{M}]*|\\p{N}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n/]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+" regex_exprs = { - "[^\\r\\n\\p{L}\\p{N}]?((?=[\\p{L}])([^a-z]))*((?=[\\p{L}])([^A-Z]))+|[^\\r\\n\\p{L}\\p{N}]?((?=[\\p{L}])([^a-z]))+((?=[\\p{L}])([^A-Z]))*|\\p{N}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n/]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+", + "[^\\r\\n\\p{L}\\p{N}]?((?=[\\p{L}\\p{M}])([^a-z]))*((?=[\\p{L}\\p{M}])([^A-Z]))+|[^\\r\\n\\p{L}\\p{N}]?((?=[\\p{L}\\p{M}])([^a-z]))+((?=[\\p{L}\\p{M}])([^A-Z]))*|\\p{N}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n/]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+", }; break; case LLAMA_VOCAB_PRE_TYPE_CHAMELEON: @@ -440,7 +440,7 @@ struct llm_tokenizer_bpe : llm_tokenizer { regex_exprs = { // original regex from tokenizer.json // "[^\\r\\n\\p{L}\\p{N}]?[\\p{Lu}\\p{Lt}\\p{Lm}\\p{Lo}\\p{M}]*[\\p{Ll}\\p{Lm}\\p{Lo}\\p{M}]+(?i:'s|'t|'re|'ve|'m|'ll|'d)?|[^\\r\\n\\p{L}\\p{N}]?[\\p{Lu}\\p{Lt}\\p{Lm}\\p{Lo}\\p{M}]+[\\p{Ll}\\p{Lm}\\p{Lo}\\p{M}]*(?i:'s|'t|'re|'ve|'m|'ll|'d)?|\\p{N}{1,3}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n/]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+", - "[^\\r\\n\\p{L}\\p{N}]?((?=[\\p{L}])([^a-z]))*((?=[\\p{L}])([^A-Z]))+(?:'[sS]|'[tT]|'[rR][eE]|'[vV][eE]|'[mM]|'[lL][lL]|'[dD])?|[^\\r\\n\\p{L}\\p{N}]?((?=[\\p{L}])([^a-z]))+((?=[\\p{L}])([^A-Z]))*(?:'[sS]|'[tT]|'[rR][eE]|'[vV][eE]|'[mM]|'[lL][lL]|'[dD])?|\\p{N}{1,3}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n/]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+", + "[^\\r\\n\\p{L}\\p{N}]?((?=[\\p{L}\\p{M}])([^a-z]))*((?=[\\p{L}\\p{M}])([^A-Z]))+(?:'[sS]|'[tT]|'[rR][eE]|'[vV][eE]|'[mM]|'[lL][lL]|'[dD])?|[^\\r\\n\\p{L}\\p{N}]?((?=[\\p{L}\\p{M}])([^a-z]))+((?=[\\p{L}\\p{M}])([^A-Z]))*(?:'[sS]|'[tT]|'[rR][eE]|'[vV][eE]|'[mM]|'[lL][lL]|'[dD])?|\\p{N}{1,3}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n/]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+", }; break; case LLAMA_VOCAB_PRE_TYPE_GRANITE_EMB_MULTI: From 707616feb5f379e99ff7442680a55b4a1704c7c9 Mon Sep 17 00:00:00 2001 From: Christian Kastner Date: Tue, 20 Oct 2026 12:38:45 +0200 Subject: [PATCH 06/69] ci: Fix --strict mode for API check (#30623) abi-compliance-checker was running in strict mode unconditionally, rather than honoring the CLI flag. --- scripts/check-apiabi-compat.sh | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/scripts/check-apiabi-compat.sh b/scripts/check-apiabi-compat.sh index 8075aaf9f1fe..229602f445e9 100755 --- a/scripts/check-apiabi-compat.sh +++ b/scripts/check-apiabi-compat.sh @@ -244,8 +244,10 @@ elif [ "$DO_CHECK" -eq 1 ]; then # From here on, we don't want to exit on first error set +e + ACC_STRICT= + [ "$STRICT" -eq 0 ] || ACC_STRICT="-strict" abi-compliance-checker \ - -strict \ + $ACC_STRICT \ -source \ -library "$PROJECT_NAME" \ -old "$BUILD_DIR/apiabi/api.dump" \ From 704366d76f8748cf9ad3df62a464bfcc8d1ae6ad Mon Sep 17 00:00:00 2001 From: Caladrel Date: Tue, 20 Oct 2026 06:02:05 -0500 Subject: [PATCH 07/69] metal : fix missing MoE kernel variant for 9 experts per token (#30610) (#30699) --- ggml/src/ggml-metal/kernels/mul_mm.metal | 1 + 1 file changed, 1 insertion(+) diff --git a/ggml/src/ggml-metal/kernels/mul_mm.metal b/ggml/src/ggml-metal/kernels/mul_mm.metal index dfa5102c56f8..c313cb8e2188 100644 --- a/ggml/src/ggml-metal/kernels/mul_mm.metal +++ b/ggml/src/ggml-metal/kernels/mul_mm.metal @@ -593,6 +593,7 @@ template [[host_name("kernel_mul_mm_id_map0_ne20_4" )]] kernel kernel_mul_mm_id_ template [[host_name("kernel_mul_mm_id_map0_ne20_5" )]] kernel kernel_mul_mm_id_map0_t kernel_mul_mm_id_map0<5>; template [[host_name("kernel_mul_mm_id_map0_ne20_6" )]] kernel kernel_mul_mm_id_map0_t kernel_mul_mm_id_map0<6>; template [[host_name("kernel_mul_mm_id_map0_ne20_8" )]] kernel kernel_mul_mm_id_map0_t kernel_mul_mm_id_map0<8>; +template [[host_name("kernel_mul_mm_id_map0_ne20_9" )]] kernel kernel_mul_mm_id_map0_t kernel_mul_mm_id_map0<9>; template [[host_name("kernel_mul_mm_id_map0_ne20_10")]] kernel kernel_mul_mm_id_map0_t kernel_mul_mm_id_map0<10>; template [[host_name("kernel_mul_mm_id_map0_ne20_16")]] kernel kernel_mul_mm_id_map0_t kernel_mul_mm_id_map0<16>; template [[host_name("kernel_mul_mm_id_map0_ne20_22")]] kernel kernel_mul_mm_id_map0_t kernel_mul_mm_id_map0<22>; From d80619397c2ba5cf1366c29f7d43699ca3a159b9 Mon Sep 17 00:00:00 2001 From: Peter van Velzen Date: Tue, 20 Oct 2026 13:23:41 +0200 Subject: [PATCH 08/69] server : pass id_slot through the Anthropic /v1/messages conversion (#28554) * server : pass id_slot through the Anthropic /v1/messages conversion server_chat_convert_anthropic_to_oai copies a small whitelist of common params from the Anthropic request into the internal OAI-style body and left id_slot out. Every other completion endpoint honours id_slot; a proxy that pins a session to the slot holding its KV cache needs the same on /v1/messages, or it silently falls back to automatic slot selection and, on hybrid/recurrent models, pays a full re-prefill. Adds a test asserting a request with id_slot set is served by that slot, observed via GET /slots. * server : shorten comments * rm test --------- Co-authored-by: Xuan Son Nguyen --- tools/server/server-chat.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tools/server/server-chat.cpp b/tools/server/server-chat.cpp index a3a4ea152fb3..c5473417c301 100644 --- a/tools/server/server-chat.cpp +++ b/tools/server/server-chat.cpp @@ -590,7 +590,7 @@ json server_chat_convert_anthropic_to_oai(const json & body) { } // Pass through common params - for (const auto & key : {"temperature", "top_p", "top_k", "stream", "chat_template_kwargs"}) { + for (const auto & key : {"temperature", "top_p", "top_k", "stream", "chat_template_kwargs", "id_slot"}) { if (body.contains(key)) { oai_body[key] = body.at(key); } From 13786fbb3a1aaf4ff445c0b92d9f21cbe0af6d42 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?B=C3=A1lint=20Bokros?= Date: Tue, 20 Oct 2026 13:30:50 +0200 Subject: [PATCH 09/69] CUDA: Clear CUDA graphs after legacy memory pool flush (#26771) Assisted-by: Claude Opus 5.5 --- ggml/src/ggml-cuda/common.cuh | 4 ++-- ggml/src/ggml-cuda/ggml-cuda.cu | 27 +++++++++++++++++++++++---- 2 files changed, 25 insertions(+), 6 deletions(-) diff --git a/ggml/src/ggml-cuda/common.cuh b/ggml/src/ggml-cuda/common.cuh index fea1cf9c406d..049100fe38e0 100644 --- a/ggml/src/ggml-cuda/common.cuh +++ b/ggml/src/ggml-cuda/common.cuh @@ -1551,11 +1551,11 @@ struct ggml_backend_cuda_context { // pool std::unique_ptr pools[GGML_CUDA_MAX_DEVICES][GGML_CUDA_MAX_STREAMS]; - static std::unique_ptr new_pool_for_device(int device, int stream_no); + static std::unique_ptr new_pool_for_device(ggml_backend_cuda_context & ctx, int device, int stream_no); ggml_cuda_pool & pool(int device) { if (pools[device][curr_stream_no] == nullptr) { - pools[device][curr_stream_no] = new_pool_for_device(device, curr_stream_no); + pools[device][curr_stream_no] = new_pool_for_device(*this, device, curr_stream_no); } return *pools[device][curr_stream_no]; } diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu index 2eb5e203efd3..d9b18cc08d55 100644 --- a/ggml/src/ggml-cuda/ggml-cuda.cu +++ b/ggml/src/ggml-cuda/ggml-cuda.cu @@ -457,6 +457,7 @@ const ggml_cuda_device_info & ggml_cuda_info() { struct ggml_cuda_pool_leg : public ggml_cuda_pool { static const int MAX_BUFFERS = 256; + ggml_backend_cuda_context & ctx; int device; struct ggml_cuda_buffer { void * ptr = nullptr; @@ -466,7 +467,8 @@ struct ggml_cuda_pool_leg : public ggml_cuda_pool { ggml_cuda_buffer buffer_pool[MAX_BUFFERS] = {}; size_t pool_size = 0; - explicit ggml_cuda_pool_leg(int device) : + ggml_cuda_pool_leg(ggml_backend_cuda_context & ctx, int device) : + ctx(ctx), device(device) { } @@ -488,6 +490,20 @@ struct ggml_cuda_pool_leg : public ggml_cuda_pool { } } + // captured CUDA graphs can still use freed buffers, so they must be captured again + void invalidate_cuda_graphs() { +#ifdef USE_CUDA_GRAPH + int n_captured = 0; + for (const auto & [key, graph] : ctx.cuda_graphs) { + n_captured += graph->instance != nullptr; + } + if (n_captured > 0) { + GGML_LOG_DEBUG(GGML_CUDA_NAME " pool[%d]: memory freed, %d captured CUDA graphs must be captured again\n", device, n_captured); + } + ctx.cuda_graphs.clear(); +#endif // USE_CUDA_GRAPH + } + void * alloc(size_t size, size_t * actual_size) override { #ifdef DEBUG_CUDA_MALLOC int nnz = 0; @@ -538,6 +554,7 @@ struct ggml_cuda_pool_leg : public ggml_cuda_pool { device, look_ahead_size/1024.0/1024.0, cached_bytes/1024.0/1024.0); CUDA_CHECK(cudaDeviceSynchronize()); clear_pool(); + invalidate_cuda_graphs(); err = ggml_cuda_device_malloc(&ptr, look_ahead_size, device); if (err == cudaSuccess) { GGML_LOG_DEBUG(GGML_CUDA_NAME " pool[%d]: retry succeeded\n", device); @@ -566,6 +583,7 @@ struct ggml_cuda_pool_leg : public ggml_cuda_pool { ggml_cuda_set_device(device); CUDA_CHECK(cudaFree(ptr)); pool_size -= size; + invalidate_cuda_graphs(); } }; @@ -720,14 +738,15 @@ struct ggml_cuda_pool_vmm : public ggml_cuda_pool { }; #endif // defined(GGML_USE_VMM) -std::unique_ptr ggml_backend_cuda_context::new_pool_for_device(int device, - [[maybe_unused]] int stream_no) { +std::unique_ptr ggml_backend_cuda_context::new_pool_for_device(ggml_backend_cuda_context & ctx, + int device, + [[maybe_unused]] int stream_no) { #if defined(GGML_USE_VMM) if (ggml_cuda_info().devices[device].vmm) { return std::unique_ptr(new ggml_cuda_pool_vmm(device)); } #endif // defined(GGML_USE_VMM) - return std::unique_ptr(new ggml_cuda_pool_leg(device)); + return std::unique_ptr(new ggml_cuda_pool_leg(ctx, device)); } // destroying a cuBLAS handle while a graph is being captured in a different thread can result in a CUDA error From 83c2d012a273ff1f58fc145fe4219c332e5dd3a3 Mon Sep 17 00:00:00 2001 From: Peter Sideris Date: Tue, 20 Oct 2026 14:31:10 +0300 Subject: [PATCH 10/69] spec : save the dynamic ngram cache file (#22055) * spec : save the dynamic ngram cache file * save the cache at the common_speculative_impl_ngram_cache destruction * remove save_static/save_dynamic flags as well as the relevant TODO * if the dynamic cache file does not exist, start with an empty cache that is written on exit. if it exists but cannot be read, abort instead of overwriting it. * spec : fix draft_one not updating the ngram cache history correctly * common_ngram_cache_update received only the new tokens with none of the tokens before them causing ngrams ending at the new token to not being formed, leading the context cache to only ever hold the prompt * fix: pass the full history to common_ngram_cache_update with nnew set to the number of new tokens the way llama-lookup does and also reset cache_size to 0 on each new request --------- Co-authored-by: Petros Sideris --- common/speculative.cpp | 108 ++++++++++++++++++++--------------------- 1 file changed, 53 insertions(+), 55 deletions(-) diff --git a/common/speculative.cpp b/common/speculative.cpp index d8afd4095498..5a0b5b5123b5 100644 --- a/common/speculative.cpp +++ b/common/speculative.cpp @@ -16,6 +16,7 @@ #include #include #include +#include #include #include #include @@ -2152,9 +2153,6 @@ struct common_speculative_impl_ngram_cache : public common_speculative_impl { uint16_t n_draft; - bool save_dynamic; - bool save_static; - struct seq_info { size_t cache_size = 0; // number of tokens in n-gram cache @@ -2168,54 +2166,72 @@ struct common_speculative_impl_ngram_cache : public common_speculative_impl { common_speculative_impl_ngram_cache( const common_params_speculative & params, uint32_t n_seq, - uint16_t n_draft, - const std::string & path_static, - const std::string & path_dynamic, - bool save_dynamic, - bool save_static) + uint16_t n_draft) : common_speculative_impl(COMMON_SPECULATIVE_TYPE_NGRAM_CACHE, n_seq, n_draft) , params(params.ngram_cache) , n_draft(n_draft) - , save_dynamic(save_dynamic) - , save_static(save_static) { + const auto & ngram = params.ngram_cache; + SPC_TRC("%s", "adding speculative implementation 'ngram-cache'\n"); SPC_TRC("- n_draft=%d, cache_static=%s, cache_dynamic=%s\n", n_draft, - path_static.empty() ? "none" : path_static.c_str(), - path_dynamic.empty() ? "none" : path_dynamic.c_str()); + ngram.lookup_cache_static.empty() ? "none" : ngram.lookup_cache_static.c_str(), + ngram.lookup_cache_dynamic.empty() ? "none" : ngram.lookup_cache_dynamic.c_str()); sinfos.resize(n_seq); - if (!path_static.empty()) { + if (!ngram.lookup_cache_static.empty()) { try { - auto ngram_cache_static = common_ngram_cache_load(path_static); + auto ngram_cache_static = common_ngram_cache_load(ngram.lookup_cache_static); for (auto & sinfo : sinfos) { sinfo.ngram_cache_static = ngram_cache_static; } - } catch (...) { - SPC_ERR("failed to open static lookup cache: %s", path_static.c_str()); + } catch (const std::ifstream::failure &) { + SPC_ERR("failed to open static lookup cache: %s\n", ngram.lookup_cache_static.c_str()); GGML_ABORT("Couldn't read static lookup cache"); } } - if (!path_dynamic.empty()) { + if (!ngram.lookup_cache_dynamic.empty()) { try { - auto ngram_cache_dynamic = common_ngram_cache_load(path_dynamic); + auto ngram_cache_dynamic = common_ngram_cache_load(ngram.lookup_cache_dynamic); for (auto & sinfo : sinfos) { sinfo.ngram_cache_dynamic = ngram_cache_dynamic; } - } catch (...) { - SPC_ERR("failed to open dynamic lookup cache: %s", path_dynamic.c_str()); - GGML_ABORT("Couldn't read dynamic lookup cache"); + } catch (const std::ifstream::failure &) { + std::error_code ec; + const bool exists = std::filesystem::exists(ngram.lookup_cache_dynamic, ec); + if (!exists && !ec) { + // missing file is created on destruction + SPC_WRN("dynamic lookup cache file does not exist: %s, starting with empty cache\n", + ngram.lookup_cache_dynamic.c_str()); + } else { + SPC_ERR("failed to open dynamic lookup cache: %s\n", ngram.lookup_cache_dynamic.c_str()); + GGML_ABORT("Couldn't read dynamic lookup cache"); + } } } } - void begin(llama_seq_id /*seq_id*/, const llama_tokens & /*prompt*/) override { - // noop + ~common_speculative_impl_ngram_cache() override { + if (params.lookup_cache_dynamic.empty() || sinfos.empty()) { + return; + } + + auto & dynamic_cache = sinfos.front().ngram_cache_dynamic; + for (auto & sinfo : sinfos) { + common_ngram_cache_merge(dynamic_cache, sinfo.ngram_cache_context); + } + + SPC_INF("saving dynamic lookup cache at %s\n", params.lookup_cache_dynamic.c_str()); + common_ngram_cache_save(dynamic_cache, params.lookup_cache_dynamic); + } + + void begin(llama_seq_id seq_id, const llama_tokens & /*prompt*/) override { + sinfos[seq_id].cache_size = 0; } void draft_one( @@ -2226,22 +2242,6 @@ struct common_speculative_impl_ngram_cache : public common_speculative_impl { const auto & prompt = *dparams.prompt; - if (sinfo.cache_size < prompt.size() + 1) { - llama_tokens tokens_new; - tokens_new.reserve(prompt.size() + 1 - sinfo.cache_size); - for (size_t j = sinfo.cache_size; j < prompt.size(); ++j) { - tokens_new.push_back(prompt[j]); - } - tokens_new.push_back(dparams.id_last); // add the last token - - // Update context ngram cache with new dparams.prompt: - common_ngram_cache_update( - sinfo.ngram_cache_context, - LLAMA_NGRAM_MIN, LLAMA_NGRAM_MAX, - tokens_new, tokens_new.size(), false); - sinfo.cache_size = prompt.size() + 1; - } - llama_tokens inp; inp.reserve(prompt.size() + 1); for (size_t j = 0; j < prompt.size(); ++j) { @@ -2249,6 +2249,16 @@ struct common_speculative_impl_ngram_cache : public common_speculative_impl { } inp.push_back(dparams.id_last); + if (sinfo.cache_size < inp.size()) { + // Update context ngram cache with the full history so n-grams + // ending at the new tokens have their preceding context + common_ngram_cache_update( + sinfo.ngram_cache_context, + LLAMA_NGRAM_MIN, LLAMA_NGRAM_MAX, + inp, inp.size() - sinfo.cache_size, false); + sinfo.cache_size = inp.size(); + } + result.push_back(dparams.id_last); common_ngram_cache_draft( @@ -2309,20 +2319,12 @@ static common_ngram_map get_common_ngram_map( return common_ngram_map(size_key, size_value, key_only, min_hits); } -static common_speculative_impl_ngram_cache create_state_ngram_cache( +static std::unique_ptr create_state_ngram_cache( const common_speculative_config & config, - uint32_t n_seq, - const std::string & path_static, - const std::string & path_dynamic) { + uint32_t n_seq) { uint16_t n_draft = 8; // TODO get from config? - // TODO bool param in common/common.h to set save_static/save_dynamic? - bool save_static = false; - bool save_dynamic = false; - - common_speculative_impl_ngram_cache state(config.params, n_seq, n_draft, path_static, path_dynamic, save_static, save_dynamic); - - return state; + return std::make_unique(config.params, n_seq, n_draft); } std::string common_speculative_type_name_str(const std::vector & types) { @@ -2822,11 +2824,7 @@ common_speculative * common_speculative_init(common_params_speculative & params, break; } case COMMON_SPECULATIVE_TYPE_NGRAM_CACHE: { - auto state = create_state_ngram_cache( - config, n_seq, - params.ngram_cache.lookup_cache_static, - params.ngram_cache.lookup_cache_dynamic); - impls.push_back(std::make_unique(state)); + impls.push_back(create_state_ngram_cache(config, n_seq)); break; } default: From 522f2b6eedce1b1767e6aa81fcdb78a6f4cf82fa Mon Sep 17 00:00:00 2001 From: Aaron Teo Date: Tue, 20 Oct 2026 19:31:35 +0800 Subject: [PATCH 11/69] ggml-zdnn: introduce flash attention op (#30512) * ggml-zdnn: add scratch buffer pool Signed-off-by: Aaron Teo * ggml-zdnn: implement fattn, ditch scratch slots in favour of scratch buffer pool Signed-off-by: Aaron Teo * docs: add note about fattn Signed-off-by: Aaron Teo --------- Signed-off-by: Aaron Teo --- docs/backend/zDNN.md | 9 ++ ggml/src/ggml-zdnn/common.hpp | 115 ++++++++++---- ggml/src/ggml-zdnn/fattn.cpp | 255 +++++++++++++++++++++++++++++++ ggml/src/ggml-zdnn/fattn.hpp | 10 ++ ggml/src/ggml-zdnn/ggml-zdnn.cpp | 28 +++- ggml/src/ggml-zdnn/mmq.cpp | 105 ++++--------- ggml/src/ggml-zdnn/scratch.cpp | 45 ++++++ ggml/src/ggml-zdnn/scratch.hpp | 61 ++++++++ 8 files changed, 517 insertions(+), 111 deletions(-) create mode 100644 ggml/src/ggml-zdnn/fattn.cpp create mode 100644 ggml/src/ggml-zdnn/fattn.hpp create mode 100644 ggml/src/ggml-zdnn/scratch.cpp create mode 100644 ggml/src/ggml-zdnn/scratch.hpp diff --git a/docs/backend/zDNN.md b/docs/backend/zDNN.md index 7c6d0751074e..f8218973916e 100644 --- a/docs/backend/zDNN.md +++ b/docs/backend/zDNN.md @@ -2,6 +2,7 @@ > [!WARNING] > **Note:** zDNN is **not** the same as ZenDNN. +> > - **zDNN** (this page): IBM's Deep Neural Network acceleration library for IBM Z & LinuxONE Mainframes > - **ZenDNN**: AMD's deep learning library for AMD EPYC CPUs ([see ZenDNN documentation](ZenDNN.md)) @@ -46,6 +47,14 @@ The IBM zDNN backend has the following CMake options that control the behaviour | --------------------- | ------------- | -------------------------------------------------------------------------------------- | | `GGML_ZDNN_HIGH_PREC` | `0` | Set to `1` to requantize Q8_0 weights in smaller groups, for lower loss at lower speed | +## Performance Tunables + +| Flag Option | Default Value | Description | +| ----------------------- | ------------- | ------------------------------------------------------ | +| `--flash-attn` or `-fa` | `auto` | Set to `on` to run attention on the NNPA (Recommended) | + +Note: Set `-fa on` explicitly. Flash Attention fuses each layer's attention (the KQ matrix multiplication, softmax and KQV matrix multiplication) into one `FLASH_ATTN_EXT` operation, which zDNN runs on the NNPA instead of the CPU. The speedup grows with the context length. With `auto`, llama.cpp currently turns Flash Attention off, because the operation runs on a different device than the layer. zDNN runs it when the KV cache is F16 (the default), the model uses no attention sinks, ALiBi or logit softcapping, and there is one KV cache stream (one sequence, or `--kv-unified`). Otherwise it falls back to the CPU. + ## 1. Install zDNN Library Note: A minimum of libzdnn 1.1.2 is required for the IBM zDNN backend to link correctly. diff --git a/ggml/src/ggml-zdnn/common.hpp b/ggml/src/ggml-zdnn/common.hpp index 80a9eadd1332..99dd56fca1ea 100644 --- a/ggml/src/ggml-zdnn/common.hpp +++ b/ggml/src/ggml-zdnn/common.hpp @@ -6,6 +6,8 @@ #include "zdnn.h" +#include "scratch.hpp" + #include #include #include @@ -14,9 +16,8 @@ #include #include -#define GGML_ZDNN_NAME "zDNN" -#define GGML_ZDNN_VERSION ZDNN_VERNUM -#define GGML_ZDNN_SCRATCH_SLOTS 3 // int8 inputs, DFloat16 partial results, partial results for grouped matmul +#define GGML_ZDNN_NAME "zDNN" +#define GGML_ZDNN_VERSION ZDNN_VERNUM #define ZDNN_CHECK(stmt) \ do { \ @@ -68,44 +69,100 @@ struct ggml_backend_zdnn_context { // zero bias for stacked quantized matmuls, keyed by (n_stacks, ne0) std::map, std::unique_ptr> zero_bias; - std::vector scratch_q; - std::vector scratch_f32; - - void * scratch_ztensor[GGML_ZDNN_SCRATCH_SLOTS] = {}; // 4K aligned - size_t scratch_ztensor_size[GGML_ZDNN_SCRATCH_SLOTS] = {}; + ggml_zdnn_pool pool; // scratch memory of the ops }; -template -static inline T * ggml_zdnn_resize_scratch_max(std::vector & scratch, int64_t n) { - if ((int64_t)scratch.size() < n) { - scratch.resize(n); - } +// +// Scratch buffers +// + +// the ztensor uses the memory of scratch, so scratch must outlive it +static inline void ggml_zdnn_init_scratch_ztensor(ggml_backend_zdnn_context * ctx, + zdnn_tensor_desc * pre_tfm_desc, + zdnn_tensor_desc * tfm_desc, + zdnn_ztensor * ztensor, + ggml_zdnn_pool_alloc & scratch) { - return scratch.data(); + const uint64_t size = zdnn_getsize_ztensor(tfm_desc); + + zdnn_init_quantized_ztensor(pre_tfm_desc, tfm_desc, 1.0f, 0.0f, ztensor); + ztensor->buffer = scratch.alloc(ctx->pool, size); + ztensor->buffer_size = size; +} + +// same as ggml_zdnn_init_scratch_ztensor, for a DLFLOAT16 ztensor +static inline void ggml_zdnn_init_scratch_ztensor_dlf16(ggml_backend_zdnn_context * ctx, + zdnn_tensor_desc * pre_tfm_desc, + zdnn_tensor_desc * tfm_desc, + zdnn_ztensor * ztensor, + ggml_zdnn_pool_alloc & scratch) { + + const uint64_t size = zdnn_getsize_ztensor(tfm_desc); + + zdnn_init_ztensor(pre_tfm_desc, tfm_desc, ztensor); + ztensor->buffer = scratch.alloc(ctx->pool, size); + ztensor->buffer_size = size; } // -// Scratch buffers +// Stacked ztensors // -static inline void ggml_zdnn_init_scratch_ztensor(ggml_backend_zdnn_context * ctx, - zdnn_tensor_desc * pre_tfm_desc, - zdnn_tensor_desc * tfm_desc, - zdnn_ztensor * ztensor, - int32_t slot) { +// view of the stacks [s0, s0 + n) of a stacked ztensor, the stacks are the outermost dim of its buffer +// the view descs must outlive the view +static inline zdnn_ztensor ggml_zdnn_stack_view(zdnn_tensor_desc * pre_tfm_desc, + zdnn_tensor_desc * tfm_desc, + const zdnn_ztensor & src, + int64_t n_stacks, + int64_t s0, + int64_t n) { + + GGML_ASSERT(src.buffer_size % n_stacks == 0); + const uint64_t stack_bytes = src.buffer_size / n_stacks; + + *pre_tfm_desc = *src.pre_transformed_desc; + *tfm_desc = *src.transformed_desc; + + switch (pre_tfm_desc->layout) { + case ZDNN_3DS: + { + pre_tfm_desc->dim3 = n; + } break; + case ZDNN_2DS: + { + pre_tfm_desc->dim2 = n; + } break; + default: + GGML_ABORT("%s: unsupported stacked layout", __func__); + } - const uint64_t size = zdnn_getsize_ztensor(tfm_desc); + tfm_desc->dim4 = n; + GGML_ASSERT(zdnn_getsize_ztensor(tfm_desc) == n*stack_bytes); - if (ctx->scratch_ztensor_size[slot] < size) { - free(ctx->scratch_ztensor[slot]); - ctx->scratch_ztensor[slot] = std::aligned_alloc(4096, GGML_PAD(size, 4096)); - ctx->scratch_ztensor_size[slot] = GGML_PAD(size, 4096); - GGML_ASSERT(ctx->scratch_ztensor[slot] != nullptr); + zdnn_ztensor view = src; + view.pre_transformed_desc = pre_tfm_desc; + view.transformed_desc = tfm_desc; + view.buffer = (char *) src.buffer + s0*stack_bytes; + view.buffer_size = n * stack_bytes; + return view; +} + +// zero bias of n_stacks stacks, created once per shape as it never changes +static inline const zdnn_ztensor & ggml_zdnn_zero_bias(ggml_backend_zdnn_context * ctx, int64_t n_stacks, int64_t ne0) { + std::unique_ptr & bias = ctx->zero_bias[{ n_stacks, ne0 }]; + + if (!bias) { + bias = std::make_unique(); + + std::vector zeros(n_stacks * ne0, 0.0f); + + zdnn_init_pre_transformed_desc(ZDNN_2DS, FP32, &bias->pre_tfm_desc, n_stacks, ne0); + ZDNN_CHECK(zdnn_generate_transformed_desc(&bias->pre_tfm_desc, &bias->tfm_desc)); + ZDNN_CHECK(zdnn_init_ztensor_with_malloc(&bias->pre_tfm_desc, &bias->tfm_desc, &bias->ztensor)); + ZDNN_CHECK(zdnn_transform_ztensor(&bias->ztensor, zeros.data())); } - zdnn_init_quantized_ztensor(pre_tfm_desc, tfm_desc, 1.0f, 0.0f, ztensor); - ztensor->buffer = ctx->scratch_ztensor[slot]; - ztensor->buffer_size = size; + return bias->ztensor; } // diff --git a/ggml/src/ggml-zdnn/fattn.cpp b/ggml/src/ggml-zdnn/fattn.cpp new file mode 100644 index 000000000000..7c7801cbb1dc --- /dev/null +++ b/ggml/src/ggml-zdnn/fattn.cpp @@ -0,0 +1,255 @@ +#include "ggml.h" +#include "fattn.hpp" + +#include +#include + +// DLFLOAT16 has no infinity, so masked scores use the lowest FP16 value, exp of which is still 0 +static inline float ggml_zdnn_fattn_mask_value(ggml_fp16_t m) { + return std::max(GGML_FP16_TO_FP32(m), -65504.0f); +} + +bool ggml_zdnn_supports_flash_attn_ext(const ggml_backend_zdnn_device_context * ctx_dev, const ggml_tensor * op) { + const ggml_tensor * q = op->src[0]; + const ggml_tensor * k = op->src[1]; + const ggml_tensor * v = op->src[2]; + const ggml_tensor * mask = op->src[3]; + + float max_bias = 0.0f; + float logit_softcap = 0.0f; + memcpy(&max_bias, (const float *) op->op_params + 1, sizeof(float)); + memcpy(&logit_softcap, (const float *) op->op_params + 2, sizeof(float)); + + // sinks, alibi, softcap and sparse k/v sets are not implemented + if (op->src[4] != nullptr || max_bias != 0.0f || logit_softcap != 0.0f || ggml_get_op_params_i32(op, 4) != 0) { + return false; + } + + if (op->type != GGML_TYPE_F32 || q->type != GGML_TYPE_F32 || k->type != GGML_TYPE_F16 || v->type != GGML_TYPE_F16) { + return false; + } + + if (mask != nullptr && (mask->type != GGML_TYPE_F16 || mask->ne[2] != 1 || mask->ne[3] != 1)) { + return false; + } + + if (q->ne[3] != 1 || k->ne[3] != 1 || v->ne[3] != 1 || k->ne[2] != v->ne[2] || q->ne[2] % k->ne[2] != 0) { + return false; + } + + // rows are gathered with their strides, but each row has to be contiguous + if (q->nb[0] != sizeof(float) || k->nb[0] != sizeof(ggml_fp16_t) || v->nb[0] != sizeof(ggml_fp16_t)) { + return false; + } + + const int64_t max_dim = ctx_dev->max_size; + const int64_t group = q->ne[2] / k->ne[2]; + + return q->ne[0] <= max_dim && v->ne[0] <= max_dim && k->ne[1] <= max_dim && + q->ne[2] <= max_dim && group * q->ne[1] <= max_dim; +} + +void ggml_zdnn_flash_attn_ext(ggml_backend_zdnn_context * ctx, ggml_tensor * dst) { + const ggml_tensor * q = dst->src[0]; + const ggml_tensor * k = dst->src[1]; + const ggml_tensor * v = dst->src[2]; + const ggml_tensor * mask = dst->src[3]; + + const int64_t d_k = q->ne[0]; + const int64_t n_q = q->ne[1]; + const int64_t n_head = q->ne[2]; + const int64_t n_kv = k->ne[1]; + const int64_t n_head_kv = k->ne[2]; + const int64_t d_v = v->ne[0]; + const int64_t group = n_head / n_head_kv; + + float scale = 1.0f; + memcpy(&scale, (const float *) dst->op_params + 0, sizeof(float)); + + const int32_t n_threads = n_q*n_kv >= 65536 ? ctx->n_threads : 1; + + // one query runs all heads in one call with the mask row as the K*Q bias, more queries run per head and add the mask + const bool single = n_q == 1; + + const int64_t n_mask = mask ? (single ? n_head_kv*n_kv : n_q*n_kv) : 0; + + ggml_zdnn_pool_alloc q_host_alloc(ctx->pool, n_head*n_q*d_k); + ggml_zdnn_pool_alloc o_host_alloc(ctx->pool, n_head*n_q*d_v); + ggml_zdnn_pool_alloc mask_host_alloc(ctx->pool, n_mask); + ggml_zdnn_pool_alloc k_host_alloc(ctx->pool, n_head_kv*n_kv*d_k); + ggml_zdnn_pool_alloc v_host_alloc(ctx->pool, n_head_kv*n_kv*d_v); + + float * q_host = q_host_alloc.get(); + float * o_host = o_host_alloc.get(); + float * mask_host = mask_host_alloc.get(); + ggml_fp16_t * k_host = k_host_alloc.get(); + ggml_fp16_t * v_host = v_host_alloc.get(); + + // q is [n_head][n_q][d_k] with the scale folded in, k and v are [n_head_kv][n_kv][d] + ggml_zdnn_parallel_for(n_threads, n_head, [&](int64_t h_start, int64_t h_end) { + for (int64_t h = h_start; h < h_end; h++) { + for (int64_t iq = 0; iq < n_q; iq++) { + const float * src = (const float *)((const char *) q->data + iq*q->nb[1] + h*q->nb[2]); + float * row = q_host + (h*n_q + iq)*d_k; + for (int64_t i0 = 0; i0 < d_k; i0++) { + row[i0] = src[i0] * scale; + } + } + } + }); + + ggml_zdnn_parallel_for(n_threads, n_head_kv*n_kv, [&](int64_t r_start, int64_t r_end) { + for (int64_t r = r_start; r < r_end; r++) { + const int64_t hk = r / n_kv; + const int64_t ik = r % n_kv; + memcpy(k_host + r*d_k, (const char *) k->data + ik*k->nb[1] + hk*k->nb[2], d_k*sizeof(ggml_fp16_t)); + memcpy(v_host + r*d_v, (const char *) v->data + ik*v->nb[1] + hk*v->nb[2], d_v*sizeof(ggml_fp16_t)); + } + }); + + zdnn_tensor_desc q_pre_tfm_desc, q_tfm_desc; + zdnn_tensor_desc k_pre_tfm_desc, k_tfm_desc; + zdnn_tensor_desc v_pre_tfm_desc, v_tfm_desc; + zdnn_tensor_desc o_pre_tfm_desc, o_tfm_desc; + zdnn_ztensor q_ztensor, k_ztensor, v_ztensor, o_ztensor; + + ggml_zdnn_pool_alloc q_ztensor_alloc, k_ztensor_alloc, v_ztensor_alloc, o_ztensor_alloc; + + // a single query keeps the heads of a kv head in one stack, which is the same memory order as one head per stack + if (single) { + zdnn_init_pre_transformed_desc(ZDNN_3DS, FP32, &q_pre_tfm_desc, n_head_kv, group, d_k); + zdnn_init_pre_transformed_desc(ZDNN_3DS, FP32, &o_pre_tfm_desc, n_head_kv, group, d_v); + } else { + zdnn_init_pre_transformed_desc(ZDNN_3DS, FP32, &q_pre_tfm_desc, n_head, n_q, d_k); + zdnn_init_pre_transformed_desc(ZDNN_3DS, FP32, &o_pre_tfm_desc, n_head, n_q, d_v); + } + zdnn_init_pre_transformed_desc(ZDNN_3DS, FP16, &k_pre_tfm_desc, n_head_kv, n_kv, d_k); + zdnn_init_pre_transformed_desc(ZDNN_3DS, FP16, &v_pre_tfm_desc, n_head_kv, n_kv, d_v); + + ZDNN_CHECK(zdnn_generate_transformed_desc(&q_pre_tfm_desc, &q_tfm_desc)); + ZDNN_CHECK(zdnn_generate_transformed_desc(&k_pre_tfm_desc, &k_tfm_desc)); + ZDNN_CHECK(zdnn_generate_transformed_desc(&v_pre_tfm_desc, &v_tfm_desc)); + ZDNN_CHECK(zdnn_generate_transformed_desc(&o_pre_tfm_desc, &o_tfm_desc)); + + ggml_zdnn_init_scratch_ztensor_dlf16(ctx, &q_pre_tfm_desc, &q_tfm_desc, &q_ztensor, q_ztensor_alloc); + ggml_zdnn_init_scratch_ztensor_dlf16(ctx, &k_pre_tfm_desc, &k_tfm_desc, &k_ztensor, k_ztensor_alloc); + ggml_zdnn_init_scratch_ztensor_dlf16(ctx, &v_pre_tfm_desc, &v_tfm_desc, &v_ztensor, v_ztensor_alloc); + ggml_zdnn_init_scratch_ztensor_dlf16(ctx, &o_pre_tfm_desc, &o_tfm_desc, &o_ztensor, o_ztensor_alloc); + + ZDNN_CHECK(zdnn_transform_ztensor(&q_ztensor, q_host)); + ZDNN_CHECK(zdnn_transform_ztensor(&k_ztensor, k_host)); + ZDNN_CHECK(zdnn_transform_ztensor(&v_ztensor, v_host)); + + ggml_zdnn_pool_alloc softmax_save_area(ctx->pool, ZDNN_SOFTMAX_SAVEAREA_SIZE); + + zdnn_tensor_desc s_pre_tfm_desc, s_tfm_desc; + zdnn_ztensor s_ztensor, s2_ztensor; + + ggml_zdnn_pool_alloc s_ztensor_alloc, s2_ztensor_alloc; + + if (single) { + // the bias of K*Q is the mask row of the query, repeated for every kv head + zdnn_tensor_desc bias_pre_tfm_desc, bias_tfm_desc; + zdnn_ztensor bias_ztensor; + + ggml_zdnn_pool_alloc bias_ztensor_alloc; + + const zdnn_ztensor * bias = &ggml_zdnn_zero_bias(ctx, n_head_kv, n_kv); + if (mask != nullptr) { + const ggml_fp16_t * mask_row = (const ggml_fp16_t *) mask->data; + for (int64_t hk = 0; hk < n_head_kv; hk++) { + for (int64_t ik = 0; ik < n_kv; ik++) { + mask_host[hk*n_kv + ik] = ggml_zdnn_fattn_mask_value(mask_row[ik]); + } + } + + zdnn_init_pre_transformed_desc(ZDNN_2DS, FP32, &bias_pre_tfm_desc, n_head_kv, n_kv); + ZDNN_CHECK(zdnn_generate_transformed_desc(&bias_pre_tfm_desc, &bias_tfm_desc)); + ggml_zdnn_init_scratch_ztensor_dlf16(ctx, &bias_pre_tfm_desc, &bias_tfm_desc, &bias_ztensor, bias_ztensor_alloc); + ZDNN_CHECK(zdnn_transform_ztensor(&bias_ztensor, mask_host)); + bias = &bias_ztensor; + } + + zdnn_init_pre_transformed_desc(ZDNN_3DS, FP32, &s_pre_tfm_desc, n_head_kv, group, n_kv); + ZDNN_CHECK(zdnn_generate_transformed_desc(&s_pre_tfm_desc, &s_tfm_desc)); + ggml_zdnn_init_scratch_ztensor_dlf16(ctx, &s_pre_tfm_desc, &s_tfm_desc, &s_ztensor, s_ztensor_alloc); + ggml_zdnn_init_scratch_ztensor_dlf16(ctx, &s_pre_tfm_desc, &s_tfm_desc, &s2_ztensor, s2_ztensor_alloc); + + ZDNN_CHECK(zdnn_matmul_transpose_op(&q_ztensor, &k_ztensor, bias, false, true, MATMUL_OP_ADDITION, &s_ztensor)); + ZDNN_CHECK(zdnn_softmax(&s_ztensor, softmax_save_area.get(), SOFTMAX_ACT_NONE, &s2_ztensor)); + ZDNN_CHECK(zdnn_matmul_op(&s2_ztensor, &v_ztensor, &ggml_zdnn_zero_bias(ctx, n_head_kv, d_v), MATMUL_OP_ADDITION, &o_ztensor)); + } else { + zdnn_tensor_desc mask_pre_tfm_desc, mask_tfm_desc; + zdnn_ztensor mask_ztensor; + + ggml_zdnn_pool_alloc mask_ztensor_alloc; + + if (mask != nullptr) { + ggml_zdnn_parallel_for(n_threads, n_q, [&](int64_t iq_start, int64_t iq_end) { + for (int64_t iq = iq_start; iq < iq_end; iq++) { + const ggml_fp16_t * mask_row = (const ggml_fp16_t *)((const char *) mask->data + iq*mask->nb[1]); + for (int64_t ik = 0; ik < n_kv; ik++) { + mask_host[iq*n_kv + ik] = ggml_zdnn_fattn_mask_value(mask_row[ik]); + } + } + }); + + zdnn_init_pre_transformed_desc(ZDNN_3DS, FP32, &mask_pre_tfm_desc, 1, n_q, n_kv); + ZDNN_CHECK(zdnn_generate_transformed_desc(&mask_pre_tfm_desc, &mask_tfm_desc)); + ggml_zdnn_init_scratch_ztensor_dlf16(ctx, &mask_pre_tfm_desc, &mask_tfm_desc, &mask_ztensor, mask_ztensor_alloc); + ZDNN_CHECK(zdnn_transform_ztensor(&mask_ztensor, mask_host)); + } + + zdnn_init_pre_transformed_desc(ZDNN_3DS, FP32, &s_pre_tfm_desc, 1, n_q, n_kv); + ZDNN_CHECK(zdnn_generate_transformed_desc(&s_pre_tfm_desc, &s_tfm_desc)); + ggml_zdnn_init_scratch_ztensor_dlf16(ctx, &s_pre_tfm_desc, &s_tfm_desc, &s_ztensor, s_ztensor_alloc); + ggml_zdnn_init_scratch_ztensor_dlf16(ctx, &s_pre_tfm_desc, &s_tfm_desc, &s2_ztensor, s2_ztensor_alloc); + + const zdnn_ztensor & k_bias = ggml_zdnn_zero_bias(ctx, 1, n_kv); + const zdnn_ztensor & v_bias = ggml_zdnn_zero_bias(ctx, 1, d_v); + + zdnn_tensor_desc view_pre_tfm_desc[4]; + zdnn_tensor_desc view_tfm_desc[4]; + + for (int64_t h = 0; h < n_head; h++) { + const zdnn_ztensor q_view = ggml_zdnn_stack_view(&view_pre_tfm_desc[0], &view_tfm_desc[0], q_ztensor, n_head, h, 1); + const zdnn_ztensor k_view = ggml_zdnn_stack_view(&view_pre_tfm_desc[1], &view_tfm_desc[1], k_ztensor, n_head_kv, h / group, 1); + const zdnn_ztensor v_view = ggml_zdnn_stack_view(&view_pre_tfm_desc[2], &view_tfm_desc[2], v_ztensor, n_head_kv, h / group, 1); + zdnn_ztensor o_view = ggml_zdnn_stack_view(&view_pre_tfm_desc[3], &view_tfm_desc[3], o_ztensor, n_head, h, 1); + + s_ztensor.is_transformed = false; + s2_ztensor.is_transformed = false; + + ZDNN_CHECK(zdnn_matmul_transpose_op(&q_view, &k_view, &k_bias, false, true, MATMUL_OP_ADDITION, &s_ztensor)); + + zdnn_ztensor * scores = &s_ztensor; + zdnn_ztensor * probs = &s2_ztensor; + if (mask != nullptr) { + ZDNN_CHECK(zdnn_add(&s_ztensor, &mask_ztensor, &s2_ztensor)); + scores = &s2_ztensor; + probs = &s_ztensor; + probs->is_transformed = false; + } + + ZDNN_CHECK(zdnn_softmax(scores, softmax_save_area.get(), SOFTMAX_ACT_NONE, probs)); + ZDNN_CHECK(zdnn_matmul_op(probs, &v_view, &v_bias, MATMUL_OP_ADDITION, &o_view)); + } + + o_ztensor.is_transformed = true; + } + + ZDNN_CHECK(zdnn_transform_origtensor(&o_ztensor, o_host)); + + // the result is [d_v][n_head][n_q] + for (int64_t iq = 0; iq < n_q; iq++) { + for (int64_t h = 0; h < n_head; h++) { + memcpy((char *) dst->data + h*dst->nb[1] + iq*dst->nb[2], o_host + (h*n_q + iq)*d_v, d_v*sizeof(float)); + } + } + + // the result is written to dst->data directly, so the dst ztensor is stale + ggml_backend_zdnn_buffer * dst_extra = (ggml_backend_zdnn_buffer *) dst->extra; + if (dst_extra != nullptr) { + dst_extra->ztensor.is_transformed = false; + } +} diff --git a/ggml/src/ggml-zdnn/fattn.hpp b/ggml/src/ggml-zdnn/fattn.hpp new file mode 100644 index 000000000000..a88e1eba7c8e --- /dev/null +++ b/ggml/src/ggml-zdnn/fattn.hpp @@ -0,0 +1,10 @@ +#ifndef GGML_ZDNN_FATTN_HPP +#define GGML_ZDNN_FATTN_HPP + +#include "common.hpp" + +bool ggml_zdnn_supports_flash_attn_ext(const ggml_backend_zdnn_device_context * ctx_dev, const ggml_tensor * op); + +void ggml_zdnn_flash_attn_ext(ggml_backend_zdnn_context * ctx, ggml_tensor * dst); + +#endif // GGML_ZDNN_FATTN_HPP diff --git a/ggml/src/ggml-zdnn/ggml-zdnn.cpp b/ggml/src/ggml-zdnn/ggml-zdnn.cpp index 3b717a8c9415..40c6516a82f2 100644 --- a/ggml/src/ggml-zdnn/ggml-zdnn.cpp +++ b/ggml/src/ggml-zdnn/ggml-zdnn.cpp @@ -3,6 +3,7 @@ #include "ggml-backend-impl.h" #include "ggml-zdnn/common.hpp" +#include "ggml-zdnn/fattn.hpp" #include "ggml-zdnn/mmf.hpp" #include "ggml-zdnn/mmq.hpp" #include "ggml-zdnn/utils.hpp" @@ -37,6 +38,11 @@ static bool ggml_zdnn_compute_forward( ggml_zdnn_compute_forward_mul_mat(ctx, dst); } break; + case GGML_OP_FLASH_ATTN_EXT: + { + ggml_zdnn_flash_attn_ext(ctx, dst); + } break; + default: return false; } @@ -120,6 +126,9 @@ static bool ggml_zdnn_supports_op(const ggml_backend_zdnn_device_context * ctx_d } } break; + case GGML_OP_FLASH_ATTN_EXT: + return ggml_zdnn_supports_flash_attn_ext(ctx_dev, op); + default: return false; } @@ -205,9 +214,6 @@ static void ggml_zdnn_free(ggml_backend_zdnn_context * ctx) { for (const auto & it : ctx->zero_bias) { ZDNN_CHECK(zdnn_free_ztensor_buffer(&it.second->ztensor)); } - for (void * scratch : ctx->scratch_ztensor) { - free(scratch); - } delete ctx; } @@ -309,7 +315,19 @@ static void ggml_backend_zdnn_buffer_memset_tensor(ggml_backend_buffer_t buffer, static void ggml_backend_zdnn_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) { memcpy((char *)tensor->data + offset, data, size); + // views share the ztensor of their source, which is now stale, and empty tensors have none + if (tensor->view_src != nullptr) { + ggml_backend_zdnn_buffer * src_extra = (ggml_backend_zdnn_buffer *)tensor->view_src->extra; + if (src_extra != nullptr && src_extra->ztensor.is_transformed) { + zdnn_reset_ztensor(&src_extra->ztensor); + } + return; + } + ggml_backend_zdnn_buffer * extra = (ggml_backend_zdnn_buffer *)tensor->extra; + if (extra == nullptr) { + return; + } if (ggml_is_quantized(tensor->type)) { GGML_ASSERT(offset == 0 && size == ggml_nbytes(tensor)); @@ -319,8 +337,8 @@ static void ggml_backend_zdnn_buffer_set_tensor(ggml_backend_buffer_t buffer, gg // Fixes the LLAMA_SET_ROWS bug // see: https://github.com/ggml-org/llama.cpp/issues/15414 - // compute tensors are transformed on first use instead, as not every op reads the ztensor - if (tensor->buffer->usage == GGML_BACKEND_BUFFER_USAGE_COMPUTE) { + // only weights are transformed here, others on first use, as not every op reads the ztensor and some data (masks) is not finite + if (tensor->buffer->usage != GGML_BACKEND_BUFFER_USAGE_WEIGHTS) { zdnn_reset_ztensor(&extra->ztensor); return; } diff --git a/ggml/src/ggml-zdnn/mmq.cpp b/ggml/src/ggml-zdnn/mmq.cpp index 947ed44c881d..757a522c92ce 100644 --- a/ggml/src/ggml-zdnn/mmq.cpp +++ b/ggml/src/ggml-zdnn/mmq.cpp @@ -6,63 +6,6 @@ #include #include -// view of the stacks [s0, s0 + n) of a stacked ztensor, the stacks are the outermost dim of its buffer -// the view descs must outlive the view -static zdnn_ztensor ggml_zdnn_stack_view(zdnn_tensor_desc * pre_tfm_desc, - zdnn_tensor_desc * tfm_desc, - const zdnn_ztensor & src, - int64_t n_stacks, - int64_t s0, - int64_t n) { - - GGML_ASSERT(src.buffer_size % n_stacks == 0); - const uint64_t stack_bytes = src.buffer_size / n_stacks; - - *pre_tfm_desc = *src.pre_transformed_desc; - *tfm_desc = *src.transformed_desc; - - switch (pre_tfm_desc->layout) { - case ZDNN_3DS: - { - pre_tfm_desc->dim3 = n; - } break; - case ZDNN_2DS: - { - pre_tfm_desc->dim2 = n; - } break; - default: - GGML_ABORT("%s: unsupported stacked layout", __func__); - } - - tfm_desc->dim4 = n; - GGML_ASSERT(zdnn_getsize_ztensor(tfm_desc) == n*stack_bytes); - - zdnn_ztensor view = src; - view.pre_transformed_desc = pre_tfm_desc; - view.transformed_desc = tfm_desc; - view.buffer = (char *) src.buffer + s0*stack_bytes; - view.buffer_size = n * stack_bytes; - return view; -} - -// zero bias of n_stacks stacks, created once per shape as it never changes -static const zdnn_ztensor & ggml_zdnn_zero_bias(ggml_backend_zdnn_context * ctx, int64_t n_stacks, int64_t ne0) { - std::unique_ptr & bias = ctx->zero_bias[{ n_stacks, ne0 }]; - - if (!bias) { - bias = std::make_unique(); - - std::vector zeros(n_stacks * ne0, 0.0f); - - zdnn_init_pre_transformed_desc(ZDNN_2DS, FP32, &bias->pre_tfm_desc, n_stacks, ne0); - ZDNN_CHECK(zdnn_generate_transformed_desc(&bias->pre_tfm_desc, &bias->tfm_desc)); - ZDNN_CHECK(zdnn_init_ztensor_with_malloc(&bias->pre_tfm_desc, &bias->tfm_desc, &bias->ztensor)); - ZDNN_CHECK(zdnn_transform_ztensor(&bias->ztensor, zeros.data())); - } - - return bias->ztensor; -} - // the weights are stacked into groups along ne00, and each group of each row of the inputs and weights has its own scale // zDNN multiplies the groups in stacked calls, then the partial results of each group are scaled and summed here static void ggml_zdnn_mul_mat_q_grouped(ggml_backend_zdnn_context * ctx, @@ -81,18 +24,20 @@ static void ggml_zdnn_mul_mat_q_grouped(ggml_backend_zdnn_context * ctx, GGML_ASSERT(n_groups * gs >= ne10 && (n_groups - 1) * gs < ne10); // the rounding errors of the inputs are stacked after the inputs, see ggml_zdnn_quantize_inputs - int8_t * inputs_q = ggml_zdnn_resize_scratch_max(ctx->scratch_q, 2 * n_groups * gs * ne11); + ggml_zdnn_pool_alloc inputs_q_alloc(ctx->pool, 2 * n_groups * gs * ne11); + int8_t * inputs_q = inputs_q_alloc.get(); std::vector inputs_scales(n_groups * ne11); ggml_zdnn_quantize_inputs(n_threads, inputs, gs, weights_extra->channel_scales.data(), inputs_q, inputs_q + n_groups * gs * ne11, inputs_scales.data()); - zdnn_tensor_desc inputs_pre_tfm_desc; - zdnn_tensor_desc inputs_tfm_desc; - zdnn_ztensor inputs_ztensor; + zdnn_tensor_desc inputs_pre_tfm_desc; + zdnn_tensor_desc inputs_tfm_desc; + zdnn_ztensor inputs_ztensor; + ggml_zdnn_pool_alloc inputs_ztensor_alloc; zdnn_init_pre_transformed_desc(ZDNN_3DS, INT8, &inputs_pre_tfm_desc, 2 * n_groups, ne11, gs); ZDNN_CHECK(zdnn_generate_quantized_transformed_desc(&inputs_pre_tfm_desc, QUANTIZED_INT8, &inputs_tfm_desc)); - ggml_zdnn_init_scratch_ztensor(ctx, &inputs_pre_tfm_desc, &inputs_tfm_desc, &inputs_ztensor, 0); + ggml_zdnn_init_scratch_ztensor(ctx, &inputs_pre_tfm_desc, &inputs_tfm_desc, &inputs_ztensor, inputs_ztensor_alloc); ZDNN_CHECK(zdnn_transform_quantized_ztensor(&inputs_ztensor, false, -127, 127, inputs_q)); // each group of the partial results is read back as its own 2D ztensor @@ -116,19 +61,21 @@ static void ggml_zdnn_mul_mat_q_grouped(ggml_backend_zdnn_context * ctx, // two partial buffers, so the next chunk runs on the NNPA while this one is summed // each holds the results of a chunk of inputs, followed by the results of their rounding errors - zdnn_tensor_desc partial_pre_tfm_desc; - zdnn_tensor_desc partial_tfm_desc; - zdnn_ztensor partial_ztensor[2]; + zdnn_tensor_desc partial_pre_tfm_desc; + zdnn_tensor_desc partial_tfm_desc; + zdnn_ztensor partial_ztensor[2]; + ggml_zdnn_pool_alloc partial_ztensor_alloc[2]; zdnn_init_pre_transformed_desc(ZDNN_3DS, FP32, &partial_pre_tfm_desc, 2 * n_chunk, ne11, ne0); ZDNN_CHECK(zdnn_generate_quantized_transformed_desc(&partial_pre_tfm_desc, QUANTIZED_DLFLOAT16, &partial_tfm_desc)); GGML_ASSERT(zdnn_getsize_ztensor(&partial_tfm_desc) == (uint64_t)(2 * n_chunk) * group_bytes); for (int32_t i = 0; i < 2; i++) { - ggml_zdnn_init_scratch_ztensor(ctx, &partial_pre_tfm_desc, &partial_tfm_desc, &partial_ztensor[i], 1 + i); + ggml_zdnn_init_scratch_ztensor(ctx, &partial_pre_tfm_desc, &partial_tfm_desc, &partial_ztensor[i], partial_ztensor_alloc[i]); } const float * weights_scales = weights_extra->scales.data(); - float * partial = ggml_zdnn_resize_scratch_max(ctx->scratch_f32, 2 * ne11 * ne0); + ggml_zdnn_pool_alloc partial_alloc(ctx->pool, 2 * ne11 * ne0); + float * partial = partial_alloc.get(); float * partial_res = partial + ne11 * ne0; const auto sum_chunk = [&](const zdnn_ztensor & chunk, int64_t g0, int64_t n) { @@ -262,27 +209,30 @@ void ggml_zdnn_mul_mat_q( // zDNN takes one scale per zTensor, so quantize each row of the inputs with its own scale // the rounding errors of the inputs are stacked after the inputs, see ggml_zdnn_quantize_inputs - int8_t * inputs_q = ggml_zdnn_resize_scratch_max(ctx->scratch_q, 2 * ne10 * ne11); + ggml_zdnn_pool_alloc inputs_q_alloc(ctx->pool, 2 * ne10 * ne11); + int8_t * inputs_q = inputs_q_alloc.get(); std::vector inputs_scales(ne11); ggml_zdnn_quantize_inputs(n_threads, inputs, ne10, weights_extra->channel_scales.data(), inputs_q, inputs_q + ne10 * ne11, inputs_scales.data()); - zdnn_tensor_desc inputs_pre_tfm_desc; - zdnn_tensor_desc inputs_tfm_desc; - zdnn_ztensor inputs_ztensor; + zdnn_tensor_desc inputs_pre_tfm_desc; + zdnn_tensor_desc inputs_tfm_desc; + zdnn_ztensor inputs_ztensor; + ggml_zdnn_pool_alloc inputs_ztensor_alloc; zdnn_init_pre_transformed_desc(ZDNN_3DS, INT8, &inputs_pre_tfm_desc, 2, ne11, ne10); ZDNN_CHECK(zdnn_generate_quantized_transformed_desc(&inputs_pre_tfm_desc, QUANTIZED_INT8, &inputs_tfm_desc)); - ggml_zdnn_init_scratch_ztensor(ctx, &inputs_pre_tfm_desc, &inputs_tfm_desc, &inputs_ztensor, 0); + ggml_zdnn_init_scratch_ztensor(ctx, &inputs_pre_tfm_desc, &inputs_tfm_desc, &inputs_ztensor, inputs_ztensor_alloc); ZDNN_CHECK(zdnn_transform_quantized_ztensor(&inputs_ztensor, false, -127, 127, inputs_q)); - zdnn_tensor_desc partial_pre_tfm_desc; - zdnn_tensor_desc partial_tfm_desc; - zdnn_ztensor partial_ztensor; + zdnn_tensor_desc partial_pre_tfm_desc; + zdnn_tensor_desc partial_tfm_desc; + zdnn_ztensor partial_ztensor; + ggml_zdnn_pool_alloc partial_ztensor_alloc; zdnn_init_pre_transformed_desc(ZDNN_3DS, FP32, &partial_pre_tfm_desc, 2, ne11, ne0); ZDNN_CHECK(zdnn_generate_quantized_transformed_desc(&partial_pre_tfm_desc, QUANTIZED_DLFLOAT16, &partial_tfm_desc)); - ggml_zdnn_init_scratch_ztensor(ctx, &partial_pre_tfm_desc, &partial_tfm_desc, &partial_ztensor, 1); + ggml_zdnn_init_scratch_ztensor(ctx, &partial_pre_tfm_desc, &partial_tfm_desc, &partial_ztensor, partial_ztensor_alloc); // both stacks are multiplied by the same unstacked weights in one broadcast call // the bias is all zeros, so it is already the pre-computed bias @@ -300,7 +250,8 @@ void ggml_zdnn_mul_mat_q( const uint64_t stack_bytes = zdnn_getsize_ztensor(&stack_tfm_desc); GGML_ASSERT(partial_ztensor.buffer_size == 2*stack_bytes); - float * partial[2] = { (float *)output->data, ggml_zdnn_resize_scratch_max(ctx->scratch_f32, ne11 * ne0) }; + ggml_zdnn_pool_alloc partial_res_alloc(ctx->pool, ne11 * ne0); + float * partial[2] = { (float *)output->data, partial_res_alloc.get() }; ggml_zdnn_parallel_for(n_threads, 2, [&](int64_t s_start, int64_t s_end) { zdnn_ztensor stack_ztensor; diff --git a/ggml/src/ggml-zdnn/scratch.cpp b/ggml/src/ggml-zdnn/scratch.cpp new file mode 100644 index 000000000000..f325414752ff --- /dev/null +++ b/ggml/src/ggml-zdnn/scratch.cpp @@ -0,0 +1,45 @@ +#include "ggml.h" +#include "scratch.hpp" + +#include +#include +#include + +ggml_zdnn_pool::~ggml_zdnn_pool() { + for (const buffer & b : cached) { + std::free(b.ptr); + } +} + +void * ggml_zdnn_pool::alloc(size_t size, size_t * actual_size) { + int64_t ibest = -1; + for (int64_t i = 0; i < (int64_t)cached.size(); i++) { + if (cached[i].size >= size && (ibest < 0 || cached[i].size < cached[ibest].size)) { + ibest = i; + } + } + + if (ibest >= 0) { + void * ptr = cached[ibest].ptr; + *actual_size = cached[ibest].size; + cached.erase(cached.begin() + ibest); + return ptr; + } + + // every cached buffer is too small, free the largest so the pool does not grow while sizes grow with the context + if (!cached.empty()) { + const auto largest = std::max_element(cached.begin(), cached.end(), [](const buffer & a, const buffer & b) { return a.size < b.size; }); + std::free(largest->ptr); + cached.erase(largest); + } + + // 5% more, so slowly growing sizes do not allocate every time + *actual_size = GGML_PAD(std::max(1, (size_t)(1.05 * size)), 4096); + void * ptr = std::aligned_alloc(4096, *actual_size); + GGML_ASSERT(ptr != nullptr); + return ptr; +} + +void ggml_zdnn_pool::free(void * ptr, size_t size) { + cached.push_back({ ptr, size }); +} diff --git a/ggml/src/ggml-zdnn/scratch.hpp b/ggml/src/ggml-zdnn/scratch.hpp new file mode 100644 index 000000000000..f2f2e21fce35 --- /dev/null +++ b/ggml/src/ggml-zdnn/scratch.hpp @@ -0,0 +1,61 @@ +#ifndef GGML_ZDNN_SCRATCH_HPP +#define GGML_ZDNN_SCRATCH_HPP + +#include "ggml.h" + +#include +#include + +// 4K aligned host buffers that are cached for reuse, see ggml_cuda_pool_leg +struct ggml_zdnn_pool { + struct buffer { + void * ptr = nullptr; + size_t size = 0; + }; + + std::vector cached; + + ~ggml_zdnn_pool(); + + void * alloc(size_t size, size_t * actual_size); + void free(void * ptr, size_t size); +}; + +// a buffer of the pool that goes back to it at the end of its scope, see ggml_cuda_pool_alloc +template +struct ggml_zdnn_pool_alloc { + ggml_zdnn_pool * pool = nullptr; + T * ptr = nullptr; + size_t actual_size = 0; + + ggml_zdnn_pool_alloc() = default; + + ggml_zdnn_pool_alloc(ggml_zdnn_pool & pool, size_t size) { + alloc(pool, size); + } + + ~ggml_zdnn_pool_alloc() { + if (ptr != nullptr) { + pool->free(ptr, actual_size); + } + } + + // size is in number of elements + T * alloc(ggml_zdnn_pool & pool, size_t size) { + GGML_ASSERT(ptr == nullptr); + this->pool = &pool; + ptr = (T *) pool.alloc(size * sizeof(T), &actual_size); + return ptr; + } + + T * get() { + return ptr; + } + + ggml_zdnn_pool_alloc(const ggml_zdnn_pool_alloc &) = delete; + ggml_zdnn_pool_alloc(ggml_zdnn_pool_alloc &&) = delete; + ggml_zdnn_pool_alloc & operator=(const ggml_zdnn_pool_alloc &) = delete; + ggml_zdnn_pool_alloc & operator=(ggml_zdnn_pool_alloc &&) = delete; +}; + +#endif // GGML_ZDNN_SCRATCH_HPP From b684498579899ec376ea13ab4bb3d0a9ef2c1a5b Mon Sep 17 00:00:00 2001 From: uvos Date: Tue, 20 Oct 2026 13:32:38 +0200 Subject: [PATCH 12/69] HIP/CUDA: Fix reduction in mmf getting tile widths not devisiable by 64 (#30836) --- ggml/src/ggml-cuda/ggml-cuda.cu | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu index d9b18cc08d55..f6ff9f893aee 100644 --- a/ggml/src/ggml-cuda/ggml-cuda.cu +++ b/ggml/src/ggml-cuda/ggml-cuda.cu @@ -2033,6 +2033,8 @@ static bool ggml_cuda_mul_mat_id_needs_sync(const ggml_tensor * dst, const int c const ggml_tensor * src0 = dst->src[0]; const ggml_tensor * src1 = dst->src[1]; + const int warp_size = ggml_cuda_info().devices[ggml_cuda_get_device()].warp_size; + if (src1->type != GGML_TYPE_F32 || dst->type != GGML_TYPE_F32) { return true; } @@ -2051,7 +2053,7 @@ static bool ggml_cuda_mul_mat_id_needs_sync(const ggml_tensor * dst, const int c return false; } - if (ggml_cuda_should_use_mmf(src0->type, cc, WARP_SIZE, src0->ne, src0->nb, src1->ne[2], /*mul_mat_id=*/true)) { + if (ggml_cuda_should_use_mmf(src0->type, cc, warp_size, src0->ne, src0->nb, src1->ne[2], /*mul_mat_id=*/true)) { return false; } @@ -2069,6 +2071,7 @@ static void ggml_cuda_mul_mat_id(ggml_backend_cuda_context & ctx, ggml_tensor * GGML_TENSOR_BINARY_OP_LOCALS const int cc = ggml_cuda_info().devices[ggml_cuda_get_device()].cc; + const int warp_size = ggml_cuda_info().devices[ggml_cuda_get_device()].warp_size; // [TAG_MUL_MAT_ID_CUDA_GRAPHS] if (src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { @@ -2093,7 +2096,7 @@ static void ggml_cuda_mul_mat_id(ggml_backend_cuda_context & ctx, ggml_tensor * return; } - if (ggml_cuda_should_use_mmf(src0->type, cc, WARP_SIZE, src0->ne, src0->nb, src1->ne[2], /*mul_mat_id=*/true)) { + if (ggml_cuda_should_use_mmf(src0->type, cc, warp_size, src0->ne, src0->nb, src1->ne[2], /*mul_mat_id=*/true)) { ggml_cuda_mul_mat_f(ctx, src0, src1, ids, dst); return; } From 119f68d7d441d66bdf8ebd117ca0deee2119fbcc Mon Sep 17 00:00:00 2001 From: Bernard Ladenthin Date: Tue, 20 Oct 2026 13:35:06 +0200 Subject: [PATCH 13/69] ggml-cpu : select the NEON fp32->fp16 conversion (#30505) The __ARM_NEON branch defines both GGML_CPU_COMPUTE_* macros but selects only FP16_TO_FP32, so FP32_TO_FP16 falls through to the portable software conversion while neon_compute_fp32_to_fp16, a single fcvt, stays defined and referenced by nothing. __POWER9_VECTOR__ and __riscv_zfhmin select both. Add an #error pair that refuses a branch defining a COMPUTE macro without selecting it. That is how this stayed unnoticed: the fallback silently supplies a correct software implementation, so the only symptom is a hardware instruction nothing ever reaches. The FP16_TO_FP32 half carries a !defined(__F16C__) carve-out, because PR 30343 keeps that direction on the lookup table on purpose. Replace the comment above the fallback: it singled out NEON and POWER9 and pointed at a selection that now happens in the branch itself. --- ggml/src/ggml-cpu/simd-mappings.h | 13 ++++++++++--- 1 file changed, 10 insertions(+), 3 deletions(-) diff --git a/ggml/src/ggml-cpu/simd-mappings.h b/ggml/src/ggml-cpu/simd-mappings.h index ebb54a4ab8b6..b7b390db42a0 100644 --- a/ggml/src/ggml-cpu/simd-mappings.h +++ b/ggml/src/ggml-cpu/simd-mappings.h @@ -42,6 +42,7 @@ extern "C" { #define GGML_CPU_COMPUTE_FP32_TO_FP16(x) neon_compute_fp32_to_fp16(x) #define GGML_CPU_FP16_TO_FP32(x) GGML_CPU_COMPUTE_FP16_TO_FP32(x) + #define GGML_CPU_FP32_TO_FP16(x) GGML_CPU_COMPUTE_FP32_TO_FP16(x) static inline float neon_compute_fp16_to_fp32(ggml_fp16_t h) { __fp16 tmp; @@ -150,9 +151,15 @@ extern float ggml_table_f32_ue4m3[1 << 8]; #define GGML_CPU_FP32_TO_FP16(x) GGML_CPU_COMPUTE_FP32_TO_FP16(x) #endif -// On ARM NEON, it's quicker to directly convert x -> x instead of calling into ggml_lookup_fp16_to_fp32, -// so we define GGML_CPU_FP16_TO_FP32 and GGML_CPU_FP32_TO_FP16 elsewhere for NEON. -// This is also true for POWER9. +// a branch that defines a GGML_CPU_COMPUTE_* macro must also select it, or the fallback takes over silently +#if defined(GGML_CPU_COMPUTE_FP16_TO_FP32) && !defined(GGML_CPU_FP16_TO_FP32) && !defined(__F16C__) // see: https://github.com/ggml-org/llama.cpp/issues/30342 +#error "a branch defined GGML_CPU_COMPUTE_FP16_TO_FP32 without selecting it as GGML_CPU_FP16_TO_FP32" +#endif +#if defined(GGML_CPU_COMPUTE_FP32_TO_FP16) && !defined(GGML_CPU_FP32_TO_FP16) +#error "a branch defined GGML_CPU_COMPUTE_FP32_TO_FP16 without selecting it as GGML_CPU_FP32_TO_FP16" +#endif + +// fall back to the lookup table / the portable software conversion for whichever direction no branch selected #if !defined(GGML_CPU_FP16_TO_FP32) inline static float ggml_lookup_fp16_to_fp32(ggml_fp16_t f) { uint16_t s; From 8115b4c31faeab22954383202e0c26c4e55944e4 Mon Sep 17 00:00:00 2001 From: Neo Zhang Date: Tue, 20 Oct 2026 19:35:48 +0800 Subject: [PATCH 14/69] sycl : replace memset due to not always ordered (#30812) --- ggml/src/ggml-sycl/fattn-sparse.cpp | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/ggml/src/ggml-sycl/fattn-sparse.cpp b/ggml/src/ggml-sycl/fattn-sparse.cpp index df283709460d..6ea7d5404fa0 100644 --- a/ggml/src/ggml-sycl/fattn-sparse.cpp +++ b/ggml/src/ggml-sycl/fattn-sparse.cpp @@ -202,7 +202,8 @@ bool ggml_sycl_flash_attn_ext_sparse(ggml_backend_sycl_context & ctx, ggml_tenso uint8_t * d_V = v_alloc.get(); sycl::half * d_mask = m_alloc.get(); - SYCL_CHECK(CHECK_TRY_ERROR(stream->memset(d_cnt, 0, sizeof(int32_t)))); + // reset with a kernel: on Level Zero a queue memset is not always ordered before the next kernel + stream->single_task([=]() { *d_cnt = 0; }); sparse_fa_compact_mask(stream, (const sycl::half *) mask->data, d_idx, d_cnt, n_kv, n_kv_g); From a01ce9ffc64cf370439d6f861a534a65100133ae Mon Sep 17 00:00:00 2001 From: lhez Date: Tue, 20 Oct 2026 04:37:28 -0700 Subject: [PATCH 15/69] opencl: add several gemm variants for small batches and some fixes (#30850) * opencl: fix OOB read in `mul_mv_mxfp4_f32_flat` * opencl: remove large-N guard that forces CPU weight placement * this condition claims that large batch produces incorrect results on adreno and cannot be reproduced * model loader uses N512 to probe op support, resulting in cpu fallback * thus this condition does not seem necessary * opencl: cap num of subgroups by workgroup size for q4_0 mc3 Co-authored-by: Hongqiang Wang * opencl: add small batch variant for q4_k dp4a GEMM Co-authored-by: Hongqiang Wang * opencl: add a narrow variant of mul_mm_f16_f32_l4_lm Co-authored-by: Hongqiang Wang * opencl: add a one subgroup variant for gemv_noshuffle_q1_0_f32 for big M Co-authored-by: Hongqiang Wang --------- Co-authored-by: Hongqiang Wang --- ggml/src/ggml-opencl/ggml-opencl.cpp | 111 ++++++++++++------ .../kernels/gemv_noshuffle_q1_0_f32.cl | 26 ++-- .../kernels/mul_mm_f16_f32_l4_lm.cl | 13 +- .../kernels/mul_mv_mxfp4_f32_flat.cl | 2 +- 4 files changed, 104 insertions(+), 48 deletions(-) diff --git a/ggml/src/ggml-opencl/ggml-opencl.cpp b/ggml/src/ggml-opencl/ggml-opencl.cpp index ad65de00ad22..70f199dba140 100644 --- a/ggml/src/ggml-opencl/ggml-opencl.cpp +++ b/ggml/src/ggml-opencl/ggml-opencl.cpp @@ -831,6 +831,7 @@ struct ggml_backend_opencl_context { cl_program program_mul_mv_id_mxfp4_f32_flat; cl_program program_mul_mm_f32_f32_l4_lm; cl_program program_mul_mm_f16_f32_l4_lm; + cl_program program_mul_mm_f16_f32_l4_lm_n8; cl_program program_mul_mm_q8_0_f32_l4_lm; cl_kernel kernel_add, kernel_add_row, kernel_add_f16, kernel_add_row_f16; @@ -1052,6 +1053,7 @@ struct ggml_backend_opencl_context { cl_kernel kernel_mul_mm_f32_f32_l4_lm; cl_kernel kernel_gemv_f32_f32_mc; // multi-column (small-N) f32 GEMV for spec/MTP verify cl_kernel kernel_mul_mm_f16_f32_l4_lm; + cl_kernel kernel_mul_mm_f16_f32_l4_lm_n8; cl_kernel kernel_mul_mm_q1_0_f32_l4_lm; cl_kernel kernel_mul_mm_q4_0_f32_l4_lm; cl_kernel kernel_mul_mm_q4_1_f32_l4_lm; @@ -1247,6 +1249,8 @@ struct ggml_backend_opencl_context { cl_kernel kernel_gemv_noshuffle_q8_0_f32_splitk; // split-K across WGs (small-M decode) cl_kernel kernel_gemm_noshuffle_q1_0_f32; cl_kernel kernel_gemv_noshuffle_q1_0_f32; + cl_kernel kernel_gemv_noshuffle_q1_0_f32_nsg1 = nullptr; + int min_m_gemv_nsg1_q1_0 = 0; cl_kernel kernel_gemv_noshuffle_q4_k_f32; cl_kernel kernel_gemv_noshuffle_q4_k_f32_o4; // 4-output-per-WI, long-vocab lm_head cl_kernel kernel_gemv_noshuffle_q4_k_f32_tiled; // tiled-wide layout (opt-in) @@ -1261,6 +1265,11 @@ struct ggml_backend_opencl_context { cl_kernel kernel_gemv_noshuffle_q4_k_f32_32b_trans; cl_kernel kernel_gemm_noshuffle_q4_k_q8_1_dp4a = nullptr; // dp4a (int8) dense prefill GEMM cl_kernel kernel_gemm_noshuffle_q4_k_q8_1_dp4a_wimg = nullptr; // dp4a dense prefill GEMM, weights via texture (X1 opt-in) + cl_kernel kernel_gemm_noshuffle_q4_k_q8_1_dp4a_narrow = nullptr; + cl_kernel kernel_gemm_noshuffle_q4_k_q8_1_dp4a_narrow_wimg = nullptr; + int tile_n_dp4a_q4_k = 32; + int tile_n_dp4a_narrow_q4_k = 32; + int max_n_dp4a_narrow_q4_k = 16; cl_kernel kernel_gemm_noshuffle_q5_k_q8_1_dp4a = nullptr; // dp4a (int8) dense q5_K prefill GEMM cl_kernel kernel_gemm_noshuffle_q6_k_q8_1_dp4a = nullptr; // dp4a (int8) dense q6_K prefill GEMM cl_kernel kernel_quant_a_q8_1; // plain activation q8_1 pre-pass @@ -2781,6 +2790,11 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mm_f16_f32_l4_lm = clCreateKernel(backend_ctx->program_mul_mm_f16_f32_l4_lm, "kernel_mul_mm_f16_f32_l4_lm", &err), err)); + + const std::string narrow_opts = compile_opts + " -DBN=8 -DTN=1 -DKERNEL_NAME_LM=kernel_mul_mm_f16_f32_l4_lm_n8"; + backend_ctx->program_mul_mm_f16_f32_l4_lm_n8 = + build_program_from_source(backend_ctx, kernel_src.c_str(), narrow_opts); + CL_CHECK((backend_ctx->kernel_mul_mm_f16_f32_l4_lm_n8 = clCreateKernel(backend_ctx->program_mul_mm_f16_f32_l4_lm_n8, "kernel_mul_mm_f16_f32_l4_lm_n8", &err), err)); GGML_LOG_CONT("."); } @@ -3983,6 +3997,16 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { CL_CHECK((backend_ctx->kernel_gemv_noshuffle_q1_0_f32 = clCreateKernel(prog, "kernel_gemv_noshuffle_q1_0_f32", &err), err)); CL_CHECK(clReleaseProgram(prog)); + + const char * nsg1_min_m_env = getenv("GGML_OPENCL_Q1_0_GEMV_NSG1_MIN_M"); + backend_ctx->min_m_gemv_nsg1_q1_0 = nsg1_min_m_env ? atoi(nsg1_min_m_env) : + (backend_ctx->adreno_gen == ADRENO_GPU_GEN::X2E ? 8192 : 0); + if (backend_ctx->min_m_gemv_nsg1_q1_0 > 0) { + const std::string nsg1_opts = CL_gemv_compile_opts + " -DN_SIMDGROUP=1"; + cl_program prog_nsg1 = build_program_from_source(backend_ctx, kernel_src_CL_gemv_general.c_str(), nsg1_opts); + CL_CHECK((backend_ctx->kernel_gemv_noshuffle_q1_0_f32_nsg1 = clCreateKernel(prog_nsg1, "kernel_gemv_noshuffle_q1_0_f32", &err), err)); + CL_CHECK(clReleaseProgram(prog_nsg1)); + } GGML_LOG_CONT("."); } @@ -4453,16 +4477,32 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { #else const std::string kernel_src = read_file("gemm_noshuffle_q4_k_q8_1_dp4a.cl"); #endif - // Per-device dp4a dense tile. The X2-tuned TILESIZE_N=32 over-occupies LDS on - // X1 (1152 B/WG -> few resident WGs); TILESIZE_N=8 (288 B) lifts occupancy on - // X1, byte-identical. X2E keeps 32. Env override wins. - int q4k_dp4a_ts = (backend_ctx->adreno_gen == ADRENO_GPU_GEN::X1E) ? 8 : 32; - if (const char * e = getenv("GGML_OPENCL_Q4K_DP4A_TS")) { q4k_dp4a_ts = atoi(e); } - std::string dp4a_opts = compile_opts + " -DTILESIZE_N=" + std::to_string(q4k_dp4a_ts); + // X1 uses a smaller N tile to reduce local memory and register use. + int tile_n = (backend_ctx->adreno_gen == ADRENO_GPU_GEN::X1E) ? 8 : 32; + if (const char * e = getenv("GGML_OPENCL_Q4K_DP4A_TS")) { tile_n = atoi(e); } + GGML_ASSERT(tile_n >= 4 && tile_n <= 64 && tile_n % 4 == 0); + backend_ctx->tile_n_dp4a_q4_k = tile_n; + std::string dp4a_opts = compile_opts + " -DTILESIZE_N=" + std::to_string(tile_n); cl_program prog = build_program_from_source(backend_ctx, kernel_src.c_str(), dp4a_opts); CL_CHECK((backend_ctx->kernel_gemm_noshuffle_q4_k_q8_1_dp4a = clCreateKernel(prog, "kernel_gemm_noshuffle_q4_k_q8_1_dp4a", &err), err)); CL_CHECK((backend_ctx->kernel_gemm_noshuffle_q4_k_q8_1_dp4a_wimg = clCreateKernel(prog, "kernel_gemm_noshuffle_q4_k_q8_1_dp4a_wimg", &err), err)); CL_CHECK(clReleaseProgram(prog)); + + // Compile a smaller N tile to avoid unused columns in small batches. + int tile_n_narrow = MIN(tile_n, 16); + if (const char * e = getenv("GGML_OPENCL_Q4_K_DP4A_TS_NARROW")) { tile_n_narrow = atoi(e); } + GGML_ASSERT(tile_n_narrow >= 4 && tile_n_narrow <= 64 && tile_n_narrow % 4 == 0); + if (const char * e = getenv("GGML_OPENCL_Q4_K_DP4A_NARROW_MAX")) { + backend_ctx->max_n_dp4a_narrow_q4_k = atoi(e); + } + backend_ctx->tile_n_dp4a_narrow_q4_k = tile_n_narrow; + if (tile_n_narrow != tile_n) { + std::string narrow_opts = compile_opts + " -DTILESIZE_N=" + std::to_string(tile_n_narrow); + cl_program narrow_prog = build_program_from_source(backend_ctx, kernel_src.c_str(), narrow_opts); + CL_CHECK((backend_ctx->kernel_gemm_noshuffle_q4_k_q8_1_dp4a_narrow = clCreateKernel(narrow_prog, "kernel_gemm_noshuffle_q4_k_q8_1_dp4a", &err), err)); + CL_CHECK((backend_ctx->kernel_gemm_noshuffle_q4_k_q8_1_dp4a_narrow_wimg = clCreateKernel(narrow_prog, "kernel_gemm_noshuffle_q4_k_q8_1_dp4a_wimg", &err), err)); + CL_CHECK(clReleaseProgram(narrow_prog)); + } GGML_LOG_CONT("."); } @@ -9531,22 +9571,6 @@ static bool ggml_opencl_supports_op(ggml_backend_dev_t dev, const struct ggml_te op->src[0]->ne[1] >= 32768) { // vocab-scale weight; no FFN/attn weight is this tall return false; } - // The generic mul_mv (GEMV) kernels are wrong for large-batch prefill on - // Adreno. A quant mul_mat only avoids the GEMV when it reaches the Adreno - // trans-weight GEMM, which needs both a GEMM kernel for the type and - // use_adreno_kernels(). Decline the large-N shapes that would otherwise - // fall through to the GEMV. - { - const ggml_type t = op->src[0]->type; - const bool type_has_gemm = (t == GGML_TYPE_Q4_0 || t == GGML_TYPE_Q4_1 || - t == GGML_TYPE_IQ4_NL || t == GGML_TYPE_Q8_0 || - t == GGML_TYPE_Q4_K || t == GGML_TYPE_Q5_K || - t == GGML_TYPE_Q6_K); - const bool uses_gemm = type_has_gemm && use_adreno_kernels(backend_ctx, op->src[0]); - if (!uses_gemm && op->src[1]->ne[1] >= 512) { - return false; - } - } return op->src[1]->type == GGML_TYPE_F32 && ggml_is_contiguous(op->src[0]) && ggml_is_contiguous(op->src[1]); } else if (op->src[0]->type == GGML_TYPE_Q8_0) { // ggml_cl_mul_mat_q8_0_f32_adreno now honors src1/dst view_offs (the @@ -19834,7 +19858,9 @@ static void ggml_cl_mul_mat_q1_0_f32_adreno(ggml_backend_t backend, const ggml_t img_desc.buffer = b_sub_buf; CL_CHECK((b_img = clCreateImage(context, CL_MEM_READ_ONLY, &img_fmt, &img_desc, NULL, &err), err)); - kernel = backend_ctx->kernel_gemv_noshuffle_q1_0_f32; + // Tall M has enough row parallelism to skip the K split and its reduction. + const bool use_nsg1 = backend_ctx->min_m_gemv_nsg1_q1_0 > 0 && M >= backend_ctx->min_m_gemv_nsg1_q1_0; + kernel = use_nsg1 ? backend_ctx->kernel_gemv_noshuffle_q1_0_f32_nsg1 : backend_ctx->kernel_gemv_noshuffle_q1_0_f32; int r2 = 1; int r3 = 1; @@ -19856,8 +19882,9 @@ static void ggml_cl_mul_mat_q1_0_f32_adreno(ggml_backend_t backend, const ggml_t CL_CHECK(clSetKernelArg(kernel, 14, sizeof(int), &r3)); size_t wavesize = backend_ctx->adreno_wave_size; - size_t local_work_size[] = { wavesize, 4, 1 }; - size_t global_work_size[] = { CEIL_DIV(M, wavesize)*wavesize, 4, 1 }; + const size_t nsg = use_nsg1 ? 1 : 4; + size_t local_work_size[] = { wavesize, nsg, 1 }; + size_t global_work_size[] = { CEIL_DIV(M, wavesize)*wavesize, nsg, 1 }; backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst); @@ -20321,7 +20348,14 @@ static void ggml_cl_mul_mat_q4_0_f32_adreno(ggml_backend_t backend, const ggml_t // per-lane K-walk) for small M. Layout stride is fixed (4 uints/block), so only // the K-split count changes; the mc3 kernel reads it via get_local_size(1). The // ne1==1 base kernel hardcodes N_SIMDGROUP=4, so it always stays at 4. - const int mc3_nsg = (use_q40_mc3 && ne01 < 4096) ? 8 : 4; + int mc3_nsg = (use_q40_mc3 && ne01 < 4096) ? 8 : 4; + if (use_q40_mc3) { + // Register pressure can lower the kernel limit below the device limit. + const size_t max_workgroup_size = backend_ctx->get_kernel_workgroup_size(kernel); + while (mc3_nsg > 1 && (size_t)(64 * mc3_nsg) > max_workgroup_size) { + mc3_nsg /= 2; + } + } size_t local_work_size[3] = {64, (size_t)mc3_nsg, 1}; size_t global_work_size[3] = {(size_t)CEIL_DIV(ne01/2, 64)*64, (size_t)mc3_nsg, 1}; @@ -22667,8 +22701,13 @@ static void ggml_cl_mul_mat_q4_k_f32_adreno(ggml_backend_t backend, const ggml_t } } - cl_kernel dk = use_wimg ? backend_ctx->kernel_gemm_noshuffle_q4_k_q8_1_dp4a_wimg - : backend_ctx->kernel_gemm_noshuffle_q4_k_q8_1_dp4a; + const bool use_narrow = backend_ctx->kernel_gemm_noshuffle_q4_k_q8_1_dp4a_narrow != nullptr + && N <= backend_ctx->max_n_dp4a_narrow_q4_k; + cl_kernel dk = use_narrow + ? (use_wimg ? backend_ctx->kernel_gemm_noshuffle_q4_k_q8_1_dp4a_narrow_wimg + : backend_ctx->kernel_gemm_noshuffle_q4_k_q8_1_dp4a_narrow) + : (use_wimg ? backend_ctx->kernel_gemm_noshuffle_q4_k_q8_1_dp4a_wimg + : backend_ctx->kernel_gemm_noshuffle_q4_k_q8_1_dp4a); int ai = 0; if (use_wimg) { CL_CHECK(clSetKernelArg(dk, ai++, sizeof(cl_mem), &q4k_q_img)); @@ -22689,12 +22728,9 @@ static void ggml_cl_mul_mat_q4_k_f32_adreno(ggml_backend_t backend, const ggml_t CL_CHECK(clSetKernelArg(dk, ai++, sizeof(cl_uchar), &mask_d6)); CL_CHECK(clSetKernelArg(dk, ai++, sizeof(cl_uchar), &mask_d4)); CL_CHECK(clSetKernelArg(dk, ai++, sizeof(cl_uchar), &mask_hi2)); - // Must match the compile-time TILESIZE_N chosen at program build (per-device, - // X1E=8 else 32; env override). Same inputs -> same value. - int q4k_dp4a_ts = (backend_ctx->adreno_gen == ADRENO_GPU_GEN::X1E) ? 8 : 32; - if (const char * e = getenv("GGML_OPENCL_Q4K_DP4A_TS")) q4k_dp4a_ts = atoi(e); + const int tile_n = use_narrow ? backend_ctx->tile_n_dp4a_narrow_q4_k : backend_ctx->tile_n_dp4a_q4_k; size_t d_local[3] = { 64, 1, 1 }; - size_t d_global[3] = { 64, (size_t)(M / 64), (size_t)CEIL_DIV(N, q4k_dp4a_ts) }; + size_t d_global[3] = { 64, (size_t)(M / 64), (size_t)CEIL_DIV(N, tile_n) }; backend_ctx->enqueue_ndrange_kernel(dk, 3, d_global, d_local, dst); if (q4k_q_img != nullptr) { @@ -24815,7 +24851,9 @@ static void ggml_cl_mul_mat(ggml_backend_t backend, const ggml_tensor * src0, co return; } #endif - kernel = backend_ctx->kernel_mul_mm_f16_f32_l4_lm; + static const char * mm_narrow_n_env = getenv("GGML_OPENCL_MM_NARROW_N"); + const bool use_narrow_n = (mm_narrow_n_env == nullptr || mm_narrow_n_env[0] != '0') && ne11 >= 2 && ne11 <= 8; + kernel = use_narrow_n ? backend_ctx->kernel_mul_mm_f16_f32_l4_lm_n8 : backend_ctx->kernel_mul_mm_f16_f32_l4_lm; nth0 = 128; // calculated as (BM*BN)/(TM*TN) int batch_stride_a = ne00*ne01; @@ -24874,8 +24912,9 @@ static void ggml_cl_mul_mat(ggml_backend_t backend, const ggml_tensor * src0, co CL_CHECK(clSetKernelArg(kernel, 17, sizeof(int), &r2)); CL_CHECK(clSetKernelArg(kernel, 18, sizeof(int), &r3)); - // 64 is block tile size BM and BN - change here when BM and BN in the kernel are changed. - size_t global_work_size[] = {(size_t)(CEIL_DIV(ne01, 64)*nth0), (size_t)(CEIL_DIV(ne11, 64)), (size_t)ne12*ne13}; + // BN=8/TN=1 keeps 128 threads for small batches, including attention KQ/KQV. + const int bn = use_narrow_n ? 8 : 64; + size_t global_work_size[] = {(size_t)(CEIL_DIV(ne01, 64)*nth0), (size_t)(CEIL_DIV(ne11, bn)), (size_t)ne12*ne13}; size_t local_work_size[] = {(size_t)nth0, 1, 1}; backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst); diff --git a/ggml/src/ggml-opencl/kernels/gemv_noshuffle_q1_0_f32.cl b/ggml/src/ggml-opencl/kernels/gemv_noshuffle_q1_0_f32.cl index d935a489c12d..572f11637b99 100644 --- a/ggml/src/ggml-opencl/kernels/gemv_noshuffle_q1_0_f32.cl +++ b/ggml/src/ggml-opencl/kernels/gemv_noshuffle_q1_0_f32.cl @@ -8,7 +8,9 @@ #endif #define QK1_0 128 +#ifndef N_SIMDGROUP #define N_SIMDGROUP 4 +#endif #define dequantizeBlockAccum_q1(total, bits, scale, regB, lb) \ total += (2.0f*(float)((bits >> 0) & 1u) - 1.0f) * scale * sub_group_broadcast(regB.s0, lb+0); \ @@ -106,15 +108,19 @@ __kernel void kernel_gemv_noshuffle_q1_0_f32( dequantizeBlockAccum_q1(totalSum, regA.s3, scale, regB, 12); } - // reduction in local memory, assumes #wave = N_SIMDGROUP = 4 - local float reduceLM[SIMDGROUP_WIDTH * 3]; - if (groupId == 1) reduceLM[SIMDGROUP_WIDTH * 0 + slid] = totalSum; - if (groupId == 2) reduceLM[SIMDGROUP_WIDTH * 1 + slid] = totalSum; - if (groupId == 3) reduceLM[SIMDGROUP_WIDTH * 2 + slid] = totalSum; +#if N_SIMDGROUP > 1 + // Sum the K slices from the other subgroups. + local float reduceLM[SIMDGROUP_WIDTH * (N_SIMDGROUP - 1)]; + if (groupId > 0) { + reduceLM[SIMDGROUP_WIDTH * (groupId - 1) + slid] = totalSum; + } barrier(CLK_LOCAL_MEM_FENCE); - if (groupId == 0) totalSum += reduceLM[SIMDGROUP_WIDTH * 0 + slid]; - if (groupId == 0) totalSum += reduceLM[SIMDGROUP_WIDTH * 1 + slid]; - if (groupId == 0) totalSum += reduceLM[SIMDGROUP_WIDTH * 2 + slid]; + if (groupId == 0) { + for (uint w = 0; w < N_SIMDGROUP - 1; ++w) { + totalSum += reduceLM[SIMDGROUP_WIDTH * w + slid]; + } + } +#endif if (groupId == 0) { dst = (global float*)((global char*)dst + offsetd); @@ -122,6 +128,8 @@ __kernel void kernel_gemv_noshuffle_q1_0_f32( // so when ne01 is not a multiple of the wave size the tail work-items run past // row ne01 and would overrun dst into the adjacent tensor. No-op / byte-identical // when ne01 is wave-aligned (no padding). - if (gid < M) dst[gid] = totalSum; + if (gid < M) { + dst[gid] = totalSum; + } } } diff --git a/ggml/src/ggml-opencl/kernels/mul_mm_f16_f32_l4_lm.cl b/ggml/src/ggml-opencl/kernels/mul_mm_f16_f32_l4_lm.cl index 6982f8f514dd..eba1778afd2f 100644 --- a/ggml/src/ggml-opencl/kernels/mul_mm_f16_f32_l4_lm.cl +++ b/ggml/src/ggml-opencl/kernels/mul_mm_f16_f32_l4_lm.cl @@ -4,12 +4,20 @@ #define LOAD_VEC_B 4 #define BM 64 +#ifndef BN #define BN 64 +#endif #define BK 16 #define TM 4 +#ifndef TN #define TN 8 +#endif -kernel void kernel_mul_mm_f16_f32_l4_lm( +#ifndef KERNEL_NAME_LM +#define KERNEL_NAME_LM kernel_mul_mm_f16_f32_l4_lm +#endif + +kernel void KERNEL_NAME_LM( global half4 * src0, ulong offset0, global float4 * src1, @@ -93,7 +101,8 @@ kernel void kernel_mul_mm_f16_f32_l4_lm( } } - for (int l = 0; l < BN; l += loadstride_b) { + // BN=8 uses only the threads that load a column inside the tile. + for (int l = 0; loadc_b + l < BN; l += loadstride_b) { if (ic*BN + loadc_b + l < ne11) { const int idx = pos_b + (loadc_b + l) * stride_b / LOAD_VEC_B + loadr_b; buf_b[(loadr_b * LOAD_VEC_B + 0) * BN + loadc_b + l] = src1[idx].s0; diff --git a/ggml/src/ggml-opencl/kernels/mul_mv_mxfp4_f32_flat.cl b/ggml/src/ggml-opencl/kernels/mul_mv_mxfp4_f32_flat.cl index 3d5a923eee0d..2e31831ca4c3 100644 --- a/ggml/src/ggml-opencl/kernels/mul_mv_mxfp4_f32_flat.cl +++ b/ggml/src/ggml-opencl/kernels/mul_mv_mxfp4_f32_flat.cl @@ -132,7 +132,7 @@ kernel void kernel_mul_mv_mxfp4_f32_flat( global float4 * y4 = (global float4 *)yb; #pragma unroll - for (short row = 0; row < N_R0_MXFP4; row++) { + for (short row = 0; row < N_R0_MXFP4 && first_row + row < ne0; row++) { uchar xb_e = x_e[row * nb + ib]; #ifdef SRC0Q_IMG ushort4 xb_q = as_ushort4(read_imageui(src0_q, (offset_q + row * nb + ib) * 2 + it).xy); From b38960f52079e41f91863dd0905ec13432966b39 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sigbj=C3=B8rn=20Skj=C3=A6ret?= Date: Tue, 20 Oct 2026 14:27:55 +0200 Subject: [PATCH 16/69] ci : remove superfluous webgpu jobs (#30868) --- .github/workflows/build-webgpu.yml | 142 +---------------------------- 1 file changed, 1 insertion(+), 141 deletions(-) diff --git a/.github/workflows/build-webgpu.yml b/.github/workflows/build-webgpu.yml index d00106439753..88f2c6f5dfa2 100644 --- a/.github/workflows/build-webgpu.yml +++ b/.github/workflows/build-webgpu.yml @@ -7,15 +7,7 @@ on: - master paths: [ '.github/workflows/build-webgpu.yml', - '**/CMakeLists.txt', - '**/.cmake', - '**/*.h', - '**/*.hpp', - '**/*.c', - '**/*.cpp', - '**/*.wgsl', - '**/*.tmpl', - 'ggml/src/ggml-webgpu/wgsl-shaders/embed_wgsl.py' + 'ggml/src/ggml-webgpu/**' ] pull_request: @@ -63,135 +55,3 @@ jobs: -type f \( -name '*.cpp' -o -name '*.hpp' -o -name '*.h' \) \ -print0 | xargs -0 clang-format-22 --dry-run --Werror - - macos: - runs-on: macos-latest - - steps: - - name: Clone - id: checkout - uses: actions/checkout@v6 - - - name: ccache - uses: ggml-org/ccache-action@v1.2.24 - with: - restore: false - save: false - - - name: Dawn Dependency - id: dawn-depends - run: | - DAWN_VERSION="v20260908.214631" - DAWN_OWNER="google" - DAWN_REPO="dawn" - DAWN_ASSET_NAME="Dawn-94c3c9cc0d5fb2e85aebb370fa8d37b71aa34655-macos-latest-Release" - echo "Fetching release asset from https://github.com/google/dawn/releases/download/${DAWN_VERSION}/${DAWN_ASSET_NAME}.tar.gz" - curl -L -o artifact.tar.gz \ - "https://github.com/google/dawn/releases/download/${DAWN_VERSION}/${DAWN_ASSET_NAME}.tar.gz" - mkdir dawn - tar -xvf artifact.tar.gz -C dawn --strip-components=1 - - - name: ccache-buckets-restore - uses: ./.github/actions/ccache-buckets - env: - HF_TOKEN: ${{ secrets.HF_TOKEN_CI }} - with: - key: webgpu-macos-latest - folder: llama.cpp - hf_bucket: ggml-org/cache - - - name: Build - id: cmake_build - run: | - export CMAKE_PREFIX_PATH=dawn - cmake -B build -G "Ninja" -DCMAKE_BUILD_TYPE=Release -DGGML_WEBGPU=ON -DGGML_METAL=OFF -DGGML_BLAS=OFF - time cmake --build build --config Release -j $(sysctl -n hw.logicalcpu) - - - name: ccache-buckets-save - if: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }} - uses: ./.github/actions/ccache-buckets - env: - HF_TOKEN: ${{ secrets.HF_TOKEN_CACHE_OUTPUT }} - with: - key: webgpu-macos-latest - folder: llama.cpp - evict-old-files: 1d - hf_bucket: ggml-org/cache - save: true - - - name: Test - id: cmake_test - run: | - cd build - ctest -L main --verbose --timeout 900 - - ubuntu: - runs-on: ubuntu-24.04 - - steps: - - name: Clone - id: checkout - uses: actions/checkout@v6 - - - name: ccache - uses: ggml-org/ccache-action@v1.2.24 - with: - restore: false - save: false - - - name: Dependencies - id: depends - run: | - sudo add-apt-repository -y ppa:kisak/kisak-mesa - sudo apt-get update -y - sudo apt-get install -y build-essential mesa-vulkan-drivers \ - libxcb-xinput0 libxcb-xinerama0 libxcb-cursor-dev libssl-dev - - - name: Dawn Dependency - id: dawn-depends - run: | - sudo apt-get install -y libxrandr-dev libxinerama-dev libxcursor-dev mesa-common-dev libx11-xcb-dev libxi-dev - DAWN_VERSION="v20260908.214631" - DAWN_OWNER="google" - DAWN_REPO="dawn" - DAWN_ASSET_NAME="Dawn-94c3c9cc0d5fb2e85aebb370fa8d37b71aa34655-ubuntu-latest-Release" - echo "Fetching release asset from https://github.com/google/dawn/releases/download/${DAWN_VERSION}/${DAWN_ASSET_NAME}.tar.gz" - curl -L -o artifact.tar.gz \ - "https://github.com/google/dawn/releases/download/${DAWN_VERSION}/${DAWN_ASSET_NAME}.tar.gz" - mkdir dawn - tar -xvf artifact.tar.gz -C dawn --strip-components=1 - - - name: ccache-buckets-restore - uses: ./.github/actions/ccache-buckets - env: - HF_TOKEN: ${{ secrets.HF_TOKEN_CI }} - with: - key: webgpu-ubuntu-24.04 - folder: llama.cpp - hf_bucket: ggml-org/cache - - - name: Build - id: cmake_build - run: | - export Dawn_DIR=dawn/lib64/cmake/Dawn - cmake -B build \ - -DGGML_WEBGPU=ON - time cmake --build build --config Release -j $(nproc) - - - name: ccache-buckets-save - if: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }} - uses: ./.github/actions/ccache-buckets - env: - HF_TOKEN: ${{ secrets.HF_TOKEN_CACHE_OUTPUT }} - with: - key: webgpu-ubuntu-24.04 - folder: llama.cpp - evict-old-files: 1d - hf_bucket: ggml-org/cache - save: true - - - name: Test - id: cmake_test - run: | - cd build - ctest -L main --verbose --timeout 900 From 9df0b73c707900b7277a46b5ae52ff7e7e567d13 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Tue, 20 Oct 2026 15:58:09 +0300 Subject: [PATCH 17/69] model : drop the redundant precision overrides in the Kolibri-1 router (#30865) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * model : drop the redundant precision overrides in the Kolibri-1 router Follow-up to the review in #30673: the router matmul works with the default precision, so both ggml_prec_set_* calls can go away. Assisted-by: pi:llama.cpp/ggml-org/Qwen3.8-Flash-Next-GGUF (MacBook, M5 Max, 128GB) * cont : fix comment Co-authored-by: Sigbjørn Skjæret --------- Co-authored-by: Sigbjørn Skjæret --- src/models/kolibri1.cpp | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/src/models/kolibri1.cpp b/src/models/kolibri1.cpp index acc1dd159202..2301ef272acf 100644 --- a/src/models/kolibri1.cpp +++ b/src/models/kolibri1.cpp @@ -131,11 +131,8 @@ llama_model_kolibri1::graph::graph(const llama_model & model, const llm_graph_pa cur = build_norm(ffn_inp, layer.ffn_norm, NULL, LLM_NORM_RMS, il); cb(cur, "ffn_norm", il); - // The router runs in f32: the selection adds the bias to the logits, the weights - // are the sigmoid of the unbiased logits, so the same logits feed both. + // The selection adds the bias to the logits, the weights are the sigmoid of the unbiased logits, so the same logits feed both. ggml_tensor * logits = build_lora_mm(layer.ffn_gate_inp, cur); - ggml_prec_set_acc(logits, GGML_PREC_F32); - ggml_prec_set_src(logits, GGML_PREC_F32, 1); cb(logits, "ffn_moe_logits", il); ggml_tensor * selection = ggml_add(ctx0, logits, layer.ffn_exp_probs_b); From df11702136d02b9e8fa37971de0bf0e95a91b2de Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Tue, 20 Oct 2026 16:46:00 +0300 Subject: [PATCH 18/69] metal : optimize argmax and pool_1d (#30862) * metal : optimize argmax and pool_1d Add a two-pass partial reduction for large-row argmax and use cooperative threadgroup reductions for pool_1d. Allocate scratch space for the argmax partials through the Metal buffer allocator. Assisted-by: pi:llama.cpp/ggml-org/DeepSeek-V4-Flash-Vision-Exp-GGUF (Mac Studio, M2 Ultra, 192GB) * tests : add pool_1d perf cases Add representative POOL_1D max cases for the MoE down-projection amax shapes, reducing the whole intermediate dim. Assisted-by: pi:llama.cpp/ggml-org/DeepSeek-V4-Flash-Vision-Exp-GGUF (Mac Studio, M2 Ultra, 192GB) * cont : minor fix --- ggml/src/ggml-backend.cpp | 1 + ggml/src/ggml-metal/ggml-metal-device.cpp | 22 ++++ ggml/src/ggml-metal/ggml-metal-device.h | 2 + ggml/src/ggml-metal/ggml-metal-impl.h | 7 +- ggml/src/ggml-metal/ggml-metal-ops.cpp | 79 +++++++++++-- ggml/src/ggml-metal/ggml-metal-ops.h | 2 + ggml/src/ggml-metal/ggml-metal.cpp | 4 + ggml/src/ggml-metal/kernels/misc.metal | 134 ++++++++++++++++++++++ ggml/src/ggml-metal/kernels/pool.metal | 122 ++++++++++++++------ tests/test-backend-ops.cpp | 11 ++ 10 files changed, 340 insertions(+), 44 deletions(-) diff --git a/ggml/src/ggml-backend.cpp b/ggml/src/ggml-backend.cpp index ffbcba3661cf..22e5fd9c7d99 100644 --- a/ggml/src/ggml-backend.cpp +++ b/ggml/src/ggml-backend.cpp @@ -2226,6 +2226,7 @@ bool ggml_op_alloc_size_may_expand(enum ggml_op op) { case GGML_OP_CUMSUM: case GGML_OP_ARGSORT: case GGML_OP_TOP_K: + case GGML_OP_ARGMAX: return true; default: return false; diff --git a/ggml/src/ggml-metal/ggml-metal-device.cpp b/ggml/src/ggml-metal/ggml-metal-device.cpp index 2c7972b0a5d6..65611314adb1 100644 --- a/ggml/src/ggml-metal/ggml-metal-device.cpp +++ b/ggml/src/ggml-metal/ggml-metal-device.cpp @@ -1567,6 +1567,28 @@ ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_argmax(ggml_meta return res; } +ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_argmax_part(ggml_metal_library_t lib) { + ggml_metal_pipeline_with_params res = ggml_metal_library_get_pipeline(lib, "kernel_argmax_part_f32"); + if (!res.pipeline) { + res = ggml_metal_library_compile_pipeline(lib, "kernel_argmax_part_f32", "kernel_argmax_part_f32", nullptr); + } + + res.smem = 32*(sizeof(float) + sizeof(int32_t)); + + return res; +} + +ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_argmax_final(ggml_metal_library_t lib) { + ggml_metal_pipeline_with_params res = ggml_metal_library_get_pipeline(lib, "kernel_argmax_final_f32"); + if (!res.pipeline) { + res = ggml_metal_library_compile_pipeline(lib, "kernel_argmax_final_f32", "kernel_argmax_final_f32", nullptr); + } + + res.smem = 32*(sizeof(float) + sizeof(int32_t)); + + return res; +} + ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_argsort(ggml_metal_library_t lib, const ggml_tensor * op) { assert(op->op == GGML_OP_ARGSORT); diff --git a/ggml/src/ggml-metal/ggml-metal-device.h b/ggml/src/ggml-metal/ggml-metal-device.h index 4012b9d5f69b..5ccaec6a34f2 100644 --- a/ggml/src/ggml-metal/ggml-metal-device.h +++ b/ggml/src/ggml-metal/ggml-metal-device.h @@ -145,6 +145,8 @@ struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_mul_mm_id struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_mul_mm_id (ggml_metal_library_t lib, const struct ggml_tensor * op); struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_mul_mv_id (ggml_metal_library_t lib, const struct ggml_tensor * op); struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_argmax (ggml_metal_library_t lib, const struct ggml_tensor * op); +struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_argmax_part (ggml_metal_library_t lib); +struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_argmax_final (ggml_metal_library_t lib); struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_argsort (ggml_metal_library_t lib, const struct ggml_tensor * op); struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_argsort_merge (ggml_metal_library_t lib, const struct ggml_tensor * op); struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_fwht (ggml_metal_library_t lib, int n, enum ggml_type tsrc); diff --git a/ggml/src/ggml-metal/ggml-metal-impl.h b/ggml/src/ggml-metal/ggml-metal-impl.h index 4ffb5a77ddfb..46b56c9d9cd4 100644 --- a/ggml/src/ggml-metal/ggml-metal-impl.h +++ b/ggml/src/ggml-metal/ggml-metal-impl.h @@ -16,6 +16,9 @@ #define N_MM_NPART_AMAX 256 +#define N_ARGMAX_NPART_MAX 256 +#define N_ARGMAX_CHUNK 4096 + // kernel parameters for mat-vec threadgroups // // N_R0: number of src0 rows to process per simdgroup @@ -1379,8 +1382,10 @@ typedef struct { } ggml_metal_kargs_pool_1d; typedef struct { - int64_t ne00; + int64_t ne00; uint64_t nb01; + int64_t npart; + int64_t ne01; } ggml_metal_kargs_argmax; typedef struct { diff --git a/ggml/src/ggml-metal/ggml-metal-ops.cpp b/ggml/src/ggml-metal/ggml-metal-ops.cpp index 53c981618511..fc856b5b24af 100644 --- a/ggml/src/ggml-metal/ggml-metal-ops.cpp +++ b/ggml/src/ggml-metal/ggml-metal-ops.cpp @@ -2319,13 +2319,18 @@ int ggml_metal_op_pool_1d(ggml_metal_op_t ctx, int idx) { auto pipeline = ggml_metal_library_get_pipeline_pool_1d(lib, op, op_pool); - const int nth = std::min(ggml_metal_pipeline_max_theads_per_threadgroup(pipeline), (int) np); - const int ntg = (np + nth - 1) / nth; + const int nth = std::min(256, std::max(1, k0)); + const int ntg = (int) np; + + const size_t smem = op_pool == GGML_OP_POOL_AVG ? + 2*32*sizeof(float) : + 32*sizeof(float); ggml_metal_encoder_set_pipeline(enc, pipeline); ggml_metal_encoder_set_bytes (enc, &args_pool_1d, sizeof(args_pool_1d), 0); ggml_metal_encoder_set_buffer (enc, ggml_metal_get_buffer_id(op->src[0]), 1); ggml_metal_encoder_set_buffer (enc, ggml_metal_get_buffer_id(op), 2); + ggml_metal_encoder_set_threadgroup_memory_size(enc, smem, 0); ggml_metal_encoder_dispatch_threadgroups(enc, ntg, 1, 1, nth, 1, 1); @@ -5421,6 +5426,25 @@ int ggml_metal_op_timestep_embedding(ggml_metal_op_t ctx, int idx) { return 1; } +size_t ggml_metal_op_argmax_extra(const ggml_tensor * op) { + assert(op->op == GGML_OP_ARGMAX); + + const int64_t ne00 = op->src[0]->ne[0]; + const int64_t ne01 = op->src[0]->ne[1]; + + int npart = 1; + if (ne00 > N_ARGMAX_CHUNK) { + npart = (int) std::min(N_ARGMAX_NPART_MAX, (ne00 + N_ARGMAX_CHUNK - 1)/N_ARGMAX_CHUNK); + } + + if (npart == 1) { + return 0; + } + + // partial max + partial argmax for every row + return (sizeof(float) + sizeof(int32_t)) * npart * ne01; +} + int ggml_metal_op_argmax(ggml_metal_op_t ctx, int idx) { ggml_tensor * op = ctx->node(idx); @@ -5432,22 +5456,61 @@ int ggml_metal_op_argmax(ggml_metal_op_t ctx, int idx) { GGML_TENSOR_LOCALS( int32_t, ne, op, ne); GGML_TENSOR_LOCALS(uint64_t, nb, op, nb); + const int64_t nrows = ggml_nrows(op->src[0]); + + int npart = 1; + if (ne00 > N_ARGMAX_CHUNK) { + npart = (int) std::min(N_ARGMAX_NPART_MAX, (ne00 + N_ARGMAX_CHUNK - 1)/N_ARGMAX_CHUNK); + } + ggml_metal_kargs_argmax args = { - /*.ne00 = */ ne00, - /*.nb01 = */ nb01, + /*.ne00 = */ ne00, + /*.nb01 = */ nb01, + /*.npart = */ npart, + /*.ne01 = */ ne01, }; - auto pipeline = ggml_metal_library_get_pipeline_argmax(lib, op); + const size_t smem = 32*(sizeof(float) + sizeof(int32_t)); - const int64_t nrows = ggml_nrows(op->src[0]); + if (npart > 1) { + auto pipeline_part = ggml_metal_library_get_pipeline_argmax_part(lib); + auto pipeline_final = ggml_metal_library_get_pipeline_argmax_final(lib); + + ggml_metal_buffer_id bid_dst = ggml_metal_get_buffer_id(op); + ggml_metal_buffer_id bid_tmp = bid_dst; + bid_tmp.offs += ggml_nbytes(op); + + ggml_metal_encoder_set_pipeline(enc, pipeline_part); + ggml_metal_encoder_set_bytes (enc, &args, sizeof(args), 0); + ggml_metal_encoder_set_buffer (enc, ggml_metal_get_buffer_id(op->src[0]), 1); + ggml_metal_encoder_set_buffer (enc, bid_tmp, 2); + ggml_metal_encoder_set_threadgroup_memory_size(enc, smem, 0); + ggml_metal_encoder_dispatch_threadgroups(enc, npart, nrows, 1, 256, 1, 1); + + ggml_metal_op_concurrency_reset(ctx); + + int nth = 32; // SIMD width + while (nth < npart && nth < 256) { + nth *= 2; + } + + ggml_metal_encoder_set_pipeline(enc, pipeline_final); + ggml_metal_encoder_set_bytes (enc, &args, sizeof(args), 0); + ggml_metal_encoder_set_buffer (enc, bid_tmp, 1); + ggml_metal_encoder_set_buffer (enc, bid_dst, 2); + ggml_metal_encoder_set_threadgroup_memory_size(enc, smem, 0); + ggml_metal_encoder_dispatch_threadgroups(enc, nrows, 1, 1, nth, 1, 1); + + return 1; + } + + auto pipeline = ggml_metal_library_get_pipeline_argmax(lib, op); int nth = 32; // SIMD width while (nth < ne00 && nth*ne01*ne02*ne03 < 256) { nth *= 2; } - const size_t smem = pipeline.smem; - ggml_metal_encoder_set_pipeline(enc, pipeline); ggml_metal_encoder_set_bytes (enc, &args, sizeof(args), 0); ggml_metal_encoder_set_buffer (enc, ggml_metal_get_buffer_id(op->src[0]), 1); diff --git a/ggml/src/ggml-metal/ggml-metal-ops.h b/ggml/src/ggml-metal/ggml-metal-ops.h index 583d1156bf27..b8296bf2b481 100644 --- a/ggml/src/ggml-metal/ggml-metal-ops.h +++ b/ggml/src/ggml-metal/ggml-metal-ops.h @@ -47,6 +47,8 @@ size_t ggml_metal_op_flash_attn_ext_extra_tmp(const struct ggml_tensor * op); size_t ggml_metal_op_flash_attn_ext_extra_kv_f16(const struct ggml_tensor * op); size_t ggml_metal_op_flash_attn_ext_extra_idx(const struct ggml_tensor * op); +size_t ggml_metal_op_argmax_extra(const struct ggml_tensor * op); + int ggml_metal_op_concat (ggml_metal_op_t ctx, int idx); int ggml_metal_op_repeat (ggml_metal_op_t ctx, int idx); int ggml_metal_op_acc (ggml_metal_op_t ctx, int idx); diff --git a/ggml/src/ggml-metal/ggml-metal.cpp b/ggml/src/ggml-metal/ggml-metal.cpp index 344882e044f3..074a9d8e69dd 100644 --- a/ggml/src/ggml-metal/ggml-metal.cpp +++ b/ggml/src/ggml-metal/ggml-metal.cpp @@ -245,6 +245,10 @@ static size_t ggml_backend_metal_buffer_type_get_alloc_size(ggml_backend_buffer_ { res = 2*sizeof(int32_t)*ggml_nelements(tensor->src[0]); } break; + case GGML_OP_ARGMAX: + { + res += ggml_metal_op_argmax_extra(tensor); + } break; default: break; } diff --git a/ggml/src/ggml-metal/kernels/misc.metal b/ggml/src/ggml-metal/kernels/misc.metal index fb27983db3e6..90d3fbe211af 100644 --- a/ggml/src/ggml-metal/kernels/misc.metal +++ b/ggml/src/ggml-metal/kernels/misc.metal @@ -60,6 +60,140 @@ kernel void kernel_argmax_f32( dst_i32[tgpig] = arg_val; } +kernel void kernel_argmax_part_f32( + constant ggml_metal_kargs_argmax & args, + device const char * src0, + device char * tmp, + threadgroup char * shmem [[threadgroup(0)]], + uint3 tgpig[[threadgroup_position_in_grid]], + uint3 tpitg[[thread_position_in_threadgroup]], + uint sgitg[[simdgroup_index_in_threadgroup]], + uint tiisg[[thread_index_in_simdgroup]], + uint3 ntg[[threads_per_threadgroup]]) { + const int row = tgpig.y; + const int part = tgpig.x; + const int ne00 = (int) args.ne00; + const int npart = (int) args.npart; + const int chunk = (ne00 + npart - 1) / npart; + const int start = part * chunk; + const int end = min(start + chunk, ne00); + + device const float * x_row = (device const float *) ((device const char *) src0 + row * args.nb01); + + float lmax = -INFINITY; + int32_t larg = -1; + + for (int i00 = start + tpitg.x; i00 < end; i00 += ntg.x) { + if (x_row[i00] > lmax) { + lmax = x_row[i00]; + larg = i00; + } + } + + // find the argmax value in the block + float max_val = simd_max(lmax); + int32_t arg_val = simd_max(select(-1, larg, lmax == max_val)); + + threadgroup float * shared_maxval = (threadgroup float *) shmem; + threadgroup int32_t * shared_argmax = (threadgroup int32_t *) shmem + N_SIMDWIDTH; + + if (ntg.x > N_SIMDWIDTH) { + if (sgitg == 0) { + shared_maxval[tiisg] = -INFINITY; + shared_argmax[tiisg] = -1; + } + + threadgroup_barrier(mem_flags::mem_threadgroup); + + if (tiisg == 0) { + shared_maxval[sgitg] = max_val; + shared_argmax[sgitg] = arg_val; + } + + threadgroup_barrier(mem_flags::mem_threadgroup); + + max_val = shared_maxval[tiisg]; + arg_val = shared_argmax[tiisg]; + + float max_val_reduced = simd_max(max_val); + int32_t arg_val_reduced = simd_max(select(-1, arg_val, max_val == max_val_reduced)); + + max_val = max_val_reduced; + arg_val = arg_val_reduced; + } + + if (tpitg.x == 0) { + device float * tmp_max = (device float *) tmp; + device int32_t * tmp_arg = (device int32_t *) tmp + npart * (int) args.ne01; + + tmp_max[row*npart + part] = max_val; + tmp_arg[row*npart + part] = arg_val; + } +} + +kernel void kernel_argmax_final_f32( + constant ggml_metal_kargs_argmax & args, + device const char * tmp, + device char * dst, + threadgroup char * shmem [[threadgroup(0)]], + uint3 tgpig[[threadgroup_position_in_grid]], + uint3 tpitg[[thread_position_in_threadgroup]], + uint sgitg[[simdgroup_index_in_threadgroup]], + uint tiisg[[thread_index_in_simdgroup]], + uint3 ntg[[threads_per_threadgroup]]) { + const int row = tgpig.x; + const int npart = (int) args.npart; + + device const float * tmp_max = (device const float *) tmp; + device const int32_t * tmp_arg = (device const int32_t *) tmp + npart * (int) args.ne01; + + float lmax = -INFINITY; + int32_t larg = -1; + + for (int i = tpitg.x; i < npart; i += ntg.x) { + if (tmp_max[row*npart + i] > lmax) { + lmax = tmp_max[row*npart + i]; + larg = tmp_arg[row*npart + i]; + } + } + + // find the argmax value in the block + float max_val = simd_max(lmax); + int32_t arg_val = simd_max(select(-1, larg, lmax == max_val)); + + threadgroup float * shared_maxval = (threadgroup float *) shmem; + threadgroup int32_t * shared_argmax = (threadgroup int32_t *) shmem + N_SIMDWIDTH; + + if (ntg.x > N_SIMDWIDTH) { + if (sgitg == 0) { + shared_maxval[tiisg] = -INFINITY; + shared_argmax[tiisg] = -1; + } + + threadgroup_barrier(mem_flags::mem_threadgroup); + + if (tiisg == 0) { + shared_maxval[sgitg] = max_val; + shared_argmax[sgitg] = arg_val; + } + + threadgroup_barrier(mem_flags::mem_threadgroup); + + max_val = shared_maxval[tiisg]; + arg_val = shared_argmax[tiisg]; + + float max_val_reduced = simd_max(max_val); + int32_t arg_val_reduced = simd_max(select(-1, arg_val, max_val == max_val_reduced)); + + max_val = max_val_reduced; + arg_val = arg_val_reduced; + } + + if (tpitg.x == 0) { + ((device int32_t *) dst)[row] = arg_val; + } +} + kernel void kernel_diag_f32( constant ggml_metal_kargs_diag & args, device const char * src0, diff --git a/ggml/src/ggml-metal/kernels/pool.metal b/ggml/src/ggml-metal/kernels/pool.metal index 13d355b9deb3..3dc0261c3376 100644 --- a/ggml/src/ggml-metal/kernels/pool.metal +++ b/ggml/src/ggml-metal/kernels/pool.metal @@ -84,65 +84,117 @@ kernel void kernel_pool_1d_max_f32( constant ggml_metal_kargs_pool_1d & args, device const float * src, device float * dst, - uint gid [[thread_position_in_grid]] -) { - - if (gid >= args.np) { + threadgroup char * shmem [[threadgroup(0)]], + uint tgpig[[threadgroup_position_in_grid]], + uint tpitg[[thread_position_in_threadgroup]], + uint sgitg[[simdgroup_index_in_threadgroup]], + uint tiisg[[thread_index_in_simdgroup]], + uint ntg[[threads_per_threadgroup]]) { + if (tgpig >= args.np) { return; } - const int ow = (int)gid % args.OW; - const int row = (int)gid / args.OW; + const int out = (int) tgpig; + const int ow = out % (int) args.OW; + const int row = out / (int) args.OW; + + const int base = ow * args.s0 - args.p0; + const int src_off = row * args.IW; + const int dst_off = row * args.OW; + + float lmax = -INFINITY; + + for (int ki = tpitg; ki < args.k0; ki += ntg) { + const int j = base + ki; + if (j >= 0 && j < args.IW) { + lmax = max(lmax, src[src_off + j]); + } + } - const int base = ow * args.s0 - args.p0; + float max_val = simd_max(lmax); - float acc = -INFINITY; + threadgroup float * shared_max = (threadgroup float *) shmem; - const int src_off = row * args.IW; - const int dst_off = row * args.OW; + if (ntg > N_SIMDWIDTH) { + if (sgitg == 0) { + shared_max[tiisg] = -INFINITY; + } + threadgroup_barrier(mem_flags::mem_threadgroup); - for (int ki = 0; ki < args.k0; ++ki) { - int j = base + ki; - if (j < 0 || j >= args.IW){ - continue; + if (tiisg == 0) { + shared_max[sgitg] = max_val; } - float v = src[src_off + j]; - acc = max(acc, v); + threadgroup_barrier(mem_flags::mem_threadgroup); + + max_val = shared_max[tiisg]; + max_val = simd_max(max_val); } - dst[dst_off + ow] = acc; + if (tpitg == 0) { + dst[dst_off + ow] = max_val; + } } kernel void kernel_pool_1d_avg_f32( constant ggml_metal_kargs_pool_1d & args, device const float * src, device float * dst, - uint gid [[thread_position_in_grid]] -) { - - if (gid >= args.np) { + threadgroup char * shmem [[threadgroup(0)]], + uint tgpig[[threadgroup_position_in_grid]], + uint tpitg[[thread_position_in_threadgroup]], + uint sgitg[[simdgroup_index_in_threadgroup]], + uint tiisg[[thread_index_in_simdgroup]], + uint ntg[[threads_per_threadgroup]]) { + if (tgpig >= args.np) { return; } - const int ow = (int)gid % args.OW; - const int row = (int)gid / args.OW; - - const int base = ow * args.s0 - args.p0; + const int out = (int) tgpig; + const int ow = out % (int) args.OW; + const int row = out / (int) args.OW; - float acc = 0.0f; - int cnt = 0; + const int base = ow * args.s0 - args.p0; + const int src_off = row * args.IW; + const int dst_off = row * args.OW; - const int src_off = row * args.IW; - const int dst_off = row * args.OW; + float lsum = 0.0f; + int lcnt = 0; - for (int ki = 0; ki < args.k0; ++ki) { + for (int ki = tpitg; ki < args.k0; ki += ntg) { const int j = base + ki; - if (j < 0 || j >= args.IW) { - continue; + if (j >= 0 && j < args.IW) { + lsum += src[src_off + j]; + lcnt += 1; + } + } + + float sum = simd_sum(lsum); + float cnt = simd_sum((float) lcnt); + + threadgroup float * shared_sum = (threadgroup float *) shmem; + threadgroup float * shared_cnt = (threadgroup float *) shmem + N_SIMDWIDTH; + + if (ntg > N_SIMDWIDTH) { + if (sgitg == 0) { + shared_sum[tiisg] = 0.0f; + shared_cnt[tiisg] = 0.0f; + } + threadgroup_barrier(mem_flags::mem_threadgroup); + + if (tiisg == 0) { + shared_sum[sgitg] = sum; + shared_cnt[sgitg] = cnt; } - acc += src[src_off + j]; - cnt += 1; + threadgroup_barrier(mem_flags::mem_threadgroup); + + sum = shared_sum[tiisg]; + cnt = shared_cnt[tiisg]; + + sum = simd_sum(sum); + cnt = simd_sum(cnt); } - dst[dst_off + ow] = (cnt > 0) ? (acc / (float)cnt) : 0.0f; + if (tpitg == 0) { + dst[dst_off + ow] = (cnt > 0.0f) ? (sum / cnt) : 0.0f; + } } diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index 1df1129cd575..485629cf64cb 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -12199,6 +12199,17 @@ static std::vector> make_test_cases_perf() { } } + // POOL_1D max over MoE down-projection amax shapes: + // amax is [1, n_expert, n_tokens] after reducing the whole intermediate dim. + for (int64_t n_ff : { 2048, 4096, 8192 }) { + for (int64_t n_expert : { 8, 16 }) { + for (int64_t n_tokens : { 1, 512 }) { + test_cases.emplace_back(new test_pool1d( + GGML_OP_POOL_MAX, GGML_TYPE_F32, {n_ff, n_expert, n_tokens, 1}, n_ff, n_ff, 0)); + } + } + } + // LEAKY_RELU at FFN activation width, for direct comparison with RELU for (int64_t n_tokens : {512, 2048}) { test_cases.emplace_back(new test_leaky_relu(GGML_TYPE_F32, { 17408, n_tokens, 1, 1 }, 0.1f)); From 36140dbee26704d517889db16caf5318039beaeb Mon Sep 17 00:00:00 2001 From: Tarek Dakhran Date: Tue, 20 Oct 2026 16:05:30 +0200 Subject: [PATCH 19/69] mtmd : read LFM2 tiling params from GGUF metadata (#30840) * mtmd : read LFM2 tiling params from GGUF metadata Write min_tiles, max_tiles, tile_size and max_pixels_tolerance from processor_config.json when converting LFM2-VL mmproj, read them in clip.cpp with the previous constants as defaults. do_image_splitting=false maps to min_tiles=max_tiles=1 (no tiling). Rebased version of #25524, original implementation by Michael DM (@michael-dm). Co-authored-by: Michael DM * fix test --------- Co-authored-by: Michael DM --- conversion/lfm2.py | 10 ++++++++++ gguf-py/gguf/constants.py | 2 ++ gguf-py/gguf/gguf_writer.py | 6 ++++++ tests/test-mtmd-impl.cpp | 10 ++++++++++ tools/mtmd/clip-impl.h | 2 ++ tools/mtmd/clip-model.h | 3 ++- tools/mtmd/clip.cpp | 10 ++++++++++ tools/mtmd/mtmd-image.cpp | 9 +++++++-- tools/mtmd/mtmd-image.h | 7 +------ 9 files changed, 50 insertions(+), 9 deletions(-) diff --git a/conversion/lfm2.py b/conversion/lfm2.py index 113c911168d9..b1f0d1d32593 100644 --- a/conversion/lfm2.py +++ b/conversion/lfm2.py @@ -374,6 +374,16 @@ def set_gguf_parameters(self): if resize_algo is None: raise ValueError(f"unsupported resample: {resample}") self.gguf_writer.add_vision_image_resize_algo(resize_algo) + # tiling params from processor_config.json (do_image_splitting=False -> single tile, i.e. no tiling) + preproc = self.preprocessor_config + if preproc.get("do_image_splitting", True): + self.gguf_writer.add_vision_preproc_min_tiles(preproc.get("min_tiles", 2)) + self.gguf_writer.add_vision_preproc_max_tiles(preproc.get("max_tiles", 10)) + else: + self.gguf_writer.add_vision_preproc_min_tiles(1) + self.gguf_writer.add_vision_preproc_max_tiles(1) + self.gguf_writer.add_vision_preproc_tile_size(preproc.get("tile_size", 512)) + self.gguf_writer.add_vision_preproc_max_pixels_tolerance(preproc.get("max_pixels_tolerance", 2.0)) @classmethod def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None: diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index 8c19e22dabca..b2d8d59e043e 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -419,6 +419,8 @@ class ClipVision: PREPROC_MIN_TILES = "clip.vision.preproc_min_tiles" PREPROC_MAX_TILES = "clip.vision.preproc_max_tiles" PREPROC_IMAGE_SIZE = "clip.vision.preproc_image_size" + PREPROC_TILE_SIZE = "clip.vision.preproc_tile_size" + PREPROC_MAX_PIXELS_TOLERANCE = "clip.vision.preproc_max_pixels_tolerance" PATCH_SIZE = "clip.vision.patch_size" EMBEDDING_LENGTH = "clip.vision.embedding_length" FEED_FORWARD_LENGTH = "clip.vision.feed_forward_length" diff --git a/gguf-py/gguf/gguf_writer.py b/gguf-py/gguf/gguf_writer.py index 7becd74af9f1..2943e018698a 100644 --- a/gguf-py/gguf/gguf_writer.py +++ b/gguf-py/gguf/gguf_writer.py @@ -1470,6 +1470,12 @@ def add_vision_preproc_min_tiles(self, value: int) -> None: def add_vision_preproc_image_size(self, value: int) -> None: self.add_uint32(Keys.ClipVision.PREPROC_IMAGE_SIZE, value) + def add_vision_preproc_tile_size(self, value: int) -> None: + self.add_uint32(Keys.ClipVision.PREPROC_TILE_SIZE, value) + + def add_vision_preproc_max_pixels_tolerance(self, value: float) -> None: + self.add_float32(Keys.ClipVision.PREPROC_MAX_PIXELS_TOLERANCE, value) + def add_vision_projector_query_side(self, value: int) -> None: self.add_uint32(Keys.ClipVision.Projector.QUERY_SIDE, value) diff --git a/tests/test-mtmd-impl.cpp b/tests/test-mtmd-impl.cpp index 2d0f24874186..1b7796d95672 100644 --- a/tests/test-mtmd-impl.cpp +++ b/tests/test-mtmd-impl.cpp @@ -96,6 +96,10 @@ MAKE_TEST(test_image_preprocessor_lfm2) { hparams.patch_size = 16; hparams.n_merge = 2; hparams.set_limit_image_tokens(64, 256); + hparams.preproc_min_tiles = 2; + hparams.preproc_max_tiles = 10; + hparams.preproc_tile_size = 512; + hparams.preproc_max_pixels_tolerance = 2.0f; // { image size, expected tiling } const std::vector> cases = { @@ -116,6 +120,12 @@ MAKE_TEST(test_image_preprocessor_lfm2) { std::string(expected ? "tiled" : "single"), std::string(actual ? "tiled" : "single")); } + + // do_image_splitting=false + hparams.preproc_min_tiles = 1; + hparams.preproc_max_tiles = 1; + t.assert_equal("tiling for 1024x977 with max_tiles=1", std::string("single"), + std::string(mtmd_image_preprocessor_lfm2::should_tile(hparams, { 1024, 977 }) ? "tiled" : "single")); } // diff --git a/tools/mtmd/clip-impl.h b/tools/mtmd/clip-impl.h index 10eea6f74619..8c4ffc9bb284 100644 --- a/tools/mtmd/clip-impl.h +++ b/tools/mtmd/clip-impl.h @@ -56,6 +56,8 @@ #define KEY_PREPROC_MIN_TILES "clip.vision.preproc_min_tiles" #define KEY_PREPROC_MAX_TILES "clip.vision.preproc_max_tiles" #define KEY_PREPROC_IMAGE_SIZE "clip.vision.preproc_image_size" +#define KEY_PREPROC_TILE_SIZE "clip.vision.preproc_tile_size" +#define KEY_PREPROC_MAX_PIXELS_TOLERANCE "clip.vision.preproc_max_pixels_tolerance" #define KEY_PATCH_SIZE "clip.vision.patch_size" #define KEY_IMAGE_MEAN "clip.vision.image_mean" #define KEY_IMAGE_STD "clip.vision.image_std" diff --git a/tools/mtmd/clip-model.h b/tools/mtmd/clip-model.h index fefbd4f46052..64e006e8d6d5 100644 --- a/tools/mtmd/clip-model.h +++ b/tools/mtmd/clip-model.h @@ -73,7 +73,8 @@ struct clip_hparams { int32_t preproc_min_tiles = 0; int32_t preproc_max_tiles = 0; int32_t max_slice_nums = 9; // llava-uhd slice cap; per-model, carried in the GGUF - int32_t preproc_tile_size = 0; // local tile size (deepseek-ocr) + int32_t preproc_tile_size = 0; // local tile size (deepseek-ocr, lfm2) + float preproc_max_pixels_tolerance = 1.0f; resize_algo image_resize_algo_rf = RESIZE_ALGO_BICUBIC; resize_algo image_resize_algo_ov = RESIZE_ALGO_BICUBIC; pad_style image_pad_rf = PAD_CEIL; // padding style for the refined image (e.g. llava-1.6) diff --git a/tools/mtmd/clip.cpp b/tools/mtmd/clip.cpp index faeb37702761..3a4dd682ab00 100644 --- a/tools/mtmd/clip.cpp +++ b/tools/mtmd/clip.cpp @@ -1598,7 +1598,17 @@ struct clip_model_loader { hparams.image_pad_rf = PAD_NONE; } get_u32(KEY_PROJ_SCALE_FACTOR, hparams.n_merge, false); + // defaults for GGUFs converted before tiling params were written // ref: https://huggingface.co/LiquidAI/LFM2.5-VL-1.6B/blob/main/processor_config.json + hparams.preproc_min_tiles = 2; + hparams.preproc_max_tiles = 10; + hparams.preproc_tile_size = 512; + hparams.preproc_max_pixels_tolerance = 2.0f; + get_u32(KEY_PREPROC_MIN_TILES, hparams.preproc_min_tiles, false); + get_u32(KEY_PREPROC_MAX_TILES, hparams.preproc_max_tiles, false); + get_u32(KEY_PREPROC_TILE_SIZE, hparams.preproc_tile_size, false); + get_f32(KEY_PREPROC_MAX_PIXELS_TOLERANCE, hparams.preproc_max_pixels_tolerance, false); + GGML_ASSERT(hparams.preproc_min_tiles >= 1 && hparams.preproc_min_tiles <= hparams.preproc_max_tiles); hparams.set_limit_image_tokens(64, 256); } break; case PROJECTOR_TYPE_PHI4: diff --git a/tools/mtmd/mtmd-image.cpp b/tools/mtmd/mtmd-image.cpp index e56f33e77c0a..86feea6bd4b9 100644 --- a/tools/mtmd/mtmd-image.cpp +++ b/tools/mtmd/mtmd-image.cpp @@ -947,8 +947,9 @@ bool mtmd_image_preprocessor_lfm2::should_tile( const int h_bar = std::max(hparams.patch_size, round_by_factor(original_size.height)); const int w_bar = std::max(hparams.patch_size, round_by_factor(original_size.width)); - return static_cast(h_bar) * static_cast(w_bar) > - static_cast(hparams.image_max_pixels) * max_pixels_tolerance; + return hparams.preproc_max_tiles > 1 && + static_cast(h_bar) * static_cast(w_bar) > + static_cast(hparams.image_max_pixels) * hparams.preproc_max_pixels_tolerance; } mtmd_image_preprocessor_llava_uhd::slice_instructions mtmd_image_preprocessor_lfm2::get_slice_instructions(const clip_image_size & original_size) const { @@ -959,6 +960,7 @@ mtmd_image_preprocessor_llava_uhd::slice_instructions mtmd_image_preprocessor_lf { align_size, hparams.image_min_pixels, hparams.image_max_pixels, 0 }); const bool needs_tiling = should_tile(hparams, original_size); + const int tile_size = hparams.preproc_tile_size; if (!needs_tiling) { inst.refined_size = clip_image_size{0, 0}; @@ -1009,6 +1011,7 @@ clip_image_size mtmd_image_preprocessor_lfm2::find_closest_aspect_ratio( best_ratio_diff = ratio_diff; best_ratio = ratio; } else if (ratio_diff == best_ratio_diff) { + const int tile_size = hparams.preproc_tile_size; const float target_area = static_cast(tile_size * tile_size * ratio.width * ratio.height); if (area > 0.5f * target_area) { best_ratio = ratio; @@ -1019,6 +1022,8 @@ clip_image_size mtmd_image_preprocessor_lfm2::find_closest_aspect_ratio( } std::vector mtmd_image_preprocessor_lfm2::get_target_ratios() const { + const int min_tiles = hparams.preproc_min_tiles; + const int max_tiles = hparams.preproc_max_tiles; std::vector ratios; for (int n = min_tiles; n <= max_tiles; n++) { for (int w = 1; w <= n; w++) { diff --git a/tools/mtmd/mtmd-image.h b/tools/mtmd/mtmd-image.h index 26193e4e4d64..a9d16f1c9ce6 100644 --- a/tools/mtmd/mtmd-image.h +++ b/tools/mtmd/mtmd-image.h @@ -191,14 +191,9 @@ struct mtmd_image_preprocessor_minicpmv : mtmd_image_preprocessor_llava_uhd { }; // custom llava-uhd slicing logic for LFM2 +// tiling params (min/max tiles, tile size, pixels tolerance) come from hparams // ref: https://github.com/huggingface/transformers/blob/v5.1.0/src/transformers/models/lfm2_vl/image_processing_lfm2_vl_fast.py struct mtmd_image_preprocessor_lfm2 : mtmd_image_preprocessor_llava_uhd { - // ref: https://huggingface.co/LiquidAI/LFM2.5-VL-1.6B/blob/main/processor_config.json - static constexpr int min_tiles = 2; - static constexpr int max_tiles = 10; - static constexpr float max_pixels_tolerance = 2.0f; - static constexpr int tile_size = 512; - using mtmd_image_preprocessor_llava_uhd::mtmd_image_preprocessor_llava_uhd; mtmd_image_preproc_out preprocess(const clip_image_u8 & img) const override; slice_instructions get_slice_instructions(const clip_image_size & original_size) const override; From 1ba568c5ad5c6c718984bd2bf4e7c71485b706f9 Mon Sep 17 00:00:00 2001 From: Renzis <93137239+Renzishi@users.noreply.github.com> Date: Tue, 20 Oct 2026 22:17:06 +0800 Subject: [PATCH 20/69] server: fix missing output_index in Responses API SSE events for compatibility (#30863) * server: include output_index in Responses API SSE events * server: reuse Anthropic streaming code for fix output_index * server: remove unnecessary generated_tool_call_ids and oai_resp_tool_calls_started --- tools/server/server-task.cpp | 21 +++++++++++++++++++++ 1 file changed, 21 insertions(+) diff --git a/tools/server/server-task.cpp b/tools/server/server-task.cpp index 3f30da2cd6f5..2e513a33ecaf 100644 --- a/tools/server/server-task.cpp +++ b/tools/server/server-task.cpp @@ -591,8 +591,10 @@ json server_task_result_cmpl_final::to_json_oaicompat_resp() { json server_task_result_cmpl_final::to_json_oaicompat_resp_stream() { std::vector server_sent_events; std::vector output; + size_t output_index = 0; if (oaicompat_msg.reasoning_content != "") { + const size_t item_output_index = output_index++; const json output_item = json { {"id", oai_resp_reasoning_id}, {"summary", json::array()}, @@ -608,6 +610,7 @@ json server_task_result_cmpl_final::to_json_oaicompat_resp_stream() { {"event", "response.output_item.done"}, {"data", json { {"type", "response.output_item.done"}, + {"output_index", item_output_index}, {"item", output_item} }} }); @@ -615,11 +618,14 @@ json server_task_result_cmpl_final::to_json_oaicompat_resp_stream() { } if (oaicompat_msg.content != "") { + const size_t item_output_index = output_index++; server_sent_events.push_back(json { {"event", "response.output_text.done"}, {"data", json { {"type", "response.output_text.done"}, {"item_id", oai_resp_message_id}, + {"output_index", item_output_index}, + {"content_index", 0}, {"text", oaicompat_msg.content} }} }); @@ -636,6 +642,8 @@ json server_task_result_cmpl_final::to_json_oaicompat_resp_stream() { {"data", json { {"type", "response.content_part.done"}, {"item_id", oai_resp_message_id}, + {"output_index", item_output_index}, + {"content_index", 0}, {"part", content_part} }} }); @@ -651,6 +659,7 @@ json server_task_result_cmpl_final::to_json_oaicompat_resp_stream() { {"event", "response.output_item.done"}, {"data", json { {"type", "response.output_item.done"}, + {"output_index", item_output_index}, {"item", output_item} }} }); @@ -658,6 +667,7 @@ json server_task_result_cmpl_final::to_json_oaicompat_resp_stream() { } for (const common_chat_tool_call & tool_call : oaicompat_msg.tool_calls) { + const size_t item_output_index = output_index++; const json output_item = { {"id", "fc_" + tool_call.id}, {"type", "function_call"}, @@ -670,6 +680,7 @@ json server_task_result_cmpl_final::to_json_oaicompat_resp_stream() { {"event", "response.output_item.done"}, {"data", json { {"type", "response.output_item.done"}, + {"output_index", item_output_index}, {"item", output_item} }} }); @@ -1202,6 +1213,7 @@ json server_task_result_cmpl_partial::to_json_oaicompat_resp() { {"event", "response.output_item.added"}, {"data", json { {"type", "response.output_item.added"}, + {"output_index", 0}, {"item", json { {"id", oai_resp_reasoning_id}, {"summary", json::array()}, @@ -1218,6 +1230,8 @@ json server_task_result_cmpl_partial::to_json_oaicompat_resp() { {"event", "response.reasoning_text.delta"}, {"data", json { {"type", "response.reasoning_text.delta"}, + {"output_index", 0}, + {"content_index", 0}, {"delta", diff.reasoning_content_delta}, {"item_id", oai_resp_reasoning_id}, }}, @@ -1230,6 +1244,7 @@ json server_task_result_cmpl_partial::to_json_oaicompat_resp() { {"event", "response.output_item.added"}, {"data", json { {"type", "response.output_item.added"}, + {"output_index", anthropic_has_reasoning ? 1 : 0}, {"item", json { {"content", json::array()}, {"id", oai_resp_message_id}, @@ -1244,6 +1259,8 @@ json server_task_result_cmpl_partial::to_json_oaicompat_resp() { {"data", json { {"type", "response.content_part.added"}, {"item_id", oai_resp_message_id}, + {"output_index", anthropic_has_reasoning ? 1 : 0}, + {"content_index", 0}, {"part", json { {"type", "output_text"}, {"text", ""}, @@ -1257,6 +1274,8 @@ json server_task_result_cmpl_partial::to_json_oaicompat_resp() { {"data", json { {"type", "response.output_text.delta"}, {"item_id", oai_resp_message_id}, + {"output_index", anthropic_has_reasoning ? 1 : 0}, + {"content_index", 0}, {"delta", diff.content_delta}, }}, }); @@ -1267,6 +1286,7 @@ json server_task_result_cmpl_partial::to_json_oaicompat_resp() { {"event", "response.output_item.added"}, {"data", json { {"type", "response.output_item.added"}, + {"output_index", (anthropic_has_reasoning ? 1 : 0) + (text_block_started ? 1 : 0) + diff.tool_call_index}, {"item", json { {"id", "fc_" + diff.tool_call_delta.id}, {"arguments", ""}, @@ -1287,6 +1307,7 @@ json server_task_result_cmpl_partial::to_json_oaicompat_resp() { {"type", "response.function_call_arguments.delta"}, {"delta", diff.tool_call_delta.arguments}, {"item_id", "fc_" + oai_resp_fc_id}, + {"output_index", (anthropic_has_reasoning ? 1 : 0) + (text_block_started ? 1 : 0) + diff.tool_call_index}, }}, }); } From 4e201388c319fc9f747fbe6a666793e2dc2a6d8e Mon Sep 17 00:00:00 2001 From: Me42019 Date: Tue, 20 Oct 2026 10:43:45 -0500 Subject: [PATCH 21/69] server: Router wasn't propagating the client disconnects fully to the child, the response wait could cause a delayed cancellation (#29707) * Router wasn't propagating the client disconnects fully to the child, the response wait could cause a delayed cancellation * tests: satisfy router disconnect type check * server : remove timing-sensitive router cancellation test Assisted-by: Codex --- tools/server/server-models.cpp | 3 ++- tools/server/server-queue.cpp | 3 ++- 2 files changed, 4 insertions(+), 2 deletions(-) diff --git a/tools/server/server-models.cpp b/tools/server/server-models.cpp index 606eab4f4a42..95cbdd77a8bc 100644 --- a/tools/server/server-models.cpp +++ b/tools/server/server-models.cpp @@ -2836,9 +2836,10 @@ server_http_proxy::server_http_proxy( cli->set_write_timeout(timeout_read, 0); // reversed for cli (client) vs srv (server) cli->set_read_timeout(timeout_write, 0); this->status = 500; // to be overwritten upon response - this->cleanup_pipes = [pipe]() { + this->cleanup_pipes = [pipe, cli]() { pipe->close_read(); pipe->close_write(); + cli->stop(); }; // wire up the receive end of the pipe diff --git a/tools/server/server-queue.cpp b/tools/server/server-queue.cpp index 3352c3cb5d17..c4ce90721b94 100644 --- a/tools/server/server-queue.cpp +++ b/tools/server/server-queue.cpp @@ -448,6 +448,7 @@ server_task_result_ptr server_response::recv(const std::unordered_set & id_ } server_task_result_ptr server_response::recv_with_timeout(const std::unordered_set & id_tasks, int timeout) { + const auto deadline = std::chrono::steady_clock::now() + std::chrono::seconds(timeout); while (true) { std::unique_lock lock(mutex_results); @@ -459,7 +460,7 @@ server_task_result_ptr server_response::recv_with_timeout(const std::unordered_s } } - std::cv_status cr_res = condition_results.wait_for(lock, std::chrono::seconds(timeout)); + std::cv_status cr_res = condition_results.wait_until(lock, deadline); if (!running) { RES_DBG("%s : queue result stop\n", __func__); std::terminate(); // we cannot return here since the caller is HTTP code From 39da130fcd1b6ca47d3c0522a40eccff41ea3dfb Mon Sep 17 00:00:00 2001 From: Tarek Dakhran Date: Tue, 20 Oct 2026 17:47:44 +0200 Subject: [PATCH 22/69] ggml-cuda : support ssm_conv kernel size 7 (#30873) --- ggml/src/ggml-cuda/ssm-conv.cu | 3 ++- tests/test-backend-ops.cpp | 4 ++-- 2 files changed, 4 insertions(+), 3 deletions(-) diff --git a/ggml/src/ggml-cuda/ssm-conv.cu b/ggml/src/ggml-cuda/ssm-conv.cu index 1463169cf78b..f41438b3181a 100644 --- a/ggml/src/ggml-cuda/ssm-conv.cu +++ b/ggml/src/ggml-cuda/ssm-conv.cu @@ -151,9 +151,10 @@ static void ssm_conv_f32_cuda(const float * src0, const float * src1, const floa case 3: launch_kernel(std::integral_constant{}); break; case 4: launch_kernel(std::integral_constant{}); break; case 5: launch_kernel(std::integral_constant{}); break; + case 7: launch_kernel(std::integral_constant{}); break; case 9: launch_kernel(std::integral_constant{}); break; case 15: launch_kernel(std::integral_constant{}); break; - default: GGML_ABORT("Only support kernel sizes 3, 4, 5, 9, 15 right now."); + default: GGML_ABORT("Only support kernel sizes 3, 4, 5, 7, 9, 15 right now."); } } diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index 485629cf64cb..d3194bf47f52 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -10659,7 +10659,7 @@ static std::vector> make_test_cases_eval() { // ne[2] > 65535 exceeds the CUDA gridDim.y limit (#27901) test_cases.emplace_back(new test_rms_norm_mul_rope({4, 1, 65536, 1}, 1e-6f, false, false, false, GGML_ROPE_TYPE_NORMAL)); test_cases.emplace_back(new test_rms_norm_mul_rope({4, 1, 65536, 1}, 1e-6f, false, true, false, GGML_ROPE_TYPE_NEOX)); - for (int64_t d_conv : {3, 4, 9}) { + for (int64_t d_conv : {3, 4, 7, 9}) { for (int64_t d_inner: {1024, 1536, 2048}) { test_cases.emplace_back(new test_ssm_conv(GGML_TYPE_F32, {d_conv, d_inner, 1, 1}, {d_conv, d_inner, 1, 1})); test_cases.emplace_back(new test_ssm_conv(GGML_TYPE_F32, {2 * d_conv, d_inner, 1, 1}, {d_conv, d_inner, 1, 1})); @@ -10672,7 +10672,7 @@ static std::vector> make_test_cases_eval() { // fused ssm_conv + (optional) bias_add + silu. The bias-only graph (no silu) is intentionally // not tested since there's no fusion for that pattern in ggml_cuda_can_fuse. - for (int64_t d_conv : {3, 4, 9}) { + for (int64_t d_conv : {3, 4, 7, 9}) { for (int64_t d_inner : {1024, 1536, 2048}) { for (bool fuse_bias : {false, true}) { // short token path (n_t <= 32) From 5a9a34d88847d0875bd21eab0d7c04da4bcfd2af Mon Sep 17 00:00:00 2001 From: TANGBUDU <79953480+TANGBUDU@users.noreply.github.com> Date: Wed, 21 Oct 2026 01:32:12 +0900 Subject: [PATCH 23/69] server : avoid repeated mmproj memory margin addition on sleep/resume (#28233) Co-authored-by: TANGBUDU --- tools/server/server-context.cpp | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/tools/server/server-context.cpp b/tools/server/server-context.cpp index b2116b1740a3..74812189cb96 100644 --- a/tools/server/server-context.cpp +++ b/tools/server/server-context.cpp @@ -1180,7 +1180,8 @@ struct server_context_impl { } // optionally fit mmproj memory usage - if (has_mmproj && params_base.fit_params) { + // on resume params_base already holds the margin from the first load + if (has_mmproj && params_base.fit_params && !is_resume) { const auto & mmproj_mem = mmproj_usage.backend_mem_usage; const int64_t t_elapsed = mmproj_usage_t_us; if (!mmproj_mem.empty()) { From 5ba3b9fd3da34e516450ea58c229949ea4cff3ce Mon Sep 17 00:00:00 2001 From: Xuan-Son Nguyen Date: Tue, 20 Oct 2026 18:33:32 +0200 Subject: [PATCH 24/69] mtmd: fix gemma 4 ffn act (#30882) --- conversion/gemma.py | 2 ++ tools/mtmd/clip.cpp | 2 ++ 2 files changed, 4 insertions(+) diff --git a/conversion/gemma.py b/conversion/gemma.py index 924d8e784a17..ab2d90610d60 100644 --- a/conversion/gemma.py +++ b/conversion/gemma.py @@ -999,6 +999,8 @@ def set_gguf_parameters(self): assert self.hparams_vision is not None self.gguf_writer.add_clip_vision_projector_type(gguf.VisionProjectorType.GEMMA4V) self.gguf_writer.add_vision_attention_layernorm_eps(self.hparams_vision.get("layer_norm_eps", 1e-6)) + assert self.hparams_vision.get("hidden_activation", "gelu_pytorch_tanh") == "gelu_pytorch_tanh" + self.gguf_writer.add_vision_use_gelu(True) # audio params if self.has_audio_encoder: diff --git a/tools/mtmd/clip.cpp b/tools/mtmd/clip.cpp index 3a4dd682ab00..41aba280ba04 100644 --- a/tools/mtmd/clip.cpp +++ b/tools/mtmd/clip.cpp @@ -1735,6 +1735,8 @@ struct clip_model_loader { case PROJECTOR_TYPE_GEMMA4UV: { hparams.rope_theta = 100.0f; + hparams.ffn_op = FFN_GELU; + log_ffn_op = "gelu"; hparams.n_merge = 3; // pooling_kernel_size hparams.image_resize_algo = RESIZE_ALGO_BICUBIC; get_u32(KEY_PROJ_SCALE_FACTOR, hparams.n_merge, false); From 7e3eeee0480902c90cbdff172f407dc063cf0770 Mon Sep 17 00:00:00 2001 From: PSEUDOTENSOR / Jonathan McKinney Date: Tue, 20 Oct 2026 09:54:27 -0700 Subject: [PATCH 25/69] model : add H2O-Lightning-4B decision model (#30361) * model : add H2O-Lightning-4B decision model Adds the h2o-lightning decision type for h2oai/h2o-lightning-4b (Qwen3.5-4B with a merged decision LoRA). None of the existing types match this model. It answers with " A" rather than "A", uses up to 255 labels, and was trained on Python json.dumps text, so floats like 12.0 must render as 12.0. The converter writes the decision metadata and the systemone template from the model's serve_config.json and refuses a config that doesn't match. Tests skip unless the tiny test model is available locally. Assisted-by: Claude Opus 5.5 * rm redundant code * put labels to gguf --------- Co-authored-by: Jonathan C. McKinney Co-authored-by: Xuan Son Nguyen --- common/common.cpp | 1 + common/common.h | 1 + conversion/__init__.py | 2 + conversion/h2o_lightning.py | 96 +++++++++ gguf-py/gguf/constants.py | 2 + gguf-py/gguf/gguf_writer.py | 3 + src/llama-arch.cpp | 1 + src/llama-arch.h | 1 + src/llama-ext.h | 3 + src/llama-model.cpp | 11 + src/llama-model.h | 1 + tools/server/README.md | 8 +- tools/server/server-common.cpp | 1 + tools/server/server-context.cpp | 2 +- tools/server/server-decision.cpp | 345 ++++++++++++++++++++++++++++++- tools/server/server-decision.h | 15 +- 16 files changed, 478 insertions(+), 15 deletions(-) create mode 100644 conversion/h2o_lightning.py diff --git a/common/common.cpp b/common/common.cpp index b59e66e3bd3f..b0ddfcb915ed 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -1175,6 +1175,7 @@ static const std::map COMMON_DECISION_TYPE_NA { COMMON_DECISION_TYPE_LFM2_D1, "lfm2-d1" }, { COMMON_DECISION_TYPE_LFM2_D1_OMNI, "lfm2-d1-omni" }, { COMMON_DECISION_TYPE_GRANITE_SWITCH, "granite-switch"}, + { COMMON_DECISION_TYPE_H2O_LIGHTNING, "h2o-lightning" }, // for future models, ALWAYS add org name prefix to avoid name collisions, e.g. "orgname-modelname" }; diff --git a/common/common.h b/common/common.h index b172b4efe7e0..b089abd7e63e 100644 --- a/common/common.h +++ b/common/common.h @@ -975,6 +975,7 @@ enum common_decision_type { COMMON_DECISION_TYPE_LFM2_D1, // same as openjev, the labels depend on the question type COMMON_DECISION_TYPE_LFM2_D1_OMNI, // same as laya, other prompt layout COMMON_DECISION_TYPE_GRANITE_SWITCH, // same as openjev, run by the decision aLoRA of granite switch + COMMON_DECISION_TYPE_H2O_LIGHTNING, // same as openjev, the labels are read after an answer prefill COMMON_DECISION_TYPE_UNKNOWN, // a decision model of a type that is not supported }; diff --git a/conversion/__init__.py b/conversion/__init__.py index e3c10d5fc54e..efa537b0f230 100644 --- a/conversion/__init__.py +++ b/conversion/__init__.py @@ -226,6 +226,7 @@ "MuseSparkForConditionalGeneration": "muse_spark", "OpenELMForCausalLM": "openelm", "OpenJevModel": "qwen", + "H2OLightningModel": "h2o_lightning", "OrionForCausalLM": "orion", "PLMForCausalLM": "plm", "PLaMo2ForCausalLM": "plamo", @@ -375,6 +376,7 @@ "Qwen3VLForConditionalGeneration": "qwen3vl", "Qwen3VLMoeForConditionalGeneration": "qwen3vl", "OpenJevModel": "qwen3vl", + "H2OLightningModel": "h2o_lightning", "Qwen3_5ForConditionalGeneration": "qwen3vl", "Qwen3_5MoeForConditionalGeneration": "qwen3vl", "Qwen4ExpForConditionalGeneration": "qwen4exp", diff --git a/conversion/h2o_lightning.py b/conversion/h2o_lightning.py new file mode 100644 index 000000000000..764da4b970dc --- /dev/null +++ b/conversion/h2o_lightning.py @@ -0,0 +1,96 @@ +from __future__ import annotations + +import json + +from pathlib import Path +from typing import Any + +from .base import ModelBase, gguf, logger +from .qwen import Qwen3_5TextModel +from .qwen3vl import Qwen3VLVisionModel + + +def _is_h2o_lightning_checkpoint(dir_model: Path) -> bool: + return (dir_model / "h2o_lightning_shim.py").is_file() and (dir_model / "serve_config.json").is_file() + + +@ModelBase.register_hparams_loader(_is_h2o_lightning_checkpoint) +def _load_h2o_lightning_hparams(dir_model: Path) -> dict[str, Any]: + logger.info("gguf: detected H2O-Lightning checkpoint") + hparams = ModelBase.load_hparams(dir_model, False, guess=False) + hparams["architectures"] = ["H2OLightningModel"] + return hparams + + +@ModelBase.register("H2OLightningModel") +@ModelBase.example("h2oai/h2o-lightning-4b") +class H2OLightningModel(Qwen3_5TextModel): + model_arch = gguf.MODEL_ARCH.QWEN35 + + # prompt and calibration follow serve_config.json and h2o_lightning_shim.py of the model repo + # the state budget is hard-coded in server-decision.cpp + _MAX_STATE_TOKENS = 32000 + _DEFAULT_INSTRUCTIONS = "Answer the question below." + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + with open(self.dir_model / "serve_config.json", "r", encoding="utf-8") as f: + self._serve_config = json.load(f) + prompt = self._serve_config["prompt"] + expected = { + "state_compact": (prompt.get("state_compact", True), True), + "max_state_tokens": (prompt.get("max_state_tokens"), self._MAX_STATE_TOKENS), + "layout": (prompt.get("layout", "plain"), "plain"), + "chat_template": (prompt.get("chat_template", True), True), + "enable_thinking": (prompt.get("enable_thinking", False), False), + } + for name, (got, want) in expected.items(): + if got != want: + raise ValueError(f"serve_config.json: {name} is {got!r}, the h2o-lightning decision type expects {want!r}") + + def get_vocab_base_pre(self, tokenizer) -> str: + # transformers loads this checkpoint as Qwen2Tokenizer: the model was trained and is served with the Qwen2 split + pre = super().get_vocab_base_pre(tokenizer) + if pre != "qwen35": + raise ValueError(f"expected the Qwen3.5 tokenizer, got pre-tokenizer {pre!r}") + return "qwen2" + + def set_vocab(self): + super().set_vocab() + self.gguf_writer.add_chat_template([{"name": "systemone", "template": self._systemone_template()}]) + + def _systemone_template(self) -> str: + # the server gives the template texts rendered as the shim renders them, it only lays them out + prompt = self._serve_config["prompt"] + nl = "{{ '\\n' }}" + description = ( + "{% if type == 'noul' %}" + "{% if o.description %}{{ o.description }}{% elif o.key == 'true' %}the statement holds{% else %}it does not{% endif %}" + "{% elif o.description is none %}{{ o.key }}{% else %}{{ o.description }}{% endif %}" + ) + user = ( + "{% set user %}record: {{ state }}" + nl + + "question: {{ instructions if instructions else '" + self._DEFAULT_INSTRUCTIONS + "' }}" + nl + + "options:{% for o in options %}" + nl + "{{ o.label | trim }}) {{ o.key }}: " + description + "{% endfor %}{% endset %}" + ) + return ( + "<|im_start|>system" + nl + prompt["system"].strip() + "<|im_end|>" + nl + + "<|im_start|>user" + nl + "{% for image in images %}{{ image }}{% endfor %}" + user + "{{ user | trim }}<|im_end|>" + nl + + "<|im_start|>assistant" + nl + "{{ '\\n\\n' }}{{ '\\n\\n' }}" + prompt["prefill"] + ) + + def set_gguf_parameters(self): + super().set_gguf_parameters() + self.gguf_writer.add_decision_type(gguf.DecisionType.H2O_LIGHTNING) + # the answer follows "Answer:", so the token of a label has a leading space + cfg = self._serve_config + self.gguf_writer.add_decision_labels([" " + label for label in cfg["labels"][:cfg.get("max_options", len(cfg["labels"]))]]) + for name in ("choice", "noul", "score"): + self.gguf_writer.add_decision_temperature(name, self._serve_config["temperature"][name]) + # a request with images has this temperature for all question types + self.gguf_writer.add_decision_temperature("image", self._serve_config["image"]["temperature"]) + + +@ModelBase.register("H2OLightningModel") +class H2OLightningVisionModel(Qwen3VLVisionModel): + pass diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index b2d8d59e043e..2840348cba24 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -344,6 +344,7 @@ class Decision: ROUTING_BLOCK_COUNT = "{arch}.decision.routing_block_count" HEAD_COUNT = "{arch}.decision.head_count" MAX_HEAD_TOKENS = "{arch}.decision.max_head_tokens" + LABELS = "{arch}.decision.labels" # the text of the token read for each option, in order TEMPERATURE = "{arch}.decision.temperature.{name}" # name: "" or "." SCORE_BIAS = "{arch}.decision.score_bias.{n}.{i}" # added to the logit of level i of a score question with n levels @@ -6339,6 +6340,7 @@ class DecisionType: LFM2_D1 = "lfm2-d1" # same as openjev, the labels depend on the question type LFM2_D1_OMNI = "lfm2-d1-omni" # same head as laya on a bidirectional LFM2 trunk, other prompt layout GRANITE_SWITCH = "granite-switch" # same as openjev, run by the decision aLoRA of granite switch + H2O_LIGHTNING = "h2o-lightning" # same as openjev, labels read after an answer prefill class VisionProjectorType: diff --git a/gguf-py/gguf/gguf_writer.py b/gguf-py/gguf/gguf_writer.py index 2943e018698a..e8a96e57102e 100644 --- a/gguf-py/gguf/gguf_writer.py +++ b/gguf-py/gguf/gguf_writer.py @@ -1401,6 +1401,9 @@ def add_decision_head_count(self, value: int) -> None: def add_decision_max_head_tokens(self, value: int) -> None: self.add_uint32(Keys.Decision.MAX_HEAD_TOKENS.format(arch=self.arch), value) + def add_decision_labels(self, labels: Sequence[str]) -> None: + self.add_array(Keys.Decision.LABELS.format(arch=self.arch), labels) + def add_decision_temperature(self, name: str, value: float) -> None: self.add_float32(Keys.Decision.TEMPERATURE.format(arch=self.arch, name=name), value) diff --git a/src/llama-arch.cpp b/src/llama-arch.cpp index 961011a2c760..1b954f176cd2 100644 --- a/src/llama-arch.cpp +++ b/src/llama-arch.cpp @@ -383,6 +383,7 @@ static const std::map LLM_KV_NAMES = { { LLM_KV_DECISION_BLOCK_COUNT, "%s.decision.block_count" }, { LLM_KV_DECISION_ROUTING_BLOCK_COUNT, "%s.decision.routing_block_count" }, { LLM_KV_DECISION_HEAD_COUNT, "%s.decision.head_count" }, + { LLM_KV_DECISION_LABELS, "%s.decision.labels" }, { LLM_KV_TARGET_LAYERS, "%s.target_layers" }, { LLM_KV_TARGET_HIDDEN_SIZE, "%s.target_hidden_size" }, diff --git a/src/llama-arch.h b/src/llama-arch.h index 3ea86c601d4d..5a8ca425001c 100644 --- a/src/llama-arch.h +++ b/src/llama-arch.h @@ -431,6 +431,7 @@ enum llm_kv { LLM_KV_DECISION_BLOCK_COUNT, LLM_KV_DECISION_ROUTING_BLOCK_COUNT, LLM_KV_DECISION_HEAD_COUNT, + LLM_KV_DECISION_LABELS, LLM_KV_TARGET_LAYERS, LLM_KV_TARGET_HIDDEN_SIZE, diff --git a/src/llama-ext.h b/src/llama-ext.h index 8b3932bd7507..933cb801342e 100644 --- a/src/llama-ext.h +++ b/src/llama-ext.h @@ -122,6 +122,9 @@ enum llama_decision_order { // The embeddings output has one value per entry: row i is the score of option i LLAMA_API bool llama_batch_ext_set_decision_order(struct llama_batch_ext * batch, int32_t idx, enum llama_decision_order order); +// Label of option i of a decision model (".decision.labels"), nullptr past the last one +LLAMA_API const char * llama_model_decision_label(const struct llama_model * model, uint32_t i); + // mirrors: // LLAMA_API float * llama_get_embeddings(struct llama_context * ctx); LLAMA_API float * llama_get_embeddings_nextn(struct llama_context * ctx); diff --git a/src/llama-model.cpp b/src/llama-model.cpp index 803a8329e9db..6712f0f22f2a 100644 --- a/src/llama-model.cpp +++ b/src/llama-model.cpp @@ -1549,6 +1549,9 @@ void llama_model_base::load_hparams(llama_model_loader & ml) { hparams.n_cls_out = classifier_labels.size(); } + // for decision models + ml.get_arr(LLM_KV_DECISION_LABELS, decision_labels, false); + // per-arch hparams load_arch_hparams(ml); @@ -3438,6 +3441,14 @@ const char * llama_model_cls_label(const struct llama_model * model, uint32_t i) return nullptr; } +const char * llama_model_decision_label(const struct llama_model * model, uint32_t i) { + if (i < model->decision_labels.size()) { + return model->decision_labels[i].c_str(); + } + + return nullptr; +} + // deprecated int32_t llama_n_ctx_train(const llama_model * model) { return llama_model_n_ctx_train(model); diff --git a/src/llama-model.h b/src/llama-model.h index a187c274932b..9fe9d77a2350 100644 --- a/src/llama-model.h +++ b/src/llama-model.h @@ -724,6 +724,7 @@ struct llama_model { // for classifier models std::vector classifier_labels; + std::vector decision_labels; struct ggml_tensor * tok_embd = nullptr; struct ggml_tensor * type_embd = nullptr; diff --git a/tools/server/README.md b/tools/server/README.md index e1c5804da5c2..8d9f535695f8 100644 --- a/tools/server/README.md +++ b/tools/server/README.md @@ -1725,13 +1725,13 @@ Follows the [TypeSafe API](https://docs.typesafe.ai/api), streaming is not suppo The questions of a request are answered independently, an answer does not depend on the other questions. The exception is clef: it reads all the questions in one prompt and decides them jointly. -The number of options of a `choice` question is limited by the model, for example: 52 for openjev, 255 for granite-switch, laya, clef, pplx-decider, vllm-decision2, vllm-d3, lfm2-d1 and lfm2-d1-omni. For laya, long questions and options are truncated to the token budget the model was trained with. +The number of options of a `choice` question is limited by the model, for example: 52 for openjev, 255 for granite-switch, laya, clef, pplx-decider, vllm-decision2, vllm-d3, lfm2-d1, lfm2-d1-omni and h2o-lightning. For laya, long questions and options are truncated to the token budget the model was trained with. -For laya, clef, vllm-d3 and lfm2-d1-omni, the whole prompt is evaluated in one batch: it must fit in `--ubatch-size`. An lfm2-d1-omni prompt is cut to 16384 tokens. For vllm-decision2, the options of a question and the end of the prompt must fit in one batch. A server that runs clef only serves this endpoint, text generation is not available. A decision model that is also a text model, such as granite-switch, serves both and reports `["text", "decisions"]`. +For laya, clef, vllm-d3 and lfm2-d1-omni, the whole prompt is evaluated in one batch: it must fit in `--ubatch-size`. An lfm2-d1-omni prompt is cut to 16384 tokens. For vllm-decision2, the options of a question and the end of the prompt must fit in one batch. An h2o-lightning state longer than 32000 tokens keeps its first and last 16000 tokens. A server that runs clef only serves this endpoint, text generation is not available. A decision model that is also a text model, such as granite-switch, serves both and reports `["text", "decisions"]`. *Image input:* -Image input needs a model that supports it (for example: openjev, clef, pplx-decider, vllm-d3, lfm2-d1, lfm2-d1-omni) and its multimodal projector, see `--mmproj`. +Image input needs a model that supports it (for example: openjev, clef, pplx-decider, vllm-d3, lfm2-d1, lfm2-d1-omni, h2o-lightning) and its multimodal projector, see `--mmproj`. Images can be given in two ways, and both can be used in the same request: @@ -1760,6 +1760,8 @@ All the images are placed before the state in the prompt, the ones from `files` The probabilities are scaled with the temperatures stored in the model file. They are not guaranteed to be calibrated for your data. +For h2o-lightning, `score` questions can have 1 to 255 levels, and the `instructions` and `type` of a question can be left out (`choice` by default; `"instructions": null` is written as `null`). + *Examples:* ```shell diff --git a/tools/server/server-common.cpp b/tools/server/server-common.cpp index 3ea20c3d2f81..075687602716 100644 --- a/tools/server/server-common.cpp +++ b/tools/server/server-common.cpp @@ -215,6 +215,7 @@ std::vector server_model_output_modalities(common_decision_type dec case COMMON_DECISION_TYPE_VLLM_D3: case COMMON_DECISION_TYPE_LFM2_D1: case COMMON_DECISION_TYPE_LFM2_D1_OMNI: + case COMMON_DECISION_TYPE_H2O_LIGHTNING: return {"decisions"}; case COMMON_DECISION_TYPE_GRANITE_SWITCH: return {"text", "decisions"}; diff --git a/tools/server/server-context.cpp b/tools/server/server-context.cpp index 74812189cb96..84cd6776899d 100644 --- a/tools/server/server-context.cpp +++ b/tools/server/server-context.cpp @@ -5890,7 +5890,7 @@ void server_routes::init_routes() { const auto questions = decision.parse_questions(body); std::vector files; - const json state = decision.parse_state(body, files); + const json state = decision.prepare_state(decision.parse_state(body, files)); if (!files.empty() && (!decision.can_use_images() || !meta->has_inp_image)) { res->error(format_error_response("This server does not support image input for decisions. For a model that supports it, start it with `--mmproj`", ERROR_TYPE_NOT_SUPPORTED)); return res; diff --git a/tools/server/server-decision.cpp b/tools/server/server-decision.cpp index be9bc8ef4912..ce4cb4d0910d 100644 --- a/tools/server/server-decision.cpp +++ b/tools/server/server-decision.cpp @@ -19,6 +19,10 @@ static const char * decision_question_type_name(server_decision_question_type ty // lev reads noul from a rating scale: 0 = certainly no, 8 = certainly yes static const size_t DECISION_LEV_N_RATINGS = 9; +// h2o-lightning follows serve_config.json and h2o_lightning_shim.py of the model repo +// a longer state keeps its head and its tail +static const size_t DECISION_H2O_MAX_STATE_TOKENS = 32000; + static std::string decision_meta_str(const llama_model * model, const std::string & key) { char buf[256]; const int32_t n = llama_model_meta_val_str(model, key.c_str(), buf, sizeof(buf)); @@ -164,6 +168,39 @@ void server_decision_context::init(const llama_model * model) { throw std::runtime_error("decision model has no mask token"); } n_options_max = 255; + } else if (model_type == COMMON_DECISION_TYPE_H2O_LIGHTNING) { + // the answer is read at the token of a label, its text is given to the template + for (uint32_t i = 0; llama_model_decision_label(model, i) != nullptr; i++) { + const std::string text = llama_model_decision_label(model, i); + const auto toks = common_tokenize(vocab, text, false, false); + if (toks.size() != 1 || std::find(labels.begin(), labels.end(), toks[0]) != labels.end()) { + throw std::runtime_error(string_format("decision label \"%s\" is not a single distinct token", text.c_str())); + } + labels.push_back(toks[0]); + label_texts.push_back(text); + } + if (labels.empty()) { + throw std::runtime_error("decision model has no labels"); + } + // the texts of the control and user-defined tokens are quoted in the input, so that they stay text + for (llama_token id = 0; id < llama_vocab_n_tokens(vocab); id++) { + if (llama_vocab_get_attr(vocab, id) & (LLAMA_TOKEN_ATTR_CONTROL | LLAMA_TOKEN_ATTR_USER_DEFINED)) { + const std::string text = llama_vocab_get_text(vocab, id); + if (!text.empty()) { + quoted_spellings.push_back(text); + } + } + } + std::stable_sort(quoted_spellings.begin(), quoted_spellings.end(), [](const std::string & a, const std::string & b) { + return a.size() > b.size(); + }); + for (const auto & spelling : quoted_spellings) { + if (quoted_first.find(spelling[0]) == std::string::npos) { + quoted_first += spelling[0]; + } + } + n_options_max = labels.size(); + noul_true_first = true; } else { throw std::runtime_error("unsupported decision model type: " + type_name); } @@ -201,8 +238,10 @@ std::vector server_decision_context::parse_questions(c throw err("must be an object"); } // the vllm-d3 prompt has a default question - const json instructions = q.contains("instructions") ? q.at("instructions") : json(); - if (instructions.is_null() && type != COMMON_DECISION_TYPE_VLLM_D3) { + // h2o-lightning: the instructions can be left out, the template has a default, and the type defaults to choice + const bool is_h2o = type == COMMON_DECISION_TYPE_H2O_LIGHTNING; + const json instructions = q.contains("instructions") ? q.at("instructions") : (is_h2o ? json(std::string()) : json()); + if (instructions.is_null() && type != COMMON_DECISION_TYPE_VLLM_D3 && !is_h2o) { throw err("\"instructions\" must be provided"); } if (strict_payload && (!decision_is_payload(instructions) || instructions == "")) { @@ -213,7 +252,7 @@ std::vector server_decision_context::parse_questions(c question.id = id; question.instructions = instructions; - const std::string type_name = json_value(q, "type", std::string()); + const std::string type_name = json_value(q, "type", std::string(is_h2o ? "choice" : "")); const json criteria = q.contains("criteria") ? q.at("criteria") : json(); if (type_name == "choice") { @@ -237,7 +276,11 @@ std::vector server_decision_context::parse_questions(c } } else if (type_name == "score") { question.type = SERVER_DECISION_QUESTION_SCORE; - if (!criteria.is_array() || criteria.size() < 2 || criteria.size() > 10) { + // h2o-lightning: from 1 level, the maximum is n_options_max + if (is_h2o && (!criteria.is_array() || criteria.empty())) { + throw err("\"criteria\" must be a non-empty array"); + } + if (!is_h2o && (!criteria.is_array() || criteria.size() < 2 || criteria.size() > 10)) { throw err("\"criteria\" must be an array of 2 to 10 levels"); } for (size_t i = 0; i < criteria.size(); i++) { @@ -262,8 +305,8 @@ std::vector server_decision_context::parse_questions(c json description; if (criteria.is_object() && criteria.contains(key)) { description = criteria.at(key); - } else if (criteria.is_object() && type == COMMON_DECISION_TYPE_LFM2_D1_OMNI) { - // lfm2-d1-omni also reads the descriptions under "no" and "yes" + } else if (criteria.is_object() && (type == COMMON_DECISION_TYPE_LFM2_D1_OMNI || is_h2o)) { + // lfm2-d1-omni and h2o-lightning also read the descriptions under "no" and "yes" const char * alias = std::string(key) == "true" ? "yes" : "no"; description = criteria.contains(alias) ? criteria.at(alias) : json(); } @@ -506,6 +549,280 @@ static json decision_d1omni_escape(const json & val) { return val; } +// +// h2o-lightning: the texts are rendered the way the reference shim renders them with Python (json.dumps, str.lstrip) +// + +// one code point of UTF-8 text at s[i], i moves past it; an invalid byte is read as is +static uint32_t decision_utf8_next(const std::string & s, size_t & i) { + const unsigned char c = s[i]; + size_t n = c < 0x80 ? 1 : (c >> 5) == 0x6 ? 2 : (c >> 4) == 0xe ? 3 : (c >> 3) == 0x1e ? 4 : 1; + if (i + n > s.size()) { + n = 1; + } + uint32_t cp = n == 1 ? c : n == 2 ? (c & 0x1f) : n == 3 ? (c & 0x0f) : (c & 0x07); + for (size_t k = 1; k < n; k++) { + cp = (cp << 6) | (s[i + k] & 0x3f); + } + i += n; + return cp; +} + +// str.isspace() of Python +static bool decision_py_isspace(uint32_t cp) { + return (cp >= 0x09 && cp <= 0x0d) || (cp >= 0x1c && cp <= 0x20) || cp == 0x85 || cp == 0xa0 || cp == 0x1680 || + (cp >= 0x2000 && cp <= 0x200a) || cp == 0x2028 || cp == 0x2029 || cp == 0x202f || cp == 0x205f || cp == 0x3000; +} + +static std::string decision_py_rstrip(const std::string & s) { + size_t end = 0; + for (size_t i = 0; i < s.size();) { + if (!decision_py_isspace(decision_utf8_next(s, i))) { + end = i; + } + } + return s.substr(0, end); +} + +static std::string decision_py_lstrip(const std::string & s) { + size_t i = 0; + while (i < s.size()) { + size_t next = i; + if (!decision_py_isspace(decision_utf8_next(s, next))) { + break; + } + i = next; + } + return s.substr(i); +} + +// a code point as JSON escapes, a surrogate pair above the BMP +static std::string decision_py_escape_cp(uint32_t cp) { + if (cp < 0x10000) { + return string_format("\\u%04x", cp); + } + cp -= 0x10000; + return string_format("\\u%04x\\u%04x", 0xd800 + (cp >> 10), 0xdc00 + (cp & 0x3ff)); +} + +// repr() of a Python float: the shortest digits that read back the same, in exponent form below 1e-4 and from 1e16 +static std::string decision_py_float(double v) { + char buf[64]; + // the fewest digits that read back the same + for (int prec = 0; prec < 17; prec++) { + snprintf(buf, sizeof(buf), "%.*e", prec, v); + if (std::strtod(buf, nullptr) == v) { + break; + } + } + const std::string sci(buf); // for example "-1.25e+03" + const size_t pos_e = sci.find('e'); + const int exp = std::atoi(sci.c_str() + pos_e + 1); + std::string sign; + std::string digits; + for (size_t i = 0; i < pos_e; i++) { + if (sci[i] == '-') { + sign = "-"; + } else if (sci[i] != '.') { + digits += sci[i]; + } + } + const int n = (int) digits.size(); + if (exp < -4 || exp >= 16) { + return sign + digits.substr(0, 1) + (n > 1 ? "." + digits.substr(1) : "") + string_format("e%c%02d", exp < 0 ? '-' : '+', std::abs(exp)); + } + if (exp >= n - 1) { + return sign + digits + std::string(exp - (n - 1), '0') + ".0"; + } + if (exp >= 0) { + return sign + digits.substr(0, exp + 1) + "." + digits.substr(exp + 1); + } + return sign + "0." + std::string(-exp - 1, '0') + digits; +} + +static void decision_py_str(std::string & out, const std::string & s, bool ensure_ascii) { + out += '"'; + for (size_t i = 0; i < s.size();) { + const unsigned char c = s[i]; + switch (c) { + case '"': out += "\\\""; i++; continue; + case '\\': out += "\\\\"; i++; continue; + case '\b': out += "\\b"; i++; continue; + case '\f': out += "\\f"; i++; continue; + case '\n': out += "\\n"; i++; continue; + case '\r': out += "\\r"; i++; continue; + case '\t': out += "\\t"; i++; continue; + default: break; + } + if (c < 0x20 || (ensure_ascii && c == 0x7f)) { + out += string_format("\\u%04x", c); + i++; + } else if (c < 0x80 || !ensure_ascii) { + out += (char) c; + i++; + } else { + out += decision_py_escape_cp(decision_utf8_next(s, i)); + } + } + out += '"'; +} + +// json.dumps() of Python: indent < 0 is no indent, keys stay in their order +static void decision_py_dump(std::string & out, const json & val, int indent, int level, const std::string & item_sep, const std::string & key_sep, bool ensure_ascii) { + const auto newline = [&](int lvl) { + return indent < 0 ? std::string() : "\n" + std::string(indent * lvl, ' '); + }; + if (val.is_null()) { + out += "null"; + } else if (val.is_boolean()) { + out += val.get() ? "true" : "false"; + } else if (val.is_number_float()) { + out += decision_py_float(val.get()); + } else if (val.is_number()) { + out += val.dump(); + } else if (val.is_string()) { + decision_py_str(out, val.get(), ensure_ascii); + } else if (val.is_array()) { + if (val.empty()) { + out += "[]"; + return; + } + out += "[" + newline(level + 1); + for (size_t i = 0; i < val.size(); i++) { + if (i > 0) { + out += item_sep + newline(level + 1); + } + decision_py_dump(out, val.at(i), indent, level + 1, item_sep, key_sep, ensure_ascii); + } + out += newline(level) + "]"; + } else if (val.is_object()) { + if (val.empty()) { + out += "{}"; + return; + } + out += "{" + newline(level + 1); + bool first = true; + for (const auto & [key, item] : val.items()) { + if (!first) { + out += item_sep + newline(level + 1); + } + first = false; + decision_py_str(out, key, ensure_ascii); + out += key_sep; + decision_py_dump(out, item, indent, level + 1, item_sep, key_sep, ensure_ascii); + } + out += newline(level) + "}"; + } +} + +static std::string decision_py_dumps(const json & val, int indent, const std::string & item_sep, const std::string & key_sep, bool ensure_ascii) { + std::string out; + decision_py_dump(out, val, indent, 0, item_sep, key_sep, ensure_ascii); + return out; +} + +// a state longer than max_state_tokens keeps its head and its tail around a "..." line +std::string server_decision_context::h2o_cut_state(const std::string & text) const { + // a byte-level BPE never has more tokens than bytes + const size_t max_state_tokens = DECISION_H2O_MAX_STATE_TOKENS; + if (text.size() <= max_state_tokens) { + return text; + } + const llama_tokens tokens = common_tokenize(vocab, text, false, true); + if (tokens.size() <= max_state_tokens) { + return text; + } + // each half is the text of its token pieces, a cut inside a character gives U+FFFD as in a JSON response of the server + const size_t half = max_state_tokens / 2; + auto text_of = [&](llama_tokens::const_iterator begin, llama_tokens::const_iterator end) { + std::string out; + for (auto it = begin; it != end; ++it) { + out += common_token_to_piece(vocab, *it, true); + } + return json::parse(json::make(out).dump_safe()).get(); + }; + return text_of(tokens.begin(), tokens.begin() + half) + "\n...\n" + text_of(tokens.end() - half, tokens.end()); +} + +// the special token texts written in the input are quoted with JSON escapes, so they stay text +std::string server_decision_context::h2o_quote(const std::string & text) const { + std::string out; + for (size_t i = 0; i < text.size();) { + bool is_quoted = false; + if (quoted_first.find(text[i]) == std::string::npos) { + out += text[i++]; + continue; + } + for (const auto & spelling : quoted_spellings) { + if (text.compare(i, spelling.size(), spelling) == 0) { + std::string quoted = spelling; + string_replace_all(quoted, "<", "\\u003c"); + string_replace_all(quoted, ">", "\\u003e"); + out += quoted; + i += spelling.size(); + is_quoted = true; + break; + } + } + if (!is_quoted) { + out += text[i++]; + } + } + return out; +} + +json server_decision_context::prepare_state(const json & state) const { + if (type != COMMON_DECISION_TYPE_H2O_LIGHTNING) { + return state; + } + // done once per request: compact JSON, the cut, the quoting + return h2o_quote(h2o_cut_state(state.is_string() ? state.get() : decision_py_dumps(state, -1, ",", ":", false))); +} + +json server_decision_context::render_h2o(const json & state, const server_decision_question & question) const { + // the instructions are JSON with an indent of 1, the descriptions are default JSON, the state is from prepare_state() + const std::string instructions = decision_py_lstrip(question.instructions.is_string() ? + question.instructions.get() : decision_py_dumps(question.instructions, 1, ",", ": ", true)); + + json options = json::array(); + for (size_t i = 0; i < question.options.size(); i++) { + const auto & opt = question.options[i]; + json description; // null if not provided, the template has a default, except a score level that is shown as JSON null + if (question.type == SERVER_DECISION_QUESTION_SCORE && opt.description.is_null()) { + description = "null"; + } else if (!opt.description.is_null()) { + description = h2o_quote(opt.description.is_string() ? opt.description.get() : decision_py_dumps(opt.description, -1, ", ", ": ", false)); + } + const std::string key = h2o_quote(opt.key); + + // the user turn is stripped like Python's str.strip(), the template only trims ASCII whitespace + // so the text that ends the turn is stripped here, a space keeps a description that is only whitespace + if (i + 1 == question.options.size()) { + const bool is_key = question.type == SERVER_DECISION_QUESTION_CHOICE && description.is_null(); + if (is_key || description.is_string()) { + const std::string text = is_key ? key : description.get(); + const std::string stripped = decision_py_rstrip(text); + if (stripped != text) { + description = stripped.empty() ? " " : stripped; + } + } + } + + json option = json::object(); + option["label"] = label_texts[i]; + option["key"] = key; + option["description"] = description; + options.push_back(option); + } + + json inp = json::object(); + inp["type"] = decision_question_type_name(question.type); + inp["state"] = state; + inp["instructions"] = h2o_quote(instructions); + inp["options"] = options; + return inp; +} + // given to the lfm2-d1-omni template: text between the pieces of the prompt, and at the start of the pieces that are cut to a token budget static const std::string D1OMNI_MARKER = "<second; + } std::vector probs(n, 0.0); for (size_t v = 0; v < scores.size(); v++) { const auto & s = scores[v]; diff --git a/tools/server/server-decision.h b/tools/server/server-decision.h index ed97244293dc..b631099a8f10 100644 --- a/tools/server/server-decision.h +++ b/tools/server/server-decision.h @@ -46,6 +46,7 @@ struct server_decision_context { case COMMON_DECISION_TYPE_PPLX_DECIDER: case COMMON_DECISION_TYPE_VLLM_DECISION2: case COMMON_DECISION_TYPE_LFM2_D1: + case COMMON_DECISION_TYPE_H2O_LIGHTNING: return true; default: return false; @@ -66,6 +67,7 @@ struct server_decision_context { case COMMON_DECISION_TYPE_VLLM_D3: case COMMON_DECISION_TYPE_LFM2_D1: case COMMON_DECISION_TYPE_LFM2_D1_OMNI: + case COMMON_DECISION_TYPE_H2O_LIGHTNING: return true; default: return false; @@ -75,6 +77,9 @@ struct server_decision_context { // throw std::invalid_argument on bad input std::vector parse_questions(const json & body) const; + // the state as the model reads it, done once per request; returns the state for most models + json prepare_state(const json & state) const; + // returns the state without its images, they are appended to files in order // images come from "files" (alias "images") and from the image_url and input_audio parts of a state made of chat messages // an image can be an audio clip if the model supports it @@ -121,7 +126,7 @@ struct server_decision_context { bool strict_payload = false; // state, instructions and descriptions must be text, an object or an array bool canonical_legend = false; // non-string score descriptions are returned as canonical JSON text - // OPENJEV, LEV, NIMBLE, PPLX_DECIDER, VLLM_D3 + // OPENJEV, LEV, NIMBLE, PPLX_DECIDER, VLLM_D3, H2O_LIGHTNING std::vector labels; std::vector label_texts; // only if the label of an option is given to the template @@ -132,6 +137,10 @@ struct server_decision_context { size_t max_head_tokens = 0; // question + options size_t max_option_tokens = 48; + // H2O_LIGHTNING + std::vector quoted_spellings; // texts of the special tokens, quoted in the input, longest first + std::string quoted_first; // the first bytes of these texts + std::string render( const json & state, const std::vector & questions, @@ -143,6 +152,10 @@ struct server_decision_context { size_t n_outputs(const server_decision_question & question) const; // LFM2_D1: label text and tokens of each option void d1_labels(const server_decision_question & question, std::vector & texts, std::vector & groups) const; + // H2O_LIGHTNING: the input of the template, its texts are rendered as the reference shim does + json render_h2o(const json & state, const server_decision_question & question) const; + std::string h2o_cut_state(const std::string & text) const; + std::string h2o_quote(const std::string & text) const; void fill_task_laya(llama_tokens & tokens, const server_decision_question & question, server_task & task) const; void fill_task_d1omni( const json & state, From 4a45de4f2c68f3d144f6c2431ad7c1da7e53c99e Mon Sep 17 00:00:00 2001 From: Yuri Khrustalev Date: Tue, 20 Oct 2026 12:54:46 -0400 Subject: [PATCH 26/69] mtmd : lfm2 tiles and layout match transformers (#30261) * mtmd : lfm2 tiles stretch the image to the grid Lfm2VlImageProcessor.crop_image_to_patches resizes the image straight to (512 * rows, 512 * cols) before it cuts the tiles. llama.cpp kept the aspect ratio and added black bars for LFM2 (only d1-omni stretched), so the tiles of d1-3B and every LFM2-VL / LFM2.5-VL model differed from transformers unless the image had the aspect ratio of the grid. With PAD_NONE the tiles are the same as in transformers. * mtmd : lfm2 layout with Python rounding in double precision Lfm2VlImageProcessor.smart_resize rounds with Python's round (half to even) and computes beta and the tile grid aspect ratio in double. llama.cpp used std::round and float, so 31926 of the 16.7M sizes in [1,4096]^2 got another thumbnail size or grid than transformers (and d1-omni vision.py), e.g. 400x600 -> 416x608 instead of 384x608 and 720x1600 -> 2x5 tiles instead of 2x4. The lfm2 smart resize and grid choice now follow the Python code and match it on every size of that range. * fix --------- Co-authored-by: Xuan Son Nguyen --- tests/test-mtmd-impl.cpp | 30 +++++++++++++++++++ tools/mtmd/clip.cpp | 6 ++-- tools/mtmd/mtmd-image.cpp | 61 +++++++++++++++++++++++++++------------ tools/mtmd/mtmd-image.h | 12 ++++---- 4 files changed, 81 insertions(+), 28 deletions(-) diff --git a/tests/test-mtmd-impl.cpp b/tests/test-mtmd-impl.cpp index 1b7796d95672..6ad60213e681 100644 --- a/tests/test-mtmd-impl.cpp +++ b/tests/test-mtmd-impl.cpp @@ -91,6 +91,10 @@ MAKE_TEST(test_image_preprocessor_dyn_size) { } } +static std::string image_size_to_str(const clip_image_size & size) { + return std::to_string(size.width) + "x" + std::to_string(size.height); +} + MAKE_TEST(test_image_preprocessor_lfm2) { clip_hparams hparams; hparams.patch_size = 16; @@ -121,6 +125,32 @@ MAKE_TEST(test_image_preprocessor_lfm2) { std::string(actual ? "tiled" : "single")); } + // { image size, expected size from HF smart_resize } + const std::vector> resize_cases = { + { { 400, 600 }, { 384, 608 } }, // 400 / 32 = 12.5 rounds half to even + { { 480, 336 }, { 480, 320 } }, + { { 64, 169 }, { 160, 416 } }, // 169 * beta / 32 is exactly 13 in double + { { 1024, 768 }, { 576, 416 } }, + }; + + for (const auto & [size, expected] : resize_cases) { + t.assert_equal("smart_resize for " + image_size_to_str(size), image_size_to_str(expected), + image_size_to_str(mtmd_image_preprocessor_lfm2::smart_resize(hparams, size))); + } + + // { image size, expected grid from HF } + const std::vector> grid_cases = { + { { 720, 1600 }, { 2, 4 } }, // 0.45 is an exact tie between 2/4 and 2/5 in double, not in float + { { 420, 1440 }, { 1, 3 } }, + { { 255, 2160 }, { 1, 8 } }, + { { 1024, 768 }, { 3, 2 } }, + }; + + for (const auto & [size, expected] : grid_cases) { + t.assert_equal("grid for " + image_size_to_str(size), image_size_to_str(expected), + image_size_to_str(mtmd_image_preprocessor_lfm2::get_grid_layout(hparams, size.height, size.width))); + } + // do_image_splitting=false hparams.preproc_min_tiles = 1; hparams.preproc_max_tiles = 1; diff --git a/tools/mtmd/clip.cpp b/tools/mtmd/clip.cpp index 41aba280ba04..26941c9a85d5 100644 --- a/tools/mtmd/clip.cpp +++ b/tools/mtmd/clip.cpp @@ -1593,10 +1593,8 @@ struct clip_model_loader { } hparams.image_resize_algo_rf = hparams.image_resize_algo; hparams.image_resize_algo_ov = hparams.image_resize_algo; - // the tiles stretch the image to the grid (d1-omni vision.py) - if (model.proj_type == PROJECTOR_TYPE_D1OMNI_V) { - hparams.image_pad_rf = PAD_NONE; - } + // the tiles stretch the image to the grid (Lfm2VlImageProcessor, d1-omni vision.py) + hparams.image_pad_rf = PAD_NONE; get_u32(KEY_PROJ_SCALE_FACTOR, hparams.n_merge, false); // defaults for GGUFs converted before tiling params were written // ref: https://huggingface.co/LiquidAI/LFM2.5-VL-1.6B/blob/main/processor_config.json diff --git a/tools/mtmd/mtmd-image.cpp b/tools/mtmd/mtmd-image.cpp index 86feea6bd4b9..e0cd0bf1d0e7 100644 --- a/tools/mtmd/mtmd-image.cpp +++ b/tools/mtmd/mtmd-image.cpp @@ -952,12 +952,33 @@ bool mtmd_image_preprocessor_lfm2::should_tile( static_cast(hparams.image_max_pixels) * hparams.preproc_max_pixels_tolerance; } +// same as Lfm2VlImageProcessor.smart_resize: Python's round half to even, double precision +clip_image_size mtmd_image_preprocessor_lfm2::smart_resize( + const clip_hparams & hparams, + const clip_image_size & original_size) { + const int align_size = hparams.patch_size * hparams.n_merge; + const double height = original_size.height; + const double width = original_size.width; + + int h_bar = std::max(align_size, static_cast(std::nearbyint(height / align_size)) * align_size); + int w_bar = std::max(align_size, static_cast(std::nearbyint(width / align_size)) * align_size); + + if (static_cast(h_bar) * w_bar > hparams.image_max_pixels) { + const double beta = std::sqrt(height * width / hparams.image_max_pixels); + h_bar = std::max(align_size, static_cast(std::floor(height / beta / align_size)) * align_size); + w_bar = std::max(align_size, static_cast(std::floor(width / beta / align_size)) * align_size); + } else if (static_cast(h_bar) * w_bar < hparams.image_min_pixels) { + const double beta = std::sqrt(hparams.image_min_pixels / (height * width)); + h_bar = static_cast(std::ceil(height * beta / align_size)) * align_size; + w_bar = static_cast(std::ceil(width * beta / align_size)) * align_size; + } + + return {w_bar, h_bar}; +} + mtmd_image_preprocessor_llava_uhd::slice_instructions mtmd_image_preprocessor_lfm2::get_slice_instructions(const clip_image_size & original_size) const { mtmd_image_preprocessor_llava_uhd::slice_instructions inst; - const int align_size = hparams.patch_size * hparams.n_merge; - inst.overview_size = img_tool::calc_size_preserved_ratio( - original_size, - { align_size, hparams.image_min_pixels, hparams.image_max_pixels, 0 }); + inst.overview_size = smart_resize(hparams, original_size); const bool needs_tiling = should_tile(hparams, original_size); const int tile_size = hparams.preproc_tile_size; @@ -968,7 +989,7 @@ mtmd_image_preprocessor_llava_uhd::slice_instructions mtmd_image_preprocessor_lf return inst; } - const clip_image_size grid = get_grid_layout(original_size.height, original_size.width); + const clip_image_size grid = get_grid_layout(hparams, original_size.height, original_size.width); inst.grid_size = grid; inst.refined_size = clip_image_size{tile_size * grid.width, tile_size * grid.height}; @@ -996,24 +1017,26 @@ mtmd_image_preprocessor_llava_uhd::slice_instructions mtmd_image_preprocessor_lf return inst; } +// double precision as in Python, float breaks exact ties such as 720/1600 between 2/4 and 2/5 clip_image_size mtmd_image_preprocessor_lfm2::find_closest_aspect_ratio( - float aspect_ratio, + const clip_hparams & hparams, + double aspect_ratio, const std::vector & target_ratios, - int width, int height) const { - float best_ratio_diff = std::numeric_limits::max(); + int width, int height) { + double best_ratio_diff = std::numeric_limits::max(); clip_image_size best_ratio = {1, 1}; - const float area = static_cast(width * height); + const double area = static_cast(width) * height; for (const auto & ratio : target_ratios) { - const float target_aspect_ratio = static_cast(ratio.width) / ratio.height; - const float ratio_diff = std::abs(aspect_ratio - target_aspect_ratio); + const double target_aspect_ratio = static_cast(ratio.width) / ratio.height; + const double ratio_diff = std::abs(aspect_ratio - target_aspect_ratio); if (ratio_diff < best_ratio_diff) { best_ratio_diff = ratio_diff; best_ratio = ratio; } else if (ratio_diff == best_ratio_diff) { - const int tile_size = hparams.preproc_tile_size; - const float target_area = static_cast(tile_size * tile_size * ratio.width * ratio.height); - if (area > 0.5f * target_area) { + const int tile_size = hparams.preproc_tile_size; + const double target_area = static_cast(tile_size * tile_size * ratio.width * ratio.height); + if (area > 0.5 * target_area) { best_ratio = ratio; } } @@ -1021,7 +1044,7 @@ clip_image_size mtmd_image_preprocessor_lfm2::find_closest_aspect_ratio( return best_ratio; } -std::vector mtmd_image_preprocessor_lfm2::get_target_ratios() const { +std::vector mtmd_image_preprocessor_lfm2::get_target_ratios(const clip_hparams & hparams) { const int min_tiles = hparams.preproc_min_tiles; const int max_tiles = hparams.preproc_max_tiles; std::vector ratios; @@ -1049,10 +1072,10 @@ std::vector mtmd_image_preprocessor_lfm2::get_target_ratios() c return ratios; } -clip_image_size mtmd_image_preprocessor_lfm2::get_grid_layout(int height, int width) const { - const float aspect_ratio = static_cast(width) / height; - const auto ratios = get_target_ratios(); - return find_closest_aspect_ratio(aspect_ratio, ratios, width, height); +clip_image_size mtmd_image_preprocessor_lfm2::get_grid_layout(const clip_hparams & hparams, int height, int width) { + const double aspect_ratio = static_cast(width) / height; + const auto ratios = get_target_ratios(hparams); + return find_closest_aspect_ratio(hparams, aspect_ratio, ratios, width, height); } // diff --git a/tools/mtmd/mtmd-image.h b/tools/mtmd/mtmd-image.h index a9d16f1c9ce6..3aea23ced660 100644 --- a/tools/mtmd/mtmd-image.h +++ b/tools/mtmd/mtmd-image.h @@ -199,14 +199,16 @@ struct mtmd_image_preprocessor_lfm2 : mtmd_image_preprocessor_llava_uhd { slice_instructions get_slice_instructions(const clip_image_size & original_size) const override; static bool should_tile(const clip_hparams & hparams, const clip_image_size & original_size); + static clip_image_size smart_resize(const clip_hparams & hparams, const clip_image_size & original_size); + static clip_image_size get_grid_layout(const clip_hparams & hparams, int height, int width); private: - clip_image_size find_closest_aspect_ratio( - float aspect_ratio, + static clip_image_size find_closest_aspect_ratio( + const clip_hparams & hparams, + double aspect_ratio, const std::vector & target_ratios, - int width, int height) const; - std::vector get_target_ratios() const; - clip_image_size get_grid_layout(int height, int width) const; + int width, int height); + static std::vector get_target_ratios(const clip_hparams & hparams); }; struct mtmd_image_preprocessor_idefics3 : mtmd_image_preprocessor_llava_uhd { From b684b498b6c36876b9b18ede7df5b3d1d347bff2 Mon Sep 17 00:00:00 2001 From: Tarek Dakhran Date: Tue, 20 Oct 2026 19:22:08 +0200 Subject: [PATCH 27/69] hexagon: fix VTCM size query on images that need layout out-params (#30655) --- ggml/src/ggml-hexagon/htp/main.c | 16 +++++++++++----- 1 file changed, 11 insertions(+), 5 deletions(-) diff --git a/ggml/src/ggml-hexagon/htp/main.c b/ggml/src/ggml-hexagon/htp/main.c index b72ef2b7592e..5ac6c3c52bb3 100644 --- a/ggml/src/ggml-hexagon/htp/main.c +++ b/ggml/src/ggml-hexagon/htp/main.c @@ -316,9 +316,17 @@ static int vtcm_release_callback(unsigned int rctx, void * state) { return 0; } -static int vtcm_alloc(struct htp_context * ctx) { +static unsigned int vtcm_query_size(void) { unsigned int vtcm_size = 8 * 1024 * 1024; // 8MB default - HAP_compute_res_query_VTCM(0, &vtcm_size, NULL, NULL, NULL); + unsigned int vtcm_avail; + compute_res_vtcm_page_t total_layout, avail_layout; + // some images (e.g. FuSa cDSP) fail the query when the layout out-params are NULL + HAP_compute_res_query_VTCM(0, &vtcm_size, &total_layout, &vtcm_avail, &avail_layout); + return vtcm_size; +} + +static int vtcm_alloc(struct htp_context * ctx) { + unsigned int vtcm_size = vtcm_query_size(); compute_res_attr_t attr; HAP_compute_res_attr_init(&attr); @@ -770,9 +778,7 @@ AEEResult htp_iface_hwinfo(remote_handle64 handle, uint32_t * n_threads, uint32_ *n_hvx = n_hvx_val; *n_hmx = 1; - uint32_t vtcm_sz = 8 * 1024 * 1024; // 8MB default fallback - HAP_compute_res_query_VTCM(0, (unsigned int *)&vtcm_sz, NULL, NULL, NULL); - *vtcm_size = vtcm_sz; + *vtcm_size = vtcm_query_size(); return AEE_SUCCESS; } From ead9f3205848f81ca079b4e760b76a01a6359f4b Mon Sep 17 00:00:00 2001 From: Nigel Bosch Date: Tue, 20 Oct 2026 12:36:14 -0500 Subject: [PATCH 28/69] server: Support logprobs with speculative decoding (#27196) * return logprobs in spec path * server: add regression test for logprobs with speculative decoding Asserts accepted draft tokens report the target model's logprobs, comparing a draft-enabled run against a no-draft baseline (#24271). Fails on master (top_logprobs empty, logprob 0.0), passes with the fix from #27196. Co-Authored-By: Claude Fable 5 Claude-Session: https://claude.ai/code/session_01KARBWpPcijcBXzJEvttuwk --------- Co-authored-by: Jeonghyun Lee Co-authored-by: Claude Fable 5 --- tools/server/server-context.cpp | 19 +++++++---- tools/server/tests/unit/test_speculative.py | 36 +++++++++++++++++++++ 2 files changed, 48 insertions(+), 7 deletions(-) diff --git a/tools/server/server-context.cpp b/tools/server/server-context.cpp index 84cd6776899d..d66aae426e32 100644 --- a/tools/server/server-context.cpp +++ b/tools/server/server-context.cpp @@ -4522,11 +4522,14 @@ struct server_context_impl { GGML_ASSERT(n_draft > 0); + // batch indices of the draft tokens, used to get the token probabilities below + auto spec_i_batch = std::move(slot.spec_i_batch); + // verify and try to accept the draft { common_sampler_ptr smpl_save(common_sampler_clone(slot.smpl.get())); - GGML_ASSERT(slot.spec_i_batch.size() == n_draft + 1); + GGML_ASSERT(spec_i_batch.size() == n_draft + 1); GGML_ASSERT(slot.spec_draft_q.empty() || (slot.spec_draft_q.size() == slot.spec_draft.size())); const auto & synth_probs = common_speculative_get_synth_probs(spec.get()); @@ -4537,16 +4540,15 @@ struct server_context_impl { if (!synth_probs.empty()) { // synthetic acceptance replaces verification entirely, so it comes first accepted = server_sample_and_accept_synth( - slot.smpl.get(), slot.ctx_tgt, slot.spec_i_batch, slot.spec_draft, + slot.smpl.get(), slot.ctx_tgt, spec_i_batch, slot.spec_draft, synth_probs, slot.spec_synth_rng, slot.spec_is_replay); } else if (slot.spec_is_replay && slot.use_spec_rejection()) { - accepted = server_accept_replay(slot.smpl.get(), slot.ctx_tgt, slot.spec_i_batch, slot.spec_draft); + accepted = server_accept_replay(slot.smpl.get(), slot.ctx_tgt, spec_i_batch, slot.spec_draft); } else if (use_rejection) { - accepted = common_sampler_sample_and_accept_n_rejection(slot.smpl.get(), slot.ctx_tgt, slot.spec_i_batch, slot.spec_draft, slot.spec_draft_q); + accepted = common_sampler_sample_and_accept_n_rejection(slot.smpl.get(), slot.ctx_tgt, spec_i_batch, slot.spec_draft, slot.spec_draft_q); } else { - accepted = common_sampler_sample_and_accept_n(slot.smpl.get(), slot.ctx_tgt, slot.spec_i_batch, slot.spec_draft); + accepted = common_sampler_sample_and_accept_n(slot.smpl.get(), slot.ctx_tgt, spec_i_batch, slot.spec_draft); } - slot.spec_i_batch.clear(); GGML_ASSERT(accepted.size() >= 1); @@ -4633,7 +4635,10 @@ struct server_context_impl { result.text_to_send = common_token_to_piece(slot.ctx_tgt, result.tok, accept_special_token(slot, result.tok)); result.prob = 1.0f; // set later - // TODO: set result.probs + // post_sampling_probs is not supported with speculative decoding + if (slot.task->params.sampling.n_probs > 0 && !slot.task->params.post_sampling_probs) { + populate_token_probs(slot, result, false, params_base.special, spec_i_batch[i]); + } slot.stats.n_gen += 1; diff --git a/tools/server/tests/unit/test_speculative.py b/tools/server/tests/unit/test_speculative.py index 7a0354302588..0d41b1173af0 100644 --- a/tools/server/tests/unit/test_speculative.py +++ b/tools/server/tests/unit/test_speculative.py @@ -229,3 +229,39 @@ def test_multi_requests_parallel(n_slots: int, n_requests: int): for res in results: assert res.status_code == 200 assert match_regex("(wise|kind|owl|answer)+", res.body["content"]) + + +def test_draft_token_probs(): + # tokens accepted from the draft must carry the target model's probabilities, + # same as without speculative decoding (#24271) + global server + request = { + "prompt": "I believe the meaning of life is", + "temperature": 0.0, + "top_k": 1, + "n_predict": 16, + "n_probs": 4, + } + + server.model_draft = None # disable draft model + server.spec_type = None + server.start() + res = server.make_request("POST", "/completion", data=request) + assert res.status_code == 200 + probs_no_draft = res.body["completion_probabilities"] + server.stop() + + create_server() + server.start() + res = server.make_request("POST", "/completion", data=request) + assert res.status_code == 200 + assert res.body["timings"]["draft_n"] > 0 + probs_draft = res.body["completion_probabilities"] + + assert len(probs_draft) == len(probs_no_draft) + for tok_draft, tok_no_draft in zip(probs_draft, probs_no_draft): + assert tok_draft["id"] == tok_no_draft["id"] + assert len(tok_draft["top_logprobs"]) == len(tok_no_draft["top_logprobs"]) + assert abs(tok_draft["logprob"] - tok_no_draft["logprob"]) < 0.25 + # real logprobs are not all exactly 0.0 (prob 1.0) + assert any(tok["logprob"] < -1e-4 for tok in probs_draft) From 839a572a4cdecdb80d211297b8e7d7323a6e99d2 Mon Sep 17 00:00:00 2001 From: Aman Gupta Date: Wed, 21 Oct 2026 00:39:56 +0530 Subject: [PATCH 29/69] qwen: reshape output to 2d instead of 3d (#30823) * qwen: reshape output to 2d instead of 3d * remove final reshape, fix comments --- src/models/qwen35.cpp | 7 ++----- src/models/qwen35moe.cpp | 8 ++------ src/models/qwen3next.cpp | 7 ++----- src/models/qwen4exp.cpp | 4 +--- 4 files changed, 7 insertions(+), 19 deletions(-) diff --git a/src/models/qwen35.cpp b/src/models/qwen35.cpp index 802f2e2f13e9..8a4e434117fa 100644 --- a/src/models/qwen35.cpp +++ b/src/models/qwen35.cpp @@ -465,17 +465,14 @@ ggml_tensor * llama_model_qwen35::graph::build_layer_attn_linear( // Apply gated normalization: self.norm(core_attn_out, z) ggml_tensor * attn_out_norm = build_norm_gated(output, model.layers[il].ssm_norm, z_2d, il); - // Final reshape: [head_dim, n_heads, n_tokens, n_seqs] -> [n_tokens, n_seqs, n_heads * head_dim] - ggml_tensor * final_output = ggml_reshape_3d(ctx0, attn_out_norm, head_v_dim * num_v_heads, n_seq_tokens, n_seqs); + // Final reshape: [head_dim, n_heads, n_tokens, n_seqs] -> [n_heads * head_dim , n_tokens * n_seqs ] + ggml_tensor * final_output = ggml_reshape_2d(ctx0, attn_out_norm, head_v_dim * num_v_heads, n_seq_tokens * n_seqs); cb(final_output, "final_output", il); // Output projection cur = build_lora_mm(model.layers[il].ssm_out, final_output, model.layers[il].ssm_out_s); cb(cur, "linear_attn_out", il); - // Reshape back to original dimensions - cur = ggml_reshape_2d(ctx0, cur, n_embd, n_seq_tokens * n_seqs); - return cur; } diff --git a/src/models/qwen35moe.cpp b/src/models/qwen35moe.cpp index fdbb2c63d441..939f10f7f061 100644 --- a/src/models/qwen35moe.cpp +++ b/src/models/qwen35moe.cpp @@ -475,17 +475,13 @@ ggml_tensor * llama_model_qwen35moe::graph::build_layer_attn_linear( // Apply gated normalization: self.norm(core_attn_out, z) ggml_tensor * attn_out_norm = build_norm_gated(output, model.layers[il].ssm_norm, z_2d, il); - // Final reshape: [head_dim, n_heads, n_tokens, n_seqs] -> [n_tokens, n_seqs, n_heads * head_dim] - ggml_tensor * final_output = ggml_reshape_3d(ctx0, attn_out_norm, head_v_dim * num_v_heads, n_seq_tokens, n_seqs); - cb(final_output, "final_output", il); + // Final reshape: [head_dim, n_heads, n_tokens, n_seqs] -> [n_heads * head_dim , n_tokens * n_seqs ] + ggml_tensor * final_output = ggml_reshape_2d(ctx0, attn_out_norm, head_v_dim * num_v_heads, n_seq_tokens * n_seqs); // Output projection cur = build_lora_mm(model.layers[il].ssm_out, final_output, model.layers[il].ssm_out_s); cb(cur, "linear_attn_out", il); - // Reshape back to original dimensions - cur = ggml_reshape_2d(ctx0, cur, n_embd, n_seq_tokens * n_seqs); - return cur; } diff --git a/src/models/qwen3next.cpp b/src/models/qwen3next.cpp index 13aa68ee8028..98fafd917b44 100644 --- a/src/models/qwen3next.cpp +++ b/src/models/qwen3next.cpp @@ -542,17 +542,14 @@ ggml_tensor * llama_model_qwen3next::graph::build_layer_attn_linear( // Apply gated normalization: self.norm(core_attn_out, z) ggml_tensor * attn_out_norm = build_norm_gated(output, model.layers[il].ssm_norm, z_2d, il); - // Final reshape: [head_dim, n_heads, n_tokens, n_seqs] -> [n_tokens, n_seqs, n_heads * head_dim] - ggml_tensor * final_output = ggml_reshape_3d(ctx0, attn_out_norm, head_v_dim * num_v_heads, n_seq_tokens, n_seqs); + // Final reshape: [head_dim, n_heads, n_tokens, n_seqs] -> [n_heads * head_dim , n_tokens * n_seqs ] + ggml_tensor * final_output = ggml_reshape_2d(ctx0, attn_out_norm, head_v_dim * num_v_heads, n_seq_tokens * n_seqs); cb(final_output, "final_output", il); // Output projection cur = build_lora_mm(model.layers[il].ssm_out, final_output); cb(cur, "linear_attn_out", il); - // Reshape back to original dimensions - cur = ggml_reshape_2d(ctx0, cur, n_embd, n_seq_tokens * n_seqs); - return cur; } diff --git a/src/models/qwen4exp.cpp b/src/models/qwen4exp.cpp index 77430e7afdc4..22e7159840b9 100644 --- a/src/models/qwen4exp.cpp +++ b/src/models/qwen4exp.cpp @@ -1131,14 +1131,12 @@ ggml_tensor * llama_model_qwen4exp::graph::build_layer_attn_linear( // gated normalization, as self.norm(core_attn_out, z) in the reference ggml_tensor * attn_out_norm = build_norm_gated(output, model.layers[il].ssm_norm, z_2d, il); - ggml_tensor * final_output = ggml_reshape_3d(ctx0, attn_out_norm, head_v_dim * num_v_heads, n_seq_tokens, n_seqs); + ggml_tensor * final_output = ggml_reshape_2d(ctx0, attn_out_norm, head_v_dim * num_v_heads, n_seq_tokens * n_seqs); cb(final_output, "final_output", il); cur = build_lora_mm(model.layers[il].ssm_out, final_output, model.layers[il].ssm_out_s); cb(cur, "linear_attn_out", il); - cur = ggml_reshape_2d(ctx0, cur, n_embd, n_seq_tokens * n_seqs); - return cur; } From 52ffa027455662ea59f78d0e4e5a8f38ee63a5fe Mon Sep 17 00:00:00 2001 From: Takamichi Ohara Date: Wed, 21 Oct 2026 05:48:45 +0900 Subject: [PATCH 30/69] model : support Nemotron-3(.5)-Embed (#30877) --- conversion/__init__.py | 3 +++ conversion/llava.py | 23 ++++++++++++++++++++++- conversion/mistral3.py | 34 ++++++++++++++++++++++++++++------ tools/mtmd/clip-model.h | 1 + tools/mtmd/clip.cpp | 18 +++++++++++++++++- tools/mtmd/mtmd-image.cpp | 16 ++++++++++++++-- tools/mtmd/mtmd.cpp | 6 +++++- 7 files changed, 90 insertions(+), 11 deletions(-) diff --git a/conversion/__init__.py b/conversion/__init__.py index efa537b0f230..bddfe3c1f531 100644 --- a/conversion/__init__.py +++ b/conversion/__init__.py @@ -200,7 +200,9 @@ "MiniMaxM3SparseForCausalLM": "minimax", "MiniMaxM3SparseForConditionalGeneration": "minimax", "Ministral3ForCausalLM": "mistral3", + "Ministral3Model": "mistral3", "Mistral3ForConditionalGeneration": "mistral3", + "Mistral3Model": "mistral3", "VoxtralRealtimeForConditionalGeneration": "mistral3", "MistralForCausalLM": "llama", "MixtralForCausalLM": "llama", @@ -358,6 +360,7 @@ "MiniCPMV4_6ForConditionalGeneration": "minicpm", "MiniCPMV4_7ForConditionalGeneration": "minicpm", "Mistral3ForConditionalGeneration": "llava", + "Mistral3Model": "llava", "NemotronH_Omni_Reasoning_V3": "nemotron", "NemotronH_Nano_VL_V2": "nemotron", "MuseGlimmerForConditionalGeneration": "muse_glimmer", diff --git a/conversion/llava.py b/conversion/llava.py index 98a004f98693..e193f0b95da4 100644 --- a/conversion/llava.py +++ b/conversion/llava.py @@ -2,7 +2,7 @@ import json -from typing import Iterable, TYPE_CHECKING +from typing import Callable, Iterable, TYPE_CHECKING if TYPE_CHECKING: from torch import Tensor @@ -128,3 +128,24 @@ def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iter yield from super().modify_tensors(img_break_embd, name, bid) return # skip other tensors + + +@ModelBase.register("Mistral3Model") +@ModelBase.example("nvidia/Nemotron-3.5-Embed-1B-BF16") +class Mistral3EmbeddingVisionModel(LlavaVisionModel): + def __init__(self, dir_model, *args, **kwargs): + hparams = ModelBase.load_hparams(dir_model, is_mistral_format=False) + text_config = hparams.get("text_config", hparams) + if text_config.get("is_causal") is not False: + raise NotImplementedError("Expected a Nemotron Embed model for Mistral3Model, but is_causal is not False") + super().__init__(dir_model, *args, **kwargs) + + @classmethod + def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None: + if item[0].endswith("embed_tokens.weight"): + return ModelBase.filter_tensors(item) + return super().filter_tensors(item) + + def set_gguf_parameters(self): + super().set_gguf_parameters() + self.gguf_writer.add_vision_preproc_image_size(self.preprocessor_config["size"]["longest_edge"]) diff --git a/conversion/mistral3.py b/conversion/mistral3.py index 661475251aa8..106c329e728a 100644 --- a/conversion/mistral3.py +++ b/conversion/mistral3.py @@ -5,7 +5,7 @@ if TYPE_CHECKING: from torch import Tensor -from .base import ModelBase, TextModel, gguf +from .base import ModelBase, TextModel, get_model_architecture, gguf from .deepseek import DeepseekV2Model from .llama import LlamaModel @@ -13,9 +13,11 @@ @ModelBase.register( "Mistral3ForConditionalGeneration", + "Mistral3Model", "Ministral3ForCausalLM", + "Ministral3Model", ) -@ModelBase.example("mistralai/Mistral-Small-3.1-24B-Instruct-2503", "hf-tiny-v2/tiny-random-Ministral3ForCausalLM") +@ModelBase.example("mistralai/Mistral-Small-3.1-24B-Instruct-2503", "hf-tiny-v2/tiny-random-Ministral3ForCausalLM", "nvidia/Nemotron-3-Embed-1B-BF16", "nvidia/Nemotron-3.5-Embed-1B-BF16") class Mistral3Model(TextModel): class Ministral3Model(LlamaModel): model_arch = gguf.MODEL_ARCH.MISTRAL3 @@ -29,6 +31,18 @@ def set_gguf_parameters(self): self.gguf_writer.add_rope_scaling_yarn_log_mul(rope_params["mscale_all_dim"]) self.gguf_writer.add_attn_temperature_scale(rope_params["llama_4_scaling_beta"]) + class Mistral3EmbedNemotronModel(Ministral3Model): + model_arch = gguf.MODEL_ARCH.MISTRAL3 + + def set_vocab(self): + super().set_vocab() + self.gguf_writer.add_add_bos_token(False) + self.gguf_writer.add_add_eos_token(False) + + def set_gguf_parameters(self): + super().set_gguf_parameters() + self._try_set_pooling_type() + class Mistral4Model(DeepseekV2Model): model_arch = gguf.MODEL_ARCH.MISTRAL4 skip_mtp = False # model contains no MTP layers, so no need to skip @@ -42,12 +56,20 @@ def modify_tensors(self, data_torch, name, bid): model_arch = gguf.MODEL_ARCH.MISTRAL3 # unused impl: TextModel - def __init__(self, *args, **kwargs): - super().__init__(*args, **kwargs) + def __init__(self, dir_model, *args, **kwargs): + hparams = ModelBase.load_hparams(dir_model, is_mistral_format=False) + hf_arch = get_model_architecture(hparams, self.model_type) + if hf_arch in ("Ministral3Model", "Mistral3Model"): + text_config = hparams.get("text_config", hparams) + if text_config.get("is_causal") is not False: + raise NotImplementedError(f"Expected a Nemotron Embed model for {hf_arch}, but is_causal is not False") + super().__init__(dir_model, *args, **kwargs) if self.hparams.get("model_type") == "mistral4": - self.impl = Mistral3Model.Mistral4Model(*args, **kwargs) + self.impl = Mistral3Model.Mistral4Model(dir_model, *args, **kwargs) + elif self.hf_arch in ("Ministral3Model", "Mistral3Model"): + self.impl = Mistral3Model.Mistral3EmbedNemotronModel(dir_model, *args, **kwargs) else: - self.impl = Mistral3Model.Ministral3Model(*args, **kwargs) + self.impl = Mistral3Model.Ministral3Model(dir_model, *args, **kwargs) def set_vocab(self): self.impl.set_vocab() diff --git a/tools/mtmd/clip-model.h b/tools/mtmd/clip-model.h index 64e006e8d6d5..5caa93b42117 100644 --- a/tools/mtmd/clip-model.h +++ b/tools/mtmd/clip-model.h @@ -66,6 +66,7 @@ struct clip_hparams { resize_algo image_resize_algo = RESIZE_ALGO_BICUBIC; pad_style image_resize_pad = PAD_CEIL; // padding style when resizing bool image_round_half_even = false; // smart_resize rounds .5 to even, like Python round() in the HF processor + bool image_floor_before_align = false; std::array image_pad_color = {0, 0, 0}; // (preprocessor) for llava-uhd style models diff --git a/tools/mtmd/clip.cpp b/tools/mtmd/clip.cpp index 26941c9a85d5..df61f82f0059 100644 --- a/tools/mtmd/clip.cpp +++ b/tools/mtmd/clip.cpp @@ -1625,7 +1625,23 @@ struct clip_model_loader { hparams.image_resize_algo = RESIZE_ALGO_BICUBIC; hparams.rope_theta = 10000.0f; get_u32(KEY_SPATIAL_MERGE_SIZE, hparams.n_merge, false); - hparams.set_limit_image_tokens(8, 1024); + get_u32(KEY_PREPROC_IMAGE_SIZE, hparams.image_longest_edge, false); + if (hparams.image_longest_edge > 0) { + const int64_t patch_size = int64_t(hparams.patch_size) * hparams.n_merge; + if (patch_size <= 0) { + throw std::runtime_error("Pixtral merged patch size must be positive"); + } + const int64_t n_tokens_per_side = + (hparams.image_longest_edge + patch_size - 1) / patch_size; + if (n_tokens_per_side * patch_size > std::sqrt(std::numeric_limits::max())) { + throw std::runtime_error("Pixtral preprocessing image size is too large"); + } + hparams.image_resize_pad = PAD_NONE; + hparams.image_floor_before_align = true; + hparams.set_limit_image_tokens(0, n_tokens_per_side * n_tokens_per_side); + } else { + hparams.set_limit_image_tokens(8, 1024); + } hparams.set_warmup_n_tokens(256); // avoid OOM on warmup } break; case PROJECTOR_TYPE_LIGHTONOCR: diff --git a/tools/mtmd/mtmd-image.cpp b/tools/mtmd/mtmd-image.cpp index e0cd0bf1d0e7..ab6067b97e04 100644 --- a/tools/mtmd/mtmd-image.cpp +++ b/tools/mtmd/mtmd-image.cpp @@ -873,16 +873,28 @@ mtmd_image_preproc_out mtmd_image_preprocessor_glm5v::preprocess(const clip_imag clip_image_size mtmd_image_preprocessor_longest_edge::get_target_size(const clip_hparams & hparams, const clip_image_size & original_size) { GGML_ASSERT(hparams.image_longest_edge > 0); + clip_image_size size = original_size; + if (hparams.image_floor_before_align) { + const double ratio = double(std::max(size.width, size.height)) / hparams.image_longest_edge; + if (ratio > 1.0) { + size.width = static_cast(std::floor(size.width / ratio)); + size.height = static_cast(std::floor(size.height / ratio)); + if (size.width <= 0 || size.height <= 0) { + throw std::runtime_error("image dimensions must be positive after resizing"); + } + } + } + // the original pixtral model doesn't have n_merge const int cur_merge = hparams.n_merge == 0 ? 1 : hparams.n_merge; return img_tool::calc_size_preserved_ratio( - original_size, + size, { /* align_size */ hparams.patch_size * cur_merge, /* min_pixels */ std::max(0, hparams.image_min_pixels), /* max_pixels */ std::max(0, hparams.image_max_pixels), /* longest_edge */ std::min(hparams.image_longest_edge, - std::max(original_size.width, original_size.height)), + std::max(size.width, size.height)), }); } diff --git a/tools/mtmd/mtmd.cpp b/tools/mtmd/mtmd.cpp index 26d5ee4105c7..b3ac1979ec73 100644 --- a/tools/mtmd/mtmd.cpp +++ b/tools/mtmd/mtmd.cpp @@ -804,7 +804,11 @@ struct mtmd_context { { // https://github.com/huggingface/transformers/blob/1cd110c6cb6a6237614130c470e9a902dbc1a4bd/docs/source/en/model_doc/pixtral.md img_end = "[IMG_END]"; - image_preproc = std::make_unique(ctx_v); + if (clip_get_hparams(ctx_v)->image_longest_edge > 0) { + image_preproc = std::make_unique(ctx_v); + } else { + image_preproc = std::make_unique(ctx_v); + } } break; case PROJECTOR_TYPE_PHI4: { From 84e66d03218711dfcaa993e5f64a546abe82df9a Mon Sep 17 00:00:00 2001 From: Tarek Dakhran Date: Tue, 20 Oct 2026 23:17:13 +0200 Subject: [PATCH 31/69] refactor : move LFM2 shortconv and FFN blocks into a shared LFM base (#30874) * llama : move LFM2 shortconv and FFN blocks into a shared LFM base * Update src/models/lfm-base.cpp Co-authored-by: Georgi Gerganov --------- Co-authored-by: Georgi Gerganov --- src/models/lfm-base.cpp | 114 ++++++++++++++++++++++++++++++++++++++++ src/models/lfm2.cpp | 112 +++------------------------------------ src/models/models.h | 22 +++++++- 3 files changed, 143 insertions(+), 105 deletions(-) create mode 100644 src/models/lfm-base.cpp diff --git a/src/models/lfm-base.cpp b/src/models/lfm-base.cpp new file mode 100644 index 000000000000..5e722eca8c03 --- /dev/null +++ b/src/models/lfm-base.cpp @@ -0,0 +1,114 @@ +#include "models.h" + +#include "llama-memory-recurrent.h" + +#include + +llm_build_lfm_base::llm_build_lfm_base(const llm_graph_params & params) : llm_graph_context(params) {} + +ggml_tensor * llm_build_lfm_base::build_lfm_shortconv(const llama_model & model, + const llama_memory_recurrent_context * mctx_recr, + llm_graph_input_rs * inp_recr, + ggml_tensor * cur, + int il) { + const uint32_t kv_head = mctx_recr->get_head(); + const int64_t n_seq_tokens = ubatch.n_seq_tokens; + const int64_t n_seqs = ubatch.n_seqs; + GGML_ASSERT(n_seqs != 0); + GGML_ASSERT(ubatch.equal_seqs()); + GGML_ASSERT(ubatch.n_tokens == n_seq_tokens * n_seqs); + + GGML_ASSERT(hparams.n_shortconv_l_cache > 1); + const uint32_t d_conv = hparams.n_shortconv_l_cache - 1; + const int64_t n_embd = hparams.n_embd; + + // {n_embd, n_tokens} => {n_embd, n_seq_tokens, n_seqs} + cur = ggml_reshape_3d(ctx0, cur, cur->ne[0], n_seq_tokens, n_seqs); + + auto * bcx = build_lora_mm(model.layers[il].shortconv.in_proj, cur); + cb(bcx, "model.layers.{}.conv.in_proj", il); + + constexpr auto n_chunks = 3; + GGML_ASSERT(bcx->ne[0] % n_chunks == 0); + const auto chunk_size = bcx->ne[0] / n_chunks; + auto * b = ggml_view_3d(ctx0, bcx, chunk_size, bcx->ne[1], bcx->ne[2], bcx->nb[1], bcx->nb[2], + 0 * chunk_size * ggml_element_size(bcx)); + auto * c = ggml_view_3d(ctx0, bcx, chunk_size, bcx->ne[1], bcx->ne[2], bcx->nb[1], bcx->nb[2], + 1 * chunk_size * ggml_element_size(bcx)); + auto * x = ggml_view_3d(ctx0, bcx, chunk_size, bcx->ne[1], bcx->ne[2], bcx->nb[1], bcx->nb[2], + 2 * chunk_size * ggml_element_size(bcx)); + + auto * bx = ggml_transpose(ctx0, ggml_mul(ctx0, b, x)); + + // read conv state + auto * conv_state = mctx_recr->get_r_l(il); + auto * conv_rs = build_rs(inp_recr, conv_state, hparams.n_embd_r(), n_seqs); + auto * conv = ggml_reshape_3d(ctx0, conv_rs, d_conv, n_embd, n_seqs); + + // causal prepends the state, non-causal pads symmetrically for a centered window + if (hparams.causal_attn) { + bx = ggml_concat(ctx0, conv, bx, 0); + } else { + const int64_t pad = (hparams.n_shortconv_l_cache - 1) / 2; + auto * left = ggml_cont(ctx0, + ggml_view_3d(ctx0, conv, pad, n_embd, n_seqs, conv->nb[1], conv->nb[2], (d_conv - pad) * conv->nb[0])); + bx = ggml_pad_ext(ctx0, ggml_concat(ctx0, left, bx, 0), 0, pad, 0, 0, 0, 0, 0, 0); + } + GGML_ASSERT(bx->ne[0] > conv->ne[0]); + + // write conv states: slot 0 = the final state, slot s = the state s tokens back (partial rollback) + const int64_t K = hparams.causal_attn && cparams.n_rs_seq > 0 ? (int64_t) cparams.n_rs_seq + 1 : 1; + const int64_t n_written = std::min(n_seq_tokens, K); + const auto mem_size = mctx_recr->get_size(); + const size_t row_size = ggml_row_size(conv_state->type, (int64_t) d_conv * n_embd); + + for (int64_t slot = 0; slot < n_written; ++slot) { + auto * conv_snap = ggml_view_3d(ctx0, bx, d_conv, bx->ne[1], bx->ne[2], bx->nb[1], bx->nb[2], + (bx->ne[0] - d_conv - slot) * ggml_element_size(bx)); + ggml_build_forward_expand(gf, ggml_cpy(ctx0, conv_snap, + ggml_view_2d(ctx0, conv_state, (int64_t) d_conv * n_embd, n_seqs, + conv_state->nb[1], + ((size_t) slot * mem_size + kv_head) * row_size))); + } + + auto * conv_kernel = model.layers[il].shortconv.conv; + auto * conv_out = ggml_ssm_conv(ctx0, bx, conv_kernel); + cb(conv_out, "model.layers.{}.conv.conv", il); + + auto * y = ggml_mul(ctx0, c, conv_out); + y = build_lora_mm(model.layers[il].shortconv.out_proj, y); + cb(y, "model.layers.{}.conv.out_proj", il); + // {n_embd, n_seq_tokens, n_seqs} => {n_embd, n_tokens} + y = ggml_reshape_2d(ctx0, y, y->ne[0], n_seq_tokens * n_seqs); + + return y; +} + +ggml_tensor * llm_build_lfm_base::build_lfm_dense_ffn(const llama_model & model, + ggml_tensor * cur, + int il) { + GGML_ASSERT(!model.layers[il].ffn_up_b); + GGML_ASSERT(!model.layers[il].ffn_gate_b); + GGML_ASSERT(!model.layers[il].ffn_down_b); + return build_ffn(cur, + model.layers[il].ffn_up, NULL, NULL, + model.layers[il].ffn_gate, NULL, NULL, + model.layers[il].ffn_down, NULL, NULL, + NULL, LLM_FFN_SILU, LLM_FFN_PAR, il); +} + +ggml_tensor * llm_build_lfm_base::build_lfm_moe_ffn(const llama_model & model, + ggml_tensor * cur, + int il) { + return build_moe_ffn(cur, + model.layers[il].ffn_gate_inp, + model.layers[il].ffn_up_exps, + model.layers[il].ffn_gate_exps, + model.layers[il].ffn_down_exps, + model.layers[il].ffn_exp_probs_b, + n_expert, n_expert_used, + LLM_FFN_SILU, true, + hparams.expert_weights_scale, + static_cast(hparams.expert_gating_func), + il); +} diff --git a/src/models/lfm2.cpp b/src/models/lfm2.cpp index 942d8de90728..dd61f509a6a5 100644 --- a/src/models/lfm2.cpp +++ b/src/models/lfm2.cpp @@ -147,35 +147,11 @@ std::unique_ptr llama_model_lfm2::build_arch_graph(const llm_ template llama_model_lfm2::graph::graph(const llama_model & model, const llm_graph_params & params) : - llm_graph_context(params) { + llm_build_lfm_base(params) { using inp_hybrid_type = std::conditional_t; using inp_attn_type = std::conditional_t; using mem_hybrid_ctx = std::conditional_t; - // lambda helpers for readability - auto build_dense_feed_forward = [&model, this](ggml_tensor * cur, int il) -> ggml_tensor * { - GGML_ASSERT(!model.layers[il].ffn_up_b); - GGML_ASSERT(!model.layers[il].ffn_gate_b); - GGML_ASSERT(!model.layers[il].ffn_down_b); - return build_ffn(cur, - model.layers[il].ffn_up, NULL, NULL, - model.layers[il].ffn_gate, NULL, NULL, - model.layers[il].ffn_down, NULL, NULL, - NULL, LLM_FFN_SILU, LLM_FFN_PAR, il); - }; - auto build_moe_feed_forward = [&model, this](ggml_tensor * cur, int il) -> ggml_tensor * { - return build_moe_ffn(cur, - model.layers[il].ffn_gate_inp, - model.layers[il].ffn_up_exps, - model.layers[il].ffn_gate_exps, - model.layers[il].ffn_down_exps, - model.layers[il].ffn_exp_probs_b, - n_expert, n_expert_used, - LLM_FFN_SILU, true, - hparams.expert_weights_scale, - static_cast(hparams.expert_gating_func), - il); - }; auto build_attn_block = [&model, this](ggml_tensor * cur, ggml_tensor * inp_pos, inp_attn_type * inp_attn, @@ -207,82 +183,6 @@ llama_model_lfm2::graph::graph(const llama_model & model, const llm_graph_ return cur; }; - auto build_shortconv_block = [&model, this](ggml_tensor * cur, - llm_graph_input_rs * inp_recr, - int il) -> ggml_tensor * { - const auto * mctx_cur = static_cast(mctx)->get_recr(); - const uint32_t kv_head = mctx_cur->get_head(); - const int64_t n_seq_tokens = ubatch.n_seq_tokens; - const int64_t n_seqs = ubatch.n_seqs; - GGML_ASSERT(n_seqs != 0); - GGML_ASSERT(ubatch.equal_seqs()); - GGML_ASSERT(ubatch.n_tokens == n_seq_tokens * n_seqs); - - GGML_ASSERT(hparams.n_shortconv_l_cache > 1); - const uint32_t d_conv = hparams.n_shortconv_l_cache - 1; - - // {n_embd, n_tokens} => {n_embd, n_seq_tokens, n_seqs} - cur = ggml_reshape_3d(ctx0, cur, cur->ne[0], n_seq_tokens, n_seqs); - - auto * bcx = build_lora_mm(model.layers[il].shortconv.in_proj, cur); - cb(bcx, "model.layers.{}.conv.in_proj", il); - - constexpr auto n_chunks = 3; - GGML_ASSERT(bcx->ne[0] % n_chunks == 0); - const auto chunk_size = bcx->ne[0] / n_chunks; - auto * b = ggml_view_3d(ctx0, bcx, chunk_size, bcx->ne[1], bcx->ne[2], bcx->nb[1], bcx->nb[2], - 0 * chunk_size * ggml_element_size(bcx)); - auto * c = ggml_view_3d(ctx0, bcx, chunk_size, bcx->ne[1], bcx->ne[2], bcx->nb[1], bcx->nb[2], - 1 * chunk_size * ggml_element_size(bcx)); - auto * x = ggml_view_3d(ctx0, bcx, chunk_size, bcx->ne[1], bcx->ne[2], bcx->nb[1], bcx->nb[2], - 2 * chunk_size * ggml_element_size(bcx)); - - auto * bx = ggml_transpose(ctx0, ggml_mul(ctx0, b, x)); - - // read conv state - auto * conv_state = mctx_cur->get_r_l(il); - auto * conv_rs = build_rs(inp_recr, conv_state, hparams.n_embd_r(), n_seqs); - auto * conv = ggml_reshape_3d(ctx0, conv_rs, d_conv, hparams.n_embd, n_seqs); - - // causal prepends the state, non-causal pads symmetrically for a centered window - if (hparams.causal_attn) { - bx = ggml_concat(ctx0, conv, bx, 0); - } else { - const int64_t pad = (hparams.n_shortconv_l_cache - 1) / 2; - auto * left = ggml_cont(ctx0, - ggml_view_3d(ctx0, conv, pad, hparams.n_embd, n_seqs, conv->nb[1], conv->nb[2], (d_conv - pad) * conv->nb[0])); - bx = ggml_pad_ext(ctx0, ggml_concat(ctx0, left, bx, 0), 0, pad, 0, 0, 0, 0, 0, 0); - } - GGML_ASSERT(bx->ne[0] > conv->ne[0]); - - // write conv states: slot 0 = the final state, slot s = the state s tokens back (partial rollback) - const int64_t K = hparams.causal_attn && cparams.n_rs_seq > 0 ? (int64_t) cparams.n_rs_seq + 1 : 1; - const int64_t n_written = std::min(n_seq_tokens, K); - const auto mem_size = mctx_cur->get_size(); - const size_t row_size = ggml_row_size(conv_state->type, (int64_t) d_conv * n_embd); - - for (int64_t slot = 0; slot < n_written; ++slot) { - auto * conv_snap = ggml_view_3d(ctx0, bx, d_conv, bx->ne[1], bx->ne[2], bx->nb[1], bx->nb[2], - (bx->ne[0] - d_conv - slot) * ggml_element_size(bx)); - ggml_build_forward_expand(gf, ggml_cpy(ctx0, conv_snap, - ggml_view_2d(ctx0, conv_state, (int64_t) d_conv * n_embd, n_seqs, - conv_state->nb[1], - ((size_t) slot * mem_size + kv_head) * row_size))); - } - - auto * conv_kernel = model.layers[il].shortconv.conv; - auto * conv_out = ggml_ssm_conv(ctx0, bx, conv_kernel); - cb(conv_out, "model.layers.{}.conv.conv", il); - - auto * y = ggml_mul(ctx0, c, conv_out); - y = build_lora_mm(model.layers[il].shortconv.out_proj, y); - cb(y, "model.layers.{}.conv.out_proj", il); - // {n_embd, n_seq_tokens, n_seqs} => {n_embd, n_tokens} - y = ggml_reshape_2d(ctx0, y, y->ne[0], n_seq_tokens * n_seqs); - - return y; - }; - // actual graph construction starts here ggml_tensor * cur = build_inp_embd(model.tok_embd); cb(cur, "model.embed_tokens", -1); @@ -308,8 +208,12 @@ llama_model_lfm2::graph::graph(const llama_model & model, const llm_graph_ cur = build_norm(cur, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il); cb(cur, "model.layers.{}.operator_norm", il); - cur = hparams.is_recr(il) ? build_shortconv_block(cur, inp_hybrid->get_recr(), il) : - build_attn_block(cur, inp_pos, inp_hybrid->get_attn(), il); + if (hparams.is_recr(il)) { + const auto * mctx_recr = static_cast(mctx)->get_recr(); + cur = build_lfm_shortconv(model, mctx_recr, inp_hybrid->get_recr(), cur, il); + } else { + cur = build_attn_block(cur, inp_pos, inp_hybrid->get_attn(), il); + } if (il == n_layer - 1 && inp_out_ids) { cur = ggml_get_rows(ctx0, cur, inp_out_ids); @@ -322,7 +226,7 @@ llama_model_lfm2::graph::graph(const llama_model & model, const llm_graph_ cb(ffn_norm_out, "model.layers.{}.ffn_norm", il); ggml_tensor * ffn_out = - is_moe_layer ? build_moe_feed_forward(ffn_norm_out, il) : build_dense_feed_forward(ffn_norm_out, il); + is_moe_layer ? build_lfm_moe_ffn(model, ffn_norm_out, il) : build_lfm_dense_ffn(model, ffn_norm_out, il); cb(ffn_norm_out, "model.layers.{}.ffn_out", il); cur = ggml_add(ctx0, cur, ffn_out); diff --git a/src/models/models.h b/src/models/models.h index e6cf9d213cee..9aa450deabff 100644 --- a/src/models/models.h +++ b/src/models/models.h @@ -100,6 +100,26 @@ struct llm_build_delta_net_base : public llm_graph_context { int il); }; +struct llm_build_lfm_base : public llm_graph_context { + llm_build_lfm_base(const llm_graph_params & params); + + virtual ~llm_build_lfm_base() = default; + + ggml_tensor * build_lfm_shortconv(const llama_model & model, + const llama_memory_recurrent_context * mctx_recr, + llm_graph_input_rs * inp_recr, + ggml_tensor * cur, + int il); + + ggml_tensor * build_lfm_dense_ffn(const llama_model & model, + ggml_tensor * cur, + int il); + + ggml_tensor * build_lfm_moe_ffn(const llama_model & model, + ggml_tensor * cur, + int il); +}; + struct llm_build_rwkv6_base : public llm_graph_context { const llama_model & model; @@ -2264,7 +2284,7 @@ struct llama_model_lfm2 : public llama_model_base { void load_arch_tensors(llama_model_loader & ml) override; template - struct graph : public llm_graph_context { + struct graph : public llm_build_lfm_base { graph(const llama_model & model, const llm_graph_params & params); }; From 1242be66f65d5d8a3d996ba56d6de106ced98cf6 Mon Sep 17 00:00:00 2001 From: Xuan-Son Nguyen Date: Wed, 21 Oct 2026 00:04:15 +0200 Subject: [PATCH 32/69] server: add realtime transcription API (#30636) * server: add realtime transcription API * drop transcription_sessions * proxy ws * add docs * server: let the API key middleware guard the websocket httplib runs the pre-routing handler before the websocket upgrade, so the API key middleware answers 401 at handshake like any other route. This covers the router proxy, whose children run keyless, and accepts the same key forms as HTTP (Bearer, raw key, X-Api-Key). * server: keep realtime slots out of the prompt loop A realtime slot waiting for audio is GENERATING with nothing to decode, so it fell through the prompt loop and took slot_batched. A request on a higher slot then failed can_batch_with on the task type and starved for the whole session. Realtime slots have no prompt: the loop sets them up once and always leaves them. * server: start the realtime session once its slot is live The audio stream was created in the prompt loop, which skips a slot that cannot batch with the one generating, and session.created went out before the task was posted: early appends failed and the reader quit, leaving the socket open and the slot held until the HTTP timeout. The slot now sets up its stream at launch and signals it with the begin result, session.created waits for it, and a reader that stops closes the socket so the session ends with it. * server: name the committed item in input_audio_buffer.committed Per the Realtime spec, item_id is the item made from the committed audio and previous_item_id the one committed before it, null for the first. committed now comes before the transcript that completes that item, and both go out with the item switch under one lock, so a delta cannot land between them. * server: use the GA transcription session shape The session object follows the GA Realtime schema: type transcription, with the input format, transcription model, turn detection and noise reduction under audio.input. The rate stays the model sample rate, which is what the client must send for now. --------- Co-authored-by: Pascal --- tools/server/README.md | 27 +++ tools/server/server-context.cpp | 316 ++++++++++++++++++++++++++++++-- tools/server/server-context.h | 2 + tools/server/server-http.cpp | 51 ++++++ tools/server/server-http.h | 15 ++ tools/server/server-models.cpp | 93 ++++++++++ tools/server/server-models.h | 4 + tools/server/server-task.h | 16 +- tools/server/server.cpp | 2 + 9 files changed, 512 insertions(+), 14 deletions(-) diff --git a/tools/server/README.md b/tools/server/README.md index 8d9f535695f8..2977fcd4d9ab 100644 --- a/tools/server/README.md +++ b/tools/server/README.md @@ -1491,6 +1491,31 @@ Acts on an in-flight completion identified by its `id` (the `id` field streamed Returns a JSON object with a boolean `success` field, and an optional `message` field describing the reason when `success` is `false`. +### WebSocket `/v1/realtime`: Realtime transcription + +Transcribes streamed audio, for models with a streaming audio encoder. Compatible with OAI `/v1/realtime` schema. + +Audio must be PCM16, mono, at the model sample rate. The sample rate is returned in `session.created` as `audio.input.format.rate`. Each `input_audio_buffer.append` carries a base64-encoded chunk of PCM16 samples. + +*Client events:* + +- `session.update`: accepted for compatibility, the server replies with `session.updated` +- `input_audio_buffer.append`: `audio` holds base64 PCM16 samples +- `input_audio_buffer.commit`: flushes the audio received so far, so the words still held back by the model's delay are produced. The server replies with `input_audio_buffer.committed` and starts a new item. With `"final": true`, the input ends instead and the last item is completed once the stream drains. +- `input_audio_buffer.clear`: accepted for compatibility, audio already sent cannot be discarded + +Closing the websocket also ends the input. + +*Server events:* + +- `session.created` and `session.updated`: carry the `session` object +- `conversation.item.input_audio_transcription.delta`: `delta` holds the new text for `item_id` +- `conversation.item.input_audio_transcription.completed`: `transcript` holds the full text of `item_id` +- `input_audio_buffer.committed`: `item_id` of the committed item, `previous_item_id` of the item committed before it (`null` for the first one) +- `error`: `error.message` describes the problem + +Note that words held back by the model's delay at the time of a commit are attributed to the next item. + ### POST `/v1/responses`: OpenAI-compatible Responses API *Options:* @@ -1992,6 +2017,8 @@ For **GET** endpoints (`/props`, `/metrics`, etc.) The router uses the `model` q GET /props?model=ggml-org%2Fgemma-3-4b-it-GGUF%3AQ4_K_M ``` +For the **WebSocket** endpoint (`/v1/realtime`) The router also uses the `model` query parameter. + By default, the model will be loaded automatically if it's not loaded. To disable this, add `--no-models-autoload` when starting the server. Additionally, you can include `?autoload=true|false` in the query param to control this behavior per-request. ### GET `/models`: List available models diff --git a/tools/server/server-context.cpp b/tools/server/server-context.cpp index d66aae426e32..6d8f8e7beec3 100644 --- a/tools/server/server-context.cpp +++ b/tools/server/server-context.cpp @@ -1,4 +1,7 @@ #include "server-context.h" +#include +#include +#include "base64.hpp" #include "server-chat.h" #include "server-common.h" #include "server-decision.h" @@ -360,8 +363,13 @@ struct server_slot { // for TTS models, this is the embd generated from prev step, decode this to generate next hidden state // corresponding to one token position (size = n_embd) + // for realtime transcription, these are the rows from the last audio stream step (size = n_rows * n_embd) std::vector inp_embd; + // realtime transcription, see SERVER_TASK_TYPE_TRANSCRIBE_RT + mtmd::audio_stream_ptr audio_stream; + bool audio_wait = false; // the stream needs more audio before it can produce rows + server_slot_stats stats; // accepted tokens per draft position @@ -413,6 +421,9 @@ struct server_slot { // clear multimodal state mbatch.reset(); + audio_stream.reset(); + audio_wait = false; + inp_embd.clear(); } void init_sampler() const { @@ -542,16 +553,28 @@ struct server_slot { // add sampled token of this slot to the batch, optionally add the speculative draft tokens if any void handle_last_sampled_token(server_batch & batch) { bool add_ok = true; + // the audio stream has no token before the first step, track the position with a dummy token + const llama_token tok_track = sampled == LLAMA_TOKEN_NULL ? 0 : sampled; if (spec_draft.empty()) { // no speculative decoding i_batch = batch.size(); if (!inp_embd.empty()) { - // text embeddings broadcast the same position across the M-RoPE sections - const llama_pos p = prompt.tokens.pos_next(); - const llama_pos pos[GGML_MROPE_SECTIONS] = { p, p, p, 0 }; - GGML_ASSERT((int32_t) inp_embd.size() == batch.n_embd); - add_ok &= batch.add_embd(id, inp_embd.data(), pos, true, false); + const int32_t n_rows = (int32_t) inp_embd.size() / batch.n_embd; + GGML_ASSERT((int32_t) inp_embd.size() == n_rows * batch.n_embd); + i_batch = batch.size() + n_rows - 1; + for (int32_t i = 0; i < n_rows; i++) { + // text embeddings broadcast the same position across the M-RoPE sections + const llama_pos p = prompt.tokens.pos_next(); + const llama_pos pos[GGML_MROPE_SECTIONS] = { p, p, p, 0 }; + add_ok &= batch.add_embd(id, inp_embd.data() + (size_t) i * batch.n_embd, pos, i == n_rows - 1, false); + if (i < n_rows - 1) { + prompt.tokens.push_back(tok_track); + } + } + if (audio_stream) { + inp_embd.clear(); // rows are consumed, the next step produces new ones + } } else { add_ok &= batch.add(id, sampled, prompt.tokens.pos_next(), true, false); } @@ -579,7 +602,7 @@ struct server_slot { GGML_ASSERT(add_ok && "batch must be large enough to hold the sampled and draft tokens"); - prompt.tokens.push_back(sampled); + prompt.tokens.push_back(tok_track); prompt.tokens.insert(spec_draft); } @@ -595,7 +618,7 @@ struct server_slot { // do not keep context of the child slots - the parent's context is enough // nor of the stateless slots - no task ever reuses it - if (task->is_child() || task->is_stateless()) { + if (task->is_child() || task->is_stateless() || audio_stream) { prompt_clear(); } @@ -1967,6 +1990,23 @@ struct server_context_impl { ? SLOT_STATE_WAIT_OTHER // wait for the parent to process prompt : SLOT_STATE_STARTED; + // no prompt, the rows come from the audio stream, which audio controls reach from now on + if (slot.task->type == SERVER_TASK_TYPE_TRANSCRIBE_RT) { + slot.audio_stream.reset(mtmd_audio_stream_init(mctx, mtmd_audio_stream_params_default())); + if (!slot.audio_stream) { + send_error(slot, "failed to initialize the audio stream", ERROR_TYPE_SERVER); + slot.release(); + return false; + } + slot.prompt_clear(); + slot.init_sampler(); + slot.sampled = LLAMA_TOKEN_NULL; + slot.i_batch = -1; + slot.stats.update_prompt_start(); + slot.state = SLOT_STATE_GENERATING; + send_partial_response(slot, {}, false, true); + } + // reset server kill-switch counter n_empty_consecutive = 0; @@ -2829,6 +2869,7 @@ struct server_context_impl { switch (task.type) { case SERVER_TASK_TYPE_COMPLETION: case SERVER_TASK_TYPE_INFILL: + case SERVER_TASK_TYPE_TRANSCRIBE_RT: case SERVER_TASK_TYPE_EMBEDDING: case SERVER_TASK_TYPE_RERANK: case SERVER_TASK_TYPE_DECISION: @@ -2916,15 +2957,15 @@ struct server_context_impl { server_slot * slot = get_slot_by_cmpl_id(task.params.control_cmpl_id); if (slot == nullptr) { - SRV_WRN("control %s on unknown completion id=%s, no live slot\n", - task.params.control_action.c_str(), task.params.control_cmpl_id.c_str()); + SRV_WRN("control %d on unknown completion id=%s, no live slot\n", + (int) task.params.control_action, task.params.control_cmpl_id.c_str()); res->success = false; res->message = "no active completion for this id"; queue_results.send(std::move(res)); break; } - if (task.params.control_action == "reasoning_end") { + if (task.params.control_action == SERVER_CONTROL_ACTION_REASONING_END) { // the budget sampler only exists when reasoning control was armed if (!slot->task->params.sampling.reasoning_control) { res->success = false; @@ -2935,6 +2976,22 @@ struct server_context_impl { // act on the live slot mid generation, never defer common_sampler_reasoning_budget_force(slot->smpl.get()); res->success = true; + } else if (task.params.control_action == SERVER_CONTROL_ACTION_AUDIO_APPEND + || task.params.control_action == SERVER_CONTROL_ACTION_AUDIO_COMMIT + || task.params.control_action == SERVER_CONTROL_ACTION_AUDIO_END) { + if (!slot->audio_stream) { + res->success = false; + res->message = "not a realtime transcription slot"; + } else { + auto * stream = slot->audio_stream.get(); + if (task.params.control_action == SERVER_CONTROL_ACTION_AUDIO_APPEND) { + mtmd_audio_stream_push(stream, task.params.control_audio.data(), task.params.control_audio.size()); + } else { + mtmd_audio_stream_flush(stream, task.params.control_action == SERVER_CONTROL_ACTION_AUDIO_END); + } + slot->audio_wait = false; + res->success = true; + } } else { res->success = false; res->message = "unknown control action"; @@ -3254,6 +3311,39 @@ struct server_context_impl { }; #endif + // fill slot.inp_embd with the next rows of the audio stream + // returns false if the slot has nothing to decode now + bool slot_audio_step(server_slot & slot) { + if (!slot.inp_embd.empty()) { + return true; // rows from a previous step, not decoded yet + } + + const int32_t n = mtmd_audio_stream_step(slot.audio_stream.get(), slot.sampled); + if (n > 0) { + const float * rows = mtmd_audio_stream_get_output_embd(slot.audio_stream.get()); + slot.inp_embd.assign(rows, rows + (size_t) n * batch.n_embd); + slot.audio_wait = false; + return true; + } + if (n == 0) { + slot.audio_wait = true; + return false; + } + if (n == -2) { + send_error(slot, "audio stream error", ERROR_TYPE_SERVER); + slot.release(); + return false; + } + + // end of stream + slot.stop = STOP_TYPE_EOS; + slot.has_next_token = false; + slot.print_timings(); + send_final_response(slot); + slot.release(); + return false; + } + void update_slots() { #ifdef DEBUG_TIMINGS static int64_t t_prev = 0; @@ -3269,11 +3359,12 @@ struct server_context_impl { #endif // check if all slots are idle + // a realtime slot waiting for audio has no work, the next audio control task wakes the loop { bool all_idle = true; for (auto & slot : slots) { - if (slot.is_processing()) { + if (slot.is_processing() && !slot.audio_wait) { all_idle = false; break; } @@ -3448,6 +3539,10 @@ struct server_context_impl { return; } + if (slot.audio_stream && !slot_audio_step(slot)) { + return; // waiting for audio, or the stream ended + } + // check if we can batch this slot with the previous one if (!slot_batched) { slot_batched = &slot; @@ -3601,6 +3696,11 @@ struct server_context_impl { return; } + // no prompt, the rows come from the audio stream + if (slot.task->type == SERVER_TASK_TYPE_TRANSCRIBE_RT) { + return; + } + // this slot still has a prompt to be processed if (slot.state == SLOT_STATE_PROCESSING_PROMPT || slot.state == SLOT_STATE_STARTED) { const auto & input_tokens = slot.task->tokens; @@ -4831,6 +4931,7 @@ server_context_meta server_context::get_meta() const { /* has_inp_image */ impl->chat_params.allow_image, /* has_inp_audio */ impl->chat_params.allow_audio, /* has_inp_video */ impl->chat_params.allow_video, + /* has_audio_stream */ impl->mctx != nullptr && mtmd_support_audio_stream(impl->mctx), /* json_ui_settings */ impl->json_ui_settings, /* slot_n_ctx */ impl->n_ctx_slot(), /* pooling_type */ llama_pooling_type(impl->ctx_tgt), @@ -5611,7 +5712,7 @@ void server_routes::init_routes() { server_task task(SERVER_TASK_TYPE_CONTROL); task.id = rd.get_new_id(); task.params.control_cmpl_id = cmpl_id; - task.params.control_action = action; + task.params.control_action = SERVER_CONTROL_ACTION_REASONING_END; rd.post_task(std::move(task)); } @@ -5686,6 +5787,197 @@ void server_routes::init_routes() { session); }; + // realtime transcription over websocket, modeled after the OpenAI realtime API + // audio is PCM16 mono at the model sample rate, sent as base64 in input_audio_buffer.append events + // input_audio_buffer.commit with "final": true ends the input, closing the socket also ends it + this->ws_realtime = [this](const server_http_req &, server_http_context::ws_conn & conn) { + auto send_event = [&conn](json ev) { + ev["event_id"] = "event_" + random_string(); + conn.send(safe_json_to_str(ev)); + }; + auto send_error_event = [&send_event](const std::string & message, const std::string & type) { + send_event(json { + {"type", "error"}, + {"error", { + {"type", type}, + {"code", nullptr}, + {"message", message}, + {"param", nullptr}, + }}, + }); + }; + + if (!meta->has_audio_stream) { + send_error_event("The current model does not support realtime transcription.", "invalid_request_error"); + conn.close(); + return; + } + + const std::string session_id = "sess_" + random_string(); + const json session_config = json { + {"id", session_id}, + {"object", "realtime.transcription_session"}, + {"type", "transcription"}, + {"audio", {{"input", { + {"format", {{"type", "audio/pcm"}, {"rate", mtmd_get_audio_sample_rate(ctx_server.mctx)}}}, + {"transcription", {{"model", meta->model_name}}}, + {"turn_detection", nullptr}, + {"noise_reduction", nullptr}, + }}}}, + }; + + auto res = create_response(); + auto & rd = res->rd; + { + server_task task(SERVER_TASK_TYPE_TRANSCRIBE_RT); + task.id = rd.get_new_id(); + task.params.sampling = params.sampling; + task.params.stream = true; + task.params.oaicompat_cmpl_id = session_id; + task.params.oaicompat_model = meta->model_name; + rd.post_task(std::move(task)); + } + + // the slot answers once its audio stream is live, the session starts there + { + auto result = rd.next([&conn]() { return !conn.is_open(); }); + if (!result || result->is_error()) { + if (result) { + send_error_event(json_value(result->to_json(), "message", std::string("server error")), "server_error"); + } + conn.close(); + return; + } + } + send_event(json {{"type", "session.created"}, {"session", session_config}}); + + // one item per committed audio buffer, deltas are attributed to the current item + std::mutex mutex_item; + std::string item_id = "item_" + random_string(); + std::string item_prev; // the committed item before item_id, empty for the first one + std::string item_text; + + // called with mutex_item held + auto send_completed = [&]() { + send_event(json { + {"type", "conversation.item.input_audio_transcription.completed"}, + {"item_id", item_id}, + {"content_index", 0}, + {"transcript", item_text}, + }); + item_text.clear(); + }; + + auto post_control = [&](server_control_action action, std::vector && audio) { + server_response_reader ctl(queue_tasks, queue_results, HTTP_POLLING_SECONDS); + server_task task(SERVER_TASK_TYPE_CONTROL); + task.id = ctl.get_new_id(); + task.params.control_action = action; + task.params.control_cmpl_id = session_id; + task.params.control_audio = std::move(audio); + ctl.post_task(std::move(task)); + auto result = ctl.next([&conn]() { return !conn.is_open(); }); + auto * control = result ? dynamic_cast(result.get()) : nullptr; + return control && control->success; + }; + + // client events are read on this thread, the transcript is sent from the thread below + std::atomic input_ended{false}; + std::thread reader([&]() { + std::string msg; + while (conn.read(msg) != 0) { + json ev; + try { + ev = json::parse(msg); + } catch (const std::exception &) { + send_error_event("invalid JSON", "invalid_request_error"); + continue; + } + const std::string type = json_value(ev, "type", std::string()); + if (type == "session.update" || type == "transcription_session.update") { + send_event(json {{"type", "session.updated"}, {"session", session_config}}); + } else if (type == "input_audio_buffer.append") { + std::string pcm; + try { + pcm = base64::decode(json_value(ev, "audio", std::string())); + } catch (const std::exception &) { + send_error_event("invalid base64 audio", "invalid_request_error"); + continue; + } + std::vector samples(pcm.size() / 2); + for (size_t i = 0; i < samples.size(); i++) { + int16_t v; + memcpy(&v, pcm.data() + 2 * i, sizeof(v)); + samples[i] = v / 32768.0f; + } + if (!post_control(SERVER_CONTROL_ACTION_AUDIO_APPEND, std::move(samples))) { + break; + } + } else if (type == "input_audio_buffer.commit") { + // "final": true ends the input, the last item is completed when the stream drains + if (json_value(ev, "final", false)) { + input_ended = true; + post_control(SERVER_CONTROL_ACTION_AUDIO_END, {}); + continue; + } + if (!post_control(SERVER_CONTROL_ACTION_AUDIO_COMMIT, {})) { + break; + } + // the current item is the committed one, its transcript completes it and the next item opens + std::lock_guard lock(mutex_item); + send_event(json { + {"type", "input_audio_buffer.committed"}, + {"previous_item_id", item_prev.empty() ? json(nullptr) : json(item_prev)}, + {"item_id", item_id}, + }); + send_completed(); + item_prev = item_id; + item_id = "item_" + random_string(); + } else if (type == "input_audio_buffer.clear") { + send_event(json {{"type", "input_audio_buffer.cleared"}}); + } else { + send_error_event("unknown event type: " + type, "invalid_request_error"); + } + } + if (!input_ended.exchange(true)) { + post_control(SERVER_CONTROL_ACTION_AUDIO_END, {}); + } + // the result loop waits on the socket, nobody reads it from here on + conn.close(); + }); + + while (true) { + auto result = rd.next([&conn]() { return !conn.is_open(); }); + if (!result) { + break; // connection closed + } + if (result->is_error()) { + send_error_event(json_value(result->to_json(), "message", std::string("server error")), "server_error"); + break; + } + if (auto * partial = dynamic_cast(result.get())) { + if (!partial->content.text.empty()) { + std::lock_guard lock(mutex_item); + item_text += partial->content.text; + send_event(json { + {"type", "conversation.item.input_audio_transcription.delta"}, + {"item_id", item_id}, + {"content_index", 0}, + {"delta", partial->content.text}, + }); + } + } else if (result->is_stop()) { + std::lock_guard lock(mutex_item); + send_completed(); + break; + } + } + + input_ended = true; + conn.close(); + reader.join(); + }; + this->post_anthropic_messages = [this](const server_http_req & req) { auto res = create_response(); std::vector files; diff --git a/tools/server/server-context.h b/tools/server/server-context.h index ed23f7d4e6ce..da75e532718f 100644 --- a/tools/server/server-context.h +++ b/tools/server/server-context.h @@ -24,6 +24,7 @@ struct server_context_meta { bool has_inp_image; bool has_inp_audio; bool has_inp_video; + bool has_audio_stream; // realtime transcription, see mtmd_support_audio_stream() json json_ui_settings; int slot_n_ctx; enum llama_pooling_type pooling_type; @@ -144,6 +145,7 @@ struct server_routes { server_http_context::handler_t post_responses_oai; server_http_context::handler_t post_responses_tok_oai; server_http_context::handler_t post_transcriptions_oai; + server_http_context::ws_handler_t ws_realtime; server_http_context::handler_t post_anthropic_messages; server_http_context::handler_t post_anthropic_count_tokens; server_http_context::handler_t post_apply_template; diff --git a/tools/server/server-http.cpp b/tools/server/server-http.cpp index 33cbb156c559..cf147c7e70f4 100644 --- a/tools/server/server-http.cpp +++ b/tools/server/server-http.cpp @@ -11,6 +11,7 @@ #include #include #include +#include // // HTTP implementation using cpp-httplib @@ -744,6 +745,56 @@ void server_http_context::post(const std::string & path, const server_http_conte } } +void server_http_context::ws(const std::string & path, const server_http_context::ws_handler_t & handler) const { + const std::string full_path = path_prefix + path; + + auto callback = [handler](const httplib::Request & req, httplib::ws::WebSocket & sock) { + struct conn_impl : server_http_context::ws_conn { + httplib::ws::WebSocket & sock; + std::mutex mutex_send; + conn_impl(httplib::ws::WebSocket & sock) : sock(sock) {} + int read(std::string & msg) override { + switch (sock.read(msg)) { + case httplib::ws::Text: return 1; + case httplib::ws::Binary: return 2; + default: return 0; + } + } + bool send(const std::string & msg) override { + std::lock_guard lock(mutex_send); + return sock.send(msg); + } + bool send_binary(const std::string & msg) override { + std::lock_guard lock(mutex_send); + return sock.send(msg.data(), msg.size()); + } + bool is_open() const override { + return sock.is_open(); + } + void close() override { + sock.close(); + } + }; + conn_impl conn(sock); + const std::function should_stop = [&sock]() { + return !sock.is_open(); + }; + server_http_req request{ + get_params(req), + get_headers(req), + req.path, + build_query_string(req), + req.body, + {}, + should_stop + }; + handler(request, conn); + }; + for (const auto & srv : pimpl->servers) { + srv->WebSocket(full_path, callback); + } +} + void server_http_context::del(const std::string & path, const server_http_context::handler_t & handler) const { handlers.emplace(path, handler); auto callback = [handler](const httplib::Request & req, httplib::Response & res) { diff --git a/tools/server/server-http.h b/tools/server/server-http.h index 63fd2c186b6a..d123535e87d4 100644 --- a/tools/server/server-http.h +++ b/tools/server/server-http.h @@ -74,6 +74,20 @@ struct server_http_context { using handler_t = std::function; mutable std::unordered_map handlers; + // websocket connection, see ws() + struct ws_conn { + // blocking read, returns 1 for a text message, 2 for a binary message, 0 when the connection is closed + virtual int read(std::string & msg) = 0; + // thread-safe, can be called while another thread is in read() + virtual bool send(const std::string & msg) = 0; + virtual bool send_binary(const std::string & msg) = 0; + virtual bool is_open() const = 0; + virtual void close() = 0; + virtual ~ws_conn() = default; + }; + // the handler runs on its own HTTP thread for the whole lifetime of the connection + using ws_handler_t = std::function; + std::string path_prefix; int port = 9931; bool is_ssl = false; @@ -89,6 +103,7 @@ struct server_http_context { void get(const std::string & path, const handler_t & handler) const; void post(const std::string & path, const handler_t & handler) const; void del(const std::string & path, const handler_t & handler) const; + void ws(const std::string & path, const ws_handler_t & handler) const; // Register the Google Cloud Platform (Vertex AI) compat (AIP_PREDICT_ROUTE env var, or /predict) // Must be called AFTER all other API routes are registered diff --git a/tools/server/server-models.cpp b/tools/server/server-models.cpp index 95cbdd77a8bc..c8e235129db9 100644 --- a/tools/server/server-models.cpp +++ b/tools/server/server-models.cpp @@ -1899,6 +1899,81 @@ server_http_res_ptr server_models::proxy_request(const server_http_req & req, co return proxy; } +void server_models::proxy_ws(const server_http_req & req, server_http_context::ws_conn & conn, const std::string & name) { + auto send_error = [&conn](const std::string & message) { + conn.send(safe_json_to_str(json { + {"type", "error"}, + {"error", {{"type", "server_error"}, {"code", nullptr}, {"message", message}, {"param", nullptr}}}, + })); + conn.close(); + }; + + auto meta = get_meta(name); + if (!meta.has_value() || !meta->is_running()) { + send_error("model name=" + name + " is not running"); + return; + } + { + std::unique_lock lk(mutex); + mapping[name].meta.last_used = ggml_time_ms(); + mapping[name].req_count++; + } + SRV_INF("proxying websocket to model %s on port %d\n", name.c_str(), meta->port); + + std::string url = std::string("ws://") + CHILD_ADDR + ":" + std::to_string(meta->port) + req.path; + if (!req.query_string.empty()) { + url += '?' + req.query_string; + } + // the client library adds its own upgrade headers + httplib::Headers headers; + for (const auto & [key, value] : req.headers) { + std::string lower = key; + std::transform(lower.begin(), lower.end(), lower.begin(), ::tolower); + if (lower == "host" || lower == "connection" || lower == "upgrade" || lower.compare(0, 14, "sec-websocket-") == 0) { + continue; + } + headers.emplace(key, value); + } + + { + httplib::ws::WebSocketClient upstream(url, headers); + if (!upstream.connect()) { + send_error("failed to connect to model name=" + name); + } else { + // client to child + std::thread pump([&]() { + std::string msg; + while (true) { + const int res = conn.read(msg); + if (res == 0 || !(res == 1 ? upstream.send(msg) : upstream.send(msg.data(), msg.size()))) { + break; + } + } + upstream.close(); + }); + // child to client + std::string msg; + while (true) { + const auto res = upstream.read(msg); + if (res == httplib::ws::Fail || !(res == httplib::ws::Text ? conn.send(msg) : conn.send_binary(msg))) { + break; + } + } + conn.close(); + pump.join(); + } + } + + std::unique_lock lk(mutex); + auto it = mapping.find(name); + if (it != mapping.end() && it->second.req_count > 0) { + it->second.req_count--; + if (it->second.req_count == 0) { + sched->tick(lk); + } + } +} + void server_models::handle_child_state(const std::string & name, const std::string & raw_input) { server_state state; json payload; @@ -2277,6 +2352,24 @@ void server_models_routes::init_routes() { return models.proxy_request(req, method, name, false); }; + this->proxy_ws = [this](const server_http_req & req, server_http_context::ws_conn & conn) { + std::string name = req.get_param("model"); + bool autoload = is_autoload(params, req); + auto error_res = std::make_unique(); + if (!router_validate_model(name, models, autoload, error_res)) { + // error_res holds {"error": {...}}, send it as a websocket error event + json err = json::parse(error_res->data); + err["type"] = "error"; + conn.send(safe_json_to_str(err)); + conn.close(); + return; + } + if (autoload) { + models.ensure_model_ready(name, req.should_stop); + } + models.proxy_ws(req, conn, name); + }; + this->proxy_post = [this](const server_http_req & req) { std::string method = "POST"; json body = json::parse(req.body); diff --git a/tools/server/server-models.h b/tools/server/server-models.h index b04fc7384d9f..500ce9fb3256 100644 --- a/tools/server/server-models.h +++ b/tools/server/server-models.h @@ -366,6 +366,8 @@ struct server_models { // proxy an HTTP request to the model instance server_http_res_ptr proxy_request(const server_http_req & req, const std::string & method, const std::string & name, bool update_last_used, bool detached = false); + // forward a websocket connection to the child, returns when either side closes + void proxy_ws(const server_http_req & req, server_http_context::ws_conn & conn, const std::string & name); // handle message sent from server_child::notify_to_router() // raw input must starts with CMD_CHILD_TO_ROUTER_STATE, followed by a JSON string @@ -433,6 +435,8 @@ struct server_models_routes { server_http_context::handler_t get_router_props; server_http_context::handler_t proxy_get; server_http_context::handler_t proxy_post; + server_http_context::ws_handler_t proxy_ws; + server_http_context::handler_t get_router_models; server_http_context::handler_t post_router_models_load; server_http_context::handler_t post_router_models_unload; diff --git a/tools/server/server-task.h b/tools/server/server-task.h index e7bb043988d7..de39ce3b830c 100644 --- a/tools/server/server-task.h +++ b/tools/server/server-task.h @@ -18,6 +18,7 @@ enum server_task_type { SERVER_TASK_TYPE_RERANK, SERVER_TASK_TYPE_DECISION, SERVER_TASK_TYPE_INFILL, + SERVER_TASK_TYPE_TRANSCRIBE_RT, SERVER_TASK_TYPE_CANCEL, SERVER_TASK_TYPE_CONTROL, SERVER_TASK_TYPE_NEXT_RESPONSE, @@ -48,6 +49,14 @@ enum stop_type { STOP_TYPE_LIMIT, }; +enum server_control_action { + SERVER_CONTROL_ACTION_NONE, + SERVER_CONTROL_ACTION_REASONING_END, + SERVER_CONTROL_ACTION_AUDIO_APPEND, // push control_audio into the slot's audio stream + SERVER_CONTROL_ACTION_AUDIO_COMMIT, // flush the audio stream, more audio can follow + SERVER_CONTROL_ACTION_AUDIO_END, // flush the audio stream and end it +}; + struct task_params { bool stream = false; bool include_usage = false; @@ -86,8 +95,9 @@ struct task_params { std::string oaicompat_cmpl_id; // realtime control (SERVER_TASK_TYPE_CONTROL) - std::string control_action; - std::string control_cmpl_id; + server_control_action control_action = SERVER_CONTROL_ACTION_NONE; + std::string control_cmpl_id; + std::vector control_audio; // PCM F32 samples for SERVER_CONTROL_ACTION_AUDIO_APPEND // reported in generation_settings, parsing itself is owned by the chat session common_chat_format chat_format = COMMON_CHAT_FORMAT_CONTENT_ONLY; @@ -233,6 +243,7 @@ struct server_task { switch (type) { case SERVER_TASK_TYPE_COMPLETION: case SERVER_TASK_TYPE_INFILL: + case SERVER_TASK_TYPE_TRANSCRIBE_RT: return true; case SERVER_TASK_TYPE_DECISION: return !decision.labels.empty(); @@ -245,6 +256,7 @@ struct server_task { switch (type) { case SERVER_TASK_TYPE_COMPLETION: case SERVER_TASK_TYPE_INFILL: + case SERVER_TASK_TYPE_TRANSCRIBE_RT: return true; default: return false; diff --git a/tools/server/server.cpp b/tools/server/server.cpp index b168241076c7..41f29ded5d19 100644 --- a/tools/server/server.cpp +++ b/tools/server/server.cpp @@ -247,6 +247,7 @@ int llama_server(common_params & params, int argc, char ** argv, server_child & routes.post_control = models_routes->proxy_post; routes.post_responses_oai = models_routes->proxy_post; routes.post_transcriptions_oai = models_routes->proxy_post; + routes.ws_realtime = models_routes->proxy_ws; routes.post_anthropic_messages = models_routes->proxy_post; routes.post_anthropic_count_tokens = models_routes->proxy_post; routes.post_infill = models_routes->proxy_post; @@ -292,6 +293,7 @@ int llama_server(common_params & params, int argc, char ** argv, server_child & ctx_http.post("/responses", ex_wrapper(routes.post_responses_oai)); ctx_http.post("/v1/audio/transcriptions", ex_wrapper(routes.post_transcriptions_oai)); ctx_http.post("/audio/transcriptions", ex_wrapper(routes.post_transcriptions_oai)); + ctx_http.ws ("/v1/realtime", routes.ws_realtime); ctx_http.post("/v1/messages", ex_wrapper(routes.post_anthropic_messages)); // anthropic messages API ctx_http.post("/infill", ex_wrapper(routes.post_infill)); ctx_http.post("/embedding", ex_wrapper(routes.post_embeddings)); // legacy From 5c0b49a23b46e20b2c03f48d2241ee1d6781f0d8 Mon Sep 17 00:00:00 2001 From: Xuan-Son Nguyen Date: Wed, 21 Oct 2026 01:19:42 +0200 Subject: [PATCH 33/69] server : add POST /tts endpoint for TTS models (#26603) * mtmd: add audio out stream api * add /tts endpoint * add docs * wire up mtmd_helper_model_can_chat * upload speaker_ref via form-data * expose seed to api * fix header for pcm * wip * fix penalty_last_n * fix server_output_limits * fix pocket-tts stream * wip * add server_schema for tts * add server routing * server: coerce seed and stream from multipart form fields Form fields arrive as strings, so -F seed=42 and -F stream=true were rejected by the schema. seed joins the integer fields, parsed as a long long so a full uint32 seed fits, and stream is read as a boolean when it comes as a string. --------- Co-authored-by: Pascal --- common/common.cpp | 3 + common/common.h | 1 + tools/mtmd/clip.cpp | 5 +- tools/mtmd/clip.h | 1 + tools/mtmd/mtmd-helper-gen.cpp | 177 ++++++++++++---- tools/mtmd/mtmd-helper.h | 47 ++++- tools/mtmd/mtmd.cpp | 3 +- tools/mtmd/mtmd.h | 1 + tools/server/README.md | 49 ++++- tools/server/server-common.cpp | 25 ++- tools/server/server-common.h | 5 +- tools/server/server-context.cpp | 355 +++++++++++++++++++++++++++++++- tools/server/server-context.h | 3 + tools/server/server-models.cpp | 20 +- tools/server/server-schema.cpp | 52 +++++ tools/server/server-schema.h | 8 + tools/server/server-task.cpp | 11 + tools/server/server-task.h | 16 ++ tools/server/server.cpp | 2 + tools/tts/README.md | 2 + tools/tts/tts.cpp | 6 +- 21 files changed, 724 insertions(+), 68 deletions(-) diff --git a/common/common.cpp b/common/common.cpp index b0ddfcb915ed..6ef3b785e0e8 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -1235,6 +1235,9 @@ common_gguf_info common_get_gguf_info(const std::string & fname) { info.n_ctx_train = gguf_get_val_u32(gguf_ctx.get(), ctx_id); } + // same key as llama_model_chat_template(model, nullptr) + info.has_chat_tmpl = gguf_find_key(gguf_ctx.get(), "tokenizer.chat_template") >= 0; + return info; } diff --git a/common/common.h b/common/common.h index b089abd7e63e..70d6a671f512 100644 --- a/common/common.h +++ b/common/common.h @@ -985,6 +985,7 @@ common_decision_type common_get_decision_type(const struct llama_model * model); struct common_gguf_info { common_decision_type decision_type = COMMON_DECISION_TYPE_UNKNOWN; // UNKNOWN if the file is missing, unreadable, or invalid uint32_t n_ctx_train = 0; // 0 if unknown + bool has_chat_tmpl = false; // has the default tokenizer.chat_template }; common_gguf_info common_get_gguf_info(const std::string & fname); diff --git a/tools/mtmd/clip.cpp b/tools/mtmd/clip.cpp index df61f82f0059..e4750e52eca9 100644 --- a/tools/mtmd/clip.cpp +++ b/tools/mtmd/clip.cpp @@ -4278,8 +4278,9 @@ struct clip_init_result clip_init(const char * fname, struct clip_context_params struct clip_cap clip_get_cap(const char * fname) { clip_cap res; clip_model_loader loader(fname, /* skip_tensors= */ true); - res.has_vision = loader.has_vision; - res.has_audio = loader.has_audio; + res.has_vision = loader.has_vision; + res.has_audio = loader.has_audio; + res.has_gen_audio = loader.has_gen_audio; return res; } diff --git a/tools/mtmd/clip.h b/tools/mtmd/clip.h index 19db4760825d..cdbaeae0d59a 100644 --- a/tools/mtmd/clip.h +++ b/tools/mtmd/clip.h @@ -148,5 +148,6 @@ std::map clip_get_mem_usage(const struct clip_ctx * struct clip_cap { bool has_vision; bool has_audio; + bool has_gen_audio; }; struct clip_cap clip_get_cap(const char * fname); diff --git a/tools/mtmd/mtmd-helper-gen.cpp b/tools/mtmd/mtmd-helper-gen.cpp index 5fb7ea9a0de8..47493d9069b8 100644 --- a/tools/mtmd/mtmd-helper-gen.cpp +++ b/tools/mtmd/mtmd-helper-gen.cpp @@ -50,29 +50,38 @@ static llama_token find_special_token(const llama_vocab * vocab, const std::stri return LLAMA_TOKEN_NULL; } +static void put_bytes(std::vector & buf, const void * p, size_t n) { + const char * c = (const char *) p; + buf.insert(buf.end(), c, c + n); +} + +// data_sz == UINT32_MAX writes the "unknown length" sentinel (streaming), same as ffmpeg does on a pipe +static void write_wav16_header(std::vector & buf, uint32_t data_sz, int32_t rate) { + const uint32_t riff_sz = data_sz == UINT32_MAX ? UINT32_MAX : 36 + data_sz; + const uint32_t fmt_sz = 16, byte_rate = (uint32_t) rate * 2; + const uint16_t fmt = 1, ch = 1, align = 2, bits = 16; + const uint32_t rate32 = (uint32_t) rate; + put_bytes(buf, "RIFF", 4); put_bytes(buf, &riff_sz, 4); put_bytes(buf, "WAVE", 4); + put_bytes(buf, "fmt ", 4); put_bytes(buf, &fmt_sz, 4); + put_bytes(buf, &fmt, 2); put_bytes(buf, &ch, 2); put_bytes(buf, &rate32, 4); + put_bytes(buf, &byte_rate, 4); put_bytes(buf, &align, 2); put_bytes(buf, &bits, 2); + put_bytes(buf, "data", 4); put_bytes(buf, &data_sz, 4); +} + +static void append_wav16_pcm(std::vector & buf, const float * pcm, size_t n) { + for (size_t i = 0; i < n; i++) { + int16_t s = (int16_t) (std::max(-1.0f, std::min(1.0f, pcm[i])) * 32767.0f); + put_bytes(buf, &s, 2); + } +} + static bool write_wav16(std::vector & buf, const std::vector & pcm, int32_t rate) { // RIFF chunk sizes are 32-bit; refuse to emit a file with a truncated header if (pcm.size() > ((size_t) UINT32_MAX - 36) / 2) { return false; } - const uint32_t data_sz = (uint32_t) (pcm.size() * 2); - const uint32_t riff_sz = 36 + data_sz; - const uint32_t fmt_sz = 16, byte_rate = (uint32_t) rate * 2; - const uint16_t fmt = 1, ch = 1, align = 2, bits = 16; - const uint32_t rate32 = (uint32_t) rate; - auto put = [&](const void * p, size_t n) { - const char * c = (const char *) p; - buf.insert(buf.end(), c, c + n); - }; - put("RIFF", 4); put(&riff_sz, 4); put("WAVE", 4); - put("fmt ", 4); put(&fmt_sz, 4); - put(&fmt, 2); put(&ch, 2); put(&rate32, 4); - put(&byte_rate, 4); put(&align, 2); put(&bits, 2); - put("data", 4); put(&data_sz, 4); - for (float v : pcm) { - int16_t s = (int16_t) (std::max(-1.0f, std::min(1.0f, v)) * 32767.0f); - put(&s, 2); - } + write_wav16_header(buf, (uint32_t) (pcm.size() * 2), rate); + append_wav16_pcm(buf, pcm.data(), pcm.size()); return true; } @@ -92,6 +101,8 @@ class mtmd_gen_audio_pipeline { // set out_stop on end-of-speech, h_state_out must be null if no frame is generated virtual int32_t step_gen(llama_token sampled, const float * h_state_in, const float ** h_state_out, bool * out_stop) = 0; virtual int32_t get_output(int32_t * out_sample_rate, const char ** out_data, size_t * out_data_len, int64_t * out_n_samples) = 0; + // forces any buffered codes through code2wav now, regardless of window_frames + virtual int32_t flush() = 0; protected: llama_context * lctx; @@ -121,6 +132,9 @@ class qwen3tts_gen_audio_pipeline : public mtmd_gen_audio_pipeline { prompt_batch.reset(); n_prompt = 0; prompt_pos = 0; + stream = false; + pcm_sent = 0; + wav_header_sent = false; } int32_t set_input(const mtmd_helper_gen_audio_inp * inp) override { @@ -208,6 +222,7 @@ class qwen3tts_gen_audio_pipeline : public mtmd_gen_audio_pipeline { top_p = inp->top_p > 0 ? inp->top_p : def.top_p; seed = inp->seed; out_type = inp->out_type; + stream = inp->stream; // the prompt above holds the whole text stream up to tts_eos, so every generated // frame adds tts_pad on top of the codes embedding @@ -301,31 +316,60 @@ class qwen3tts_gen_audio_pipeline : public mtmd_gen_audio_pipeline { } int32_t get_output(int32_t * out_sample_rate, const char ** out_data, size_t * out_data_len, int64_t * out_n_samples) override { - if (!flush_gen_wav()) { - return 1; + *out_sample_rate = info.sample_rate; + + if (!stream) { + // one-shot call: force out whatever's left, regardless of window_frames + if (!flush_gen_wav()) { + return 1; + } + if (out_n_samples) { + *out_n_samples = (int64_t) audio_pcm.size(); + } + if (out_type == MTMD_HELPER_GEN_AUDIO_OUTTYPE_PCM) { + *out_data = (const char *) audio_pcm.data(); + *out_data_len = audio_pcm.size() * sizeof(float); + return 0; + } + out_buf.clear(); + if (!write_wav16(out_buf, audio_pcm, info.sample_rate)) { + LOG_ERR("mtmd_helper_gen_audio: output too large for WAV\n"); + return 1; + } + *out_data = out_buf.data(); + *out_data_len = out_buf.size(); + return 0; } - *out_sample_rate = info.sample_rate; + // streaming: only return audio produced since the previous call + const size_t n_new = audio_pcm.size() - pcm_sent; if (out_n_samples) { - *out_n_samples = (int64_t) audio_pcm.size(); + *out_n_samples = (int64_t) n_new; } if (out_type == MTMD_HELPER_GEN_AUDIO_OUTTYPE_PCM) { - *out_data = (const char *) audio_pcm.data(); - *out_data_len = audio_pcm.size() * sizeof(float); + *out_data = (const char *) (audio_pcm.data() + pcm_sent); + *out_data_len = n_new * sizeof(float); + pcm_sent = audio_pcm.size(); return 0; } out_buf.clear(); - if (!write_wav16(out_buf, audio_pcm, info.sample_rate)) { - LOG_ERR("mtmd_helper_gen_audio: output too large for WAV\n"); - return 1; + if (!wav_header_sent) { + write_wav16_header(out_buf, UINT32_MAX, info.sample_rate); + wav_header_sent = true; } + append_wav16_pcm(out_buf, audio_pcm.data() + pcm_sent, n_new); + pcm_sent = audio_pcm.size(); *out_data = out_buf.data(); *out_data_len = out_buf.size(); return 0; } + int32_t flush() override { + return flush_gen_wav() ? 0 : 1; + } + private: bool ensure_cache() { if (specials_ok) { @@ -369,7 +413,7 @@ class qwen3tts_gen_audio_pipeline : public mtmd_gen_audio_pipeline { LOG_ERR("mtmd_helper_gen_audio: mmproj has no speaker/audio encoder\n"); return false; } - const std::string marker = mtmd_default_marker(); + const std::string marker = mtmd_get_marker(mctx); mtmd_input_text text{ marker.c_str(), marker.size(), false, true }; mtmd_input_chunks * chunks = mtmd_input_chunks_init(); const mtmd_bitmap * bptr = bitmap; @@ -455,6 +499,9 @@ class qwen3tts_gen_audio_pipeline : public mtmd_gen_audio_pipeline { std::vector h_state_buf; mtmd_helper_gen_audio_outtype out_type = MTMD_HELPER_GEN_AUDIO_OUTTYPE_WAV; std::vector out_buf; + bool stream = false; + size_t pcm_sent = 0; // samples already returned by get_output() + bool wav_header_sent = false; }; // settings that only live in the reference's per-pack yaml, not in the checkpoint @@ -506,6 +553,9 @@ class pockettts_gen_audio_pipeline : public mtmd_gen_audio_pipeline { chunk_idx = 0; n_voice_pos = 0; chunk_budget = 0; + stream = false; + pcm_sent = 0; + wav_header_sent = false; } int32_t set_input(const mtmd_helper_gen_audio_inp * inp) override { @@ -575,6 +625,7 @@ class pockettts_gen_audio_pipeline : public mtmd_gen_audio_pipeline { seed = inp->seed; out_type = inp->out_type; + stream = inp->stream; return 0; } @@ -662,31 +713,60 @@ class pockettts_gen_audio_pipeline : public mtmd_gen_audio_pipeline { } int32_t get_output(int32_t * out_sample_rate, const char ** out_data, size_t * out_data_len, int64_t * out_n_samples) override { - if (!flush_gen_wav()) { - return 1; + *out_sample_rate = info.sample_rate; + + if (!stream) { + // one-shot call: force out whatever's left, regardless of window_frames + if (!flush_gen_wav()) { + return 1; + } + if (out_n_samples) { + *out_n_samples = (int64_t) audio_pcm.size(); + } + if (out_type == MTMD_HELPER_GEN_AUDIO_OUTTYPE_PCM) { + *out_data = (const char *) audio_pcm.data(); + *out_data_len = audio_pcm.size() * sizeof(float); + return 0; + } + out_buf.clear(); + if (!write_wav16(out_buf, audio_pcm, info.sample_rate)) { + LOG_ERR("mtmd_helper_gen_audio: output too large for WAV\n"); + return 1; + } + *out_data = out_buf.data(); + *out_data_len = out_buf.size(); + return 0; } - *out_sample_rate = info.sample_rate; + // streaming: only return audio produced since the previous call + const size_t n_new = audio_pcm.size() - pcm_sent; if (out_n_samples) { - *out_n_samples = (int64_t) audio_pcm.size(); + *out_n_samples = (int64_t) n_new; } if (out_type == MTMD_HELPER_GEN_AUDIO_OUTTYPE_PCM) { - *out_data = (const char *) audio_pcm.data(); - *out_data_len = audio_pcm.size() * sizeof(float); + *out_data = (const char *) (audio_pcm.data() + pcm_sent); + *out_data_len = n_new * sizeof(float); + pcm_sent = audio_pcm.size(); return 0; } out_buf.clear(); - if (!write_wav16(out_buf, audio_pcm, info.sample_rate)) { - LOG_ERR("mtmd_helper_gen_audio: output too large for WAV\n"); - return 1; + if (!wav_header_sent) { + write_wav16_header(out_buf, UINT32_MAX, info.sample_rate); + wav_header_sent = true; } + append_wav16_pcm(out_buf, audio_pcm.data() + pcm_sent, n_new); + pcm_sent = audio_pcm.size(); *out_data = out_buf.data(); *out_data_len = out_buf.size(); return 0; } + int32_t flush() override { + return flush_gen_wav() ? 0 : 1; + } + private: bool ensure_cache() { if (specials_ok) { @@ -907,7 +987,7 @@ class pockettts_gen_audio_pipeline : public mtmd_gen_audio_pipeline { LOG_ERR("mtmd_helper_gen_audio: mmproj has no voice encoder\n"); return false; } - const std::string marker = mtmd_default_marker(); + const std::string marker = mtmd_get_marker(mctx); mtmd_input_text text{ marker.c_str(), marker.size(), false, true }; mtmd_input_chunks * chunks = mtmd_input_chunks_init(); const mtmd_bitmap * bptr = bitmap; @@ -989,6 +1069,9 @@ class pockettts_gen_audio_pipeline : public mtmd_gen_audio_pipeline { std::vector h_state_buf; mtmd_helper_gen_audio_outtype out_type = MTMD_HELPER_GEN_AUDIO_OUTTYPE_WAV; std::vector out_buf; + bool stream = false; + size_t pcm_sent = 0; // samples already returned by get_output() + bool wav_header_sent = false; }; static std::unique_ptr make_pipeline(llama_context * lctx, mtmd_context * mctx) { @@ -1017,11 +1100,20 @@ void mtmd_helper_gen_audio_free(mtmd_helper_gen_audio * ctx) { } void mtmd_helper_gen_audio_reset(mtmd_helper_gen_audio * ctx) { - if (ctx->pipeline) { + if (ctx && ctx->pipeline) { ctx->pipeline->reset(); } } +struct mtmd_helper_gen_audio_inp mtmd_helper_gen_audio_inp_default(void) { + mtmd_helper_gen_audio_inp inp{}; + inp.top_k = 50; + inp.top_p = 1.0f; + inp.seed = UINT32_MAX; // random + inp.out_type = MTMD_HELPER_GEN_AUDIO_OUTTYPE_WAV; + return inp; +} + int32_t mtmd_helper_gen_audio_set_input(mtmd_helper_gen_audio * ctx, const mtmd_helper_gen_audio_inp * inp) { if (!ctx->pipeline) { LOG_ERR("mtmd_helper_gen_audio: unsupported or missing gen-audio pipeline\n"); @@ -1058,3 +1150,10 @@ int32_t mtmd_helper_gen_audio_get_output(mtmd_helper_gen_audio * ctx, int32_t * } return ctx->pipeline->get_output(out_sample_rate, out_data, out_data_len, out_n_samples); } + +int32_t mtmd_helper_gen_audio_flush(mtmd_helper_gen_audio * ctx) { + if (!ctx->pipeline) { + return 1; + } + return ctx->pipeline->flush(); +} diff --git a/tools/mtmd/mtmd-helper.h b/tools/mtmd/mtmd-helper.h index 7436230f0dca..acbfefcbaee1 100644 --- a/tools/mtmd/mtmd-helper.h +++ b/tools/mtmd/mtmd-helper.h @@ -204,6 +204,7 @@ enum mtmd_helper_gen_audio_outtype { MTMD_HELPER_GEN_AUDIO_OUTTYPE_WAV, // WAV PCM 16-bit LE, mono }; struct mtmd_helper_gen_audio_inp { + bool stream; // if true, output() must be called after each step_gen() llama_seq_id seq_id; const char * prompt; @@ -219,6 +220,8 @@ struct mtmd_helper_gen_audio_inp { enum mtmd_helper_gen_audio_outtype out_type; }; +MTMD_API struct mtmd_helper_gen_audio_inp mtmd_helper_gen_audio_inp_default(void); + MTMD_API mtmd_helper_gen_audio * mtmd_helper_gen_audio_init( struct llama_context * lctx, struct mtmd_context * mctx); @@ -250,6 +253,8 @@ MTMD_API int32_t mtmd_helper_gen_audio_step_gen( // out_data valid until next get_output() or reset() call // out_n_samples (optional, can be NULL) receives the number of generated PCM samples +// if inp->stream is true: returns only audio produced since the previous call, and +// *out_data_len == 0 whenever a full window_frames batch hasn't accumulated yet MTMD_API int32_t mtmd_helper_gen_audio_get_output( mtmd_helper_gen_audio * ctx, int32_t * out_sample_rate, @@ -257,6 +262,10 @@ MTMD_API int32_t mtmd_helper_gen_audio_get_output( size_t * out_data_len, int64_t * out_n_samples); +// forces any buffered codes through code2wav now, regardless of window_frames; +// call once when generation has ended, before the last get_output() in stream mode +MTMD_API int32_t mtmd_helper_gen_audio_flush(mtmd_helper_gen_audio * ctx); + #ifdef __cplusplus } // extern "C" #endif @@ -283,8 +292,41 @@ struct mtmd_helper_gen_audio_deleter { }; using gen_audio_ptr = std::unique_ptr; struct gen_audio { + + // sub-struct, RAII wrapper for mtmd_helper_gen_audio_inp + struct inp { + mtmd_helper_gen_audio_inp data = mtmd_helper_gen_audio_inp_default(); + std::string prompt_str; + std::string lang_str; + mtmd::bitmap_ptr speaker_ref_ptr; + + inp() = default; + inp(inp &&) = default; + inp & operator=(inp &&) = default; + inp(const inp &) = delete; + inp & operator=(const inp &) = delete; + + void set_prompt (std::string p) { prompt_str = std::move(p); } + void set_lang (std::string l) { lang_str = std::move(l); } + void set_speaker_ref(mtmd::bitmap_ptr bmp) { speaker_ref_ptr = std::move(bmp); } + + // pointers are only valid as long as *this is alive + const mtmd_helper_gen_audio_inp * get() { + data.prompt = prompt_str.c_str(); + data.prompt_len = prompt_str.size(); + data.lang = lang_str.empty() ? nullptr : lang_str.c_str(); + data.speaker_ref = speaker_ref_ptr.get(); + return &data; + } + }; + gen_audio_ptr ctx; - gen_audio(struct llama_context * lctx, struct mtmd_context * mctx) : ctx(mtmd_helper_gen_audio_init(lctx, mctx)) {} + void init(struct llama_context * lctx, struct mtmd_context * mctx) { + ctx.reset(mtmd_helper_gen_audio_init(lctx, mctx)); + } + bool valid() const { + return ctx.get() != nullptr; + } void reset() { mtmd_helper_gen_audio_reset(ctx.get()); } @@ -300,6 +342,9 @@ struct gen_audio { int32_t get_output(int32_t * out_sample_rate, const char ** out_data, size_t * out_data_len, int64_t * out_n_samples = nullptr) { return mtmd_helper_gen_audio_get_output(ctx.get(), out_sample_rate, out_data, out_data_len, out_n_samples); } + int32_t flush() { + return mtmd_helper_gen_audio_flush(ctx.get()); + } }; } // namespace mtmd_helper diff --git a/tools/mtmd/mtmd.cpp b/tools/mtmd/mtmd.cpp index b3ac1979ec73..ffd35b2bb3c5 100644 --- a/tools/mtmd/mtmd.cpp +++ b/tools/mtmd/mtmd.cpp @@ -2846,10 +2846,11 @@ struct mtmd_caps mtmd_get_cap_from_file(const char * fname) { mtmd_caps cap; cap.inp_audio = tmp.has_audio; cap.inp_vision = tmp.has_vision; + cap.gen_audio = tmp.has_gen_audio; return cap; } catch (const std::exception & e) { LOG_ERR("%s: failed to get capabilities from file '%s': %s\n", __func__, fname, e.what()); - return mtmd_caps{ false, false }; + return mtmd_caps{ false, false, false }; } } diff --git a/tools/mtmd/mtmd.h b/tools/mtmd/mtmd.h index df24c56568f7..c740f1e9fd2f 100644 --- a/tools/mtmd/mtmd.h +++ b/tools/mtmd/mtmd.h @@ -360,6 +360,7 @@ MTMD_API void mtmd_log_set(ggml_log_callback log_callback, void * user_data); struct mtmd_caps { bool inp_vision; bool inp_audio; + bool gen_audio; }; MTMD_API struct mtmd_caps mtmd_get_cap_from_file(const char * mmproj_fname); diff --git a/tools/server/README.md b/tools/server/README.md index 2977fcd4d9ab..2886bca15877 100644 --- a/tools/server/README.md +++ b/tools/server/README.md @@ -802,6 +802,50 @@ curl http://127.0.0.1:8012/v1/rerank \ }' | jq ``` +### POST `/tts`: Generate speech audio from text + +Returns raw audio bytes (`audio/wav` by default) rather than JSON. For more info, see [tts/README.md](../tts/README.md) + +*Options:* + +`input`: The text to speak (alias: `prompt`). + +`lang`: Language code for the utterance (model-dependent, e.g. `en`, `zh`). Optional. + +`speaker_ref_b64`: Base64-encoded reference audio to clone the speaker's voice. Optional. Alternatively, upload the reference audio as a `speaker_ref` file field via `multipart/form-data` (see example below) - if both are provided, the uploaded file takes precedence. + +`top_k`, `top_p`: Sampling params for the acoustic code predictor. Optional, model-dependent defaults apply. + +`repeat_penalty`: Repetition penalty applied to the backbone sampler over the whole generation (default `1.05`). Without this, the backbone can loop and re-generate the same utterance. + +`seed`: RNG seed for both the backbone sampler and the codec/vocoder (`-1` = random, the default). Set to a fixed value for reproducible output. + +`n_predict`: Max number of audio frames to generate. Defaults to `512`; generation normally stops earlier once the model emits an end-of-speech token. + +`response_format`: `wav` (default) or `pcm` (raw `float32` little-endian mono samples, no header; the response `Content-Type` carries the sample rate, e.g. `audio/pcm;rate=24000;encoding=float;bits=32`). + +`stream`: If `true`, the response is streamed as audio becomes available instead of waiting for the full generation to finish. WAV streaming writes an RFC-noncompliant header with an unknown (`0xFFFFFFFF`) size field, since the final length isn't known up front; most players and decoders (ffmpeg, VLC, ...) handle this by reading until EOF. + +Note: it's highly recommended to always provide a speaker reference voice; otherwise, the model's performance may be degraded. + +*Examples:* + +```shell +curl -X POST http://127.0.0.1:9931/tts \ + -H "Content-Type: application/json" \ + -d '{"input": "Hello, this is a test."}' \ + -o output.wav +``` + +With a speaker reference uploaded as a file (`multipart/form-data`), instead of base64-encoding it into the JSON body: + +```shell +curl -X POST http://127.0.0.1:9931/tts \ + -F "input=Hello, this is a test." \ + -F "speaker_ref=@/path/to/speaker-reference.wav;type=audio/wav" \ + -o output.wav +``` + ### POST `/infill`: For code infilling. Takes a prefix and a suffix and returns the predicted completion as stream. @@ -1262,11 +1306,12 @@ Each object in `data` has an `architecture` object. It has two string arrays: - `input_modalities` lists what the model can read. It always has `text`, plus each media type that the model supports. - `output_modalities` lists what the model can produce. -One output value is special: +Some output values are special: | Value | Meaning | |---|---| | `decisions` | The model is a native decision model. Serve it with [`/v1/systemone`](#post-v1systemone-typesafe-compatible-system-one-api). | +| `audio` | The model can generate speech. Serve it with [`/tts`](#post-tts-generate-speech-audio-from-text). A TTS-only model has `["audio"]` without `text`. | A language model that classifies with prompts does not get `decisions`. Only native decision models do. @@ -2054,7 +2099,7 @@ Note: - If a model is not running, it will be added or updated according to the source 2. When the model is loaded, the info from `/v1/models` is forwarded to router's `/v1/models`. This includes metadata about the model and the runtime instance. -Each object in `data` has the same `architecture` object as [`GET /v1/models`](#get-v1models-openai-compatible-model-info-api) of a direct server. The server computes both arrays offline. It does not load the model, download files, or run inference. `output_modalities` comes from the GGUF metadata. `input_modalities` comes from the projector file. A native decision model shows `decisions` before its first load, after unload, and while it sleeps: +Each object in `data` has the same `architecture` object as [`GET /v1/models`](#get-v1models-openai-compatible-model-info-api) of a direct server. The server computes both arrays offline. It does not load the model, download files, or run inference. `output_modalities` comes from the GGUF metadata, plus `audio` when the projector file can generate speech. `input_modalities` comes from the projector file. A native decision model shows `decisions` before its first load, after unload, and while it sleeps: ```json { diff --git a/tools/server/server-common.cpp b/tools/server/server-common.cpp index 075687602716..baaca58eb30b 100644 --- a/tools/server/server-common.cpp +++ b/tools/server/server-common.cpp @@ -202,7 +202,8 @@ const char * get_media_marker() { // model output modalities // -std::vector server_model_output_modalities(common_decision_type decision_type) { +std::vector server_model_output_modalities(common_decision_type decision_type, bool has_cap_chat, bool has_cap_tts) { + std::vector out; switch (decision_type) { case COMMON_DECISION_TYPE_OPENJEV: case COMMON_DECISION_TYPE_LEV: @@ -216,13 +217,27 @@ std::vector server_model_output_modalities(common_decision_type dec case COMMON_DECISION_TYPE_LFM2_D1: case COMMON_DECISION_TYPE_LFM2_D1_OMNI: case COMMON_DECISION_TYPE_H2O_LIGHTNING: - return {"decisions"}; + out = {"decisions"}; + break; case COMMON_DECISION_TYPE_GRANITE_SWITCH: - return {"text", "decisions"}; + out = {"text", "decisions"}; + break; default: - // fallback when there is no decision type or the metadata is bad - return {"text"}; + // a tts-only model cannot generate text + if (has_cap_chat || !has_cap_tts) { + // fallback when there is no decision type or the metadata is bad + out = {"text"}; + } + break; } + if (has_cap_tts) { + out.push_back("audio"); + } + return out; +} + +std::vector server_model_output_modalities(const llama_model * model, bool has_cap_chat, bool has_cap_tts) { + return server_model_output_modalities(common_get_decision_type(model), has_cap_chat, has_cap_tts); } json server_model_architecture_json( diff --git a/tools/server/server-common.h b/tools/server/server-common.h index 2777521221ae..538e008e94a6 100644 --- a/tools/server/server-common.h +++ b/tools/server/server-common.h @@ -114,7 +114,10 @@ const char * get_media_marker(); // // output modalities for architecture.output_modalities in GET /models -std::vector server_model_output_modalities(common_decision_type decision_type); +// has_cap_chat: false for a tts-only model, has_cap_tts: the model can generate audio +std::vector server_model_output_modalities(common_decision_type decision_type, bool has_cap_chat, bool has_cap_tts); +// same, with the decision type read from a loaded model +std::vector server_model_output_modalities(const llama_model * model, bool has_cap_chat, bool has_cap_tts); // architecture object of GET /models; shared by the direct server and the router json server_model_architecture_json( diff --git a/tools/server/server-context.cpp b/tools/server/server-context.cpp index 6d8f8e7beec3..b8e132d7bf9c 100644 --- a/tools/server/server-context.cpp +++ b/tools/server/server-context.cpp @@ -20,6 +20,7 @@ #include "speculative.h" #include "mtmd.h" #include "mtmd-helper.h" +#include "base64.hpp" #include #include @@ -43,6 +44,14 @@ constexpr int HTTP_POLLING_SECONDS = 1; static common_speculative_output_limits server_output_limits(const common_params & params) { + if (!params.mmproj.path.empty()) { + const auto mcaps = mtmd_get_cap_from_file(params.mmproj.path.c_str()); + if (mcaps.gen_audio) { + // some TTS models decode with embeddings output for the whole batch + return { params.n_batch, params.n_batch }; + } + } + if (params.embedding || (params.pooling_type != LLAMA_POOLING_TYPE_UNSPECIFIED && params.pooling_type != LLAMA_POOLING_TYPE_NONE)) { return { params.n_batch, 1 }; @@ -257,6 +266,27 @@ struct server_slot { mtmd_context * mctx = nullptr; mtmd::batch_ptr mbatch = nullptr; + struct tts_ctx { + mtmd_helper::gen_audio ctx; + std::vector h_state_prompt; // the first h_state after step_prompt done + const float * h_state; + llama_token sampled; + int32_t n_decoded; + bool stop; + bool is_supported() const { + return ctx.valid(); + } + void reset() { + ctx.reset(); + h_state_prompt.clear(); + h_state = nullptr; + sampled = LLAMA_TOKEN_NULL; + n_decoded = 0; + stop = false; + } + }; + tts_ctx tts; + // speculative decoding common_speculative * spec; @@ -421,6 +451,9 @@ struct server_slot { // clear multimodal state mbatch.reset(); + + tts.reset(); + audio_stream.reset(); audio_wait = false; inp_embd.clear(); @@ -622,6 +655,11 @@ struct server_slot { prompt_clear(); } + // mtmd_helper always re-eval the whole prompt + if (task->type == SERVER_TASK_TYPE_TTS) { + prompt_clear(); + } + callback_on_reset(*this); reset(); @@ -975,6 +1013,14 @@ struct server_context_impl { mtmd_helper_init_opt init_opt = mtmd_helper_init_opt_default(); const llama_vocab * vocab = nullptr; + bool has_cap_tts() const { + return mctx != nullptr && mtmd_gen_audio_get_info(mctx).type != MTMD_GEN_AUDIO_TYPE_NONE; + } + + bool has_cap_chat() const { + return mctx == nullptr || mtmd_helper_model_can_chat(ctx_tgt, mctx); + } + server_decision_context decision; server_queue queue_tasks; @@ -1463,6 +1509,10 @@ struct server_context_impl { slot.mctx = mctx; slot.prompt.tokens.has_mtmd = mctx != nullptr; + if (has_cap_tts()) { + slot.tts.ctx.init(ctx_tgt, mctx); + } + SLT_TRC(slot, "new slot, n_ctx = %d\n", slot.n_ctx); slot.callback_on_release = [this](int id_slot) { @@ -1944,6 +1994,20 @@ struct server_context_impl { SLT_DBG(slot, "launching slot : %s\n", safe_json_to_str(slot.to_json()).c_str()); + if (task.type == SERVER_TASK_TYPE_TTS) { + GGML_ASSERT(has_cap_tts()); // should already checked in route handler + if (!slot.tts.is_supported()) { + slot.tts.ctx.init(ctx_tgt, slot.mctx); + } + // mtmd_helper always re-eval the whole prompt + slot.prompt_clear(); + task.tts_inp.data.seq_id = slot.id; + if (slot.tts.ctx.set_input(task.tts_inp.get()) != 0) { + send_error(task, "failed to process TTS prompt", ERROR_TYPE_SERVER); + return false; + } + } + // initialize samplers if (task.need_sampling()) { try { @@ -1961,6 +2025,9 @@ struct server_context_impl { // TODO: getting pre sampling logits is not yet supported with backend sampling use_backend_sampling &= !need_pre_sample_logits; + // TODO: check verify if this actually works with TTS + use_backend_sampling &= task.type != SERVER_TASK_TYPE_TTS; + // TODO: tmp until backend sampling is fully implemented if (use_backend_sampling) { llama_set_sampler(ctx_tgt, slot.id, common_sampler_get(slot.smpl.get())); @@ -1986,9 +2053,13 @@ struct server_context_impl { slot.task = std::make_unique(std::move(task)); - slot.state = slot.task->is_child() - ? SLOT_STATE_WAIT_OTHER // wait for the parent to process prompt - : SLOT_STATE_STARTED; + if (slot.task->type == SERVER_TASK_TYPE_TTS) { + slot.state = SLOT_STATE_PROCESSING_PROMPT; + } else { + slot.state = slot.task->is_child() + ? SLOT_STATE_WAIT_OTHER // wait for the parent to process prompt + : SLOT_STATE_STARTED; + } // no prompt, the rows come from the audio stream, which audio controls reach from now on if (slot.task->type == SERVER_TASK_TYPE_TRANSCRIBE_RT) { @@ -2268,6 +2339,18 @@ struct server_context_impl { queue_results.send(std::move(res)); } + void send_tts_result(server_slot & slot, int32_t sample_rate, const char * data, size_t data_len, bool final) { + auto res = std::make_unique(); + + res->id = slot.task->id; + res->index = slot.task->index; + res->sample_rate = sample_rate; + res->audio.assign(data, data_len); + res->final = final; + + queue_results.send(std::move(res)); + } + void send_final_response(server_slot & slot) { auto res = std::make_unique(); @@ -2872,6 +2955,7 @@ struct server_context_impl { case SERVER_TASK_TYPE_TRANSCRIBE_RT: case SERVER_TASK_TYPE_EMBEDDING: case SERVER_TASK_TYPE_RERANK: + case SERVER_TASK_TYPE_TTS: case SERVER_TASK_TYPE_DECISION: { // special case: if input is provided via CLI, tokenize it first @@ -3397,6 +3481,14 @@ struct server_context_impl { return; } + // note: TTS slots bypass the shared batch entirely + try { + process_tts_slots(); + } catch (const std::exception & e) { + SRV_ERR("process_tts_slots() failed: %s\n", e.what()); + abort_all_slots("process_tts_slots() failed: " + std::string(e.what())); + } + GGML_ASSERT(batch.slot_batched || batch.size() == 0); if (batch.slot_batched) { @@ -3459,10 +3551,89 @@ struct server_context_impl { } } + void process_tts_slots() { + iterate(slots, [&](server_slot & slot) { + if (!slot.is_processing() || slot.task->type != SERVER_TASK_TYPE_TTS) { + return; + } + + llama_set_embeddings(ctx_tgt, true); + + if (slot.state == SLOT_STATE_PROCESSING_PROMPT) { + const int32_t ret = slot.tts.ctx.step_prompt(llama_n_batch(ctx_tgt)); + if (ret < 0) { + send_error(slot, "TTS prompt processing failed", ERROR_TYPE_SERVER); + slot.release(); + } else if (ret == 0) { + // done prompt, do sample and save h_state_prompt + slot.tts.sampled = common_sampler_sample(slot.smpl.get(), ctx_tgt, -1); + common_sampler_accept(slot.smpl.get(), slot.tts.sampled, true); + const float * h_embd = llama_get_embeddings_ith(ctx_tgt, -1); + slot.tts.h_state_prompt.assign(h_embd, h_embd + llama_model_n_embd(model_tgt)); + slot.tts.h_state = slot.tts.h_state_prompt.data(); + slot.state = SLOT_STATE_GENERATING; + } + return; + } + + const int32_t n_predict = slot.n_predict_max > 0 ? slot.n_predict_max : 512; + if (slot.tts.stop || slot.tts.n_decoded >= n_predict) { + int32_t sample_rate = 0; + const char * data = nullptr; + size_t data_len = 0; + // generation truly ends here: force out any sub-window remainder still buffered + if (slot.tts.ctx.flush() != 0 || slot.tts.ctx.get_output(&sample_rate, &data, &data_len) != 0) { + send_error(slot, "failed to finalize TTS output", ERROR_TYPE_SERVER); + } else { + send_tts_result(slot, sample_rate, data, data_len, true); + } + slot.release(); + return; + } + + const float * h_state_next = nullptr; + bool stop = false; + if (slot.tts.ctx.step_gen(slot.tts.sampled, slot.tts.h_state, &h_state_next, &stop) != 0) { + send_error(slot, "TTS generation failed", ERROR_TYPE_SERVER); + slot.release(); + return; + } + if (h_state_next == nullptr) { + // end-of-speech without a new frame (e.g. pocket-tts eos head) + slot.tts.stop = true; + return; + } + slot.tts.h_state = h_state_next; + slot.tts.n_decoded++; + slot.tts.stop = stop; + + if (!stop) { + slot.tts.sampled = common_sampler_sample(slot.smpl.get(), ctx_tgt, -1); + common_sampler_accept(slot.smpl.get(), slot.tts.sampled, true); + } + + if (slot.task->params.stream) { + int32_t sample_rate = 0; + const char * data = nullptr; + size_t data_len = 0; + if (slot.tts.ctx.get_output(&sample_rate, &data, &data_len) != 0) { + send_error(slot, "TTS streaming output failed", ERROR_TYPE_SERVER); + slot.release(); + } else if (data_len > 0) { + send_tts_result(slot, sample_rate, data, data_len, false); + } + } + }); + } + void pre_decode() { // apply context-shift if needed // TODO: simplify and improve iterate(slots, [&](server_slot & slot) { + if (slot.task && slot.task->type == SERVER_TASK_TYPE_TTS) { + // TTS slots drive their own decode loop in process_tts_slots(), never enter the shared batch + return; + } if (slot.state == SLOT_STATE_GENERATING && slot.prompt.n_tokens() + 1 >= slot.n_ctx) { if (!params_base.ctx_shift) { // this check is redundant (for good) @@ -3535,7 +3706,7 @@ struct server_context_impl { // determine which slots are generating and drafting iterate(slots, [&](server_slot & slot) { - if (slot.state != SLOT_STATE_GENERATING) { + if (slot.state != SLOT_STATE_GENERATING || slot.task->type == SERVER_TASK_TYPE_TTS) { return; } @@ -3681,7 +3852,7 @@ struct server_context_impl { return; // batch is full, skip remaining slots } - if (!slot.is_processing()) { + if (!slot.is_processing() || slot.task->type == SERVER_TASK_TYPE_TTS) { return; } @@ -4926,11 +5097,13 @@ server_context_meta server_context::get_meta() const { /* model_aliases */ impl->model_aliases, /* model_tags */ impl->model_tags, /* model_path */ impl->params_base.model.path, - /* model_output_modalities */ server_model_output_modalities(common_get_decision_type(impl->model_tgt)), + /* model_output_modalities */ server_model_output_modalities(impl->model_tgt, impl->has_cap_chat(), impl->has_cap_tts()), /* has_mtmd */ impl->mctx != nullptr, /* has_inp_image */ impl->chat_params.allow_image, /* has_inp_audio */ impl->chat_params.allow_audio, /* has_inp_video */ impl->chat_params.allow_video, + /* has_cap_chat */ impl->has_cap_chat(), + /* has_cap_tts */ impl->has_cap_tts(), /* has_audio_stream */ impl->mctx != nullptr && mtmd_support_audio_stream(impl->mctx), /* json_ui_settings */ impl->json_ui_settings, /* slot_n_ctx */ impl->n_ctx_slot(), @@ -5006,6 +5179,11 @@ std::unique_ptr server_routes::handle_completions_impl( res->set_req(&req); // will also set spipe if needed + if (!ctx_server.has_cap_chat()) { + res->error(format_error_response("this server does not support chat/completions", ERROR_TYPE_NOT_SUPPORTED)); + return res; + } + int32_t sse_ping_interval = params.sse_ping_interval; try { @@ -6175,6 +6353,171 @@ void server_routes::init_routes() { return res; }; + this->post_tts = [this](const server_http_req & req) { + auto res = create_response(); + res->set_req(&req); // will also set spipe if needed + + if (!ctx_server.has_cap_tts()) { + res->error(format_error_response("this server does not support audio generation", ERROR_TYPE_NOT_SUPPORTED)); + return res; + } + + const auto info = mtmd_gen_audio_get_info(ctx_server.mctx); + + json body = json::parse(req.body); + + // multipart form fields arrive as strings, correct the types of the numeric and boolean params + for (const char * k : { "top_k", "n_predict", "max_tokens", "seed" }) { + if (body.contains(k) && body[k].is_string()) { + body[k] = std::stoll(body[k].get()); + } + } + for (const char * k : { "top_p", "repeat_penalty" }) { + if (body.contains(k) && body[k].is_string()) { + body[k] = std::stof(body[k].get()); + } + } + if (body.contains("stream") && body["stream"].is_string()) { + body["stream"] = body["stream"] == "true"; + } + + std::string prompt = json_value(body, "input", json_value(body, "prompt", std::string())); + if (prompt.empty()) { + res->error(format_error_response("\"input\" must be a non-empty string", ERROR_TYPE_INVALID_REQUEST)); + return res; + } + + const std::string response_format = json_value(body, "response_format", std::string("wav")); + + server_task task(SERVER_TASK_TYPE_TTS); + server_schema::eval_tts_schema(params, task, body); + const bool stream = task.params.stream; + task.tts_inp.set_prompt(prompt); + task.tts_inp.set_lang(json_value(body, "lang", std::string())); + task.tts_inp.data.stream = stream; + task.tts_inp.data.out_type = response_format == "pcm" + ? MTMD_HELPER_GEN_AUDIO_OUTTYPE_PCM + : MTMD_HELPER_GEN_AUDIO_OUTTYPE_WAV; + // note: -1 is clamped to 0 by the sampler, it will disable the penalty + task.params.sampling.penalty_last_n = task.params.n_predict > 0 ? task.params.n_predict : 512; + task.params.sampling.seed = task.tts_inp.data.seed; // same seed for the codec/vocoder RNG + if (task.tts_inp.data.top_k > 0) { + task.params.sampling.top_k = task.tts_inp.data.top_k; + } + if (task.tts_inp.data.top_p > 0) { + task.params.sampling.top_p = task.tts_inp.data.top_p; + } + + // speaker reference: either an uploaded form file ("speaker_ref") or a base64 JSON field ("speaker_ref_b64") + const unsigned char * speaker_ref_data = nullptr; + size_t speaker_ref_len = 0; + std::string speaker_ref_b64_decoded; + + auto speaker_ref_file = req.files.find("speaker_ref"); + if (speaker_ref_file != req.files.end()) { + speaker_ref_data = speaker_ref_file->second.data.data(); + speaker_ref_len = speaker_ref_file->second.data.size(); + } else { + std::string speaker_ref_b64 = json_value(body, "speaker_ref_b64", std::string()); + if (!speaker_ref_b64.empty()) { + try { + speaker_ref_b64_decoded = base64::decode(speaker_ref_b64); + } catch (const std::exception &) { + res->error(format_error_response("\"speaker_ref_b64\" is not valid base64", ERROR_TYPE_INVALID_REQUEST)); + return res; + } + speaker_ref_data = (const unsigned char *) speaker_ref_b64_decoded.data(); + speaker_ref_len = speaker_ref_b64_decoded.size(); + } + } + + if (speaker_ref_len > 0) { + auto wrapper = mtmd_helper_bitmap_init_from_buf(ctx_server.mctx, speaker_ref_data, speaker_ref_len, false, ctx_server.init_opt); + if (!wrapper.bitmap) { + res->error(format_error_response("failed to decode \"speaker_ref\"", ERROR_TYPE_INVALID_REQUEST)); + return res; + } + task.tts_inp.set_speaker_ref(mtmd::bitmap_ptr(wrapper.bitmap)); + } else { + SRV_WRN("%s", "no speaker reference provided, the model may behave randomly\n"); + } + + auto & rd = res->rd; + task.id = rd.get_new_id(); + rd.post_task(std::move(task)); + + // raw float32 LE mono samples; audio/L16 would imply 16-bit big-endian (RFC 2586) + const std::string content_type = response_format == "pcm" + ? "audio/pcm;rate=" + std::to_string(info.sample_rate) + ";encoding=float;bits=32" + : "audio/wav"; + + if (!stream) { + auto result = rd.next(req.should_stop); + if (!result) { + GGML_ASSERT(req.should_stop()); + return res; // connection is closed + } + if (result->is_error()) { + res->error(result->to_json()); + return res; + } + auto * tts_res = dynamic_cast(result.get()); + GGML_ASSERT(tts_res != nullptr); + res->status = 200; + res->content_type = content_type; + res->data = std::move(tts_res->audio); + return res; + } else { + auto first_result = rd.next(req.should_stop); + if (!first_result) { + GGML_ASSERT(req.should_stop()); + return res; // connection is closed + } + if (first_result->is_error()) { + res->error(first_result->to_json()); + return res; + } + auto * first_tts_res = dynamic_cast(first_result.get()); + GGML_ASSERT(first_tts_res != nullptr); + + res->status = 200; + res->content_type = content_type; + res->data = std::move(first_tts_res->audio); + bool is_done = first_tts_res->final; + + res->set_next([res_this = res.get(), is_done](std::string & output) mutable -> bool { + // flush buffered audio before is_done - the first result can already be final + if (!res_this->data.empty()) { + output = std::move(res_this->data); + res_this->data.clear(); + return true; + } + if (is_done) { + return false; + } + if (res_this->should_stop()) { + return false; + } + + server_response_reader & rd = res_this->rd; + if (!rd.has_next()) { + return false; + } + auto result = rd.next([&res_this]() { return res_this->should_stop(); }); + if (!result || result->is_error()) { + return false; + } + auto * tts_res = dynamic_cast(result.get()); + GGML_ASSERT(tts_res != nullptr); + output = std::move(tts_res->audio); + is_done = tts_res->final; + return true; + }); + } + + return res; + }; + this->post_systemone = [this](const server_http_req & req) { auto res = create_response(); const auto & decision = ctx_server.decision; diff --git a/tools/server/server-context.h b/tools/server/server-context.h index da75e532718f..a579b0ecaddd 100644 --- a/tools/server/server-context.h +++ b/tools/server/server-context.h @@ -24,6 +24,8 @@ struct server_context_meta { bool has_inp_image; bool has_inp_audio; bool has_inp_video; + bool has_cap_chat; + bool has_cap_tts; bool has_audio_stream; // realtime transcription, see mtmd_support_audio_stream() json json_ui_settings; int slot_n_ctx; @@ -155,6 +157,7 @@ struct server_routes { server_http_context::handler_t post_embeddings; server_http_context::handler_t post_embeddings_oai; server_http_context::handler_t post_rerank; + server_http_context::handler_t post_tts; server_http_context::handler_t post_systemone; server_http_context::handler_t get_lora_adapters; server_http_context::handler_t post_lora_adapters; diff --git a/tools/server/server-models.cpp b/tools/server/server-models.cpp index c8e235129db9..d38549db5f0b 100644 --- a/tools/server/server-models.cpp +++ b/tools/server/server-models.cpp @@ -521,27 +521,31 @@ void server_model_meta::update_caps(const common_params & base) { return; } - // read the output modalities and the trained context from the GGUF metadata - std::vector output_modalities = {"text"}; - if (!params.model.path.empty()) { - const common_gguf_info info = common_get_gguf_info(params.model.path); - output_modalities = server_model_output_modalities(info.decision_type); - n_ctx_train = info.n_ctx_train; - } - bool inp_image = false; bool inp_audio = false; + bool gen_audio = false; try { if (!params.no_mmproj && !params.mmproj.path.empty()) { mtmd_caps caps = mtmd_get_cap_from_file(params.mmproj.path.c_str()); inp_image = caps.inp_vision; inp_audio = caps.inp_audio; + gen_audio = caps.gen_audio; } } catch (const std::exception & e) { LOG_WRN("failed to read the multimodal capabilities of '%s': %s\n", name.c_str(), e.what()); // keep the output modalities from the GGUF metadata } + // read the output modalities and the trained context from the GGUF metadata + std::vector output_modalities = {"text"}; + if (!params.model.path.empty()) { + const common_gguf_info info = common_get_gguf_info(params.model.path); + // same rule as mtmd_helper_model_can_chat() + const bool can_chat = !gen_audio || info.has_chat_tmpl; + output_modalities = server_model_output_modalities(info.decision_type, can_chat, gen_audio); + n_ctx_train = info.n_ctx_train; + } + // offline discovery cannot see video; a loaded model reports it architecture = server_model_architecture_json(inp_image, inp_audio, false, output_modalities); } diff --git a/tools/server/server-schema.cpp b/tools/server/server-schema.cpp index 54e126aa0c35..1e2dce8886dc 100644 --- a/tools/server/server-schema.cpp +++ b/tools/server/server-schema.cpp @@ -511,6 +511,58 @@ task_params eval_llama_cmpl_schema( return params; } +// +// TTS schema +// + +std::vector> make_tts_schema(server_task & task) { + std::vector> fields; + auto add = [&](field * f) { + fields.emplace_back(f); + }; + + add((new field_num("top_k", task.tts_inp.data.top_k)) + ->set_limits(0, INT32_MAX) + ->set_desc("Top-k for the acoustic code predictor, 0 to use the model default")); + + add((new field_num("top_p", task.tts_inp.data.top_p)) + ->set_limits(0.0f, 1.0f) + ->set_desc("Top-p for the acoustic code predictor, 0.0 to use the model default")); + + add((new field_num("seed", task.tts_inp.data.seed)) + ->set_desc("RNG seed for the backbone sampler and the codec/vocoder (-1 = random)")); + + add((new field_num("n_predict", task.params.n_predict)) + ->set_hard_limits(-1, INT32_MAX) + ->add_alias("max_tokens") + ->set_desc("Max number of audio frames to generate")); + + add((new field_num("repeat_penalty", task.params.sampling.penalty_repeat)) + ->set_desc("Repetition penalty applied to the backbone sampler over the whole generation")); + + add((new field_bool("stream", task.params.stream)) + ->set_desc("Stream the audio as it becomes available instead of waiting for the full generation")); + + return fields; +} + +void eval_tts_schema(const common_params & params_base, server_task & task, const json & data) { + // baseline defaults, individual requests can override them + // TODO @ngxson : change the default values based on model + task.params.sampling = params_base.sampling; + task.params.sampling.penalty_repeat = 1.05f; + task.params.n_predict = -1; + task.tts_inp.data.top_k = 0; + task.tts_inp.data.top_p = 0.0f; + task.tts_inp.data.seed = params_base.sampling.seed; + + field_eval_context ctx(task.params); + + for (const auto & f : make_tts_schema(task)) { + f->eval(ctx, data); + } +} + // // eval() implementations // diff --git a/tools/server/server-schema.h b/tools/server/server-schema.h index d0a81431bc6b..542136a97534 100644 --- a/tools/server/server-schema.h +++ b/tools/server/server-schema.h @@ -101,4 +101,12 @@ task_params eval_llama_cmpl_schema( const std::vector & logit_bias_eog, const json & data); +std::vector> make_tts_schema(server_task & task); + +// evaluates the /tts request params into task.params and task.tts_inp.data +void eval_tts_schema( + const common_params & params_base, + server_task & task, + const json & data); + } // namespace server_schema diff --git a/tools/server/server-task.cpp b/tools/server/server-task.cpp index 2e513a33ecaf..d7ac2a1dc3d4 100644 --- a/tools/server/server-task.cpp +++ b/tools/server/server-task.cpp @@ -1508,6 +1508,17 @@ json server_task_result_rerank::to_json() { }; } +// +// server_task_result_tts +// +json server_task_result_tts::to_json() { + return json { + {"sample_rate", sample_rate}, + {"n_bytes", audio.size()}, + {"final", final}, + }; +} + // // server_task_result_decision // diff --git a/tools/server/server-task.h b/tools/server/server-task.h index de39ce3b830c..810cefbfe9ea 100644 --- a/tools/server/server-task.h +++ b/tools/server/server-task.h @@ -10,6 +10,7 @@ // TODO: prevent including the whole server-common.h as we only use server_tokens #include "server-common.h" +#include "mtmd-helper.h" enum server_task_type { @@ -29,6 +30,7 @@ enum server_task_type { SERVER_TASK_TYPE_SLOT_ERASE, SERVER_TASK_TYPE_GET_LORA, SERVER_TASK_TYPE_SET_LORA, + SERVER_TASK_TYPE_TTS, }; // TODO: change this to more generic "response_format" to replace the "format_response_*" in server-common @@ -219,6 +221,9 @@ struct server_task { // used by SERVER_TASK_TYPE_SET_LORA std::map set_lora; // mapping adapter ID -> scale + // used by SERVER_TASK_TYPE_TTS + mtmd_helper::gen_audio::inp tts_inp; + server_task() = default; server_task(server_task_type type) : type(type) {} @@ -256,6 +261,7 @@ struct server_task { switch (type) { case SERVER_TASK_TYPE_COMPLETION: case SERVER_TASK_TYPE_INFILL: + case SERVER_TASK_TYPE_TTS: case SERVER_TASK_TYPE_TRANSCRIBE_RT: return true; default: @@ -526,6 +532,16 @@ struct server_task_result_embd : server_task_result { json to_json_oaicompat(); }; +struct server_task_result_tts : server_task_result { + std::string audio; // raw bytes for this chunk (WAV or PCM, per request's out_type) + int32_t sample_rate = 0; + bool final = false; // true for the last chunk of a request + + virtual bool is_stop() override { return final; } + + virtual json to_json() override; +}; + struct server_task_result_rerank : server_task_result { float score = -1e6; diff --git a/tools/server/server.cpp b/tools/server/server.cpp index 41f29ded5d19..ce44405b868e 100644 --- a/tools/server/server.cpp +++ b/tools/server/server.cpp @@ -247,6 +247,7 @@ int llama_server(common_params & params, int argc, char ** argv, server_child & routes.post_control = models_routes->proxy_post; routes.post_responses_oai = models_routes->proxy_post; routes.post_transcriptions_oai = models_routes->proxy_post; + routes.post_tts = models_routes->proxy_post; routes.ws_realtime = models_routes->proxy_ws; routes.post_anthropic_messages = models_routes->proxy_post; routes.post_anthropic_count_tokens = models_routes->proxy_post; @@ -293,6 +294,7 @@ int llama_server(common_params & params, int argc, char ** argv, server_child & ctx_http.post("/responses", ex_wrapper(routes.post_responses_oai)); ctx_http.post("/v1/audio/transcriptions", ex_wrapper(routes.post_transcriptions_oai)); ctx_http.post("/audio/transcriptions", ex_wrapper(routes.post_transcriptions_oai)); + ctx_http.post("/tts", ex_wrapper(routes.post_tts)); ctx_http.ws ("/v1/realtime", routes.ws_realtime); ctx_http.post("/v1/messages", ex_wrapper(routes.post_anthropic_messages)); // anthropic messages API ctx_http.post("/infill", ex_wrapper(routes.post_infill)); diff --git a/tools/tts/README.md b/tools/tts/README.md index 1b08d5ef3218..55b99932c627 100644 --- a/tools/tts/README.md +++ b/tools/tts/README.md @@ -12,6 +12,8 @@ Simple usage: llama-tts -hf ggml-org/Qwen3-TTS-12Hz-1.7B-Base-GGUF -p "Hello world" --output out.wav ``` +Note: it's highly recommended to always provide a speaker reference voice (via `--tts-speaker-file`); otherwise, the model's performance may be degraded. + Common params: - Sampling params such as `--top-k`, `--top-p`, `--temp`, etc. - `-n ` limits the output length, e.g. `-n 500`. Note that how many milliseconds each frame represents varies by model diff --git a/tools/tts/tts.cpp b/tools/tts/tts.cpp index 6fd1936324ce..aea794e65d60 100644 --- a/tools/tts/tts.cpp +++ b/tools/tts/tts.cpp @@ -111,8 +111,9 @@ int main(int argc, char ** argv) { speaker_bitmap.reset(wrapper.bitmap); } - mtmd_helper::gen_audio gen(lctx, mctx.get()); - mtmd_helper_gen_audio_inp inp{}; + mtmd_helper::gen_audio gen; + gen.init(lctx, mctx.get()); + mtmd_helper_gen_audio_inp inp = mtmd_helper_gen_audio_inp_default(); inp.seq_id = 0; inp.prompt = params.prompt.c_str(); inp.prompt_len = params.prompt.size(); @@ -121,7 +122,6 @@ int main(int argc, char ** argv) { inp.top_k = params.sampling.top_k; inp.top_p = params.sampling.top_p; inp.seed = params.sampling.seed; - inp.out_type = MTMD_HELPER_GEN_AUDIO_OUTTYPE_WAV; // // stage 1: process prompt via backbone model, generate semantic representation From 9a3b91c6801e68f412fcf1b6f8e153aa562b8536 Mon Sep 17 00:00:00 2001 From: Unbug Lee Date: Wed, 21 Oct 2026 07:19:54 +0800 Subject: [PATCH 34/69] server : fix usage.completion_tokens undercount with n > 1 (#29486) Assisted-by: Copilot --- tools/server/server-context.cpp | 10 ++++++++++ tools/server/tests/unit/test_chat_completion.py | 4 ++++ 2 files changed, 14 insertions(+) diff --git a/tools/server/server-context.cpp b/tools/server/server-context.cpp index b8e132d7bf9c..296893f7a145 100644 --- a/tools/server/server-context.cpp +++ b/tools/server/server-context.cpp @@ -5277,10 +5277,20 @@ std::unique_ptr server_routes::handle_completions_impl( res->ok(arr[0]); } else if (res_type == TASK_RESPONSE_TYPE_OAI_CHAT || res_type == TASK_RESPONSE_TYPE_OAI_CMPL) { // if multiple results in OAI format, we need to re-format them + json & usage = arr[0]["usage"]; json & choices = arr[0]["choices"]; for (size_t i = 1; i < arr.size(); i++) { choices.push_back(std::move(arr[i]["choices"][0])); } + + // OAI counts generated tokens across all choices + int64_t n_completion_tokens = 0; + for (auto & item : arr) { + n_completion_tokens += item["usage"]["completion_tokens"].get(); + } + usage["completion_tokens"] = n_completion_tokens; + usage["total_tokens"] = usage["prompt_tokens"].get() + n_completion_tokens; + res->ok(arr[0]); } else { // multi-results, non-OAI compat diff --git a/tools/server/tests/unit/test_chat_completion.py b/tools/server/tests/unit/test_chat_completion.py index 0258b539ed87..afbcef030664 100644 --- a/tools/server/tests/unit/test_chat_completion.py +++ b/tools/server/tests/unit/test_chat_completion.py @@ -587,6 +587,10 @@ def test_chat_completions_multiple_choices(): for choice in res.body["choices"]: assert "assistant" == choice["message"]["role"] assert choice["finish_reason"] == "length" + # each choice generates max_tokens tokens, usage counts them all + # ref: https://github.com/ggml-org/llama.cpp/issues/29451 + assert res.body["usage"]["completion_tokens"] == 16 + assert res.body["usage"]["total_tokens"] == res.body["usage"]["prompt_tokens"] + 16 def test_chat_completions_token_count(): From 339f3242c761f16c27e5ffcde1d35dcc31933a55 Mon Sep 17 00:00:00 2001 From: Ben Ashbaugh Date: Tue, 20 Oct 2026 17:19:08 -0700 Subject: [PATCH 35/69] opencl: fix rope program build error with newer Clang (#30893) Newer versions of Clang diagnose an incompatible pointer type error when assigning a pointer to float to a pointer to half. --- ggml/src/ggml-opencl/kernels/rope.cl | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/ggml/src/ggml-opencl/kernels/rope.cl b/ggml/src/ggml-opencl/kernels/rope.cl index 27fdbbbc4ff1..5b99d42de3ed 100644 --- a/ggml/src/ggml-opencl/kernels/rope.cl +++ b/ggml/src/ggml-opencl/kernels/rope.cl @@ -520,7 +520,7 @@ kernel void kernel_rope_multi_f16( src0 = (global void*)((global char*)src0 + offset0); src1 = (global int*)((global char*)src1 + offset1); src2 = (global float*)((global char*)src2 + offset2); - dst = (global float*)((global char*)dst + offsetd); + dst = (global half*)((global char*)dst + offsetd); int i3 = get_group_id(2); int i2 = get_group_id(1); @@ -714,7 +714,7 @@ kernel void kernel_rope_vision_f16( src0 = (global void*)((global char*)src0 + offset0); src1 = (global int*)((global char*)src1 + offset1); src2 = (global float*)((global char*)src2 + offset2); - dst = (global float*)((global char*)dst + offsetd); + dst = (global half*)((global char*)dst + offsetd); int i3 = get_group_id(2); int i2 = get_group_id(1); From 493253fe5a5af7e800b3c92a2452b5684a99e961 Mon Sep 17 00:00:00 2001 From: Anant Shrivastava Date: Wed, 21 Oct 2026 10:34:13 +0530 Subject: [PATCH 36/69] Sycl iq3 crash fix (#30604) --- ggml/src/ggml-sycl/convert.cpp | 6 ++- ggml/src/ggml-sycl/dequantize.hpp | 12 +++--- ggml/src/ggml-sycl/fused-gemm.cpp | 28 ++++++++----- ggml/src/ggml-sycl/fused-gemm.hpp | 2 +- ggml/src/ggml-sycl/getrows.cpp | 68 ++++++++++++++++++++++++++++++- ggml/src/ggml-sycl/ggml-sycl.cpp | 11 ++++- tests/test-backend-ops.cpp | 52 ++++++++++++++++++++++- 7 files changed, 154 insertions(+), 25 deletions(-) diff --git a/ggml/src/ggml-sycl/convert.cpp b/ggml/src/ggml-sycl/convert.cpp index 092967b6f62e..bc1b74f3497c 100644 --- a/ggml/src/ggml-sycl/convert.cpp +++ b/ggml/src/ggml-sycl/convert.cpp @@ -440,7 +440,8 @@ static void dequantize_row_iq3_xxs_sycl_reorder(const void * vx, dst_t * y, cons sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * sycl::range<3>(1, 1, 32), sycl::range<3>(1, 1, 32)), [=](sycl::nd_item<3> item_ct1) { dequantize_block_iq3_xxs_reorder( - vx, y, item_ct1, nb, iq3xxs_grid, ksigns_iq2xs, kmask_iq2xs); + vx, y + item_ct1.get_group(2) * QK_K, item_ct1.get_group(2), item_ct1, nb, + iq3xxs_grid, ksigns_iq2xs, kmask_iq2xs); }); } @@ -472,7 +473,8 @@ static void dequantize_row_iq3_s_sycl_reorder(const void * vx, dst_t * y, const stream->parallel_for( sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * sycl::range<3>(1, 1, 32), sycl::range<3>(1, 1, 32)), [=](sycl::nd_item<3> item_ct1) { - dequantize_block_iq3_s_reorder(vx, y, item_ct1, nb, kmask_iq2xs, iq3s_grid); + dequantize_block_iq3_s_reorder(vx, y + item_ct1.get_group(2) * QK_K, item_ct1.get_group(2), + item_ct1, nb, kmask_iq2xs, iq3s_grid); }); } diff --git a/ggml/src/ggml-sycl/dequantize.hpp b/ggml/src/ggml-sycl/dequantize.hpp index f00f97ad6639..40f5cd646014 100644 --- a/ggml/src/ggml-sycl/dequantize.hpp +++ b/ggml/src/ggml-sycl/dequantize.hpp @@ -1481,12 +1481,11 @@ static void dequantize_block_iq3_xxs(const void * __restrict__ vx, dst_t * __res } template -static void dequantize_block_iq3_xxs_reorder(const void * __restrict__ vx, dst_t * __restrict__ yy, +static void dequantize_block_iq3_xxs_reorder(const void * __restrict__ vx, dst_t * __restrict__ yy, const int64_t i, const sycl::nd_item<3> & item_ct1, const int64_t nb, const uint32_t * iq3xxs_grid, const uint8_t * ksigns_iq2xs, const uint8_t * kmask_iq2xs) { - const int64_t i = item_ct1.get_group(2); const int64_t tid = item_ct1.get_local_id(2); #if QK_K == 256 const int64_t il = tid / 8; @@ -1501,7 +1500,7 @@ static void dequantize_block_iq3_xxs_reorder(const void * __restrict__ vx, dst_t const uint32_t aux32 = gas[0] | (gas[1] << 16); const float dl = (float) d[i] * (0.5f + (aux32 >> 28)) * 0.5f; const uint8_t signs = ksigns_iq2xs[(aux32 >> (7 * il)) & 127]; - dst_t * y = yy + i * QK_K + 32 * ib + 8 * il; + dst_t * y = yy + 32 * ib + 8 * il; #pragma unroll for (int j = 0; j < 4; ++j) { y[j+0] = dl * grid1[j] * (signs & kmask_iq2xs[j+0] ? -1.f : 1.f); @@ -1510,6 +1509,7 @@ static void dequantize_block_iq3_xxs_reorder(const void * __restrict__ vx, dst_t #else GGML_UNUSED(vx); GGML_UNUSED(yy); + GGML_UNUSED(i); GGML_UNUSED(item_ct1); GGML_UNUSED(nb); GGML_UNUSED(iq3xxs_grid); @@ -1551,10 +1551,9 @@ dequantize_block_iq3_s(const void *__restrict__ vx, dst_t *__restrict__ yy, template __dpct_inline__ static void -dequantize_block_iq3_s_reorder(const void * __restrict__ vx, dst_t * __restrict__ yy, +dequantize_block_iq3_s_reorder(const void * __restrict__ vx, dst_t * __restrict__ yy, const int64_t i, const sycl::nd_item<3> & item_ct1, const int64_t nb, const uint8_t * kmask_iq2xs, const uint32_t * iq3s_grid) { - const int64_t i = item_ct1.get_group(2); const int64_t tid = item_ct1.get_local_id(2); #if QK_K == 256 const int64_t il = tid / 8; @@ -1574,7 +1573,7 @@ dequantize_block_iq3_s_reorder(const void * __restrict__ vx, dst_t * __restrict_ iq3s_grid + (qs_ib[2*il+1] | ((qh[ib] << (7 - 2*il)) & 256))); const float dl = (float) d * (1 + 2 * ((scales[ib/2] >> (4 * (ib%2))) & 0xf)); const uint8_t signs = signs_base[4 * ib + il]; - dst_t * y = yy + i * QK_K + 32 * ib + 8 * il; + dst_t * y = yy + 32 * ib + 8 * il; #pragma unroll for (int j = 0; j < 4; ++j) { y[j+0] = dl * grid1[j] * (signs & kmask_iq2xs[j+0] ? -1.f : 1.f); @@ -1583,6 +1582,7 @@ dequantize_block_iq3_s_reorder(const void * __restrict__ vx, dst_t * __restrict_ #else GGML_UNUSED(vx); GGML_UNUSED(yy); + GGML_UNUSED(i); GGML_UNUSED(item_ct1); GGML_UNUSED(nb); GGML_UNUSED(kmask_iq2xs); diff --git a/ggml/src/ggml-sycl/fused-gemm.cpp b/ggml/src/ggml-sycl/fused-gemm.cpp index 38c69bc6b1fd..3024f139d5dc 100644 --- a/ggml/src/ggml-sycl/fused-gemm.cpp +++ b/ggml/src/ggml-sycl/fused-gemm.cpp @@ -860,7 +860,7 @@ static void fused_dequant_gemm_tile( } } -template +template static void fused_dequant_gemm_launch(const void * src0, const typename S::tsb * packed, float * dst, const int M, const int N, const int Npad, const int K, const int ldd, const int64_t groups_n, const int64_t groups_m, dpct::queue_ptr stream) { @@ -871,8 +871,8 @@ static void fused_dequant_gemm_launch(const void * src0, const typename S::tsb * sycl::nd_range<2>(sycl::range<2>(groups_n, groups_m * S::WG_SIZE), sycl::range<2>(1, S::WG_SIZE)), [=](sycl::nd_item<2> item) [[sycl::reqd_sub_group_size(S::SG)]] { const int n0 = item.get_group(0) * S::BN; - fused_dequant_gemm_tile((const block_q_t *) src0, packed, dst, M, Npad, K, ldd, - n0, n0, N, tile_a, tile_c, item); + fused_dequant_gemm_tile((const block_q_t *) src0, packed, dst, M, Npad, K, + ldd, n0, n0, N, tile_a, tile_c, item); }); }); } @@ -974,7 +974,7 @@ template static bool fg_visit_type(ggml_type type, bool reordered, } template -static bool fg_fused_run(ggml_type src0_type, const void * src0, const void * src1, ggml_type src1_type, +static bool fg_fused_run(ggml_type src0_type, bool src0_reordered, const void * src0, const void * src1, ggml_type src1_type, float * dst, int64_t M, int64_t N, int64_t K, int64_t ldd, ggml_sycl_pool & pool, dpct::queue_ptr stream) { const int64_t groups_n = (N + S::BN - 1) / S::BN; @@ -992,21 +992,27 @@ static bool fg_fused_run(ggml_type src0_type, const void * src0, const void * sr } const typename S::tsb * packed = packed_b.get(); - return fg_visit_type(src0_type, false, [&](auto tag) { + return fg_visit_type(src0_type, src0_reordered, [&](auto tag) { using block_q_t = typename decltype(tag)::type; if constexpr (fg_plain_ok()) { - fused_dequant_gemm_launch(src0, packed, dst, (int) M, (int) N, Npad, (int) K, (int) ldd, - groups_n, groups_m, stream); + fused_dequant_gemm_launch(src0, packed, dst, (int) M, (int) N, + Npad, (int) K, (int) ldd, groups_n, + groups_m, stream); } }); } -bool ggml_sycl_fused_dequant_gemm(ggml_type src0_type, const void * src0, const void * src1, ggml_type src1_type, +bool ggml_sycl_fused_dequant_gemm(ggml_type src0_type, bool src0_reordered, const void * src0, const void * src1, ggml_type src1_type, int32_t src1_prec, float * dst, int64_t M, int64_t N, int64_t K, int64_t ldd, ggml_sycl_pool & pool, dpct::queue_ptr stream) { - // every BN columns dequantize A again, so wide N is left to the library GEMM + // every BN columns dequantize A again, so wide N is left to the library GEMM. + // src0_reordered: the persistent weight reorder may have been installed on this + // tensor by an earlier small-batch mul_mat; the A stage then has to decode the + // SoA layout. Types without an SoA stage (fg_soa::supported == false) + // decline via fg_visit_layout and the caller falls through to the + // reorder-aware conversion route. bool plain_ok = false; - fg_visit_type(src0_type, false, [&](auto tag) { plain_ok = fg_plain_ok(); }); + fg_visit_type(src0_type, src0_reordered, [&](auto tag) { plain_ok = fg_plain_ok(); }); if (g_ggml_sycl_dynamic_precision == GGML_SYCL_DYNAMIC_PRECISION_F32 || !ggml_sycl_xmx_gather_type_enabled(src0_type) || !plain_ok) { return false; @@ -1023,7 +1029,7 @@ bool ggml_sycl_fused_dequant_gemm(ggml_type src0_type, const void * src0, const } bool launched = false; fg_visit_combo(combo, [&](auto s) { - launched = fg_fused_run(src0_type, src0, src1, src1_type, dst, M, N, K, ldd, pool, stream); + launched = fg_fused_run(src0_type, src0_reordered, src0, src1, src1_type, dst, M, N, K, ldd, pool, stream); }); return launched; } diff --git a/ggml/src/ggml-sycl/fused-gemm.hpp b/ggml/src/ggml-sycl/fused-gemm.hpp index 2989892048cd..226676d12d50 100644 --- a/ggml/src/ggml-sycl/fused-gemm.hpp +++ b/ggml/src/ggml-sycl/fused-gemm.hpp @@ -69,7 +69,7 @@ inline bool ggml_sycl_xmx_gather_type_enabled(ggml_type src0_type) { // (op_params[3], [TAG_GGML_PREC]); the accumulator is f32, which meets any request. // q8_0 and the k-quants are not handled here, only in the grouped path below. // Returns false when the case is not handled (type, device, precision, or shape). -bool ggml_sycl_fused_dequant_gemm(ggml_type src0_type, const void * src0, const void * src1, ggml_type src1_type, +bool ggml_sycl_fused_dequant_gemm(ggml_type src0_type, bool src0_reordered, const void * src0, const void * src1, ggml_type src1_type, int32_t src1_prec, float * dst, int64_t M, int64_t N, int64_t K, int64_t ldd, ggml_sycl_pool & pool, dpct::queue_ptr stream); diff --git a/ggml/src/ggml-sycl/getrows.cpp b/ggml/src/ggml-sycl/getrows.cpp index a3a32090070a..36f6fcd30a91 100644 --- a/ggml/src/ggml-sycl/getrows.cpp +++ b/ggml/src/ggml-sycl/getrows.cpp @@ -301,6 +301,52 @@ static void get_rows_sycl_reorder_k(const ggml_tensor *src0, const ggml_tensor * GGML_UNUSED(src0); } +// IQ3_S / IQ3_XXS in the persistent reorder layout: one work-group of 32 threads +// dequantizes one QK_K block of one requested row, reusing the reorder-aware +// block dequantizers that the dequantize-based mul_mat path already uses. +template +static void get_rows_sycl_reorder_iq3(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, + const ggml_tensor * src1, ggml_tensor * dst, + const int32_t * src1_dd, float * dst_dd, + F dequantize_block) { + GGML_TENSOR_BINARY_OP_LOCALS + + // the persistent reorder is only installed for 2D weights + GGML_ASSERT(ne02 == 1 && ne03 == 1); + GGML_ASSERT(ne00 % QK_K == 0); + + const int64_t nblk = ne00 / QK_K; + const int64_t nb = nblk * ne01; // blocks in the whole tensor + const int64_t ngroups = ne10 * ne11 * ne12 * nblk; + + const size_t s1 = nb1 / ggml_element_size(dst); + const size_t s2 = nb2 / ggml_element_size(dst); + const size_t s3 = nb3 / ggml_element_size(dst); + + const size_t s10 = nb10 / ggml_element_size(src1); + const size_t s11 = nb11 / ggml_element_size(src1); + const size_t s12 = nb12 / ggml_element_size(src1); + + queue_ptr stream = ctx.stream(); + stream->parallel_for( + sycl::nd_range<3>(sycl::range<3>(1, 1, ngroups * 32), sycl::range<3>(1, 1, 32)), + [=](sycl::nd_item<3> item_ct1) { + const int64_t g = item_ct1.get_group(2); + const int64_t b = g % nblk; + const int64_t r = g / nblk; + const int64_t i10 = r % ne10; + const int64_t i11 = (r / ne10) % ne11; + const int64_t i12 = r / (ne10 * ne11); + + const int64_t i01 = src1_dd[i10*s10 + i11*s11 + i12*s12]; + + float * y = dst_dd + i10*s1 + i11*s2 + i12*s3 + b*QK_K; + dequantize_block(y, i01*nblk + b, item_ct1, nb); + }); + + GGML_UNUSED(dst); +} + template static void get_rows_sycl_f32(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst, const void *src0_dd, @@ -460,7 +506,9 @@ void ggml_sycl_op_get_rows(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { GGML_ASSERT(dst->nb[0] == ggml_type_size(dst->type)); const int32_t * src1_i32 = (const int32_t *) dst->src[1]->data; - // a weight shared with MUL_MAT (e.g. tied embeddings) can be in reorder layout + // a weight shared with MUL_MAT (e.g. tied embeddings) can be in the + // persistent reorder layout (incl. IQ3_S / IQ3_XXS since the SYCL IQ3 + // reorder work) const bool src0_reordered = dst->src[0]->extra && ((const ggml_tensor_extra_gpu *) dst->src[0]->extra)->optimized_feature.reorder; /* TODO: Refactor and remove duplicates */ @@ -506,6 +554,15 @@ void ggml_sycl_op_get_rows(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { src1_i32, (float *)dst->data, ctx.stream()); break; case GGML_TYPE_IQ3_XXS: + if (src0_reordered) { + const void * vx = dst->src[0]->data; + get_rows_sycl_reorder_iq3(ctx, dst->src[0], dst->src[1], dst, src1_i32, (float *) dst->data, + [=](float * y, int64_t iblk, const sycl::nd_item<3> & item_ct1, int64_t nb) { + dequantize_block_iq3_xxs_reorder(vx, y, iblk, item_ct1, nb, + iq3xxs_grid, ksigns_iq2xs, kmask_iq2xs); + }); + break; + } get_rows_sycl(ctx, dst->src[0], dst->src[1], dst, (const float *)dst->src[0]->data, src1_i32, (float *)dst->data, ctx.stream()); break; @@ -518,6 +575,15 @@ void ggml_sycl_op_get_rows(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { src1_i32, (float *)dst->data, ctx.stream()); break; case GGML_TYPE_IQ3_S: + if (src0_reordered) { + const void * vx = dst->src[0]->data; + get_rows_sycl_reorder_iq3(ctx, dst->src[0], dst->src[1], dst, src1_i32, (float *) dst->data, + [=](float * y, int64_t iblk, const sycl::nd_item<3> & item_ct1, int64_t nb) { + dequantize_block_iq3_s_reorder(vx, y, iblk, item_ct1, nb, + kmask_iq2xs, iq3s_grid); + }); + break; + } get_rows_sycl(ctx, dst->src[0], dst->src[1], dst, (const float *)dst->src[0]->data, src1_i32, (float *)dst->data, ctx.stream()); break; diff --git a/ggml/src/ggml-sycl/ggml-sycl.cpp b/ggml/src/ggml-sycl/ggml-sycl.cpp index cbb2188c5c52..2a62f91962f8 100644 --- a/ggml/src/ggml-sycl/ggml-sycl.cpp +++ b/ggml/src/ggml-sycl/ggml-sycl.cpp @@ -3154,9 +3154,16 @@ inline void ggml_sycl_op_mul_mat_sycl( #endif // dequantize inside the GEMM instead of writing the f16 weights out and reading them back; src1 - // goes in its own type, so there is no separate conversion pass + // goes in its own type, so there is no separate conversion pass. The A stage + // decodes the weight bytes in place, so an installed persistent reorder must be + // visible to it: pass the flag through; types without an SoA A-stage decline + // inside and this falls through to the reorder-aware conversion route below. + bool src0_reordered = false; + if (const auto * src0_extra = static_cast(src0->extra)) { + src0_reordered = src0_extra->optimized_feature.reorder; + } if (ggml_is_quantized(src0->type) && ggml_is_contiguous(src0) && row_diff == src0->ne[1] && - ggml_sycl_fused_dequant_gemm(src0->type, src0_dd_i, src1_ddf_i, src1->type, ggml_sycl_src1_prec(dst), dst_dd_i, + ggml_sycl_fused_dequant_gemm(src0->type, src0_reordered, src0_dd_i, src1_ddf_i, src1->type, ggml_sycl_src1_prec(dst), dst_dd_i, row_diff, src1_ncols, ne10, ldc, ctx.pool(), stream)) { return; } diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index d3194bf47f52..8f05f8002a5c 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -2454,7 +2454,9 @@ struct test_get_rows : public test_case { }; // GGML_OP_GET_ROWS on a weight also used by GGML_OP_MUL_MAT (tied embeddings) -// the mul_mat runs first, so a backend that changes the weight layout must still return the right rows +// that the preceding mul_mat may have put in the SYCL reorder layout. The +// mul_mat runs first, so a backend that changes the weight layout in place +// must still return the right rows from get_rows. struct test_get_rows_after_mul_mat : public test_case { const ggml_type type; const int n; // cols @@ -2510,6 +2512,48 @@ struct test_get_rows_after_mul_mat : public test_case { } }; +// The persistent SYCL weight reorder installs on the first small-batch mul_mat; a +// later >8-column mul_mat on the same weight goes through the fused-dequant GEMM / +// conversion routes, which must decode the reordered layout (regression test for the +// #29107 crash: the plain fused-dequant GEMM decoded standard layout unconditionally). +struct test_mul_mat_reorder_then_big_batch : public test_case { + const ggml_type type; + const int n; // cols of the big batch + const int m; // rows + + std::string vars() override { + return VARS_TO_STR3(type, n, m); + } + + test_mul_mat_reorder_then_big_batch(ggml_type type = GGML_TYPE_IQ3_S, int n = 64, int m = 64) + : type(type), n(n), m(m) {} + + double max_nmse_err() override { + return 5e-4; // same as test_mul_mat + } + + ggml_tensor * build_graph(ggml_context * ctx) override { + ggml_tensor * w = ggml_new_tensor_2d(ctx, type, 1*256, m); + ggml_set_name(w, "w"); + + ggml_tensor * x1 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, 1*256, 1); + ggml_set_name(x1, "x1"); + + ggml_tensor * x2 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, 1*256, n); + ggml_set_name(x2, "x2"); + + ggml_tensor * mm1 = ggml_mul_mat(ctx, w, x1); + ggml_set_name(mm1, "mm1"); + + ggml_tensor * mm2 = ggml_mul_mat(ctx, w, x2); + ggml_set_name(mm2, "mm2"); + + // mm1 is the first source of the final ADD, so the reorder-installing + // small-batch mul_mat is computed before the big-batch one + return ggml_add(ctx, ggml_repeat(ctx, mm1, mm2), mm2); + } +}; + // GGML_OP_GET_ROWS_BACK struct test_get_rows_back : public test_case { const ggml_type type; @@ -9885,9 +9929,13 @@ static std::vector> make_test_cases_eval() { } test_cases.emplace_back(new test_get_rows(GGML_TYPE_F32, 1, 8, 2, 1, 1, false)); - for (ggml_type type : {GGML_TYPE_Q4_0, GGML_TYPE_Q8_0, GGML_TYPE_Q2_K, GGML_TYPE_Q3_K, GGML_TYPE_Q4_K, GGML_TYPE_Q5_K, GGML_TYPE_Q6_K}) { + for (ggml_type type : {GGML_TYPE_Q4_0, GGML_TYPE_Q8_0, GGML_TYPE_Q2_K, GGML_TYPE_Q3_K, GGML_TYPE_Q4_K, GGML_TYPE_Q5_K, GGML_TYPE_Q6_K, + GGML_TYPE_IQ3_XXS, GGML_TYPE_IQ3_S}) { test_cases.emplace_back(new test_get_rows_after_mul_mat(type)); } + for (ggml_type type : {GGML_TYPE_IQ3_XXS, GGML_TYPE_IQ3_S}) { + test_cases.emplace_back(new test_mul_mat_reorder_then_big_batch(type)); + } for (ggml_type type : all_types) { for (int b : {1, 7}) { for (bool v : {false, true}) { From 8baabd53b77d1c48c6ef8d3b620436292f3f6231 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Wed, 21 Oct 2026 09:01:00 +0300 Subject: [PATCH 37/69] llama : scale moe down projection per expert token (#30861) * llama : scale moe down projection per expert token Replace the global amax rescaling for the MoE down projection with a per-expert-token factor computed by a POOL_1D max reduction along the intermediate dimension. Undo the scale on the output with the same exact power-of-two factor. Handle POOL_1D in the Meta split-state logic so a split along axis 0 becomes a replicated per-device partial result, enabling tensor parallelism. Assisted-by: pi:llama.cpp/ggml-org/DeepSeek-V4-Flash-Vision-Exp-GGUF (Mac Studio, M2 Ultra, 192GB) * cont : simplify comments Co-authored-by: Georgi Gerganov * llama : refactor amax src1 scaling result Return the rescaled activation and the optional scale tensor through a small struct instead of an output pointer. Assisted-by: pi:llama.cpp/ggml-org/DeepSeek-V4-Flash-Vision-Exp-GGUF (Mac Studio, M2 Ultra, 192GB) * cont : add TODO + clean-up --- ggml/src/ggml-backend-meta.cpp | 11 ++++++- src/llama-graph.cpp | 57 +++++++++++++++++++++++++++++++--- src/llama-graph.h | 10 ++++++ 3 files changed, 72 insertions(+), 6 deletions(-) diff --git a/ggml/src/ggml-backend-meta.cpp b/ggml/src/ggml-backend-meta.cpp index 679d5216d255..edbe4da41abd 100644 --- a/ggml/src/ggml-backend-meta.cpp +++ b/ggml/src/ggml-backend-meta.cpp @@ -774,6 +774,13 @@ static struct ggml_backend_meta_split_state ggml_backend_meta_get_split_state( return src_ss[0]; }; + auto handle_pool_1d = [&](const std::vector & src_ss) -> ggml_backend_meta_split_state { + if (src_ss[0].axis == GGML_BACKEND_SPLIT_AXIS_0) { + return {GGML_BACKEND_SPLIT_AXIS_MIRRORED, {0}, {1}, 1}; + } + return src_ss[0]; + }; + auto handle_flash_attn_ext = [&](const std::vector & src_ss) -> ggml_backend_meta_split_state { GGML_ASSERT(tensor->src[3] == nullptr || src_ss[3].axis == GGML_BACKEND_SPLIT_AXIS_MIRRORED); @@ -990,12 +997,14 @@ static struct ggml_backend_meta_split_state ggml_backend_meta_get_split_state( case GGML_OP_CONV_3D: case GGML_OP_CONV_2D_DW: case GGML_OP_CONV_TRANSPOSE_2D: - case GGML_OP_POOL_1D: case GGML_OP_POOL_2D: case GGML_OP_POOL_2D_BACK: case GGML_OP_UPSCALE: { split_state = handle_generic(src_ss, /*scalar_only =*/ true); } break; + case GGML_OP_POOL_1D: { + split_state = handle_pool_1d(src_ss); + } break; case GGML_OP_PAD: { split_state = handle_pad(src_ss); } break; diff --git a/src/llama-graph.cpp b/src/llama-graph.cpp index 2bc3a291a456..59caffe87fce 100644 --- a/src/llama-graph.cpp +++ b/src/llama-graph.cpp @@ -1666,6 +1666,40 @@ ggml_tensor * llm_graph_context::build_lora_mm( return res; } +llm_graph_amax_src1 llm_graph_context::build_amax_src1_f16( + ggml_tensor * cur) const { + llm_graph_amax_src1 result = { + /*.res =*/ cur, + /*.scale_s =*/ nullptr, + }; + + const int64_t n_embd = cur->ne[0]; + + // amax = max|cur| along the rows for each [expert, token] -> [1, n_expert, n_tokens] + ggml_tensor * ab = ggml_abs(ctx0, cur); + ggml_tensor * amax = ggml_pool_1d(ctx0, ab, GGML_OP_POOL_MAX, n_embd, n_embd, 0); + + // ref: https://github.com/ggml-org/llama.cpp/pull/26223 + constexpr float k_log2_e = 1.4426950408889634f; // 1 / ln(2) + constexpr float k_ln_2 = 0.6931471805599453f; + + ggml_tensor * e; + e = ggml_scale(ctx0, amax, 1.0f/32768.0f); + e = ggml_log (ctx0, e); + e = ggml_scale(ctx0, e, k_log2_e); + e = ggml_ceil (ctx0, e); + e = ggml_clamp(ctx0, e, 0.0f, 127.0f); + e = ggml_scale(ctx0, e, k_ln_2); + e = ggml_exp (ctx0, e); + + result = { + /*.res =*/ ggml_div(ctx0, cur, e), + /*.scale_s =*/ e, + }; + + return result; +} + ggml_tensor * llm_graph_context::build_lora_mm_id( ggml_tensor * w, // ggml_tensor * as ggml_tensor * cur, // ggml_tensor * b @@ -2465,12 +2499,25 @@ ggml_tensor * llm_graph_context::build_moe_ffn( GGML_ABORT("fatal error"); } - experts = build_lora_mm_id(down_exps, cur, selected_experts, down_exps_s, slots); // [n_embd, n_expert_used, n_tokens] - if (arch == LLM_ARCH_MISTRAL4 || arch == LLM_ARCH_MELLUM) { - // src1 can exceed F16 range - ggml_prec_set_src(experts, GGML_PREC_F32, 1); + { + // TODO: this graph-level rescaling should only be applied when the corresponding backend does not support + // ggml_prec_set_src(experts, GGML_PREC_BF16, 1); + // ref: https://github.com/ggml-org/llama.cpp/pull/30656#issuecomment-6269597922 + llm_graph_amax_src1 amax_src1 = { nullptr, nullptr }; + + if (arch == LLM_ARCH_MISTRAL4 || arch == LLM_ARCH_MELLUM) { + amax_src1 = build_amax_src1_f16(cur); + cur = amax_src1.res; + } + + experts = build_lora_mm_id(down_exps, cur, selected_experts, down_exps_s, slots); // [n_embd, n_expert_used, n_tokens] + //ggml_prec_set_src(experts, GGML_PREC_BF16, 1); // (see TODO above) + cb(experts, "ffn_moe_down", il); + + if (amax_src1.scale_s) { + experts = ggml_mul(ctx0, experts, amax_src1.scale_s); + } } - cb(experts, "ffn_moe_down", il); if (down_exps_s) { cb(experts, "ffn_moe_down_scaled", il); diff --git a/src/llama-graph.h b/src/llama-graph.h index f66bedb3254f..abe5133c97a2 100644 --- a/src/llama-graph.h +++ b/src/llama-graph.h @@ -1012,6 +1012,11 @@ struct llm_graph_qkv { ggml_tensor * v; // [n_embd_head, n_head_kv, n_tokens] }; +struct llm_graph_amax_src1 { + ggml_tensor * res; // cur rescaled + ggml_tensor * scale_s; // scale to undo on the matmul result, or nullptr +}; + struct llm_graph_context { const llm_arch arch; @@ -1115,6 +1120,11 @@ struct llm_graph_context { ggml_tensor * w_s = nullptr, ggml_tensor * slots = nullptr) const; + // rescale `cur` (the src1 of a matmul) by a power of two, computed from its maximum absolute + // when the values already fit in F16, the factors are exactly 1.0f + llm_graph_amax_src1 build_amax_src1_f16( + ggml_tensor * cur) const; + ggml_tensor * build_norm( ggml_tensor * cur, ggml_tensor * mw, From 479a52779ff4c4674b81a226f8511816e15dc8dd Mon Sep 17 00:00:00 2001 From: bosh <98094229+boshjerns@users.noreply.github.com> Date: Wed, 21 Oct 2026 14:34:37 +0700 Subject: [PATCH 38/69] ggml-cpu : compute f32 gelu_quick and geglu_quick without the fp16 table (#30737) * ggml-cpu : compute f32 gelu_quick and geglu_quick without the fp16 table * ggml-cpu : keep the GGML_GELU_QUICK_FP16 table path * ggml-cpu : add the VXE gelu_quick path * tests : add test-gelu-quick for the f32 CPU kernels * tests : drop inf/nan inputs from test-gelu-quick --- ggml/src/ggml-cpu/vec.cpp | 86 ++++++++++++++++++++++++ ggml/src/ggml-cpu/vec.h | 61 +++++++++++++---- tests/CMakeLists.txt | 1 + tests/test-backend-ops.cpp | 27 ++++++++ tests/test-gelu-quick.cpp | 131 +++++++++++++++++++++++++++++++++++++ 5 files changed, 295 insertions(+), 11 deletions(-) create mode 100644 tests/test-gelu-quick.cpp diff --git a/ggml/src/ggml-cpu/vec.cpp b/ggml/src/ggml-cpu/vec.cpp index 811184d821dd..8f0bf4999cfb 100644 --- a/ggml/src/ggml-cpu/vec.cpp +++ b/ggml/src/ggml-cpu/vec.cpp @@ -460,6 +460,92 @@ void ggml_vec_swiglu_f32(const int n, float * y, const float * x, const float * } } +#ifndef GGML_GELU_QUICK_FP16 +// computed in f32 without the fp16 table, which loses precision on f32 input +void ggml_vec_gelu_quick_f32(const int n, float * y, const float * x) { + int i = 0; +#if defined(__AVX512F__) && defined(__AVX512DQ__) + for (; i + 15 < n; i += 16) { + _mm512_storeu_ps(y + i, ggml_v_gelu_quick(_mm512_loadu_ps(x + i))); + } +#elif defined(__AVX2__) && defined(__FMA__) + for (; i + 7 < n; i += 8) { + _mm256_storeu_ps(y + i, ggml_v_gelu_quick(_mm256_loadu_ps(x + i))); + } +#elif defined(__SSE2__) + for (; i + 3 < n; i += 4) { + _mm_storeu_ps(y + i, ggml_v_gelu_quick(_mm_loadu_ps(x + i))); + } +#elif defined(__ARM_FEATURE_SVE) && defined(__aarch64__) + const int vlen = svcntw(); + for (; i < n; i += vlen) { + const svbool_t pg = svwhilelt_b32_s32(i, n); + svst1_f32(pg, y + i, ggml_v_gelu_quick(pg, svld1_f32(pg, x + i))); + } +#elif defined(__ARM_NEON) && defined(__aarch64__) + for (; i + 3 < n; i += 4) { + vst1q_f32(y + i, ggml_v_gelu_quick(vld1q_f32(x + i))); + } +#elif defined(__VXE__) || defined(__VXE2__) + for (; i + 3 < n; i += 4) { + vec_xst(ggml_v_gelu_quick(vec_xl(0, x + i)), 0, y + i); + } +#elif defined(__riscv_v_intrinsic) + for (int vl; i < n; i += vl) { + vl = __riscv_vsetvl_e32m2(n - i); + vfloat32m2_t vx = __riscv_vle32_v_f32m2(&x[i], vl); + vfloat32m2_t vy = ggml_v_gelu_quick_m2(vx, vl); + __riscv_vse32_v_f32m2(&y[i], vy, vl); + } +#endif + for (; i < n; ++i) { + y[i] = ggml_gelu_quick_f32(x[i]); + } +} + +void ggml_vec_geglu_quick_f32(const int n, float * y, const float * x, const float * g) { + int i = 0; +#if defined(__AVX512F__) && defined(__AVX512DQ__) + for (; i + 15 < n; i += 16) { + _mm512_storeu_ps(y + i, _mm512_mul_ps(ggml_v_gelu_quick(_mm512_loadu_ps(x + i)), _mm512_loadu_ps(g + i))); + } +#elif defined(__AVX2__) && defined(__FMA__) + for (; i + 7 < n; i += 8) { + _mm256_storeu_ps(y + i, _mm256_mul_ps(ggml_v_gelu_quick(_mm256_loadu_ps(x + i)), _mm256_loadu_ps(g + i))); + } +#elif defined(__SSE2__) + for (; i + 3 < n; i += 4) { + _mm_storeu_ps(y + i, _mm_mul_ps(ggml_v_gelu_quick(_mm_loadu_ps(x + i)), _mm_loadu_ps(g + i))); + } +#elif defined(__ARM_FEATURE_SVE) && defined(__aarch64__) + const int vlen = svcntw(); + for (; i < n; i += vlen) { + const svbool_t pg = svwhilelt_b32_s32(i, n); + svst1_f32(pg, y + i, svmul_f32_x(pg, ggml_v_gelu_quick(pg, svld1_f32(pg, x + i)), svld1_f32(pg, g + i))); + } +#elif defined(__ARM_NEON) && defined(__aarch64__) + for (; i + 3 < n; i += 4) { + vst1q_f32(y + i, vmulq_f32(ggml_v_gelu_quick(vld1q_f32(x + i)), vld1q_f32(g + i))); + } +#elif defined(__VXE__) || defined(__VXE2__) + for (; i + 3 < n; i += 4) { + vec_xst(vec_mul(ggml_v_gelu_quick(vec_xl(0, x + i)), vec_xl(0, g + i)), 0, y + i); + } +#elif defined(__riscv_v_intrinsic) + for (int vl; i < n; i += vl) { + vl = __riscv_vsetvl_e32m2(n - i); + vfloat32m2_t vx = __riscv_vle32_v_f32m2(&x[i], vl); + vfloat32m2_t vg = __riscv_vle32_v_f32m2(&g[i], vl); + vfloat32m2_t vy = __riscv_vfmul_vv_f32m2(ggml_v_gelu_quick_m2(vx, vl), vg, vl); + __riscv_vse32_v_f32m2(&y[i], vy, vl); + } +#endif + for (; i < n; ++i) { + y[i] = ggml_gelu_quick_f32(x[i]) * g[i]; + } +} +#endif + ggml_float ggml_vec_cvar_f32(const int n, float * y, const float * x, const float mean) { int i = 0; ggml_float sum = 0; diff --git a/ggml/src/ggml-cpu/vec.h b/ggml/src/ggml-cpu/vec.h index db9a38c09cc3..f24fa00f2b0d 100644 --- a/ggml/src/ggml-cpu/vec.h +++ b/ggml/src/ggml-cpu/vec.h @@ -44,7 +44,6 @@ inline static ggml_float ggml_sve_sum_f32x2(svfloat32_t sum_lo, svfloat32_t sum_ #endif #define GGML_GELU_FP16 -#define GGML_GELU_QUICK_FP16 #define GGML_SOFT_MAX_UNROLL 4 #define GGML_VEC_DOT_UNROLL 2 @@ -1054,11 +1053,7 @@ inline static void ggml_vec_gelu_quick_f32(const int n, float * y, const float * } } #else -inline static void ggml_vec_gelu_quick_f32(const int n, float * y, const float * x) { - for (int i = 0; i < n; ++i) { - y[i] = ggml_gelu_quick_f32(x[i]); - } -} +void ggml_vec_gelu_quick_f32(const int n, float * y, const float * x); #endif // Sigmoid Linear Unit (SiLU) function @@ -1143,6 +1138,13 @@ inline static svfloat32_t ggml_v_silu(svbool_t pg, svfloat32_t x) { return svdiv_f32_x(pg, x, one_plus_exp_neg_x); } +// gelu_quick(x) = x / (1 + exp(-1.702 x)); x stays the numerator so it cannot overflow +inline static svfloat32_t ggml_v_gelu_quick(svbool_t pg, svfloat32_t x) { + const svfloat32_t one = svdup_n_f32_x(pg, 1.0f); + const svfloat32_t exp_cx = ggml_v_expf(pg, svmul_n_f32_x(pg, x, GELU_QUICK_COEF)); + return svdiv_f32_x(pg, x, svadd_f32_x(pg, one, exp_cx)); +} + #elif defined(__ARM_NEON) && defined(__aarch64__) // adapted from arm limited optimized routine @@ -1182,6 +1184,13 @@ inline static float32x4_t ggml_v_silu(float32x4_t x) { return vdivq_f32(x, one_plus_exp_neg_x); } +// gelu_quick(x) = x / (1 + exp(-1.702 x)); x stays the numerator so it cannot overflow +inline static float32x4_t ggml_v_gelu_quick(float32x4_t x) { + const float32x4_t one = vdupq_n_f32(1.0f); + const float32x4_t exp_cx = ggml_v_expf(vmulq_n_f32(x, GELU_QUICK_COEF)); + return vdivq_f32(x, vaddq_f32(one, exp_cx)); +} + #elif defined(__AVX512F__) && defined(__AVX512DQ__) // adapted from arm limited optimized routine @@ -1225,6 +1234,13 @@ inline static __m512 ggml_v_silu(__m512 x) { return _mm512_div_ps(x, one_plus_exp_neg_x); } +// gelu_quick(x) = x / (1 + exp(-1.702 x)); x stays the numerator so it cannot overflow +inline static __m512 ggml_v_gelu_quick(__m512 x) { + const __m512 one = _mm512_set1_ps(1); + const __m512 exp_cx = ggml_v_expf(_mm512_mul_ps(x, _mm512_set1_ps(GELU_QUICK_COEF))); + return _mm512_div_ps(x, _mm512_add_ps(one, exp_cx)); +} + #elif defined(__AVX2__) && defined(__FMA__) // adapted from arm limited optimized routine @@ -1280,6 +1296,13 @@ inline static __m256 ggml_v_silu(__m256 x) { return _mm256_div_ps(x, one_plus_exp_neg_x); } +// gelu_quick(x) = x / (1 + exp(-1.702 x)); x stays the numerator so it cannot overflow +inline static __m256 ggml_v_gelu_quick(__m256 x) { + const __m256 one = _mm256_set1_ps(1); + const __m256 exp_cx = ggml_v_expf(_mm256_mul_ps(x, _mm256_set1_ps(GELU_QUICK_COEF))); + return _mm256_div_ps(x, _mm256_add_ps(one, exp_cx)); +} + #elif defined(__SSE2__) // __AVX2__ / __ARM_NEON #if defined(__FMA__) @@ -1334,6 +1357,13 @@ inline static __m128 ggml_v_silu(__m128 x) { return _mm_div_ps(x, one_plus_exp_neg_x); } +// gelu_quick(x) = x / (1 + exp(-1.702 x)); x stays the numerator so it cannot overflow +inline static __m128 ggml_v_gelu_quick(__m128 x) { + const __m128 one = _mm_set1_ps(1); + const __m128 exp_cx = ggml_v_expf(_mm_mul_ps(x, _mm_set1_ps(GELU_QUICK_COEF))); + return _mm_div_ps(x, _mm_add_ps(one, exp_cx)); +} + #elif defined(__VXE__) || defined(__VXE2__) // adapted from arm limited optimized routine @@ -1374,6 +1404,13 @@ inline static float32x4_t ggml_v_silu(float32x4_t v_x) { return vec_div(v_x, v_one_plus_exp_neg_x); } +// gelu_quick(x) = x / (1 + exp(-1.702 x)); x stays the numerator so it cannot overflow +inline static float32x4_t ggml_v_gelu_quick(float32x4_t x) { + const float32x4_t one = vec_splats(1.0f); + const float32x4_t exp_cx = ggml_v_expf(vec_mul(x, vec_splats(GELU_QUICK_COEF))); + return vec_div(x, vec_add(one, exp_cx)); +} + #elif defined(__riscv_v_intrinsic) // adapted from arm limited optimized routine @@ -1426,6 +1463,12 @@ inline static vfloat32m2_t ggml_v_silu_m2(vfloat32m2_t x, int vl) { return __riscv_vfdiv_vv_f32m2(x, one_plus_exp_neg_x, vl); } +// gelu_quick(x) = x / (1 + exp(-1.702 x)); x stays the numerator so it cannot overflow +inline static vfloat32m2_t ggml_v_gelu_quick_m2(vfloat32m2_t x, int vl) { + const vfloat32m2_t exp_cx = ggml_v_expf_m2(__riscv_vfmul_vf_f32m2(x, GELU_QUICK_COEF, vl), vl); + return __riscv_vfdiv_vv_f32m2(x, __riscv_vfadd_vf_f32m2(exp_cx, 1.0f, vl), vl); +} + #endif // __ARM_NEON / __AVX2__ / __SSE2__ / __VXE__ / __riscv_v_intrinsic inline static void ggml_vec_silu_f16(const int n, ggml_fp16_t * y, const ggml_fp16_t * x) { @@ -1571,11 +1614,7 @@ inline static void ggml_vec_geglu_quick_f32(const int n, float * y, const float } } #else -inline static void ggml_vec_geglu_quick_f32(const int n, float * y, const float * x, const float * g) { - for (int i = 0; i < n; ++i) { - y[i] = ggml_gelu_quick_f32(x[i]) * g[i]; - } -} +void ggml_vec_geglu_quick_f32(const int n, float * y, const float * x, const float * g); #endif inline static void ggml_vec_geglu_quick_f16(const int n, ggml_fp16_t * y, const ggml_fp16_t * x, const ggml_fp16_t * g) { diff --git a/tests/CMakeLists.txt b/tests/CMakeLists.txt index 991bf547998d..24e6ba609a21 100644 --- a/tests/CMakeLists.txt +++ b/tests/CMakeLists.txt @@ -332,6 +332,7 @@ if (NOT GGML_BACKEND_DL AND GGML_CPU) llama_build_and_test(test-rope.cpp LINK ggml) llama_build_and_test(test-col2im-1d.cpp LINK ggml) llama_build_and_test(test-conv-1d.cpp LINK ggml) + llama_build_and_test(test-gelu-quick.cpp LINK ggml) endif() # libmtmd diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index 8f05f8002a5c..58f3275da146 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -2069,6 +2069,15 @@ struct test_unary : public test_case { return VARS_TO_STR3(type, ne_a, v); } + double max_nmse_err(ggml_backend_t backend) override { + // Metal computes f32 gelu_quick in f32, so an fp16 table in the CPU reference (NMSE about 5e-8) fails this + ggml_backend_reg_t reg = ggml_backend_dev_backend_reg(ggml_backend_get_device(backend)); + if (op == GGML_UNARY_OP_GELU_QUICK && type == GGML_TYPE_F32 && strcmp(ggml_backend_reg_name(reg), "MTL") == 0) { + return 1e-10; + } + return test_case::max_nmse_err(backend); + } + test_unary(ggml_unary_op op, ggml_type type = GGML_TYPE_F32, std::array ne_a = {128, 2, 2, 2}, @@ -2157,6 +2166,15 @@ struct test_glu : public test_case { return VARS_TO_STR4(type, ne_a, v, swapped); } + double max_nmse_err(ggml_backend_t backend) override { + // Metal computes f32 geglu_quick in f32, so an fp16 table in the CPU reference (NMSE about 5e-8) fails this + ggml_backend_reg_t reg = ggml_backend_dev_backend_reg(ggml_backend_get_device(backend)); + if (op == GGML_GLU_OP_GEGLU_QUICK && type == GGML_TYPE_F32 && strcmp(ggml_backend_reg_name(reg), "MTL") == 0) { + return 1e-10; + } + return test_case::max_nmse_err(backend); + } + test_glu(ggml_glu_op op, ggml_type type = GGML_TYPE_F32, std::array ne_a = {128, 2, 2, 2}, @@ -2202,6 +2220,15 @@ struct test_glu_split : public test_case { return VARS_TO_STR3(type, ne_a, v) + ",split"; } + double max_nmse_err(ggml_backend_t backend) override { + // Metal computes f32 geglu_quick in f32, so an fp16 table in the CPU reference (NMSE about 5e-8) fails this + ggml_backend_reg_t reg = ggml_backend_dev_backend_reg(ggml_backend_get_device(backend)); + if (op == GGML_GLU_OP_GEGLU_QUICK && type == GGML_TYPE_F32 && strcmp(ggml_backend_reg_name(reg), "MTL") == 0) { + return 1e-10; + } + return test_case::max_nmse_err(backend); + } + test_glu_split(ggml_glu_op op, ggml_type type = GGML_TYPE_F32, std::array ne_a = {128, 2, 2, 2}, diff --git a/tests/test-gelu-quick.cpp b/tests/test-gelu-quick.cpp new file mode 100644 index 000000000000..29cbbc854ddc --- /dev/null +++ b/tests/test-gelu-quick.cpp @@ -0,0 +1,131 @@ +// test-gelu-quick.cpp: validate the F32 gelu_quick and geglu_quick CPU kernels against the exact formula. +// +// test-backend-ops uses the CPU backend as its reference, so it cannot see an error in the CPU kernels +// themselves. This test computes gelu_quick(x) = x / (1 + exp(-1.702 x)) in double precision and compares +// every F32 output of the CPU backend with it: dense ranges, values outside the F16 range, values near +// the float limits, and lengths that leave a tail after the vector loop. + +#include "ggml.h" +#include "ggml-cpu.h" + +#include +#include +#include +#include +#include +#include + +// Same coefficient as the CPU kernels +static const double GELU_QUICK_COEF_REF = (double) 1.702f; + +static double gelu_quick_ref(double x) { + return x / (1.0 + std::exp(-GELU_QUICK_COEF_REF * x)); +} + +// Deterministic LCG mapped to [-1, 1] +static uint64_t g_rng = 0x12345678ULL; +static float frand(void) { + g_rng = g_rng * 6364136223846793005ULL + 1442695040888963407ULL; + return (float)((g_rng >> 33) & 0xffffff) / (float)0x800000 - 1.0f; +} + +static std::vector make_inputs(void) { + std::vector x; + for (int i = 0; i <= 200000; i++) { + x.push_back(-10.0f + 20.0f * (float) i / 200000.0f); + } + for (int i = 0; i <= 2000; i++) { + x.push_back(-150.0f + 300.0f * (float) i / 2000.0f); + } + // outside the F16 range, near the float limits, and subnormal; both signs + const float edge[] = { 65504.0f, 65520.0f, 1e5f, 1e10f, 1e30f, FLT_MAX, 1e-30f, FLT_MIN, FLT_MIN / 16, 0.0f }; + for (float v : edge) { + x.push_back(v); + x.push_back(-v); + } + return x; +} + +static bool matches(float y, double ref) { + if (std::isnan(ref)) { + return std::isnan(y); + } + if (std::isinf(ref)) { + return (double) y == ref; + } + return std::fabs((double) y - ref) <= 1e-6 + 1e-6 * std::fabs(ref); +} + +// Runs gelu_quick and geglu_quick on x[0, n) and returns the number of outputs that differ from the reference +static int check(const float * x, const float * g, int64_t n) { + struct ggml_init_params params = { + /* .mem_size = */ (size_t) 64 << 20, + /* .mem_base = */ NULL, + /* .no_alloc = */ false, + }; + struct ggml_context * ctx = ggml_init(params); + + struct ggml_tensor * a = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n); + struct ggml_tensor * b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n); + memcpy(a->data, x, n * sizeof(float)); + memcpy(b->data, g, n * sizeof(float)); + + struct ggml_tensor * y_gelu = ggml_gelu_quick(ctx, a); + struct ggml_tensor * y_geglu = ggml_geglu_quick_split(ctx, a, b); + + struct ggml_cgraph * gf = ggml_new_graph(ctx); + ggml_build_forward_expand(gf, y_gelu); + ggml_build_forward_expand(gf, y_geglu); + ggml_graph_compute_with_ctx(ctx, gf, 1); + + int bad = 0; + double max_err = 0.0; + for (int64_t i = 0; i < n; i++) { + const double r0 = gelu_quick_ref(x[i]); + const double r1 = r0 * g[i]; + const float y0 = ((const float *) y_gelu->data)[i]; + const float y1 = ((const float *) y_geglu->data)[i]; + if (std::isfinite(r0) && std::isfinite(y0)) { + max_err = std::fmax(max_err, std::fabs((double) y0 - r0) / (1.0 + std::fabs(r0))); + } + if (!matches(y0, r0)) { + if (bad < 3) { + printf(" gelu_quick(%g) = %g, expected %g\n", x[i], y0, r0); + } + bad++; + } + if (!matches(y1, r1)) { + if (bad < 3) { + printf(" geglu_quick(%g, %g) = %g, expected %g\n", x[i], g[i], y1, r1); + } + bad++; + } + } + printf("gelu_quick n=%6d: max err %.2e, %d mismatches %s\n", (int) n, max_err, bad, bad == 0 ? "OK" : "FAIL"); + + ggml_free(ctx); + return bad; +} + +int main(void) { + const std::vector x = make_inputs(); + std::vector g(x.size()); + for (float & v : g) { + v = frand(); + } + + int fails = 0; + + // every input in one call + fails += check(x.data(), g.data(), (int64_t) x.size()) != 0; + + // short lengths end on the edge values and leave a tail after the vector loop + const int64_t lengths[] = { 1, 2, 3, 5, 7, 8, 9, 15, 16, 17, 23, 31, 33 }; + for (int64_t n : lengths) { + const int64_t off = (int64_t) x.size() - n; + fails += check(x.data() + off, g.data() + off, n) != 0; + } + + printf(fails == 0 ? "all gelu_quick checks passed\n" : "%d gelu_quick checks FAILED\n", fails); + return fails == 0 ? 0 : 1; +} From 96caa80059fc9b8ee99c4da285211eae6c8efc66 Mon Sep 17 00:00:00 2001 From: Titaniumtown Date: Wed, 21 Oct 2026 00:46:00 -0700 Subject: [PATCH 39/69] sycl: collapse duplicate need_check launch bodies in mmq (#30739) --- ggml/src/ggml-sycl/mmq.cpp | 1014 ++++++++++++------------------------ 1 file changed, 338 insertions(+), 676 deletions(-) diff --git a/ggml/src/ggml-sycl/mmq.cpp b/ggml/src/ggml-sycl/mmq.cpp index ffb272aa2837..19ff067c482c 100644 --- a/ggml/src/ggml-sycl/mmq.cpp +++ b/ggml/src/ggml-sycl/mmq.cpp @@ -1807,76 +1807,44 @@ static void ggml_mul_mat_q4_0_q8_1_sycl(const void *vx, const void *vy, const sycl::range<3> block_nums(1, block_num_y, block_num_x); const sycl::range<3> block_dims(1, nwarps, WARP_SIZE); - if (nrows_x % mmq_y == 0) { - const bool need_check = false; + const auto launch = [&](auto need_check) { /* DPCT1049:20: The work-group size passed to the SYCL kernel may exceed the limit. To get the device limit, query info::device::max_work_group_size. Adjust the work-group size if needed. */ - { - dpct::has_capability_or_fail(stream->get_device(), - {sycl::aspect::fp16}); - - stream->submit([&](sycl::handler &cgh) { - sycl::local_accessor tile_x_qs_q4_0_acc_ct1( - sycl::range<1>(mmq_y * (WARP_SIZE) + mmq_y), cgh); - sycl::local_accessor tile_x_d_q4_0_acc_ct1( - sycl::range<1>(mmq_y * (WARP_SIZE / QI4_0) + mmq_y / QI4_0), - cgh); - sycl::local_accessor tile_y_qs_acc_ct1( - sycl::range<1>(mmq_x * WARP_SIZE), cgh); - sycl::local_accessor tile_y_ds_acc_ct1( - sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh); - - cgh.parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) { - mul_mat_q4_0( - vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, - nrows_dst, item_ct1, - get_pointer(tile_x_qs_q4_0_acc_ct1), - get_pointer(tile_x_d_q4_0_acc_ct1), - get_pointer(tile_y_qs_acc_ct1), - get_pointer(tile_y_ds_acc_ct1)); - }); - }); - } + dpct::has_capability_or_fail(stream->get_device(), + {sycl::aspect::fp16}); + + stream->submit([&](sycl::handler &cgh) { + sycl::local_accessor tile_x_qs_q4_0_acc_ct1( + sycl::range<1>(mmq_y * (WARP_SIZE) + mmq_y), cgh); + sycl::local_accessor tile_x_d_q4_0_acc_ct1( + sycl::range<1>(mmq_y * (WARP_SIZE / QI4_0) + mmq_y / QI4_0), + cgh); + sycl::local_accessor tile_y_qs_acc_ct1( + sycl::range<1>(mmq_x * WARP_SIZE), cgh); + sycl::local_accessor tile_y_ds_acc_ct1( + sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh); + + cgh.parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) { + mul_mat_q4_0( + vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, + nrows_dst, item_ct1, + get_pointer(tile_x_qs_q4_0_acc_ct1), + get_pointer(tile_x_d_q4_0_acc_ct1), + get_pointer(tile_y_qs_acc_ct1), + get_pointer(tile_y_ds_acc_ct1)); + }); + }); + }; + + if (nrows_x % mmq_y == 0) { + launch(std::false_type{}); } else { - const bool need_check = true; - /* - DPCT1049:21: The work-group size passed to the SYCL kernel may exceed - the limit. To get the device limit, query - info::device::max_work_group_size. Adjust the work-group size if needed. - */ - { - dpct::has_capability_or_fail(stream->get_device(), - {sycl::aspect::fp16}); - - stream->submit([&](sycl::handler &cgh) { - sycl::local_accessor tile_x_qs_q4_0_acc_ct1( - sycl::range<1>(mmq_y * (WARP_SIZE) + mmq_y), cgh); - sycl::local_accessor tile_x_d_q4_0_acc_ct1( - sycl::range<1>(mmq_y * (WARP_SIZE / QI4_0) + mmq_y / QI4_0), - cgh); - sycl::local_accessor tile_y_qs_acc_ct1( - sycl::range<1>(mmq_x * WARP_SIZE), cgh); - sycl::local_accessor tile_y_ds_acc_ct1( - sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh); - - cgh.parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) { - mul_mat_q4_0( - vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, - nrows_dst, item_ct1, - get_pointer(tile_x_qs_q4_0_acc_ct1), - get_pointer(tile_x_d_q4_0_acc_ct1), - get_pointer(tile_y_qs_acc_ct1), - get_pointer(tile_y_ds_acc_ct1)); - }); - }); - } + launch(std::true_type{}); } } catch (sycl::exception const &exc) { @@ -1922,76 +1890,44 @@ static void ggml_mul_mat_q4_1_q8_1_sycl(const void *vx, const void *vy, const sycl::range<3> block_nums(1, block_num_y, block_num_x); const sycl::range<3> block_dims(1, nwarps, WARP_SIZE); - if (nrows_x % mmq_y == 0) { - const bool need_check = false; + const auto launch = [&](auto need_check) { /* DPCT1049:22: The work-group size passed to the SYCL kernel may exceed the limit. To get the device limit, query info::device::max_work_group_size. Adjust the work-group size if needed. */ - { - dpct::has_capability_or_fail(stream->get_device(), - {sycl::aspect::fp16}); - - stream->submit([&](sycl::handler &cgh) { - sycl::local_accessor tile_x_qs_q4_1_acc_ct1( - sycl::range<1>(mmq_y * (WARP_SIZE) + +mmq_y), cgh); - sycl::local_accessor tile_x_dm_q4_1_acc_ct1( - sycl::range<1>(mmq_y * (WARP_SIZE / QI4_1) + mmq_y / QI4_1), - cgh); - sycl::local_accessor tile_y_qs_acc_ct1( - sycl::range<1>(mmq_x * WARP_SIZE), cgh); - sycl::local_accessor tile_y_ds_acc_ct1( - sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh); - - cgh.parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) { - mul_mat_q4_1( - vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, - nrows_dst, item_ct1, - get_pointer(tile_x_qs_q4_1_acc_ct1), - get_pointer(tile_x_dm_q4_1_acc_ct1), - get_pointer(tile_y_qs_acc_ct1), - get_pointer(tile_y_ds_acc_ct1)); - }); - }); - } + dpct::has_capability_or_fail(stream->get_device(), + {sycl::aspect::fp16}); + + stream->submit([&](sycl::handler &cgh) { + sycl::local_accessor tile_x_qs_q4_1_acc_ct1( + sycl::range<1>(mmq_y * (WARP_SIZE) + +mmq_y), cgh); + sycl::local_accessor tile_x_dm_q4_1_acc_ct1( + sycl::range<1>(mmq_y * (WARP_SIZE / QI4_1) + mmq_y / QI4_1), + cgh); + sycl::local_accessor tile_y_qs_acc_ct1( + sycl::range<1>(mmq_x * WARP_SIZE), cgh); + sycl::local_accessor tile_y_ds_acc_ct1( + sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh); + + cgh.parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) { + mul_mat_q4_1( + vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, + nrows_dst, item_ct1, + get_pointer(tile_x_qs_q4_1_acc_ct1), + get_pointer(tile_x_dm_q4_1_acc_ct1), + get_pointer(tile_y_qs_acc_ct1), + get_pointer(tile_y_ds_acc_ct1)); + }); + }); + }; + + if (nrows_x % mmq_y == 0) { + launch(std::false_type{}); } else { - const bool need_check = true; - /* - DPCT1049:23: The work-group size passed to the SYCL kernel may exceed - the limit. To get the device limit, query - info::device::max_work_group_size. Adjust the work-group size if needed. - */ - { - dpct::has_capability_or_fail(stream->get_device(), - {sycl::aspect::fp16}); - - stream->submit([&](sycl::handler &cgh) { - sycl::local_accessor tile_x_qs_q4_1_acc_ct1( - sycl::range<1>(mmq_y * (WARP_SIZE) + +mmq_y), cgh); - sycl::local_accessor tile_x_dm_q4_1_acc_ct1( - sycl::range<1>(mmq_y * (WARP_SIZE / QI4_1) + mmq_y / QI4_1), - cgh); - sycl::local_accessor tile_y_qs_acc_ct1( - sycl::range<1>(mmq_x * WARP_SIZE), cgh); - sycl::local_accessor tile_y_ds_acc_ct1( - sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh); - - cgh.parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) { - mul_mat_q4_1( - vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, - nrows_dst, item_ct1, - get_pointer(tile_x_qs_q4_1_acc_ct1), - get_pointer(tile_x_dm_q4_1_acc_ct1), - get_pointer(tile_y_qs_acc_ct1), - get_pointer(tile_y_ds_acc_ct1)); - }); - }); - } + launch(std::true_type{}); } } catch (sycl::exception const &exc) { @@ -2037,76 +1973,44 @@ static void ggml_mul_mat_q5_0_q8_1_sycl(const void *vx, const void *vy, const sycl::range<3> block_nums(1, block_num_y, block_num_x); const sycl::range<3> block_dims(1, nwarps, WARP_SIZE); - if (nrows_x % mmq_y == 0) { - const bool need_check = false; + const auto launch = [&](auto need_check) { /* DPCT1049:24: The work-group size passed to the SYCL kernel may exceed the limit. To get the device limit, query info::device::max_work_group_size. Adjust the work-group size if needed. */ - { - dpct::has_capability_or_fail(stream->get_device(), - {sycl::aspect::fp16}); - - stream->submit([&](sycl::handler &cgh) { - sycl::local_accessor tile_x_ql_q5_0_acc_ct1( - sycl::range<1>(mmq_y * (2 * WARP_SIZE) + mmq_y), cgh); - sycl::local_accessor tile_x_d_q5_0_acc_ct1( - sycl::range<1>(mmq_y * (WARP_SIZE / QI5_0) + mmq_y / QI5_0), - cgh); - sycl::local_accessor tile_y_qs_acc_ct1( - sycl::range<1>(mmq_x * WARP_SIZE), cgh); - sycl::local_accessor tile_y_ds_acc_ct1( - sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh); - - cgh.parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) { - mul_mat_q5_0( - vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, - nrows_dst, item_ct1, - get_pointer(tile_x_ql_q5_0_acc_ct1), - get_pointer(tile_x_d_q5_0_acc_ct1), - get_pointer(tile_y_qs_acc_ct1), - get_pointer(tile_y_ds_acc_ct1)); - }); - }); - } + dpct::has_capability_or_fail(stream->get_device(), + {sycl::aspect::fp16}); + + stream->submit([&](sycl::handler &cgh) { + sycl::local_accessor tile_x_ql_q5_0_acc_ct1( + sycl::range<1>(mmq_y * (2 * WARP_SIZE) + mmq_y), cgh); + sycl::local_accessor tile_x_d_q5_0_acc_ct1( + sycl::range<1>(mmq_y * (WARP_SIZE / QI5_0) + mmq_y / QI5_0), + cgh); + sycl::local_accessor tile_y_qs_acc_ct1( + sycl::range<1>(mmq_x * WARP_SIZE), cgh); + sycl::local_accessor tile_y_ds_acc_ct1( + sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh); + + cgh.parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) { + mul_mat_q5_0( + vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, + nrows_dst, item_ct1, + get_pointer(tile_x_ql_q5_0_acc_ct1), + get_pointer(tile_x_d_q5_0_acc_ct1), + get_pointer(tile_y_qs_acc_ct1), + get_pointer(tile_y_ds_acc_ct1)); + }); + }); + }; + + if (nrows_x % mmq_y == 0) { + launch(std::false_type{}); } else { - const bool need_check = true; - /* - DPCT1049:25: The work-group size passed to the SYCL kernel may exceed - the limit. To get the device limit, query - info::device::max_work_group_size. Adjust the work-group size if needed. - */ - { - dpct::has_capability_or_fail(stream->get_device(), - {sycl::aspect::fp16}); - - stream->submit([&](sycl::handler &cgh) { - sycl::local_accessor tile_x_ql_q5_0_acc_ct1( - sycl::range<1>(mmq_y * (2 * WARP_SIZE) + mmq_y), cgh); - sycl::local_accessor tile_x_d_q5_0_acc_ct1( - sycl::range<1>(mmq_y * (WARP_SIZE / QI5_0) + mmq_y / QI5_0), - cgh); - sycl::local_accessor tile_y_qs_acc_ct1( - sycl::range<1>(mmq_x * WARP_SIZE), cgh); - sycl::local_accessor tile_y_ds_acc_ct1( - sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh); - - cgh.parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) { - mul_mat_q5_0( - vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, - nrows_dst, item_ct1, - get_pointer(tile_x_ql_q5_0_acc_ct1), - get_pointer(tile_x_d_q5_0_acc_ct1), - get_pointer(tile_y_qs_acc_ct1), - get_pointer(tile_y_ds_acc_ct1)); - }); - }); - } + launch(std::true_type{}); } } catch (sycl::exception const &exc) { @@ -2152,76 +2056,44 @@ static void ggml_mul_mat_q5_1_q8_1_sycl(const void *vx, const void *vy, const sycl::range<3> block_nums(1, block_num_y, block_num_x); const sycl::range<3> block_dims(1, nwarps, WARP_SIZE); - if (nrows_x % mmq_y == 0) { - const bool need_check = false; + const auto launch = [&](auto need_check) { /* DPCT1049:26: The work-group size passed to the SYCL kernel may exceed the limit. To get the device limit, query info::device::max_work_group_size. Adjust the work-group size if needed. */ - { - dpct::has_capability_or_fail(stream->get_device(), - {sycl::aspect::fp16}); - - stream->submit([&](sycl::handler &cgh) { - sycl::local_accessor tile_x_ql_q5_1_acc_ct1( - sycl::range<1>(mmq_y * (2 * WARP_SIZE) + mmq_y), cgh); - sycl::local_accessor tile_x_dm_q5_1_acc_ct1( - sycl::range<1>(mmq_y * (WARP_SIZE / QI5_1) + mmq_y / QI5_1), - cgh); - sycl::local_accessor tile_y_qs_acc_ct1( - sycl::range<1>(mmq_x * WARP_SIZE), cgh); - sycl::local_accessor tile_y_ds_acc_ct1( - sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh); - - cgh.parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) { - mul_mat_q5_1( - vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, - nrows_dst, item_ct1, - get_pointer(tile_x_ql_q5_1_acc_ct1), - get_pointer(tile_x_dm_q5_1_acc_ct1), - get_pointer(tile_y_qs_acc_ct1), - get_pointer(tile_y_ds_acc_ct1)); - }); - }); - } + dpct::has_capability_or_fail(stream->get_device(), + {sycl::aspect::fp16}); + + stream->submit([&](sycl::handler &cgh) { + sycl::local_accessor tile_x_ql_q5_1_acc_ct1( + sycl::range<1>(mmq_y * (2 * WARP_SIZE) + mmq_y), cgh); + sycl::local_accessor tile_x_dm_q5_1_acc_ct1( + sycl::range<1>(mmq_y * (WARP_SIZE / QI5_1) + mmq_y / QI5_1), + cgh); + sycl::local_accessor tile_y_qs_acc_ct1( + sycl::range<1>(mmq_x * WARP_SIZE), cgh); + sycl::local_accessor tile_y_ds_acc_ct1( + sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh); + + cgh.parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) { + mul_mat_q5_1( + vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, + nrows_dst, item_ct1, + get_pointer(tile_x_ql_q5_1_acc_ct1), + get_pointer(tile_x_dm_q5_1_acc_ct1), + get_pointer(tile_y_qs_acc_ct1), + get_pointer(tile_y_ds_acc_ct1)); + }); + }); + }; + + if (nrows_x % mmq_y == 0) { + launch(std::false_type{}); } else { - const bool need_check = true; - /* - DPCT1049:27: The work-group size passed to the SYCL kernel may exceed - the limit. To get the device limit, query - info::device::max_work_group_size. Adjust the work-group size if needed. - */ - { - dpct::has_capability_or_fail(stream->get_device(), - {sycl::aspect::fp16}); - - stream->submit([&](sycl::handler &cgh) { - sycl::local_accessor tile_x_ql_q5_1_acc_ct1( - sycl::range<1>(mmq_y * (2 * WARP_SIZE) + mmq_y), cgh); - sycl::local_accessor tile_x_dm_q5_1_acc_ct1( - sycl::range<1>(mmq_y * (WARP_SIZE / QI5_1) + mmq_y / QI5_1), - cgh); - sycl::local_accessor tile_y_qs_acc_ct1( - sycl::range<1>(mmq_x * WARP_SIZE), cgh); - sycl::local_accessor tile_y_ds_acc_ct1( - sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh); - - cgh.parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) { - mul_mat_q5_1( - vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, - nrows_dst, item_ct1, - get_pointer(tile_x_ql_q5_1_acc_ct1), - get_pointer(tile_x_dm_q5_1_acc_ct1), - get_pointer(tile_y_qs_acc_ct1), - get_pointer(tile_y_ds_acc_ct1)); - }); - }); - } + launch(std::true_type{}); } } catch (sycl::exception const &exc) { @@ -2267,76 +2139,44 @@ static void ggml_mul_mat_q8_0_q8_1_sycl(const void *vx, const void *vy, const sycl::range<3> block_nums(1, block_num_y, block_num_x); const sycl::range<3> block_dims(1, nwarps, WARP_SIZE); - if (nrows_x % mmq_y == 0) { - const bool need_check = false; + const auto launch = [&](auto need_check) { /* DPCT1049:28: The work-group size passed to the SYCL kernel may exceed the limit. To get the device limit, query info::device::max_work_group_size. Adjust the work-group size if needed. */ - { - dpct::has_capability_or_fail(stream->get_device(), - {sycl::aspect::fp16}); - - stream->submit([&](sycl::handler &cgh) { - sycl::local_accessor tile_x_qs_q8_0_acc_ct1( - sycl::range<1>(mmq_y * (WARP_SIZE) + mmq_y), cgh); - sycl::local_accessor tile_x_d_q8_0_acc_ct1( - sycl::range<1>(mmq_y * (WARP_SIZE / QI8_0) + mmq_y / QI8_0), - cgh); - sycl::local_accessor tile_y_qs_acc_ct1( - sycl::range<1>(mmq_x * WARP_SIZE), cgh); - sycl::local_accessor tile_y_ds_acc_ct1( - sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh); - - cgh.parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) { - mul_mat_q8_0( - vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, - nrows_dst, item_ct1, - get_pointer(tile_x_qs_q8_0_acc_ct1), - get_pointer(tile_x_d_q8_0_acc_ct1), - get_pointer(tile_y_qs_acc_ct1), - get_pointer(tile_y_ds_acc_ct1)); - }); - }); - } + dpct::has_capability_or_fail(stream->get_device(), + {sycl::aspect::fp16}); + + stream->submit([&](sycl::handler &cgh) { + sycl::local_accessor tile_x_qs_q8_0_acc_ct1( + sycl::range<1>(mmq_y * (WARP_SIZE) + mmq_y), cgh); + sycl::local_accessor tile_x_d_q8_0_acc_ct1( + sycl::range<1>(mmq_y * (WARP_SIZE / QI8_0) + mmq_y / QI8_0), + cgh); + sycl::local_accessor tile_y_qs_acc_ct1( + sycl::range<1>(mmq_x * WARP_SIZE), cgh); + sycl::local_accessor tile_y_ds_acc_ct1( + sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh); + + cgh.parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) { + mul_mat_q8_0( + vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, + nrows_dst, item_ct1, + get_pointer(tile_x_qs_q8_0_acc_ct1), + get_pointer(tile_x_d_q8_0_acc_ct1), + get_pointer(tile_y_qs_acc_ct1), + get_pointer(tile_y_ds_acc_ct1)); + }); + }); + }; + + if (nrows_x % mmq_y == 0) { + launch(std::false_type{}); } else { - const bool need_check = true; - /* - DPCT1049:29: The work-group size passed to the SYCL kernel may exceed - the limit. To get the device limit, query - info::device::max_work_group_size. Adjust the work-group size if needed. - */ - { - dpct::has_capability_or_fail(stream->get_device(), - {sycl::aspect::fp16}); - - stream->submit([&](sycl::handler &cgh) { - sycl::local_accessor tile_x_qs_q8_0_acc_ct1( - sycl::range<1>(mmq_y * (WARP_SIZE) + mmq_y), cgh); - sycl::local_accessor tile_x_d_q8_0_acc_ct1( - sycl::range<1>(mmq_y * (WARP_SIZE / QI8_0) + mmq_y / QI8_0), - cgh); - sycl::local_accessor tile_y_qs_acc_ct1( - sycl::range<1>(mmq_x * WARP_SIZE), cgh); - sycl::local_accessor tile_y_ds_acc_ct1( - sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh); - - cgh.parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) { - mul_mat_q8_0( - vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, - nrows_dst, item_ct1, - get_pointer(tile_x_qs_q8_0_acc_ct1), - get_pointer(tile_x_d_q8_0_acc_ct1), - get_pointer(tile_y_qs_acc_ct1), - get_pointer(tile_y_ds_acc_ct1)); - }); - }); - } + launch(std::true_type{}); } } catch (sycl::exception const &exc) { @@ -2382,82 +2222,47 @@ static void ggml_mul_mat_q2_K_q8_1_sycl(const void *vx, const void *vy, const sycl::range<3> block_nums(1, block_num_y, block_num_x); const sycl::range<3> block_dims(1, nwarps, WARP_SIZE); - if (nrows_x % mmq_y == 0) { - const bool need_check = false; + const auto launch = [&](auto need_check) { /* DPCT1049:30: The work-group size passed to the SYCL kernel may exceed the limit. To get the device limit, query info::device::max_work_group_size. Adjust the work-group size if needed. */ - { - dpct::has_capability_or_fail(stream->get_device(), - {sycl::aspect::fp16}); - - stream->submit([&](sycl::handler &cgh) { - sycl::local_accessor tile_x_ql_q2_K_acc_ct1( - sycl::range<1>(mmq_y * (WARP_SIZE) + mmq_y), cgh); - sycl::local_accessor tile_x_dm_q2_K_acc_ct1( - sycl::range<1>(mmq_y * (WARP_SIZE / QI2_K) + mmq_y / QI2_K), - cgh); - sycl::local_accessor tile_x_sc_q2_K_acc_ct1( - sycl::range<1>(mmq_y * (WARP_SIZE / 4) + mmq_y / 4), cgh); - sycl::local_accessor tile_y_qs_acc_ct1( - sycl::range<1>(mmq_x * WARP_SIZE), cgh); - sycl::local_accessor tile_y_ds_acc_ct1( - sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh); - - cgh.parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) { - mul_mat_q2_K( - vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, - nrows_dst, item_ct1, - get_pointer(tile_x_ql_q2_K_acc_ct1), - get_pointer(tile_x_dm_q2_K_acc_ct1), - get_pointer(tile_x_sc_q2_K_acc_ct1), - get_pointer(tile_y_qs_acc_ct1), - get_pointer(tile_y_ds_acc_ct1)); - }); - }); - } + dpct::has_capability_or_fail(stream->get_device(), + {sycl::aspect::fp16}); + + stream->submit([&](sycl::handler &cgh) { + sycl::local_accessor tile_x_ql_q2_K_acc_ct1( + sycl::range<1>(mmq_y * (WARP_SIZE) + mmq_y), cgh); + sycl::local_accessor tile_x_dm_q2_K_acc_ct1( + sycl::range<1>(mmq_y * (WARP_SIZE / QI2_K) + mmq_y / QI2_K), + cgh); + sycl::local_accessor tile_x_sc_q2_K_acc_ct1( + sycl::range<1>(mmq_y * (WARP_SIZE / 4) + mmq_y / 4), cgh); + sycl::local_accessor tile_y_qs_acc_ct1( + sycl::range<1>(mmq_x * WARP_SIZE), cgh); + sycl::local_accessor tile_y_ds_acc_ct1( + sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh); + + cgh.parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) { + mul_mat_q2_K( + vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, + nrows_dst, item_ct1, + get_pointer(tile_x_ql_q2_K_acc_ct1), + get_pointer(tile_x_dm_q2_K_acc_ct1), + get_pointer(tile_x_sc_q2_K_acc_ct1), + get_pointer(tile_y_qs_acc_ct1), + get_pointer(tile_y_ds_acc_ct1)); + }); + }); + }; + + if (nrows_x % mmq_y == 0) { + launch(std::false_type{}); } else { - const bool need_check = true; - /* - DPCT1049:31: The work-group size passed to the SYCL kernel may exceed - the limit. To get the device limit, query - info::device::max_work_group_size. Adjust the work-group size if needed. - */ - { - dpct::has_capability_or_fail(stream->get_device(), - {sycl::aspect::fp16}); - - stream->submit([&](sycl::handler &cgh) { - sycl::local_accessor tile_x_ql_q2_K_acc_ct1( - sycl::range<1>(mmq_y * (WARP_SIZE) + mmq_y), cgh); - sycl::local_accessor tile_x_dm_q2_K_acc_ct1( - sycl::range<1>(mmq_y * (WARP_SIZE / QI2_K) + mmq_y / QI2_K), - cgh); - sycl::local_accessor tile_x_sc_q2_K_acc_ct1( - sycl::range<1>(mmq_y * (WARP_SIZE / 4) + mmq_y / 4), cgh); - sycl::local_accessor tile_y_qs_acc_ct1( - sycl::range<1>(mmq_x * WARP_SIZE), cgh); - sycl::local_accessor tile_y_ds_acc_ct1( - sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh); - - cgh.parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) { - mul_mat_q2_K( - vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, - nrows_dst, item_ct1, - get_pointer(tile_x_ql_q2_K_acc_ct1), - get_pointer(tile_x_dm_q2_K_acc_ct1), - get_pointer(tile_x_sc_q2_K_acc_ct1), - get_pointer(tile_y_qs_acc_ct1), - get_pointer(tile_y_ds_acc_ct1)); - }); - }); - } + launch(std::true_type{}); } } catch (sycl::exception const &exc) { @@ -2505,88 +2310,50 @@ static void ggml_mul_mat_q3_K_q8_1_sycl(const void *vx, const void *vy, const sycl::range<3> block_nums(1, block_num_y, block_num_x); const sycl::range<3> block_dims(1, nwarps, WARP_SIZE); - if (nrows_x % mmq_y == 0) { - const bool need_check = false; + const auto launch = [&](auto need_check) { /* DPCT1049:32: The work-group size passed to the SYCL kernel may exceed the limit. To get the device limit, query info::device::max_work_group_size. Adjust the work-group size if needed. */ - { - dpct::has_capability_or_fail(stream->get_device(), - {sycl::aspect::fp16}); - - stream->submit([&](sycl::handler &cgh) { - sycl::local_accessor tile_x_ql_q3_K_acc_ct1( - sycl::range<1>(mmq_y * (WARP_SIZE) + mmq_y), cgh); - sycl::local_accessor tile_x_dm_q3_K_acc_ct1( - sycl::range<1>(mmq_y * (WARP_SIZE / QI3_K) + mmq_y / QI3_K), - cgh); - sycl::local_accessor tile_x_qh_q3_K_acc_ct1( - sycl::range<1>(mmq_y * (WARP_SIZE / 2) + mmq_y / 2), cgh); - sycl::local_accessor tile_x_sc_q3_K_acc_ct1( - sycl::range<1>(mmq_y * (WARP_SIZE / 4) + mmq_y / 4), cgh); - sycl::local_accessor tile_y_qs_acc_ct1( - sycl::range<1>(mmq_x * WARP_SIZE), cgh); - sycl::local_accessor tile_y_ds_acc_ct1( - sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh); - - cgh.parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) { - mul_mat_q3_K( - vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, - nrows_dst, item_ct1, - get_pointer(tile_x_ql_q3_K_acc_ct1), - get_pointer(tile_x_dm_q3_K_acc_ct1), - get_pointer(tile_x_qh_q3_K_acc_ct1), - get_pointer(tile_x_sc_q3_K_acc_ct1), - get_pointer(tile_y_qs_acc_ct1), - get_pointer(tile_y_ds_acc_ct1)); - }); - }); - } + dpct::has_capability_or_fail(stream->get_device(), + {sycl::aspect::fp16}); + + stream->submit([&](sycl::handler &cgh) { + sycl::local_accessor tile_x_ql_q3_K_acc_ct1( + sycl::range<1>(mmq_y * (WARP_SIZE) + mmq_y), cgh); + sycl::local_accessor tile_x_dm_q3_K_acc_ct1( + sycl::range<1>(mmq_y * (WARP_SIZE / QI3_K) + mmq_y / QI3_K), + cgh); + sycl::local_accessor tile_x_qh_q3_K_acc_ct1( + sycl::range<1>(mmq_y * (WARP_SIZE / 2) + mmq_y / 2), cgh); + sycl::local_accessor tile_x_sc_q3_K_acc_ct1( + sycl::range<1>(mmq_y * (WARP_SIZE / 4) + mmq_y / 4), cgh); + sycl::local_accessor tile_y_qs_acc_ct1( + sycl::range<1>(mmq_x * WARP_SIZE), cgh); + sycl::local_accessor tile_y_ds_acc_ct1( + sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh); + + cgh.parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) { + mul_mat_q3_K( + vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, + nrows_dst, item_ct1, + get_pointer(tile_x_ql_q3_K_acc_ct1), + get_pointer(tile_x_dm_q3_K_acc_ct1), + get_pointer(tile_x_qh_q3_K_acc_ct1), + get_pointer(tile_x_sc_q3_K_acc_ct1), + get_pointer(tile_y_qs_acc_ct1), + get_pointer(tile_y_ds_acc_ct1)); + }); + }); + }; + + if (nrows_x % mmq_y == 0) { + launch(std::false_type{}); } else { - const bool need_check = true; - /* - DPCT1049:33: The work-group size passed to the SYCL kernel may exceed - the limit. To get the device limit, query - info::device::max_work_group_size. Adjust the work-group size if needed. - */ - { - dpct::has_capability_or_fail(stream->get_device(), - {sycl::aspect::fp16}); - - stream->submit([&](sycl::handler &cgh) { - sycl::local_accessor tile_x_ql_q3_K_acc_ct1( - sycl::range<1>(mmq_y * (WARP_SIZE) + mmq_y), cgh); - sycl::local_accessor tile_x_dm_q3_K_acc_ct1( - sycl::range<1>(mmq_y * (WARP_SIZE / QI3_K) + mmq_y / QI3_K), - cgh); - sycl::local_accessor tile_x_qh_q3_K_acc_ct1( - sycl::range<1>(mmq_y * (WARP_SIZE / 2) + mmq_y / 2), cgh); - sycl::local_accessor tile_x_sc_q3_K_acc_ct1( - sycl::range<1>(mmq_y * (WARP_SIZE / 4) + mmq_y / 4), cgh); - sycl::local_accessor tile_y_qs_acc_ct1( - sycl::range<1>(mmq_x * WARP_SIZE), cgh); - sycl::local_accessor tile_y_ds_acc_ct1( - sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh); - - cgh.parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) { - mul_mat_q3_K( - vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, - nrows_dst, item_ct1, - get_pointer(tile_x_ql_q3_K_acc_ct1), - get_pointer(tile_x_dm_q3_K_acc_ct1), - get_pointer(tile_x_qh_q3_K_acc_ct1), - get_pointer(tile_x_sc_q3_K_acc_ct1), - get_pointer(tile_y_qs_acc_ct1), - get_pointer(tile_y_ds_acc_ct1)); - }); - }); - } + launch(std::true_type{}); } #endif } @@ -2633,82 +2400,47 @@ static void ggml_mul_mat_q4_K_q8_1_sycl(const void *vx, const void *vy, const sycl::range<3> block_nums(1, block_num_y, block_num_x); const sycl::range<3> block_dims(1, nwarps, WARP_SIZE); - if (nrows_x % mmq_y == 0) { - const bool need_check = false; + const auto launch = [&](auto need_check) { /* DPCT1049:34: The work-group size passed to the SYCL kernel may exceed the limit. To get the device limit, query info::device::max_work_group_size. Adjust the work-group size if needed. */ - { - dpct::has_capability_or_fail(stream->get_device(), - {sycl::aspect::fp16}); - - stream->submit([&](sycl::handler &cgh) { - sycl::local_accessor tile_x_ql_q4_K_acc_ct1( - sycl::range<1>(mmq_y * (WARP_SIZE) + mmq_y), cgh); - sycl::local_accessor tile_x_dm_q4_K_acc_ct1( - sycl::range<1>(mmq_y * (WARP_SIZE / QI4_K) + mmq_y / QI4_K), - cgh); - sycl::local_accessor tile_x_sc_q4_K_acc_ct1( - sycl::range<1>(mmq_y * (WARP_SIZE / 8) + mmq_y / 8), cgh); - sycl::local_accessor tile_y_qs_acc_ct1( - sycl::range<1>(mmq_x * WARP_SIZE), cgh); - sycl::local_accessor tile_y_ds_acc_ct1( - sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh); - - cgh.parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) { - mul_mat_q4_K( - vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, - nrows_dst, item_ct1, - get_pointer(tile_x_ql_q4_K_acc_ct1), - get_pointer(tile_x_dm_q4_K_acc_ct1), - get_pointer(tile_x_sc_q4_K_acc_ct1), - get_pointer(tile_y_qs_acc_ct1), - get_pointer(tile_y_ds_acc_ct1)); - }); - }); - } + dpct::has_capability_or_fail(stream->get_device(), + {sycl::aspect::fp16}); + + stream->submit([&](sycl::handler &cgh) { + sycl::local_accessor tile_x_ql_q4_K_acc_ct1( + sycl::range<1>(mmq_y * (WARP_SIZE) + mmq_y), cgh); + sycl::local_accessor tile_x_dm_q4_K_acc_ct1( + sycl::range<1>(mmq_y * (WARP_SIZE / QI4_K) + mmq_y / QI4_K), + cgh); + sycl::local_accessor tile_x_sc_q4_K_acc_ct1( + sycl::range<1>(mmq_y * (WARP_SIZE / 8) + mmq_y / 8), cgh); + sycl::local_accessor tile_y_qs_acc_ct1( + sycl::range<1>(mmq_x * WARP_SIZE), cgh); + sycl::local_accessor tile_y_ds_acc_ct1( + sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh); + + cgh.parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) { + mul_mat_q4_K( + vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, + nrows_dst, item_ct1, + get_pointer(tile_x_ql_q4_K_acc_ct1), + get_pointer(tile_x_dm_q4_K_acc_ct1), + get_pointer(tile_x_sc_q4_K_acc_ct1), + get_pointer(tile_y_qs_acc_ct1), + get_pointer(tile_y_ds_acc_ct1)); + }); + }); + }; + + if (nrows_x % mmq_y == 0) { + launch(std::false_type{}); } else { - const bool need_check = true; - /* - DPCT1049:35: The work-group size passed to the SYCL kernel may exceed - the limit. To get the device limit, query - info::device::max_work_group_size. Adjust the work-group size if needed. - */ - { - dpct::has_capability_or_fail(stream->get_device(), - {sycl::aspect::fp16}); - - stream->submit([&](sycl::handler &cgh) { - sycl::local_accessor tile_x_ql_q4_K_acc_ct1( - sycl::range<1>(mmq_y * (WARP_SIZE) + mmq_y), cgh); - sycl::local_accessor tile_x_dm_q4_K_acc_ct1( - sycl::range<1>(mmq_y * (WARP_SIZE / QI4_K) + mmq_y / QI4_K), - cgh); - sycl::local_accessor tile_x_sc_q4_K_acc_ct1( - sycl::range<1>(mmq_y * (WARP_SIZE / 8) + mmq_y / 8), cgh); - sycl::local_accessor tile_y_qs_acc_ct1( - sycl::range<1>(mmq_x * WARP_SIZE), cgh); - sycl::local_accessor tile_y_ds_acc_ct1( - sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh); - - cgh.parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) { - mul_mat_q4_K( - vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, - nrows_dst, item_ct1, - get_pointer(tile_x_ql_q4_K_acc_ct1), - get_pointer(tile_x_dm_q4_K_acc_ct1), - get_pointer(tile_x_sc_q4_K_acc_ct1), - get_pointer(tile_y_qs_acc_ct1), - get_pointer(tile_y_ds_acc_ct1)); - }); - }); - } + launch(std::true_type{}); } } catch (sycl::exception const &exc) { @@ -2754,82 +2486,47 @@ static void ggml_mul_mat_q5_K_q8_1_sycl(const void *vx, const void *vy, const sycl::range<3> block_nums(1, block_num_y, block_num_x); const sycl::range<3> block_dims(1, nwarps, WARP_SIZE); - if (nrows_x % mmq_y == 0) { - const bool need_check = false; + const auto launch = [&](auto need_check) { /* DPCT1049:36: The work-group size passed to the SYCL kernel may exceed the limit. To get the device limit, query info::device::max_work_group_size. Adjust the work-group size if needed. */ - { - dpct::has_capability_or_fail(stream->get_device(), - {sycl::aspect::fp16}); - - stream->submit([&](sycl::handler &cgh) { - sycl::local_accessor tile_x_ql_q5_K_acc_ct1( - sycl::range<1>(mmq_y * (2 * WARP_SIZE) + mmq_y), cgh); - sycl::local_accessor tile_x_dm_q5_K_acc_ct1( - sycl::range<1>(mmq_y * (WARP_SIZE / QI5_K) + mmq_y / QI5_K), - cgh); - sycl::local_accessor tile_x_sc_q5_K_acc_ct1( - sycl::range<1>(mmq_y * (WARP_SIZE / 8) + mmq_y / 8), cgh); - sycl::local_accessor tile_y_qs_acc_ct1( - sycl::range<1>(mmq_x * WARP_SIZE), cgh); - sycl::local_accessor tile_y_ds_acc_ct1( - sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh); - - cgh.parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) { - mul_mat_q5_K( - vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, - nrows_dst, item_ct1, - get_pointer(tile_x_ql_q5_K_acc_ct1), - get_pointer(tile_x_dm_q5_K_acc_ct1), - get_pointer(tile_x_sc_q5_K_acc_ct1), - get_pointer(tile_y_qs_acc_ct1), - get_pointer(tile_y_ds_acc_ct1)); - }); - }); - } + dpct::has_capability_or_fail(stream->get_device(), + {sycl::aspect::fp16}); + + stream->submit([&](sycl::handler &cgh) { + sycl::local_accessor tile_x_ql_q5_K_acc_ct1( + sycl::range<1>(mmq_y * (2 * WARP_SIZE) + mmq_y), cgh); + sycl::local_accessor tile_x_dm_q5_K_acc_ct1( + sycl::range<1>(mmq_y * (WARP_SIZE / QI5_K) + mmq_y / QI5_K), + cgh); + sycl::local_accessor tile_x_sc_q5_K_acc_ct1( + sycl::range<1>(mmq_y * (WARP_SIZE / 8) + mmq_y / 8), cgh); + sycl::local_accessor tile_y_qs_acc_ct1( + sycl::range<1>(mmq_x * WARP_SIZE), cgh); + sycl::local_accessor tile_y_ds_acc_ct1( + sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh); + + cgh.parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) { + mul_mat_q5_K( + vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, + nrows_dst, item_ct1, + get_pointer(tile_x_ql_q5_K_acc_ct1), + get_pointer(tile_x_dm_q5_K_acc_ct1), + get_pointer(tile_x_sc_q5_K_acc_ct1), + get_pointer(tile_y_qs_acc_ct1), + get_pointer(tile_y_ds_acc_ct1)); + }); + }); + }; + + if (nrows_x % mmq_y == 0) { + launch(std::false_type{}); } else { - const bool need_check = true; - /* - DPCT1049:37: The work-group size passed to the SYCL kernel may exceed - the limit. To get the device limit, query - info::device::max_work_group_size. Adjust the work-group size if needed. - */ - { - dpct::has_capability_or_fail(stream->get_device(), - {sycl::aspect::fp16}); - - stream->submit([&](sycl::handler &cgh) { - sycl::local_accessor tile_x_ql_q5_K_acc_ct1( - sycl::range<1>(mmq_y * (2 * WARP_SIZE) + mmq_y), cgh); - sycl::local_accessor tile_x_dm_q5_K_acc_ct1( - sycl::range<1>(mmq_y * (WARP_SIZE / QI5_K) + mmq_y / QI5_K), - cgh); - sycl::local_accessor tile_x_sc_q5_K_acc_ct1( - sycl::range<1>(mmq_y * (WARP_SIZE / 8) + mmq_y / 8), cgh); - sycl::local_accessor tile_y_qs_acc_ct1( - sycl::range<1>(mmq_x * WARP_SIZE), cgh); - sycl::local_accessor tile_y_ds_acc_ct1( - sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh); - - cgh.parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) { - mul_mat_q5_K( - vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, - nrows_dst, item_ct1, - get_pointer(tile_x_ql_q5_K_acc_ct1), - get_pointer(tile_x_dm_q5_K_acc_ct1), - get_pointer(tile_x_sc_q5_K_acc_ct1), - get_pointer(tile_y_qs_acc_ct1), - get_pointer(tile_y_ds_acc_ct1)); - }); - }); - } + launch(std::true_type{}); } } catch (sycl::exception const &exc) { @@ -2875,82 +2572,47 @@ static void ggml_mul_mat_q6_K_q8_1_sycl(const void *vx, const void *vy, const sycl::range<3> block_nums(1, block_num_y, block_num_x); const sycl::range<3> block_dims(1, nwarps, WARP_SIZE); - if (nrows_x % mmq_y == 0) { - const bool need_check = false; + const auto launch = [&](auto need_check) { /* DPCT1049:38: The work-group size passed to the SYCL kernel may exceed the limit. To get the device limit, query info::device::max_work_group_size. Adjust the work-group size if needed. */ - { - dpct::has_capability_or_fail(stream->get_device(), - {sycl::aspect::fp16}); - - stream->submit([&](sycl::handler &cgh) { - sycl::local_accessor tile_x_ql_acc_ct1( - sycl::range<1>(mmq_y * (2 * WARP_SIZE) + mmq_y), cgh); - sycl::local_accessor tile_x_dm_acc_ct1( - sycl::range<1>(mmq_y * (WARP_SIZE / QI6_K) + mmq_y / QI6_K), - cgh); - sycl::local_accessor tile_x_sc_acc_ct1( - sycl::range<1>(mmq_y * (WARP_SIZE / 8) + mmq_y / 8), cgh); - sycl::local_accessor tile_y_qs_acc_ct1( - sycl::range<1>(mmq_x * WARP_SIZE), cgh); - sycl::local_accessor tile_y_ds_acc_ct1( - sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh); - - cgh.parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) { - mul_mat_q6_K( - vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, - nrows_dst, item_ct1, - get_pointer(tile_x_ql_acc_ct1), - get_pointer(tile_x_dm_acc_ct1), - get_pointer(tile_x_sc_acc_ct1), - get_pointer(tile_y_qs_acc_ct1), - get_pointer(tile_y_ds_acc_ct1)); - }); - }); - } + dpct::has_capability_or_fail(stream->get_device(), + {sycl::aspect::fp16}); + + stream->submit([&](sycl::handler &cgh) { + sycl::local_accessor tile_x_ql_acc_ct1( + sycl::range<1>(mmq_y * (2 * WARP_SIZE) + mmq_y), cgh); + sycl::local_accessor tile_x_dm_acc_ct1( + sycl::range<1>(mmq_y * (WARP_SIZE / QI6_K) + mmq_y / QI6_K), + cgh); + sycl::local_accessor tile_x_sc_acc_ct1( + sycl::range<1>(mmq_y * (WARP_SIZE / 8) + mmq_y / 8), cgh); + sycl::local_accessor tile_y_qs_acc_ct1( + sycl::range<1>(mmq_x * WARP_SIZE), cgh); + sycl::local_accessor tile_y_ds_acc_ct1( + sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh); + + cgh.parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) { + mul_mat_q6_K( + vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, + nrows_dst, item_ct1, + get_pointer(tile_x_ql_acc_ct1), + get_pointer(tile_x_dm_acc_ct1), + get_pointer(tile_x_sc_acc_ct1), + get_pointer(tile_y_qs_acc_ct1), + get_pointer(tile_y_ds_acc_ct1)); + }); + }); + }; + + if (nrows_x % mmq_y == 0) { + launch(std::false_type{}); } else { - const bool need_check = true; - /* - DPCT1049:39: The work-group size passed to the SYCL kernel may exceed - the limit. To get the device limit, query - info::device::max_work_group_size. Adjust the work-group size if needed. - */ - { - dpct::has_capability_or_fail(stream->get_device(), - {sycl::aspect::fp16}); - - stream->submit([&](sycl::handler &cgh) { - sycl::local_accessor tile_x_ql_acc_ct1( - sycl::range<1>(mmq_y * (2 * WARP_SIZE) + mmq_y), cgh); - sycl::local_accessor tile_x_dm_acc_ct1( - sycl::range<1>(mmq_y * (WARP_SIZE / QI6_K) + mmq_y / QI6_K), - cgh); - sycl::local_accessor tile_x_sc_acc_ct1( - sycl::range<1>(mmq_y * (WARP_SIZE / 8) + mmq_y / 8), cgh); - sycl::local_accessor tile_y_qs_acc_ct1( - sycl::range<1>(mmq_x * WARP_SIZE), cgh); - sycl::local_accessor tile_y_ds_acc_ct1( - sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh); - - cgh.parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) { - mul_mat_q6_K( - vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, - nrows_dst, item_ct1, - get_pointer(tile_x_ql_acc_ct1), - get_pointer(tile_x_dm_acc_ct1), - get_pointer(tile_x_sc_acc_ct1), - get_pointer(tile_y_qs_acc_ct1), - get_pointer(tile_y_ds_acc_ct1)); - }); - }); - } + launch(std::true_type{}); } } catch (sycl::exception const &exc) { From 74b115ca6e9ca7bec5c03f9c4ae0e30ee4b6345a Mon Sep 17 00:00:00 2001 From: Neo Zhang Date: Wed, 21 Oct 2026 15:46:43 +0800 Subject: [PATCH 40/69] sycl : fix fa crash for Query head dimension mismatch (neq0 != HSK / D=2048/4096) (#30286) --- ggml/src/ggml-sycl/fattn.cpp | 16 ++++++++++++++++ 1 file changed, 16 insertions(+) diff --git a/ggml/src/ggml-sycl/fattn.cpp b/ggml/src/ggml-sycl/fattn.cpp index 8bd946e09583..42e46298e220 100644 --- a/ggml/src/ggml-sycl/fattn.cpp +++ b/ggml/src/ggml-sycl/fattn.cpp @@ -281,6 +281,22 @@ static best_fattn_kernel ggml_sycl_get_best_fattn_kernel(const int device, const void ggml_sycl_flash_attn_ext(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { ggml_sycl_set_device(ctx.device); + ggml_tensor * Q = dst->src[0]; + ggml_tensor * K = dst->src[1]; + ggml_tensor * V = dst->src[2]; + if (K && Q && K->ne[0] != Q->ne[0] && Q->ne[0] > 0 && K->ne[0] % Q->ne[0] == 0) { + const int64_t d = Q->ne[0]; + const int64_t n_heads = (K->ne[0] * K->ne[2]) / d; + K->ne[0] = d; + K->ne[2] = n_heads; + } + if (V && Q && V->ne[0] != Q->ne[0] && Q->ne[0] > 0 && V->ne[0] % Q->ne[0] == 0) { + const int64_t d = Q->ne[0]; + const int64_t n_heads = (V->ne[0] * V->ne[2]) / d; + V->ne[0] = d; + V->ne[2] = n_heads; + } + // sparse nodes are gathered down to n_kv_max rows and re-dispatched here if (ggml_sycl_flash_attn_ext_sparse(ctx, dst)) { return; From b531e4c7494e5a9349e40d2ea9efc3686f635cdf Mon Sep 17 00:00:00 2001 From: lhez Date: Wed, 21 Oct 2026 00:48:13 -0700 Subject: [PATCH 41/69] opencl: add dp4a GEMM variants to vector load activation (#30753) * opencl: add dp4a GEMM variants with vector activation loads * opencl: rename kernels and env vars --- ggml/src/ggml-opencl/ggml-opencl.cpp | 50 +++++-- .../kernels/gemm_noshuffle_q4_0_q8_1_dp4a.cl | 126 ++++++++++++++++ .../kernels/gemm_noshuffle_q4_k_q8_1_dp4a.cl | 138 ++++++++++++++++++ .../kernels/gemm_noshuffle_q5_k_q8_1_dp4a.cl | 138 ++++++++++++++++++ .../kernels/gemm_noshuffle_q6_k_q8_1_dp4a.cl | 114 +++++++++++++++ .../kernels/gemm_noshuffle_q8_0_q8_1_dp4a.cl | 122 ++++++++++++++++ 6 files changed, 679 insertions(+), 9 deletions(-) diff --git a/ggml/src/ggml-opencl/ggml-opencl.cpp b/ggml/src/ggml-opencl/ggml-opencl.cpp index 70f199dba140..6071b6e58256 100644 --- a/ggml/src/ggml-opencl/ggml-opencl.cpp +++ b/ggml/src/ggml-opencl/ggml-opencl.cpp @@ -1243,6 +1243,7 @@ struct ggml_backend_opencl_context { cl_kernel kernel_gemm_noshuffle_q4_1_f32; cl_kernel kernel_gemm_noshuffle_q8_0_f32, kernel_gemm_noshuffle_q8_0_f32_bin; cl_kernel kernel_gemm_noshuffle_q8_0_q8_1_dp4a = nullptr; // dp4a (int8) dense q8_0 prefill GEMM (opt-in) + cl_kernel kernel_gemm_noshuffle_q8_0_q8_1_dp4a_act_vload4 = nullptr; cl_kernel kernel_gemm_noshuffle_q8_0_q8_1_dp4a_wimg = nullptr; // q8_0 dense dp4a, weights via texture (opt-in) cl_kernel kernel_gemm_noshuffle_q8_0_q8_1_dp4a_ila_a8_bin = nullptr; cl_kernel kernel_gemv_noshuffle_q8_0_f32; @@ -1264,6 +1265,7 @@ struct ggml_backend_opencl_context { cl_kernel kernel_gemm_noshuffle_q4_k_q8_1_dp4a_ila_a8_bin; cl_kernel kernel_gemv_noshuffle_q4_k_f32_32b_trans; cl_kernel kernel_gemm_noshuffle_q4_k_q8_1_dp4a = nullptr; // dp4a (int8) dense prefill GEMM + cl_kernel kernel_gemm_noshuffle_q4_k_q8_1_dp4a_act_vload4 = nullptr; cl_kernel kernel_gemm_noshuffle_q4_k_q8_1_dp4a_wimg = nullptr; // dp4a dense prefill GEMM, weights via texture (X1 opt-in) cl_kernel kernel_gemm_noshuffle_q4_k_q8_1_dp4a_narrow = nullptr; cl_kernel kernel_gemm_noshuffle_q4_k_q8_1_dp4a_narrow_wimg = nullptr; @@ -1271,7 +1273,9 @@ struct ggml_backend_opencl_context { int tile_n_dp4a_narrow_q4_k = 32; int max_n_dp4a_narrow_q4_k = 16; cl_kernel kernel_gemm_noshuffle_q5_k_q8_1_dp4a = nullptr; // dp4a (int8) dense q5_K prefill GEMM + cl_kernel kernel_gemm_noshuffle_q5_k_q8_1_dp4a_act_vload4 = nullptr; cl_kernel kernel_gemm_noshuffle_q6_k_q8_1_dp4a = nullptr; // dp4a (int8) dense q6_K prefill GEMM + cl_kernel kernel_gemm_noshuffle_q6_k_q8_1_dp4a_act_vload4 = nullptr; cl_kernel kernel_quant_a_q8_1; // plain activation q8_1 pre-pass cl_kernel kernel_gemm_noshuffle_q4_k_f32_r1; cl_kernel kernel_gemm_noshuffle_q4_k_f32_kimg; @@ -1305,6 +1309,7 @@ struct ggml_backend_opencl_context { cl_kernel kernel_gemm_noshuffle_iq4_nl_f32; cl_kernel kernel_gemm_noshuffle_iq4_nl_q8_1_dp4a = nullptr; // dp4a (int8) dense IQ4_NL prefill GEMM cl_kernel kernel_gemm_noshuffle_q4_0_q8_1_dp4a = nullptr; // dp4a (int8) dense q4_0 prefill GEMM + cl_kernel kernel_gemm_noshuffle_q4_0_q8_1_dp4a_act_vload4 = nullptr; cl_kernel kernel_gemm_coopk_q4_0_q8_1_dp4a_c4 = nullptr; cl_kernel kernel_gemm_coopk_q4_0_q8_1_dp4a_c2 = nullptr; int nsg_coopk_dp4a_q4_0 = 8; @@ -4347,6 +4352,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { #endif cl_program prog = build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_gemm_noshuffle_q4_0_q8_1_dp4a = clCreateKernel(prog, "kernel_gemm_noshuffle_q4_0_q8_1_dp4a", &err), err)); + CL_CHECK((backend_ctx->kernel_gemm_noshuffle_q4_0_q8_1_dp4a_act_vload4 = clCreateKernel(prog, "kernel_gemm_noshuffle_q4_0_q8_1_dp4a_act_vload4", &err), err)); CL_CHECK(clReleaseProgram(prog)); GGML_LOG_CONT("."); } @@ -4486,6 +4492,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { cl_program prog = build_program_from_source(backend_ctx, kernel_src.c_str(), dp4a_opts); CL_CHECK((backend_ctx->kernel_gemm_noshuffle_q4_k_q8_1_dp4a = clCreateKernel(prog, "kernel_gemm_noshuffle_q4_k_q8_1_dp4a", &err), err)); CL_CHECK((backend_ctx->kernel_gemm_noshuffle_q4_k_q8_1_dp4a_wimg = clCreateKernel(prog, "kernel_gemm_noshuffle_q4_k_q8_1_dp4a_wimg", &err), err)); + CL_CHECK((backend_ctx->kernel_gemm_noshuffle_q4_k_q8_1_dp4a_act_vload4 = clCreateKernel(prog, "kernel_gemm_noshuffle_q4_k_q8_1_dp4a_act_vload4", &err), err)); CL_CHECK(clReleaseProgram(prog)); // Compile a smaller N tile to avoid unused columns in small batches. @@ -4518,6 +4525,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { cl_program prog = build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_gemm_noshuffle_q8_0_q8_1_dp4a = clCreateKernel(prog, "kernel_gemm_noshuffle_q8_0_q8_1_dp4a", &err), err)); CL_CHECK((backend_ctx->kernel_gemm_noshuffle_q8_0_q8_1_dp4a_wimg = clCreateKernel(prog, "kernel_gemm_noshuffle_q8_0_q8_1_dp4a_wimg", &err), err)); + CL_CHECK((backend_ctx->kernel_gemm_noshuffle_q8_0_q8_1_dp4a_act_vload4 = clCreateKernel(prog, "kernel_gemm_noshuffle_q8_0_q8_1_dp4a_act_vload4", &err), err)); CL_CHECK(clReleaseProgram(prog)); GGML_LOG_CONT("."); } @@ -4533,6 +4541,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { #endif cl_program prog = build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_gemm_noshuffle_q5_k_q8_1_dp4a = clCreateKernel(prog, "kernel_gemm_noshuffle_q5_k_q8_1_dp4a", &err), err)); + CL_CHECK((backend_ctx->kernel_gemm_noshuffle_q5_k_q8_1_dp4a_act_vload4 = clCreateKernel(prog, "kernel_gemm_noshuffle_q5_k_q8_1_dp4a_act_vload4", &err), err)); CL_CHECK(clReleaseProgram(prog)); GGML_LOG_CONT("."); } @@ -4548,6 +4557,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { #endif cl_program prog = build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_gemm_noshuffle_q6_k_q8_1_dp4a = clCreateKernel(prog, "kernel_gemm_noshuffle_q6_k_q8_1_dp4a", &err), err)); + CL_CHECK((backend_ctx->kernel_gemm_noshuffle_q6_k_q8_1_dp4a_act_vload4 = clCreateKernel(prog, "kernel_gemm_noshuffle_q6_k_q8_1_dp4a_act_vload4", &err), err)); CL_CHECK(clReleaseProgram(prog)); GGML_LOG_CONT("."); } @@ -20430,11 +20440,13 @@ static void ggml_cl_mul_mat_q4_0_f32_adreno(ggml_backend_t backend, const ggml_t return; } - // dp4a (int8) dense prefill GEMM, default off + static const char * q4_0_act_vload4_env = getenv("GGML_OPENCL_Q4_0_DP4A_ACT_VLOAD4"); + const bool q4_0_act_vload4_on = q4_0_act_vload4_env == nullptr || atoi(q4_0_act_vload4_env) != 0; + // Enable dense prefill by default on X2E when the uint4 activation tile is enabled. static const char * q4_0_dense_dp4a_env = getenv("GGML_OPENCL_Q4_0_DENSE_DP4A"); bool q4_0_dense_dp4a_on = q4_0_dense_dp4a_env ? (atoi(q4_0_dense_dp4a_env) != 0) - : false; + : (backend_ctx->adreno_gen == ADRENO_GPU_GEN::X2E && q4_0_act_vload4_on); // dot prod has to be available q4_0_dense_dp4a_on = backend_ctx->has_integer_dot && q4_0_dense_dp4a_on; @@ -20461,7 +20473,9 @@ static void ggml_cl_mul_mat_q4_0_f32_adreno(ggml_backend_t backend, const ggml_t size_t q_global[1] = { (size_t)(((n_blocks + 63) / 64) * 64) }; backend_ctx->enqueue_ndrange_kernel(qk, 1, q_global, q_local, dst); - cl_kernel dk = backend_ctx->kernel_gemm_noshuffle_q4_0_q8_1_dp4a; + cl_kernel dk = q4_0_act_vload4_on + ? backend_ctx->kernel_gemm_noshuffle_q4_0_q8_1_dp4a_act_vload4 + : backend_ctx->kernel_gemm_noshuffle_q4_0_q8_1_dp4a; int ai = 0; CL_CHECK(clSetKernelArg(dk, ai++, sizeof(cl_mem), &extra0_q4_0->q)); CL_CHECK(clSetKernelArg(dk, ai++, sizeof(cl_mem), &extra0_q4_0->d)); @@ -21754,8 +21768,13 @@ static void ggml_cl_mul_mat_q8_0_f32_adreno(ggml_backend_t backend, const ggml_t } } - cl_kernel dk = use_wimg ? backend_ctx->kernel_gemm_noshuffle_q8_0_q8_1_dp4a_wimg - : backend_ctx->kernel_gemm_noshuffle_q8_0_q8_1_dp4a; + static const char * q8_0_act_vload4_env = getenv("GGML_OPENCL_Q8_0_DP4A_ACT_VLOAD4"); + const bool q8_0_act_vload4_on = q8_0_act_vload4_env == nullptr || atoi(q8_0_act_vload4_env) != 0; + cl_kernel dk = use_wimg + ? backend_ctx->kernel_gemm_noshuffle_q8_0_q8_1_dp4a_wimg + : q8_0_act_vload4_on + ? backend_ctx->kernel_gemm_noshuffle_q8_0_q8_1_dp4a_act_vload4 + : backend_ctx->kernel_gemm_noshuffle_q8_0_q8_1_dp4a; int ai = 0; if (use_wimg) { CL_CHECK(clSetKernelArg(dk, ai++, sizeof(cl_mem), &q8_q_img)); @@ -22703,11 +22722,16 @@ static void ggml_cl_mul_mat_q4_k_f32_adreno(ggml_backend_t backend, const ggml_t const bool use_narrow = backend_ctx->kernel_gemm_noshuffle_q4_k_q8_1_dp4a_narrow != nullptr && N <= backend_ctx->max_n_dp4a_narrow_q4_k; + static const char * q4_k_act_vload4_env = getenv("GGML_OPENCL_Q4_K_DP4A_ACT_VLOAD4"); + const bool q4_k_act_vload4_on = q4_k_act_vload4_env == nullptr || atoi(q4_k_act_vload4_env) != 0; cl_kernel dk = use_narrow ? (use_wimg ? backend_ctx->kernel_gemm_noshuffle_q4_k_q8_1_dp4a_narrow_wimg : backend_ctx->kernel_gemm_noshuffle_q4_k_q8_1_dp4a_narrow) - : (use_wimg ? backend_ctx->kernel_gemm_noshuffle_q4_k_q8_1_dp4a_wimg - : backend_ctx->kernel_gemm_noshuffle_q4_k_q8_1_dp4a); + : use_wimg + ? backend_ctx->kernel_gemm_noshuffle_q4_k_q8_1_dp4a_wimg + : q4_k_act_vload4_on + ? backend_ctx->kernel_gemm_noshuffle_q4_k_q8_1_dp4a_act_vload4 + : backend_ctx->kernel_gemm_noshuffle_q4_k_q8_1_dp4a; int ai = 0; if (use_wimg) { CL_CHECK(clSetKernelArg(dk, ai++, sizeof(cl_mem), &q4k_q_img)); @@ -23369,7 +23393,11 @@ static void ggml_cl_mul_mat_q6_K_f32_adreno(ggml_backend_t backend, const ggml_t size_t q_global[1] = { (size_t)(((n_blocks + 63) / 64) * 64) }; backend_ctx->enqueue_ndrange_kernel(qk, 1, q_global, q_local, dst); - cl_kernel dk = backend_ctx->kernel_gemm_noshuffle_q6_k_q8_1_dp4a; + static const char * q6_k_act_vload4_env = getenv("GGML_OPENCL_Q6_K_DP4A_ACT_VLOAD4"); + const bool q6_k_act_vload4_on = q6_k_act_vload4_env == nullptr || atoi(q6_k_act_vload4_env) != 0; + cl_kernel dk = q6_k_act_vload4_on + ? backend_ctx->kernel_gemm_noshuffle_q6_k_q8_1_dp4a_act_vload4 + : backend_ctx->kernel_gemm_noshuffle_q6_k_q8_1_dp4a; int ai = 0; CL_CHECK(clSetKernelArg(dk, ai++, sizeof(cl_mem), &extra0_q6_K->ql)); CL_CHECK(clSetKernelArg(dk, ai++, sizeof(cl_mem), &extra0_q6_K->qh)); @@ -23925,7 +23953,11 @@ static void ggml_cl_mul_mat_q5_K_f32_adreno(ggml_backend_t backend, const ggml_t size_t q_global[1] = { (size_t)(((n_blocks + 63) / 64) * 64) }; backend_ctx->enqueue_ndrange_kernel(qk, 1, q_global, q_local, dst); - cl_kernel dk = backend_ctx->kernel_gemm_noshuffle_q5_k_q8_1_dp4a; + static const char * q5_k_act_vload4_env = getenv("GGML_OPENCL_Q5_K_DP4A_ACT_VLOAD4"); + const bool q5_k_act_vload4_on = q5_k_act_vload4_env == nullptr || atoi(q5_k_act_vload4_env) != 0; + cl_kernel dk = q5_k_act_vload4_on + ? backend_ctx->kernel_gemm_noshuffle_q5_k_q8_1_dp4a_act_vload4 + : backend_ctx->kernel_gemm_noshuffle_q5_k_q8_1_dp4a; int ai = 0; CL_CHECK(clSetKernelArg(dk, ai++, sizeof(cl_mem), &extra0_q5_k->q)); CL_CHECK(clSetKernelArg(dk, ai++, sizeof(cl_mem), &extra0_q5_k->qh)); diff --git a/ggml/src/ggml-opencl/kernels/gemm_noshuffle_q4_0_q8_1_dp4a.cl b/ggml/src/ggml-opencl/kernels/gemm_noshuffle_q4_0_q8_1_dp4a.cl index 446a8eb18483..6418d0f8ff44 100644 --- a/ggml/src/ggml-opencl/kernels/gemm_noshuffle_q4_0_q8_1_dp4a.cl +++ b/ggml/src/ggml-opencl/kernels/gemm_noshuffle_q4_0_q8_1_dp4a.cl @@ -125,3 +125,129 @@ kernel void kernel_gemm_noshuffle_q4_0_q8_1_dp4a( } #undef NGROUPS } + +// Stage each 32-K activation block as two uint4 values. +inline int dot8_q8a_v(uint8 qw, uint4 a0, uint4 a1) { + int r = 0; + r = dot_acc_sat_4x8packed_ss_int(qw.s0, a0.x, r); + r = dot_acc_sat_4x8packed_ss_int(qw.s1, a0.y, r); + r = dot_acc_sat_4x8packed_ss_int(qw.s2, a0.z, r); + r = dot_acc_sat_4x8packed_ss_int(qw.s3, a0.w, r); + r = dot_acc_sat_4x8packed_ss_int(qw.s4, a1.x, r); + r = dot_acc_sat_4x8packed_ss_int(qw.s5, a1.y, r); + r = dot_acc_sat_4x8packed_ss_int(qw.s6, a1.z, r); + r = dot_acc_sat_4x8packed_ss_int(qw.s7, a1.w, r); + return r; +} + +__attribute__((qcom_wave_pair_mode(1))) +kernel void kernel_gemm_noshuffle_q4_0_q8_1_dp4a_act_vload4( + __global const ushort * src0_q, + __global const half * src0_d, + __global const uint * src1_qa, + __global const half * src1_da, + __global const half * src1_sa, + __global float * dst, + ulong offsetd, + int m, + int n_no_padding, + int k +) { + dst = (global float *)((global char *)dst + offsetd); + + const uint lid = get_local_id(0); + const uint block_id_m = get_global_id(1); + const uint block_id_n = get_global_id(2); + + const uint row = block_id_m * 64 + lid; + const uint col_base = block_id_n * TILESIZE_N; + const bool row_valid = row < (uint)m; + const uint rrow = row_valid ? row : 0; + + const uint k_u = (uint)k >> 2; + const uint k_b = (uint)k >> 5; + + __local uint4 sh_qa4[TILESIZE_N][2]; + __local half sh_d[TILESIZE_N]; + __local half sh_s[TILESIZE_N]; + +#define NGROUPS (TILESIZE_N / 4) + float4 acc[NGROUPS]; + #pragma unroll + for (int g = 0; g < NGROUPS; ++g) { + acc[g] = (float4)(0.0f); + } + + for (uint step = 0; step < (uint)k; step += 32) { + const uint sub = step >> 5; + + const float d_w = (float)src0_d[rrow + sub * (uint)m]; + + const uint qsbase = rrow + (step >> 2) * (uint)m; + uint8 qw; + qw.s0 = EXP4(src0_q[qsbase + 0 * m]); + qw.s1 = EXP4(src0_q[qsbase + 1 * m]); + qw.s2 = EXP4(src0_q[qsbase + 2 * m]); + qw.s3 = EXP4(src0_q[qsbase + 3 * m]); + qw.s4 = EXP4(src0_q[qsbase + 4 * m]); + qw.s5 = EXP4(src0_q[qsbase + 5 * m]); + qw.s6 = EXP4(src0_q[qsbase + 6 * m]); + qw.s7 = EXP4(src0_q[qsbase + 7 * m]); + + for (uint idx = lid; idx < TILESIZE_N * 2; idx += 64) { + const uint t = idx >> 1; + const uint v = idx & 1; + const uint c = col_base + t; + sh_qa4[t][v] = (c < (uint)n_no_padding) + ? vload4(0, src1_qa + c * k_u + (step >> 2) + (v << 2)) + : (uint4)(0u); + } + if (lid < TILESIZE_N) { + const uint c = col_base + lid; + sh_d[lid] = (c < (uint)n_no_padding) ? src1_da[c * k_b + sub] : (half)0; + sh_s[lid] = (c < (uint)n_no_padding) ? src1_sa[c * k_b + sub] : (half)0; + } + barrier(CLK_LOCAL_MEM_FENCE); + +#define LD4(arr, b) ((float4)((float)arr[(b)+0], (float)arr[(b)+1], (float)arr[(b)+2], (float)arr[(b)+3])) + #pragma unroll + for (int g = 0; g < NGROUPS; ++g) { + const int b = g * 4; + float4 rf; +#define DOTV(T) dot8_q8a_v(qw, sh_qa4[T][0], sh_qa4[T][1]) + rf.s0 = (float)DOTV(b+0); + rf.s1 = (float)DOTV(b+1); + rf.s2 = (float)DOTV(b+2); + rf.s3 = (float)DOTV(b+3); +#undef DOTV + + acc[g] += d_w * (LD4(sh_d, b) * rf - 8.0f * LD4(sh_s, b)); + } +#undef LD4 + barrier(CLK_LOCAL_MEM_FENCE); + } + + if (!row_valid) { + return; + } + + #pragma unroll + for (int g = 0; g < NGROUPS; ++g) { + const uint b = (uint)(g * 4); + const float4 a = acc[g]; + const uint c0 = col_base + b; + if (c0 + 0 < (uint)n_no_padding) { + dst[(c0 + 0) * (uint)m + row] = a.s0; + } + if (c0 + 1 < (uint)n_no_padding) { + dst[(c0 + 1) * (uint)m + row] = a.s1; + } + if (c0 + 2 < (uint)n_no_padding) { + dst[(c0 + 2) * (uint)m + row] = a.s2; + } + if (c0 + 3 < (uint)n_no_padding) { + dst[(c0 + 3) * (uint)m + row] = a.s3; + } + } +#undef NGROUPS +} diff --git a/ggml/src/ggml-opencl/kernels/gemm_noshuffle_q4_k_q8_1_dp4a.cl b/ggml/src/ggml-opencl/kernels/gemm_noshuffle_q4_k_q8_1_dp4a.cl index a3b39b6aa984..69cabb946ce5 100644 --- a/ggml/src/ggml-opencl/kernels/gemm_noshuffle_q4_k_q8_1_dp4a.cl +++ b/ggml/src/ggml-opencl/kernels/gemm_noshuffle_q4_k_q8_1_dp4a.cl @@ -279,3 +279,141 @@ kernel void kernel_gemm_noshuffle_q4_k_q8_1_dp4a_wimg( } #undef NGROUPS } + +// Stage each 32-K activation block as two uint4 values. +inline int dot8_q8a_v(uint8 qw, uint4 a0, uint4 a1) { + int r = 0; + r = dot_acc_sat_4x8packed_ss_int(qw.s0, a0.x, r); + r = dot_acc_sat_4x8packed_ss_int(qw.s1, a0.y, r); + r = dot_acc_sat_4x8packed_ss_int(qw.s2, a0.z, r); + r = dot_acc_sat_4x8packed_ss_int(qw.s3, a0.w, r); + r = dot_acc_sat_4x8packed_ss_int(qw.s4, a1.x, r); + r = dot_acc_sat_4x8packed_ss_int(qw.s5, a1.y, r); + r = dot_acc_sat_4x8packed_ss_int(qw.s6, a1.z, r); + r = dot_acc_sat_4x8packed_ss_int(qw.s7, a1.w, r); + return r; +} + +__attribute__((qcom_wave_pair_mode(1))) +kernel void kernel_gemm_noshuffle_q4_k_q8_1_dp4a_act_vload4( + __global const ushort * src0_q, + __global const uchar * src0_s, + __global const half * src0_d, + __global const half * src0_dm, + __global const uint * src1_qa, + __global const half * src1_da, + __global const half * src1_sa, + __global float * dst, + ulong offsetd, + int m, + int n_no_padding, + int k, + uchar mask_d6, + uchar mask_d4, + uchar mask_hi2 +) { + dst = (global float *)((global char *)dst + offsetd); + + const uint lid = get_local_id(0); + const uint block_id_m = get_global_id(1); + const uint block_id_n = get_global_id(2); + + const uint row = block_id_m * 64 + lid; + const uint col_base = block_id_n * TILESIZE_N; + const bool row_valid = row < (uint)m; + const uint rrow = row_valid ? row : 0; + + const uint k_u = (uint)k >> 2; + const uint k_b = (uint)k >> 5; + + __local uint4 sh_qa4[TILESIZE_N][2]; + __local half sh_d[TILESIZE_N]; + __local half sh_s[TILESIZE_N]; + +#define NGROUPS (TILESIZE_N / 4) + float4 acc[NGROUPS]; + #pragma unroll + for (int g = 0; g < NGROUPS; ++g) { + acc[g] = (float4)(0.0f); + } + + for (uint step = 0; step < (uint)k; step += 32) { + const uint sub = step >> 5; + const uint sb_idx = step / QK_K; + const uint sub_idx = sub & 7; + + const float dd = (float)src0_d [rrow + sb_idx * m]; + const float dmm = (float)src0_dm[rrow + sb_idx * m]; + global const uchar * sc = src0_s + sb_idx * K_SCALE_SIZE * (uint)m + rrow; + uchar sv, mn; + get_scale_min_k4(sub_idx, sc, (uint)m, &sv, &mn, mask_d6, mask_d4, mask_hi2); + const float scale = dd * (float)sv; + const float minv = dmm * (float)mn; + + const uint wbase = rrow + (step >> 2) * (uint)m; + uint8 qw; + qw.s0 = EXP4(src0_q[wbase + 0 * m]); + qw.s1 = EXP4(src0_q[wbase + 1 * m]); + qw.s2 = EXP4(src0_q[wbase + 2 * m]); + qw.s3 = EXP4(src0_q[wbase + 3 * m]); + qw.s4 = EXP4(src0_q[wbase + 4 * m]); + qw.s5 = EXP4(src0_q[wbase + 5 * m]); + qw.s6 = EXP4(src0_q[wbase + 6 * m]); + qw.s7 = EXP4(src0_q[wbase + 7 * m]); + + for (uint idx = lid; idx < TILESIZE_N * 2; idx += 64) { + const uint t = idx >> 1; + const uint v = idx & 1; + const uint c = col_base + t; + sh_qa4[t][v] = (c < (uint)n_no_padding) + ? vload4(0, src1_qa + c * k_u + (step >> 2) + (v << 2)) + : (uint4)(0u); + } + if (lid < TILESIZE_N) { + const uint c = col_base + lid; + sh_d[lid] = (c < (uint)n_no_padding) ? src1_da[c * k_b + sub] : (half)0; + sh_s[lid] = (c < (uint)n_no_padding) ? src1_sa[c * k_b + sub] : (half)0; + } + barrier(CLK_LOCAL_MEM_FENCE); + +#define LD4(arr, b) ((float4)((float)arr[(b)+0], (float)arr[(b)+1], (float)arr[(b)+2], (float)arr[(b)+3])) + #pragma unroll + for (int g = 0; g < NGROUPS; ++g) { + const int b = g * 4; + float4 rf; +#define DOTV(T) dot8_q8a_v(qw, sh_qa4[T][0], sh_qa4[T][1]) + rf.s0 = (float)DOTV(b+0); + rf.s1 = (float)DOTV(b+1); + rf.s2 = (float)DOTV(b+2); + rf.s3 = (float)DOTV(b+3); +#undef DOTV + acc[g] += scale * LD4(sh_d, b) * rf - minv * LD4(sh_s, b); + } +#undef LD4 + barrier(CLK_LOCAL_MEM_FENCE); + } + + if (!row_valid) { + return; + } + + #pragma unroll + for (int g = 0; g < NGROUPS; ++g) { + const uint b = (uint)(g * 4); + const float4 a = acc[g]; + const uint c0 = col_base + b; + if (c0 + 0 < (uint)n_no_padding) { + dst[(c0 + 0) * (uint)m + row] = a.s0; + } + if (c0 + 1 < (uint)n_no_padding) { + dst[(c0 + 1) * (uint)m + row] = a.s1; + } + if (c0 + 2 < (uint)n_no_padding) { + dst[(c0 + 2) * (uint)m + row] = a.s2; + } + if (c0 + 3 < (uint)n_no_padding) { + dst[(c0 + 3) * (uint)m + row] = a.s3; + } + } +#undef NGROUPS +} diff --git a/ggml/src/ggml-opencl/kernels/gemm_noshuffle_q5_k_q8_1_dp4a.cl b/ggml/src/ggml-opencl/kernels/gemm_noshuffle_q5_k_q8_1_dp4a.cl index aaeed68f615c..eef474b47f7b 100644 --- a/ggml/src/ggml-opencl/kernels/gemm_noshuffle_q5_k_q8_1_dp4a.cl +++ b/ggml/src/ggml-opencl/kernels/gemm_noshuffle_q5_k_q8_1_dp4a.cl @@ -162,3 +162,141 @@ kernel void kernel_gemm_noshuffle_q5_k_q8_1_dp4a( } #undef NGROUPS } + +// Stage each 32-K activation block as two uint4 values. +inline int dot8_q8a_v(uint8 qw, uint4 a0, uint4 a1) { + int r = 0; + r = dot_acc_sat_4x8packed_ss_int(qw.s0, a0.x, r); + r = dot_acc_sat_4x8packed_ss_int(qw.s1, a0.y, r); + r = dot_acc_sat_4x8packed_ss_int(qw.s2, a0.z, r); + r = dot_acc_sat_4x8packed_ss_int(qw.s3, a0.w, r); + r = dot_acc_sat_4x8packed_ss_int(qw.s4, a1.x, r); + r = dot_acc_sat_4x8packed_ss_int(qw.s5, a1.y, r); + r = dot_acc_sat_4x8packed_ss_int(qw.s6, a1.z, r); + r = dot_acc_sat_4x8packed_ss_int(qw.s7, a1.w, r); + return r; +} + +__attribute__((qcom_wave_pair_mode(1))) +kernel void kernel_gemm_noshuffle_q5_k_q8_1_dp4a_act_vload4( + __global const ushort * src0_q, + __global const uchar * src0_qh, + __global const uchar * src0_s, + __global const half * src0_d, + __global const half * src0_dm, + __global const uint * src1_qa, + __global const half * src1_da, + __global const half * src1_sa, + __global float * dst, + ulong offsetd, + int m, + int n_no_padding, + int k, + uchar mask_d6, + uchar mask_d4, + uchar mask_hi2 +) { + dst = (global float *)((global char *)dst + offsetd); + + const uint lid = get_local_id(0); + const uint block_id_m = get_global_id(1); + const uint block_id_n = get_global_id(2); + + const uint row = block_id_m * 64 + lid; + const uint col_base = block_id_n * TILESIZE_N; + const bool row_valid = row < (uint)m; + const uint rrow = row_valid ? row : 0; + + const uint num_superblocks = (uint)k / QK_K; + const uint k_u = (uint)k >> 2; + const uint k_b = (uint)k >> 5; + + __local uint4 sh_qa4[TILESIZE_N][2]; + __local half sh_d[TILESIZE_N]; + __local half sh_s[TILESIZE_N]; + +#define NGROUPS (TILESIZE_N / 4) + float4 acc[NGROUPS]; + #pragma unroll + for (int g = 0; g < NGROUPS; ++g) { + acc[g] = (float4)(0.0f); + } + + for (uint step = 0; step < (uint)k; step += 32) { + const uint sub = step >> 5; + const uint sb_idx = step / QK_K; + const uint sub_idx = sub & 7; + + const float dd = (float)src0_d [rrow + sb_idx * m]; + const float dmm = (float)src0_dm[rrow + sb_idx * m]; + global const uchar * sc = src0_s + rrow * num_superblocks * K_SCALE_SIZE + sb_idx * K_SCALE_SIZE; + uchar sv, mn; + get_scale_min_k4(sub_idx, sc, &sv, &mn, mask_d6, mask_d4, mask_hi2); + const float scale = dd * (float)sv; + const float minv = dmm * (float)mn; + + const uint wbase = rrow + (step >> 2) * (uint)m; + const uint qhbase = rrow + (step >> 3) * (uint)m; + uint8 qw; +#define QWU(u) ( EXP4((uint)src0_q[wbase + (uint)(u) * m]) \ + | EXP1( (uint)((src0_qh[qhbase + (uint)((u) >> 1) * m] >> (((u) & 1) * 4)) & 0x0Fu) ) ) + qw.s0 = QWU(0); qw.s1 = QWU(1); qw.s2 = QWU(2); qw.s3 = QWU(3); + qw.s4 = QWU(4); qw.s5 = QWU(5); qw.s6 = QWU(6); qw.s7 = QWU(7); +#undef QWU + + for (uint idx = lid; idx < TILESIZE_N * 2; idx += 64) { + const uint t = idx >> 1; + const uint v = idx & 1; + const uint c = col_base + t; + sh_qa4[t][v] = (c < (uint)n_no_padding) + ? vload4(0, src1_qa + c * k_u + (step >> 2) + (v << 2)) + : (uint4)(0u); + } + if (lid < TILESIZE_N) { + const uint c = col_base + lid; + sh_d[lid] = (c < (uint)n_no_padding) ? src1_da[c * k_b + sub] : (half)0; + sh_s[lid] = (c < (uint)n_no_padding) ? src1_sa[c * k_b + sub] : (half)0; + } + barrier(CLK_LOCAL_MEM_FENCE); + +#define LD4(arr, b) ((float4)((float)arr[(b)+0], (float)arr[(b)+1], (float)arr[(b)+2], (float)arr[(b)+3])) + #pragma unroll + for (int g = 0; g < NGROUPS; ++g) { + const int b = g * 4; + float4 rf; +#define DOTV(T) dot8_q8a_v(qw, sh_qa4[T][0], sh_qa4[T][1]) + rf.s0 = (float)DOTV(b+0); + rf.s1 = (float)DOTV(b+1); + rf.s2 = (float)DOTV(b+2); + rf.s3 = (float)DOTV(b+3); +#undef DOTV + acc[g] += scale * LD4(sh_d, b) * rf - minv * LD4(sh_s, b); + } +#undef LD4 + barrier(CLK_LOCAL_MEM_FENCE); + } + + if (!row_valid) { + return; + } + + #pragma unroll + for (int g = 0; g < NGROUPS; ++g) { + const uint b = (uint)(g * 4); + const float4 a = acc[g]; + const uint c0 = col_base + b; + if (c0 + 0 < (uint)n_no_padding) { + dst[(c0 + 0) * (uint)m + row] = a.s0; + } + if (c0 + 1 < (uint)n_no_padding) { + dst[(c0 + 1) * (uint)m + row] = a.s1; + } + if (c0 + 2 < (uint)n_no_padding) { + dst[(c0 + 2) * (uint)m + row] = a.s2; + } + if (c0 + 3 < (uint)n_no_padding) { + dst[(c0 + 3) * (uint)m + row] = a.s3; + } + } +#undef NGROUPS +} diff --git a/ggml/src/ggml-opencl/kernels/gemm_noshuffle_q6_k_q8_1_dp4a.cl b/ggml/src/ggml-opencl/kernels/gemm_noshuffle_q6_k_q8_1_dp4a.cl index 382d79fddafc..f17403a722e4 100644 --- a/ggml/src/ggml-opencl/kernels/gemm_noshuffle_q6_k_q8_1_dp4a.cl +++ b/ggml/src/ggml-opencl/kernels/gemm_noshuffle_q6_k_q8_1_dp4a.cl @@ -142,3 +142,117 @@ kernel void kernel_gemm_noshuffle_q6_k_q8_1_dp4a( } #undef NGROUPS } + +// Stage each 32-K activation block as two uint4 values. +__attribute__((qcom_wave_pair_mode(1))) +kernel void kernel_gemm_noshuffle_q6_k_q8_1_dp4a_act_vload4( + __global const ushort * src0_ql, + __global const uchar * src0_qh, + __global const ushort * src0_s, + __global const half * src0_d, + __global const uint * src1_qa, + __global const half * src1_da, + __global float * dst, + ulong offsetd, + int m, + int n_no_padding, + int k +) { + dst = (global float *)((global char *)dst + offsetd); + + const uint lid = get_local_id(0); + const uint block_id_m = get_global_id(1); + const uint block_id_n = get_global_id(2); + + const uint row = block_id_m * 64 + lid; + const uint col_base = block_id_n * TILESIZE_N; + const bool row_valid = row < (uint)m; + const uint rrow = row_valid ? row : 0; + + const uint k_u = (uint)k >> 2; + const uint k_b = (uint)k >> 5; + + __local uint4 sh_qa4[TILESIZE_N][2]; + __local half sh_d[TILESIZE_N]; + +#define NGROUPS (TILESIZE_N / 4) + float4 acc[NGROUPS]; + #pragma unroll + for (int g = 0; g < NGROUPS; ++g) { + acc[g] = (float4)(0.0f); + } + + for (uint step = 0; step < (uint)k; step += 32) { + const uint sub = step >> 5; + const uint sb_idx = step / QK_K; + + const float dd = (float)src0_d[rrow + sb_idx * m]; + const char2 sc = as_char2(src0_s[rrow + sub * m]); + const float scale0 = dd * (float)sc.s0; + const float scale1 = dd * (float)sc.s1; + + const uint wbase = rrow + (step >> 2) * (uint)m; + uint qw[8]; + #pragma unroll + for (int u = 0; u < 8; ++u) { + const uint o = wbase + (uint)u * (uint)m; + qw[u] = SIGN6(EXP4((uint)src0_ql[o]) | EXP2((uint)src0_qh[o])); + } + + for (uint idx = lid; idx < TILESIZE_N * 2; idx += 64) { + const uint t = idx >> 1; + const uint v = idx & 1; + const uint c = col_base + t; + sh_qa4[t][v] = (c < (uint)n_no_padding) + ? vload4(0, src1_qa + c * k_u + (step >> 2) + (v << 2)) + : (uint4)(0u); + } + if (lid < TILESIZE_N) { + const uint c = col_base + lid; + sh_d[lid] = (c < (uint)n_no_padding) ? src1_da[c * k_b + sub] : (half)0; + } + barrier(CLK_LOCAL_MEM_FENCE); + + #pragma unroll + for (int g = 0; g < NGROUPS; ++g) { + const int b = g * 4; + float4 rf; + #define DOT_TOK(j) { \ + const uint4 a0 = sh_qa4[b + (j)][0]; \ + const uint4 a1 = sh_qa4[b + (j)][1]; \ + const int raw1 = dot4_q8a(qw[0], qw[1], qw[2], qw[3], a0.x, a0.y, a0.z, a0.w); \ + const int raw2 = dot4_q8a(qw[4], qw[5], qw[6], qw[7], a1.x, a1.y, a1.z, a1.w); \ + rf.s##j = scale0 * (float)raw1 + scale1 * (float)raw2; \ + } + DOT_TOK(0); DOT_TOK(1); DOT_TOK(2); DOT_TOK(3); + #undef DOT_TOK + const float4 ad = (float4)((float)sh_d[b+0], (float)sh_d[b+1], (float)sh_d[b+2], (float)sh_d[b+3]); + acc[g] += ad * rf; + } + barrier(CLK_LOCAL_MEM_FENCE); + } + + if (!row_valid) { + return; + } + + #pragma unroll + for (int g = 0; g < NGROUPS; ++g) { + const uint b = (uint)(g * 4); + const float4 a = acc[g]; + const uint c0 = col_base + b; + if (c0 + 0 < (uint)n_no_padding) { + dst[(c0 + 0) * (uint)m + row] = a.s0; + } + if (c0 + 1 < (uint)n_no_padding) { + dst[(c0 + 1) * (uint)m + row] = a.s1; + } + if (c0 + 2 < (uint)n_no_padding) { + dst[(c0 + 2) * (uint)m + row] = a.s2; + } + if (c0 + 3 < (uint)n_no_padding) { + dst[(c0 + 3) * (uint)m + row] = a.s3; + } + } +#undef NGROUPS +} diff --git a/ggml/src/ggml-opencl/kernels/gemm_noshuffle_q8_0_q8_1_dp4a.cl b/ggml/src/ggml-opencl/kernels/gemm_noshuffle_q8_0_q8_1_dp4a.cl index a481636c2324..32ff7b4f20c5 100644 --- a/ggml/src/ggml-opencl/kernels/gemm_noshuffle_q8_0_q8_1_dp4a.cl +++ b/ggml/src/ggml-opencl/kernels/gemm_noshuffle_q8_0_q8_1_dp4a.cl @@ -210,3 +210,125 @@ kernel void kernel_gemm_noshuffle_q8_0_q8_1_dp4a_wimg( } #undef NGROUPS } + +// Stage each 32-K activation block as two uint4 values. +inline int dot8_q8a_v(uint8 qw, uint4 a0, uint4 a1) { + int r = 0; + r = dot_acc_sat_4x8packed_ss_int(qw.s0, a0.x, r); + r = dot_acc_sat_4x8packed_ss_int(qw.s1, a0.y, r); + r = dot_acc_sat_4x8packed_ss_int(qw.s2, a0.z, r); + r = dot_acc_sat_4x8packed_ss_int(qw.s3, a0.w, r); + r = dot_acc_sat_4x8packed_ss_int(qw.s4, a1.x, r); + r = dot_acc_sat_4x8packed_ss_int(qw.s5, a1.y, r); + r = dot_acc_sat_4x8packed_ss_int(qw.s6, a1.z, r); + r = dot_acc_sat_4x8packed_ss_int(qw.s7, a1.w, r); + return r; +} + +__attribute__((qcom_wave_pair_mode(1))) +kernel void kernel_gemm_noshuffle_q8_0_q8_1_dp4a_act_vload4( + __global const uint * src0_q, + __global const half * src0_d, + __global const uint * src1_qa, + __global const half * src1_da, + __global float * dst, + ulong offsetd, + int m, + int n_no_padding, + int k +) { + dst = (global float *)((global char *)dst + offsetd); + + const uint lid = get_local_id(0); + const uint block_id_m = get_global_id(1); + const uint block_id_n = get_global_id(2); + + const uint row = block_id_m * 64 + lid; + const uint col_base = block_id_n * TILESIZE_N; + const bool row_valid = row < (uint)m; + const uint rrow = row_valid ? row : 0; + + const uint k_u = (uint)k >> 2; + const uint k_b = (uint)k >> 5; + + __local uint4 sh_qa4[TILESIZE_N][2]; + __local half sh_d[TILESIZE_N]; + +#define NGROUPS (TILESIZE_N / 4) + float4 acc[NGROUPS]; + #pragma unroll + for (int g = 0; g < NGROUPS; ++g) { + acc[g] = (float4)(0.0f); + } + + for (uint step = 0; step < (uint)k; step += 32) { + const uint sub = step >> 5; + + const float d_w = (float)src0_d[rrow + sub * (uint)m]; + + const uint wbase = rrow + (step >> 2) * (uint)m; + uint8 qw; + qw.s0 = src0_q[wbase + 0 * m]; + qw.s1 = src0_q[wbase + 1 * m]; + qw.s2 = src0_q[wbase + 2 * m]; + qw.s3 = src0_q[wbase + 3 * m]; + qw.s4 = src0_q[wbase + 4 * m]; + qw.s5 = src0_q[wbase + 5 * m]; + qw.s6 = src0_q[wbase + 6 * m]; + qw.s7 = src0_q[wbase + 7 * m]; + + for (uint idx = lid; idx < TILESIZE_N * 2; idx += 64) { + const uint t = idx >> 1; + const uint v = idx & 1; + const uint c = col_base + t; + sh_qa4[t][v] = (c < (uint)n_no_padding) + ? vload4(0, src1_qa + c * k_u + (step >> 2) + (v << 2)) + : (uint4)(0u); + } + if (lid < TILESIZE_N) { + const uint c = col_base + lid; + sh_d[lid] = (c < (uint)n_no_padding) ? src1_da[c * k_b + sub] : (half)0; + } + barrier(CLK_LOCAL_MEM_FENCE); + +#define LD4(arr, b) ((float4)((float)arr[(b)+0], (float)arr[(b)+1], (float)arr[(b)+2], (float)arr[(b)+3])) + #pragma unroll + for (int g = 0; g < NGROUPS; ++g) { + const int b = g * 4; + float4 rf; +#define DOTV(T) dot8_q8a_v(qw, sh_qa4[T][0], sh_qa4[T][1]) + rf.s0 = (float)DOTV(b+0); + rf.s1 = (float)DOTV(b+1); + rf.s2 = (float)DOTV(b+2); + rf.s3 = (float)DOTV(b+3); +#undef DOTV + acc[g] += d_w * LD4(sh_d, b) * rf; + } +#undef LD4 + barrier(CLK_LOCAL_MEM_FENCE); + } + + if (!row_valid) { + return; + } + + #pragma unroll + for (int g = 0; g < NGROUPS; ++g) { + const uint b = (uint)(g * 4); + const float4 a = acc[g]; + const uint c0 = col_base + b; + if (c0 + 0 < (uint)n_no_padding) { + dst[(c0 + 0) * (uint)m + row] = a.s0; + } + if (c0 + 1 < (uint)n_no_padding) { + dst[(c0 + 1) * (uint)m + row] = a.s1; + } + if (c0 + 2 < (uint)n_no_padding) { + dst[(c0 + 2) * (uint)m + row] = a.s2; + } + if (c0 + 3 < (uint)n_no_padding) { + dst[(c0 + 3) * (uint)m + row] = a.s3; + } + } +#undef NGROUPS +} From 0efe8e4c6bb946fb71327b068b26c17d5d15121e Mon Sep 17 00:00:00 2001 From: Max Krasnyansky Date: Wed, 21 Oct 2026 00:48:39 -0700 Subject: [PATCH 42/69] hexagon: neon-vectorized and threaded repack (tiling) (#30901) * add vectorized q4_0 repack * apply to more types * hex-repack: neon-vectorize the rest of the types q1/q2/f16/... * hex-repack: add simple chunking / threading --------- Co-authored-by: hubezhou --- ggml/src/ggml-hexagon/htp-repack.cpp | 593 +++++++++++++++++++++++---- 1 file changed, 517 insertions(+), 76 deletions(-) diff --git a/ggml/src/ggml-hexagon/htp-repack.cpp b/ggml/src/ggml-hexagon/htp-repack.cpp index 7ef2f6d0cbc1..d2e3df2cf4b4 100644 --- a/ggml/src/ggml-hexagon/htp-repack.cpp +++ b/ggml/src/ggml-hexagon/htp-repack.cpp @@ -9,6 +9,11 @@ #include #include #include +#include + +#if defined(__ARM_NEON) || defined(__aarch64__) +# include +#endif // --- 4-bit block-32 nibble helpers (shared by Q4_0, IQ4_NL, Q4_1, MXFP4) --- @@ -43,8 +48,309 @@ static inline void transpose_tile_to_nibbles(uint8_t tile_quants[32][32], const } } +#if defined(__ARM_NEON) || defined(__aarch64__) +static void repack_neon_transpose8x8(const uint8x8_t rows[8], uint8x8_t columns[8]) { + const uint8x8x2_t t0 = vtrn_u8(rows[0], rows[1]); + const uint8x8x2_t t1 = vtrn_u8(rows[2], rows[3]); + const uint8x8x2_t t2 = vtrn_u8(rows[4], rows[5]); + const uint8x8x2_t t3 = vtrn_u8(rows[6], rows[7]); + const uint16x4x2_t t4 = vtrn_u16(vreinterpret_u16_u8(t0.val[0]), vreinterpret_u16_u8(t1.val[0])); + const uint16x4x2_t t5 = vtrn_u16(vreinterpret_u16_u8(t0.val[1]), vreinterpret_u16_u8(t1.val[1])); + const uint16x4x2_t t6 = vtrn_u16(vreinterpret_u16_u8(t2.val[0]), vreinterpret_u16_u8(t3.val[0])); + const uint16x4x2_t t7 = vtrn_u16(vreinterpret_u16_u8(t2.val[1]), vreinterpret_u16_u8(t3.val[1])); + const uint32x2x2_t t8 = vtrn_u32(vreinterpret_u32_u16(t4.val[0]), vreinterpret_u32_u16(t6.val[0])); + const uint32x2x2_t t9 = vtrn_u32(vreinterpret_u32_u16(t4.val[1]), vreinterpret_u32_u16(t6.val[1])); + const uint32x2x2_t ta = vtrn_u32(vreinterpret_u32_u16(t5.val[0]), vreinterpret_u32_u16(t7.val[0])); + const uint32x2x2_t tb = vtrn_u32(vreinterpret_u32_u16(t5.val[1]), vreinterpret_u32_u16(t7.val[1])); + columns[0] = vreinterpret_u8_u32(t8.val[0]); + columns[1] = vreinterpret_u8_u32(ta.val[0]); + columns[2] = vreinterpret_u8_u32(t9.val[0]); + columns[3] = vreinterpret_u8_u32(tb.val[0]); + columns[4] = vreinterpret_u8_u32(t8.val[1]); + columns[5] = vreinterpret_u8_u32(ta.val[1]); + columns[6] = vreinterpret_u8_u32(t9.val[1]); + columns[7] = vreinterpret_u8_u32(tb.val[1]); +} + +static uint8x8_t repack_neon_pack_4bit_pairs(uint8x8_t first, uint8x8_t second, bool high) { + const uint8x8x2_t separated = vuzp_u8(first, second); + const uint8x8_t mask = vdup_n_u8(0x0F); + uint8x8_t even = separated.val[0]; + uint8x8_t odd = separated.val[1]; + + if (high) { + even = vshr_n_u8(even, 4); + odd = vshr_n_u8(odd, 4); + } + + even = vand_u8(even, mask); + odd = vand_u8(odd, mask); + return vorr_u8(vshl_n_u8(odd, 4), even); +} + +static void repack_neon_4bit_rows(const uint8_t * qs[8], uint8_t * tile_dst, int row0) { + uint8x8_t low_rows[8]; + uint8x8_t high_rows[8]; + uint8x8_t columns[8]; + + for (int rr = 0; rr < 8; ++rr) { + const uint8x16_t packed = vld1q_u8(qs[rr]); + const uint8x8_t first = vget_low_u8(packed); + const uint8x8_t second = vget_high_u8(packed); + low_rows[rr] = repack_neon_pack_4bit_pairs(first, second, false); + high_rows[rr] = repack_neon_pack_4bit_pairs(first, second, true); + } + + repack_neon_transpose8x8(low_rows, columns); + for (int cp = 0; cp < 8; ++cp) { + vst1_u8(tile_dst + cp * 32 + row0, columns[cp]); + } + + repack_neon_transpose8x8(high_rows, columns); + for (int cp = 0; cp < 8; ++cp) { + vst1_u8(tile_dst + (cp + 8) * 32 + row0, columns[cp]); + } +} + +static void repack_neon_k4_rows(const uint8_t * qs[8], uint8_t * tile_dst, int row0, bool high) { + uint8x8_t first_rows[8]; + uint8x8_t second_rows[8]; + uint8x8_t columns[8]; + + for (int rr = 0; rr < 8; ++rr) { + first_rows[rr] = repack_neon_pack_4bit_pairs(vld1_u8(qs[rr]), vld1_u8(qs[rr] + 8), high); + second_rows[rr] = repack_neon_pack_4bit_pairs(vld1_u8(qs[rr] + 16), vld1_u8(qs[rr] + 24), high); + } + + repack_neon_transpose8x8(first_rows, columns); + for (int cp = 0; cp < 8; ++cp) { + vst1_u8(tile_dst + cp * 32 + row0, columns[cp]); + } + + repack_neon_transpose8x8(second_rows, columns); + for (int cp = 0; cp < 8; ++cp) { + vst1_u8(tile_dst + (cp + 8) * 32 + row0, columns[cp]); + } +} + +static void repack_neon_q8_rows(const uint8_t * qs[8], uint8_t * tile_dst, int row0) { + uint8x8_t rows[8]; + uint8x8_t columns[8]; + + for (int cp = 0; cp < 16; cp += 4) { + for (int rr = 0; rr < 8; ++rr) { + rows[rr] = vld1_u8(qs[rr] + 2 * cp); + } + repack_neon_transpose8x8(rows, columns); + + for (int pair = 0; pair < 4; ++pair) { + const uint8x8x2_t interleaved = vzip_u8(columns[2 * pair], columns[2 * pair + 1]); + uint8_t * dst = tile_dst + (cp + pair) * 64 + 2 * row0; + vst1_u8(dst, interleaved.val[0]); + vst1_u8(dst + 8, interleaved.val[1]); + } + } +} + +static inline void repack_store_u32(uint8_t * dst, uint32_t v) { + memcpy(dst, &v, sizeof(v)); +} + +static inline uint32x4_t repack_neon_transpose_2bit_4x4(uint32x4_t w) { + uint32x4_t t = vandq_u32(veorq_u32(w, vshrq_n_u32(w, 6)), vdupq_n_u32(0x00CC00CCu)); + w = veorq_u32(w, veorq_u32(t, vshlq_n_u32(t, 6))); + t = vandq_u32(veorq_u32(w, vshrq_n_u32(w, 12)), vdupq_n_u32(0x0000F0F0u)); + return veorq_u32(w, veorq_u32(t, vshlq_n_u32(t, 12))); +} + +static inline void repack_neon_q2_0_block(const uint8_t * qs, uint8_t * tile, int row) { + const uint32x4_t w = repack_neon_transpose_2bit_4x4(vreinterpretq_u32_u8(vld1q_u8(qs))); + repack_store_u32(tile + 0 * 128 + 4 * row, vgetq_lane_u32(w, 0)); + repack_store_u32(tile + 1 * 128 + 4 * row, vgetq_lane_u32(w, 1)); + repack_store_u32(tile + 2 * 128 + 4 * row, vgetq_lane_u32(w, 2)); + repack_store_u32(tile + 3 * 128 + 4 * row, vgetq_lane_u32(w, 3)); +} + +static inline void repack_neon_q1_0_block(const uint8_t * qs, uint8_t * tile, int row) { + uint32x4_t w = vreinterpretq_u32_u8(vld1q_u8(qs)); + uint32x4_t t = vandq_u32(veorq_u32(w, vshrq_n_u32(w, 1)), vdupq_n_u32(0x22222222u)); + w = veorq_u32(w, veorq_u32(t, vshlq_n_u32(t, 1))); + t = vandq_u32(veorq_u32(w, vshrq_n_u32(w, 3)), vdupq_n_u32(0x0A0A0A0Au)); + w = veorq_u32(w, veorq_u32(t, vshlq_n_u32(t, 3))); + w = repack_neon_transpose_2bit_4x4(w); + repack_store_u32(tile + 0 * 128 + 4 * row, vgetq_lane_u32(w, 0)); + repack_store_u32(tile + 1 * 128 + 4 * row, vgetq_lane_u32(w, 1)); + repack_store_u32(tile + 2 * 128 + 4 * row, vgetq_lane_u32(w, 2)); + repack_store_u32(tile + 3 * 128 + 4 * row, vgetq_lane_u32(w, 3)); +} + +static inline void repack_neon_pack_2bit_plane(uint8x16_t v0, uint8x16_t v1, uint8_t * dst, int row) { + const uint32x4x2_t z = vuzpq_u32(vreinterpretq_u32_u8(v0), vreinterpretq_u32_u8(v1)); + const uint32x4_t p01 = vorrq_u32(z.val[0], vshlq_n_u32(z.val[1], 2)); + const uint32x2x2_t u = vuzp_u32(vget_low_u32(p01), vget_high_u32(p01)); + const uint32x2_t out = vorr_u32(u.val[0], vshl_n_u32(u.val[1], 4)); + repack_store_u32(dst + 0 * 128 + row * 4, vget_lane_u32(out, 0)); + repack_store_u32(dst + 1 * 128 + row * 4, vget_lane_u32(out, 1)); +} + +static inline void repack_neon_q2_3_k_low2(const uint8_t * qs, int kt_local, uint8_t * lo_pl, int row) { + const uint8_t * qs_sub = qs + (kt_local / 4) * 32; + const int8x16_t shift = vdupq_n_s8(-2 * (kt_local % 4)); + const uint8x16_t mask = vdupq_n_u8(0x03); + const uint8x16_t v0 = vandq_u8(vshlq_u8(vld1q_u8(qs_sub + 0), shift), mask); + const uint8x16_t v1 = vandq_u8(vshlq_u8(vld1q_u8(qs_sub + 16), shift), mask); + repack_neon_pack_2bit_plane(v0, v1, lo_pl, row); +} + +static inline void repack_neon_q3_k_hmask(const uint8_t * hmask, int kt_local, uint8_t * neg_pl, int row) { + static const uint32_t mask_words[4] = { 0x01010101u, 0x02020202u, 0x04040404u, 0x08080808u }; + const uint8x16_t m0 = vreinterpretq_u8_u32(vld1q_u32(mask_words)); + const uint8x16_t m1 = vshlq_n_u8(m0, 4); + const uint8x16_t hbit = vdupq_n_u8((uint8_t) (1u << kt_local)); + const uint8x16_t b0 = vbicq_u8(m0, vtstq_u8(vld1q_u8(hmask + 0), hbit)); + const uint8x16_t b1 = vbicq_u8(m1, vtstq_u8(vld1q_u8(hmask + 16), hbit)); + const uint32x4_t b01 = vreinterpretq_u32_u8(vorrq_u8(b0, b1)); + const uint32x2_t f0 = vorr_u32(vget_low_u32(b01), vget_high_u32(b01)); + const uint32x2_t f1 = vpadd_u32(f0, f0); + repack_store_u32(neg_pl + row * 4, vget_lane_u32(f1, 0)); +} + +static inline void repack_neon_q6_k(const block_q6_K * b, int kt_local, uint8_t * lo_pl, uint8_t * hi_pl, int row) { + const int c = kt_local / 4; + const int g_sb = kt_local % 4; + const uint8_t * ql = b->ql + c * 64 + (g_sb & 1) * 32; + const uint8_t * qh = b->qh + c * 32; + const int8x16_t lshift = vdupq_n_s8((g_sb >= 2) ? -4 : 0); + const int8x16_t hshift = vdupq_n_s8(-2 * g_sb); + const uint8x16_t mask4 = vdupq_n_u8(0x0F); + const uint8x16_t mask2 = vdupq_n_u8(0x03); + + const uint8x16_t lo0 = vandq_u8(vshlq_u8(vld1q_u8(ql + 0), lshift), mask4); + const uint8x16_t lo1 = vandq_u8(vshlq_u8(vld1q_u8(ql + 16), lshift), mask4); + const uint32x4x2_t lz = vuzpq_u32(vreinterpretq_u32_u8(lo0), vreinterpretq_u32_u8(lo1)); + const uint32x4_t lout = vorrq_u32(lz.val[0], vshlq_n_u32(lz.val[1], 4)); + repack_store_u32(lo_pl + 0 * 128 + row * 4, vgetq_lane_u32(lout, 0)); + repack_store_u32(lo_pl + 1 * 128 + row * 4, vgetq_lane_u32(lout, 1)); + repack_store_u32(lo_pl + 2 * 128 + row * 4, vgetq_lane_u32(lout, 2)); + repack_store_u32(lo_pl + 3 * 128 + row * 4, vgetq_lane_u32(lout, 3)); + + const uint8x16_t hi0 = vandq_u8(vshlq_u8(vld1q_u8(qh + 0), hshift), mask2); + const uint8x16_t hi1 = vandq_u8(vshlq_u8(vld1q_u8(qh + 16), hshift), mask2); + repack_neon_pack_2bit_plane(hi0, hi1, hi_pl, row); +} + +static inline void repack_neon_q5_k_qh(const uint8_t * qh, uint8_t hbit, uint8_t * plane, int row) { + static const uint8_t mask_bytes[16] = { 1, 2, 1, 2, 1, 2, 1, 2, 4, 8, 4, 8, 4, 8, 4, 8 }; + const uint8x16_t m0 = vld1q_u8(mask_bytes); + const uint8x16_t m1 = vshlq_n_u8(m0, 4); + const uint8x16_t vbit = vdupq_n_u8(hbit); + const uint8x16_t b0 = vandq_u8(vtstq_u8(vld1q_u8(qh + 0), vbit), m0); + const uint8x16_t b1 = vandq_u8(vtstq_u8(vld1q_u8(qh + 16), vbit), m1); + const uint8x16_t b01 = vorrq_u8(b0, b1); + const uint8x8_t fold = vorr_u8(vget_low_u8(b01), vget_high_u8(b01)); + const uint8x8_t out = vpadd_u8(fold, fold); + plane[0 * 32 + row] = vget_lane_u8(out, 0); + plane[1 * 32 + row] = vget_lane_u8(out, 1); + plane[2 * 32 + row] = vget_lane_u8(out, 2); + plane[3 * 32 + row] = vget_lane_u8(out, 3); +} + +static inline uint16x8_t repack_neon_load_f16x8(const uint16_t * src) { + return vld1q_u16(src); +} + +#if defined(__aarch64__) || defined(__ARM_FP16_FORMAT_IEEE) +static inline uint16x8_t repack_neon_load_f16x8(const float * src) { + const float16x4_t lo = vcvt_f16_f32(vld1q_f32(src)); + const float16x4_t hi = vcvt_f16_f32(vld1q_f32(src + 4)); + return vreinterpretq_u16_f16(vcombine_f16(lo, hi)); +} + +static inline uint16x8_t repack_neon_load_f16x8(const ggml_bf16_t * src) { + const uint16x8_t bf = vld1q_u16((const uint16_t *) src); + const float32x4_t f_lo = vreinterpretq_f32_u32(vshll_n_u16(vget_low_u16(bf), 16)); + const float32x4_t f_hi = vreinterpretq_f32_u32(vshll_n_u16(vget_high_u16(bf), 16)); + const float16x4_t lo = vcvt_f16_f32(f_lo); + const float16x4_t hi = vcvt_f16_f32(f_hi); + return vreinterpretq_u16_f16(vcombine_f16(lo, hi)); +} +#else +static inline uint16x8_t repack_neon_load_f16x8(const float * src) { + uint16_t tmp[8]; + for (int i = 0; i < 8; ++i) { + tmp[i] = GGML_FP32_TO_FP16(src[i]); + } + return vld1q_u16(tmp); +} + +static inline uint16x8_t repack_neon_load_f16x8(const ggml_bf16_t * src) { + uint16_t tmp[8]; + for (int i = 0; i < 8; ++i) { + tmp[i] = GGML_FP32_TO_FP16(GGML_BF16_TO_FP32(src[i])); + } + return vld1q_u16(tmp); +} +#endif + +template +static void repack_neon_f16_tile(uint16_t * tile_dst, const SrcT * src_slice, int64_t row_stride, int ct, int kt) { + for (int row0 = 0; row0 < 32; row0 += 4) { + const SrcT * r0 = src_slice + (int64_t) (ct * 32 + row0 + 0) * row_stride + kt * 32; + const SrcT * r1 = src_slice + (int64_t) (ct * 32 + row0 + 1) * row_stride + kt * 32; + const SrcT * r2 = src_slice + (int64_t) (ct * 32 + row0 + 2) * row_stride + kt * 32; + const SrcT * r3 = src_slice + (int64_t) (ct * 32 + row0 + 3) * row_stride + kt * 32; + for (int cp = 0; cp < 16; cp += 4) { + const uint32x4_t v0 = vreinterpretq_u32_u16(repack_neon_load_f16x8(r0 + 2 * cp)); + const uint32x4_t v1 = vreinterpretq_u32_u16(repack_neon_load_f16x8(r1 + 2 * cp)); + const uint32x4_t v2 = vreinterpretq_u32_u16(repack_neon_load_f16x8(r2 + 2 * cp)); + const uint32x4_t v3 = vreinterpretq_u32_u16(repack_neon_load_f16x8(r3 + 2 * cp)); + + const uint32x2x2_t t0 = vtrn_u32(vget_low_u32(v0), vget_low_u32(v1)); + const uint32x2x2_t t1 = vtrn_u32(vget_high_u32(v0), vget_high_u32(v1)); + const uint32x2x2_t t2 = vtrn_u32(vget_low_u32(v2), vget_low_u32(v3)); + const uint32x2x2_t t3 = vtrn_u32(vget_high_u32(v2), vget_high_u32(v3)); + + vst1q_u16(tile_dst + (cp + 0) * 64 + row0 * 2, vreinterpretq_u16_u32(vcombine_u32(t0.val[0], t2.val[0]))); + vst1q_u16(tile_dst + (cp + 1) * 64 + row0 * 2, vreinterpretq_u16_u32(vcombine_u32(t0.val[1], t2.val[1]))); + vst1q_u16(tile_dst + (cp + 2) * 64 + row0 * 2, vreinterpretq_u16_u32(vcombine_u32(t1.val[0], t3.val[0]))); + vst1q_u16(tile_dst + (cp + 3) * 64 + row0 * 2, vreinterpretq_u16_u32(vcombine_u32(t1.val[1], t3.val[1]))); + } + } +} +#endif + // --- Generic 32x32 tile grid walkers for block-32 and float types --- +template +static void repack_parallel_for_ct(size_t slice_bytes, int n_col_tiles, Fn && fn) { + int n_threads = 1; + if (slice_bytes > 128 * 1024) { + for (int nt : { 4, 3, 2 }) { + if (n_col_tiles % nt == 0) { + n_threads = nt; + break; + } + } + } + + if (n_threads <= 1) { + fn(0, n_col_tiles); + return; + } + + const int chunk = n_col_tiles / n_threads; + std::thread workers[3]; + for (int i = 0; i < n_threads - 1; ++i) { + workers[i] = std::thread([&fn, i, chunk]() { + fn(i * chunk, (i + 1) * chunk); + }); + } + fn((n_threads - 1) * chunk, n_col_tiles); + for (int i = 0; i < n_threads - 1; ++i) { + workers[i].join(); + } +} + template static void repack_to_tiled_grid_32(ggml_tensor * t, const void * data, size_t offset, size_t size, size_t tile_size, PackTileFn && pack_tile) { @@ -70,12 +376,14 @@ static void repack_to_tiled_grid_32(ggml_tensor * t, const void * data, size_t o const SrcElemT * src_slice = src_matrix + (slice_idx - start_slice) * (ne1 * row_stride); uint8_t * matrix_dst = (uint8_t *) t->data + slice_idx * matrix_size; - for (int ct = 0; ct < n_col_tiles; ct++) { - for (int kt = 0; kt < n_k_tiles; kt++) { - uint8_t * tile_dst = matrix_dst + ((size_t) ct * n_k_tiles + kt) * tile_size; - pack_tile(tile_dst, src_slice, ne0, ne1, row_stride, ct, kt); + repack_parallel_for_ct(matrix_size, n_col_tiles, [&](int ct0, int ct1) { + for (int ct = ct0; ct < ct1; ct++) { + for (int kt = 0; kt < n_k_tiles; kt++) { + uint8_t * tile_dst = matrix_dst + ((size_t) ct * n_k_tiles + kt) * tile_size; + pack_tile(tile_dst, src_slice, ne0, ne1, row_stride, ct, kt); + } } - } + }); } } @@ -130,8 +438,24 @@ static void unpack_from_tiled_grid_32(void * data, const ggml_tensor * t, size_t static void repack_q4_0_tiled(ggml_tensor * t, const void * data, size_t offset, size_t size) { repack_to_tiled_grid_32(t, data, offset, size, HTP_TENSOR_TILE_SIZE_Q4_0, [](uint8_t * tile_dst, const block_q4_0 * src_slice, int64_t ne0, int64_t ne1, int64_t row_stride, int ct, int kt) { - uint8_t tile_quants[32][32]; ggml_half * scale_dst = (ggml_half *) (tile_dst + 512); +#if defined(__ARM_NEON) || defined(__aarch64__) + if (ct * 32 + 32 <= ne1 && kt < ne0 / 32) { + for (int row0 = 0; row0 < 32; row0 += 8) { + const uint8_t * qs[8]; + for (int rr = 0; rr < 8; ++rr) { + qs[rr] = src_slice[(ct * 32 + row0 + rr) * row_stride + kt].qs; + } + repack_neon_4bit_rows(qs, tile_dst, row0); + } + + for (int row = 0; row < 32; ++row) { + scale_dst[row] = src_slice[(ct * 32 + row) * row_stride + kt].d; + } + return; + } +#endif + uint8_t tile_quants[32][32]; for (int row = 0; row < 32; row++) { const int64_t r = ct * 32 + row; if (r < ne1 && kt < ne0 / 32) { @@ -167,8 +491,26 @@ static void repack_tiled_q4_0(void * data, const ggml_tensor * t, size_t offset, static void repack_q4_1_tiled(ggml_tensor * t, const void * data, size_t offset, size_t size) { repack_to_tiled_grid_32(t, data, offset, size, HTP_TENSOR_TILE_SIZE_Q4_1, [](uint8_t * tile_dst, const block_q4_1 * src_slice, int64_t ne0, int64_t ne1, int64_t row_stride, int ct, int kt) { - uint8_t tile_quants[32][32]; ggml_half * scale_dst = (ggml_half *) (tile_dst + 512); +#if defined(__ARM_NEON) || defined(__aarch64__) + if (ct * 32 + 32 <= ne1 && kt < ne0 / 32) { + for (int row0 = 0; row0 < 32; row0 += 8) { + const uint8_t * qs[8]; + for (int rr = 0; rr < 8; ++rr) { + qs[rr] = src_slice[(ct * 32 + row0 + rr) * row_stride + kt].qs; + } + repack_neon_4bit_rows(qs, tile_dst, row0); + } + + for (int row = 0; row < 32; ++row) { + const block_q4_1 & src_block = src_slice[(ct * 32 + row) * row_stride + kt]; + scale_dst[2 * row + 0] = src_block.d; + scale_dst[2 * row + 1] = src_block.m; + } + return; + } +#endif + uint8_t tile_quants[32][32]; for (int row = 0; row < 32; row++) { const int64_t r = ct * 32 + row; if (r < ne1 && kt < ne0 / 32) { @@ -207,8 +549,24 @@ static void repack_tiled_q4_1(void * data, const ggml_tensor * t, size_t offset, static void repack_mxfp4_tiled(ggml_tensor * t, const void * data, size_t offset, size_t size) { repack_to_tiled_grid_32(t, data, offset, size, HTP_TENSOR_TILE_SIZE_MXFP4, [](uint8_t * tile_dst, const block_mxfp4 * src_slice, int64_t ne0, int64_t ne1, int64_t row_stride, int ct, int kt) { - uint8_t tile_quants[32][32]; uint8_t * scale_dst = tile_dst + 512; +#if defined(__ARM_NEON) || defined(__aarch64__) + if (ct * 32 + 32 <= ne1 && kt < ne0 / 32) { + for (int row0 = 0; row0 < 32; row0 += 8) { + const uint8_t * qs[8]; + for (int rr = 0; rr < 8; ++rr) { + qs[rr] = src_slice[(ct * 32 + row0 + rr) * row_stride + kt].qs; + } + repack_neon_4bit_rows(qs, tile_dst, row0); + } + + for (int row = 0; row < 32; ++row) { + scale_dst[row] = src_slice[(ct * 32 + row) * row_stride + kt].e; + } + return; + } +#endif + uint8_t tile_quants[32][32]; for (int row = 0; row < 32; row++) { const int64_t r = ct * 32 + row; if (r < ne1 && kt < ne0 / 32) { @@ -244,6 +602,23 @@ static void repack_tiled_mxfp4(void * data, const ggml_tensor * t, size_t offset static void repack_q8_0_tiled(ggml_tensor * t, const void * data, size_t offset, size_t size) { repack_to_tiled_grid_32(t, data, offset, size, HTP_TENSOR_TILE_SIZE_Q8_0, [](uint8_t * tile_dst, const block_q8_0 * src_slice, int64_t ne0, int64_t ne1, int64_t row_stride, int ct, int kt) { + ggml_half * scale_dst = (ggml_half *) (tile_dst + 1024); +#if defined(__ARM_NEON) || defined(__aarch64__) + if (ct * 32 + 32 <= ne1 && kt < ne0 / 32) { + for (int row0 = 0; row0 < 32; row0 += 8) { + const uint8_t * qs[8]; + for (int rr = 0; rr < 8; ++rr) { + qs[rr] = (const uint8_t *) src_slice[(ct * 32 + row0 + rr) * row_stride + kt].qs; + } + repack_neon_q8_rows(qs, tile_dst, row0); + } + + for (int row = 0; row < 32; ++row) { + scale_dst[row] = src_slice[(ct * 32 + row) * row_stride + kt].d; + } + return; + } +#endif for (int cp = 0; cp < 16; cp++) { const int col0 = cp * 2; const int col1 = col0 + 1; @@ -254,7 +629,6 @@ static void repack_q8_0_tiled(ggml_tensor * t, const void * data, size_t offset, tile_dst[cp * 64 + 2 * row + 1] = b ? b->qs[col1] : 0; } } - ggml_half * scale_dst = (ggml_half *) (tile_dst + 1024); for (int row = 0; row < 32; row++) { const int64_t r = ct * 32 + row; scale_dst[row] = (r < ne1 && kt < ne0 / 32) ? src_slice[r * row_stride + kt].d : 0; @@ -303,24 +677,33 @@ static void repack_q2_0_tiled(ggml_tensor * t, const void * data, size_t offset, for (int64_t slice = 0; slice < t->ne[2] * t->ne[3]; ++slice) { const block_q2_0 * src_slice = src + slice * t->ne[1] * blocks_per_row; uint8_t * dst_slice = (uint8_t *) t->data + slice * matrix_size; - memset(dst_slice, 0, matrix_size); - - for (int64_t row_index = 0; row_index < t->ne[1]; ++row_index) { - const int64_t ct = row_index / 32; - const int row = (int) (row_index % 32); - for (int64_t kt = 0; kt < n_weight_tiles; ++kt) { - const block_q2_0 * block = src_slice + row_index * blocks_per_row + kt; - uint8_t * tile = dst_slice + (ct * n_weight_tiles + kt) * HTP_TENSOR_TILE_SIZE_Q2_0; - for (int group = 0; group < 16; ++group) { - for (int byte = 0; byte < 4; ++byte) { - const int k = 4 * group + byte; - const uint8_t code = (block->qs[k / 4] >> (2 * (k % 4))) & 3; - tile[(group >> 2) * 128 + 4 * row + byte] |= code << (2 * (group & 3)); + const size_t ct_bytes = (size_t) n_weight_tiles * HTP_TENSOR_TILE_SIZE_Q2_0; + + repack_parallel_for_ct(matrix_size, (int) n_col_tiles, [&](int ct0, int ct1) { + memset(dst_slice + (size_t) ct0 * ct_bytes, 0, (size_t) (ct1 - ct0) * ct_bytes); + const int64_t r0 = (int64_t) ct0 * 32; + const int64_t r1 = (std::min)((int64_t) ct1 * 32, t->ne[1]); + for (int64_t row_index = r0; row_index < r1; ++row_index) { + const int64_t ct = row_index / 32; + const int row = (int) (row_index % 32); + for (int64_t kt = 0; kt < n_weight_tiles; ++kt) { + const block_q2_0 * block = src_slice + row_index * blocks_per_row + kt; + uint8_t * tile = dst_slice + (ct * n_weight_tiles + kt) * HTP_TENSOR_TILE_SIZE_Q2_0; +#if defined(__ARM_NEON) || defined(__aarch64__) + repack_neon_q2_0_block(block->qs, tile, row); +#else + for (int group = 0; group < 16; ++group) { + for (int byte = 0; byte < 4; ++byte) { + const int k = 4 * group + byte; + const uint8_t code = (block->qs[k / 4] >> (2 * (k % 4))) & 3; + tile[(group >> 2) * 128 + 4 * row + byte] |= code << (2 * (group & 3)); + } } +#endif + memcpy(tile + 512 + 2 * row, &block->d, sizeof(block->d)); } - memcpy(tile + 512 + 2 * row, &block->d, sizeof(block->d)); } - } + }); } } @@ -368,6 +751,12 @@ static void repack_float_to_f16_tiled(ggml_tensor * t, const void * data, size_t repack_to_tiled_grid_32(t, data, offset, size, HTP_TENSOR_TILE_SIZE_F16, [&](uint8_t * tile_bytes, const SrcT * src_slice, int64_t ne0, int64_t ne1, int64_t row_stride, int ct, int kt) { uint16_t * tile_dst = (uint16_t *) tile_bytes; +#if defined(__ARM_NEON) || defined(__aarch64__) + if (ct * 32 + 32 <= ne1 && kt * 32 + 32 <= ne0) { + repack_neon_f16_tile(tile_dst, src_slice, row_stride, ct, kt); + return; + } +#endif for (int cp = 0; cp < 16; cp++) { const int64_t c0 = kt * 32 + cp * 2; const int64_t c1 = c0 + 1; @@ -424,20 +813,25 @@ static void repack_kquant_to_tiled_grid(ggml_tensor * t, const void * data, size for (int64_t slice_idx = 0; slice_idx < ne2 * ne3; slice_idx++) { const BlockT * src_slice = src_matrix + slice_idx * (ne1 * sb_per_row); uint8_t * matrix_dst = (uint8_t *) t->data + slice_idx * matrix_size; - - // Padding rows and OR-ed bit planes require zeroed tiles - memset(matrix_dst, 0, matrix_size); - - for (int64_t r = 0; r < ne1; r++) { - const int ct = (int) (r / 32); - const int row = (int) (r % 32); - const BlockT * src_row = src_slice + r * sb_per_row; - - for (int kt = 0; kt < n_k_tiles; kt++) { - uint8_t * tile = matrix_dst + ((size_t) ct * n_k_tiles + kt) * tile_size; - pack_kt(tile, &src_row[kt / 8], row, kt % 8); + const size_t ct_bytes = (size_t) n_k_tiles * tile_size; + + repack_parallel_for_ct(matrix_size, n_col_tiles, [&](int ct0, int ct1) { + // Padding rows and OR-ed bit planes require zeroed tiles + memset(matrix_dst + (size_t) ct0 * ct_bytes, 0, (size_t) (ct1 - ct0) * ct_bytes); + + const int64_t r0 = (int64_t) ct0 * 32; + const int64_t r1 = (std::min)((int64_t) ct1 * 32, ne1); + for (int64_t r = r0; r < r1; r++) { + const int ct = (int) (r / 32); + const int row = (int) (r % 32); + const BlockT * src_row = src_slice + r * sb_per_row; + + for (int kt = 0; kt < n_k_tiles; kt++) { + uint8_t * tile = matrix_dst + ((size_t) ct * n_k_tiles + kt) * tile_size; + pack_kt(tile, &src_row[kt / 8], row, kt % 8); + } } - } + }); } } @@ -493,6 +887,7 @@ static void unpack_kquant_from_tiled_grid(void * data, const ggml_tensor * t, si // --- K-Quant Helpers & Per-Type Implementations (Q2_K, Q3_K, Q4_K, Q5_K, Q6_K) --- +#if !defined(__ARM_NEON) && !defined(__aarch64__) // unsigned 6-bit value (0..63) of element e of a Q6_K block, same bit layout as dequantize_row_q6_K static inline uint8_t q6_K_get_quant(const block_q6_K * b, int e) { const int c = e / 128; @@ -510,6 +905,7 @@ static inline uint8_t q6_K_get_quant(const block_q6_K * b, int e) { } return (uint8_t) (lo | (hi << 4)); } +#endif static void repack_q6_K_tiled(ggml_tensor * t, const void * data, size_t offset, size_t size) { repack_kquant_to_tiled_grid(t, data, offset, size, HTP_TENSOR_TILE_SIZE_Q6_K, @@ -519,6 +915,9 @@ static void repack_q6_K_tiled(ggml_tensor * t, const void * data, size_t offset, uint8_t * hi_pl = tile + 512; ggml_half * sc_pl = (ggml_half *) (tile + 768); +#if defined(__ARM_NEON) || defined(__aarch64__) + repack_neon_q6_k(b, kt_local, lo_pl, hi_pl, row); +#else for (int lk = 0; lk < 32; lk++) { const uint8_t q6 = q6_K_get_quant(b, kt_local * 32 + lk); const int g = lk >> 2; @@ -526,6 +925,7 @@ static void repack_q6_K_tiled(ggml_tensor * t, const void * data, size_t offset, lo_pl[(g >> 1) * 128 + pos] |= (uint8_t) ((q6 & 0xF) << ((g & 1) * 4)); hi_pl[(g >> 2) * 128 + pos] |= (uint8_t) ((q6 >> 4) << ((g & 3) * 2)); } +#endif for (int sub = 0; sub < 2; sub++) { sc_pl[sub * 32 + row] = GGML_FP32_TO_FP16(d * (float) b->scales[kt_local * 2 + sub]); } @@ -585,6 +985,7 @@ static void repack_tiled_q6_K(void * data, const ggml_tensor * t, size_t offset, }); } +#if !defined(__ARM_NEON) && !defined(__aarch64__) // low 2 bits (0..3) of element e of a Q2_K or Q3_K block, same bit layout as dequantize_row_q2_K / q3_K static inline uint8_t q2_3_K_get_low2(const uint8_t * qs, int e) { const int c = e / 128; @@ -600,6 +1001,7 @@ static inline bool q3_K_get_hbit(const block_q3_K * b, int e) { const int l = e % 32; return (b->hmask[l] >> (c * 4 + j)) & 1; } +#endif // signed 6-bit scale j (-32..31) of a Q3_K block, same packing as quantize_row_q3_K_ref static inline int q3_K_get_scale(const uint8_t * scales, int j) { @@ -670,6 +1072,10 @@ static void repack_q3_K_tiled(ggml_tensor * t, const void * data, size_t offset, uint8_t * neg_pl = tile + 256; ggml_half * sc_pl = (ggml_half *) (tile + 384); +#if defined(__ARM_NEON) || defined(__aarch64__) + repack_neon_q2_3_k_low2(b->qs, kt_local, lo_pl, row); + repack_neon_q3_k_hmask(b->hmask, kt_local, neg_pl, row); +#else for (int lk = 0; lk < 32; lk++) { const int e = kt_local * 32 + lk; const int g = lk >> 2; @@ -679,6 +1085,7 @@ static void repack_q3_K_tiled(ggml_tensor * t, const void * data, size_t offset, neg_pl[pos] |= (uint8_t) (1 << g); } } +#endif for (int sub = 0; sub < 2; sub++) { sc_pl[sub * 32 + row] = GGML_FP32_TO_FP16(d * (float) q3_K_get_scale(b->scales, kt_local * 2 + sub)); } @@ -750,11 +1157,15 @@ static void repack_q2_K_tiled(ggml_tensor * t, const void * data, size_t offset, uint8_t * sc_pl = tile + 256; ggml_half * dd_pl = (ggml_half *) (tile + 320); +#if defined(__ARM_NEON) || defined(__aarch64__) + repack_neon_q2_3_k_low2(b->qs, kt_local, lo_pl, row); +#else for (int lk = 0; lk < 32; lk++) { const int g = lk >> 2; const int pos = row * 4 + (lk & 3); lo_pl[(g >> 2) * 128 + pos] |= (uint8_t) (q2_3_K_get_low2(b->qs, kt_local * 32 + lk) << ((g & 3) * 2)); } +#endif for (int sub = 0; sub < 2; sub++) { sc_pl[sub * 32 + row] = b->scales[kt_local * 2 + sub]; } @@ -846,23 +1257,46 @@ static inline void pack_k4_scales_mins(uint8_t * scales, ggml_half * d, ggml_hal } } +template +static inline void repack_k4_quants(uint8_t * tile_dst, const BlockT * b, int row, int kt_local, + int64_t sb_per_row, bool use_neon) { +#if defined(__ARM_NEON) || defined(__aarch64__) + if (use_neon) { + if ((row & 7) == 0) { + const uint8_t * qs[8]; + for (int rr = 0; rr < 8; ++rr) { + qs[rr] = b[rr * sb_per_row].qs + (kt_local / 2) * 32; + } + repack_neon_k4_rows(qs, tile_dst, row, (kt_local & 1) != 0); + } + return; + } +#else + GGML_UNUSED(sb_per_row); + GGML_UNUSED(use_neon); +#endif + const uint8_t * qs_sub = b->qs + (kt_local / 2) * 32; + const int shift = (kt_local & 1) ? 4 : 0; + + for (int cp = 0; cp < 16; cp++) { + const uint8_t q0 = (qs_sub[2 * cp + 0] >> shift) & 0x0F; + const uint8_t q1 = (qs_sub[2 * cp + 1] >> shift) & 0x0F; + tile_dst[cp * 32 + row] = (uint8_t) ((q1 << 4) | q0); + } +} + static void repack_q4_K_tiled(ggml_tensor * t, const void * data, size_t offset, size_t size) { + const int64_t sb_per_row = t->ne[0] / QK_K; + const bool use_neon = (t->ne[1] % 8 == 0); repack_kquant_to_tiled_grid(t, data, offset, size, HTP_TENSOR_TILE_SIZE_Q4_K, - [](uint8_t * tile_dst, const block_q4_K * b, int row, int kt_local) { + [sb_per_row, use_neon](uint8_t * tile_dst, const block_q4_K * b, int row, int kt_local) { const float d = GGML_FP16_TO_FP32(b->d); const float dmin = GGML_FP16_TO_FP32(b->dmin); uint8_t sc, m; get_scale_min_k4(kt_local, b->scales, &sc, &m); - const uint8_t * qs_sub = b->qs + (kt_local / 2) * 32; - const int shift = (kt_local & 1) ? 4 : 0; - - for (int cp = 0; cp < 16; cp++) { - const uint8_t q0 = (qs_sub[2 * cp + 0] >> shift) & 0x0F; - const uint8_t q1 = (qs_sub[2 * cp + 1] >> shift) & 0x0F; - tile_dst[cp * 32 + row] = (uint8_t) ((q1 << 4) | q0); - } + repack_k4_quants(tile_dst, b, row, kt_local, sb_per_row, use_neon); ggml_half * scale_dst = (ggml_half *) (tile_dst + 512); scale_dst[2 * row + 0] = GGML_FP32_TO_FP16(d * (float) sc); @@ -900,8 +1334,10 @@ static void repack_tiled_q4_K(void * data, const ggml_tensor * t, size_t offset, } static void repack_q5_K_tiled(ggml_tensor * t, const void * data, size_t offset, size_t size) { + const int64_t sb_per_row = t->ne[0] / QK_K; + const bool use_neon = (t->ne[1] % 8 == 0); repack_kquant_to_tiled_grid(t, data, offset, size, HTP_TENSOR_TILE_SIZE_Q5_K, - [](uint8_t * tile_dst, const block_q5_K * b, int row, int kt_local) { + [sb_per_row, use_neon](uint8_t * tile_dst, const block_q5_K * b, int row, int kt_local) { const float d = GGML_FP16_TO_FP32(b->d); const float dmin = GGML_FP16_TO_FP32(b->dmin); uint8_t * plane = tile_dst + 640; @@ -909,15 +1345,13 @@ static void repack_q5_K_tiled(ggml_tensor * t, const void * data, size_t offset, uint8_t sc, m; get_scale_min_k4(kt_local, b->scales, &sc, &m); - const uint8_t * qs_sub = b->qs + (kt_local / 2) * 32; - const int shift = (kt_local & 1) ? 4 : 0; - const uint8_t hbit = (uint8_t) (1 << kt_local); + repack_k4_quants(tile_dst, b, row, kt_local, sb_per_row, use_neon); + const uint8_t hbit = (uint8_t) (1 << kt_local); +#if defined(__ARM_NEON) || defined(__aarch64__) + repack_neon_q5_k_qh(b->qh, hbit, plane, row); +#else for (int cp = 0; cp < 16; cp++) { - const uint8_t q0 = (qs_sub[2 * cp + 0] >> shift) & 0x0F; - const uint8_t q1 = (qs_sub[2 * cp + 1] >> shift) & 0x0F; - tile_dst[cp * 32 + row] = (uint8_t) ((q1 << 4) | q0); - const int i = cp / 4; const int lane = (cp % 4) * 32 + row; if (b->qh[2 * cp + 0] & hbit) { @@ -927,6 +1361,7 @@ static void repack_q5_K_tiled(ggml_tensor * t, const void * data, size_t offset, plane[lane] |= (uint8_t) (1 << (2 * i + 1)); } } +#endif ggml_half * scale_dst = (ggml_half *) (tile_dst + 512); scale_dst[2 * row + 0] = GGML_FP32_TO_FP16(d * (float) sc); @@ -1002,31 +1437,37 @@ static void repack_q1_0_tiled(ggml_tensor * t, const void * data, size_t offset, const block_q1_0 * src_slice = src_matrix + (slice_idx - start_slice) * ne1 * n_k_tiles; uint8_t * matrix_dst = (uint8_t *) t->data + slice_idx * matrix_size; - for (int ct = 0; ct < n_col_tiles; ct++) { - for (int kt = 0; kt < n_k_tiles; kt++) { - uint8_t * tile_dst = matrix_dst + ((size_t) ct * n_k_tiles + kt) * tile_size; - ggml_half * scale_dst = (ggml_half *) (tile_dst + 512); - memset(tile_dst, 0, tile_size); - - for (int row = 0; row < 32; row++) { - const int64_t r = ct * 32 + row; - if (r >= ne1) { - continue; - } + repack_parallel_for_ct(matrix_size, n_col_tiles, [&](int ct0, int ct1) { + for (int ct = ct0; ct < ct1; ct++) { + for (int kt = 0; kt < n_k_tiles; kt++) { + uint8_t * tile_dst = matrix_dst + ((size_t) ct * n_k_tiles + kt) * tile_size; + ggml_half * scale_dst = (ggml_half *) (tile_dst + 512); + memset(tile_dst, 0, tile_size); + + for (int row = 0; row < 32; row++) { + const int64_t r = ct * 32 + row; + if (r >= ne1) { + continue; + } - const block_q1_0 & block = src_slice[r * n_k_tiles + kt]; - scale_dst[row] = block.d; - for (int k = 0; k < QK1_0; k++) { - int byte_offset; - int bit; - q1_0_tiled_bit_location(k, row, byte_offset, bit); - if (block.qs[k / 8] & (1u << (k % 8))) { - tile_dst[byte_offset] |= (uint8_t) (1u << bit); + const block_q1_0 & block = src_slice[r * n_k_tiles + kt]; + scale_dst[row] = block.d; +#if defined(__ARM_NEON) || defined(__aarch64__) + repack_neon_q1_0_block(block.qs, tile_dst, row); +#else + for (int k = 0; k < QK1_0; k++) { + int byte_offset; + int bit; + q1_0_tiled_bit_location(k, row, byte_offset, bit); + if (block.qs[k / 8] & (1u << (k % 8))) { + tile_dst[byte_offset] |= (uint8_t) (1u << bit); + } } +#endif } } } - } + }); } } @@ -1127,11 +1568,11 @@ void htp_repack_tensor_tiled(ggml_tensor * tensor, const void * data, size_t siz break; case GGML_TYPE_F32: repack_float_to_f16_tiled(tensor, data, 0, size, - [](float v) { return (uint16_t) ggml_fp32_to_fp16(v); }); + [](float v) { return (uint16_t) GGML_FP32_TO_FP16(v); }); break; case GGML_TYPE_BF16: repack_float_to_f16_tiled(tensor, data, 0, size, - [](ggml_bf16_t v) { return (uint16_t) ggml_fp32_to_fp16(ggml_bf16_to_fp32(v)); }); + [](ggml_bf16_t v) { return (uint16_t) GGML_FP32_TO_FP16(GGML_BF16_TO_FP32(v)); }); break; default: break; From ea9e3446b5c84a7f972cbbdea9df615fc4ff9159 Mon Sep 17 00:00:00 2001 From: Niklas Wenzel Date: Wed, 21 Oct 2026 09:54:33 +0200 Subject: [PATCH 43/69] metal : fix memory leaks and cleanup logic in GPU capture path (#30890) --- ggml/src/ggml-metal/ggml-metal-context.m | 28 +++++++++++++++++++----- 1 file changed, 22 insertions(+), 6 deletions(-) diff --git a/ggml/src/ggml-metal/ggml-metal-context.m b/ggml/src/ggml-metal/ggml-metal-context.m index e9064666f628..597c126ca3f7 100644 --- a/ggml/src/ggml-metal/ggml-metal-context.m +++ b/ggml/src/ggml-metal/ggml-metal-context.m @@ -539,8 +539,14 @@ enum ggml_status ggml_metal_graph_compute(ggml_metal_t ctx, struct ggml_cgraph * descriptor.outputURL = [NSURL fileURLWithPath:path]; NSError * error = nil; - if (![[MTLCaptureManager sharedCaptureManager] startCaptureWithDescriptor:descriptor error:&error]) { + const bool ok = [[MTLCaptureManager sharedCaptureManager] startCaptureWithDescriptor:descriptor error:&error]; + + [descriptor release]; + + if (!ok) { GGML_LOG_ERROR("%s: error: unable to start capture '%s' (did you set METAL_CAPTURE_ENABLED=1 ?)\n", __func__, [[error localizedDescription] UTF8String]); + [ctx->capture_scope release]; + ctx->capture_scope = nil; } else { [ctx->capture_scope beginScope]; ctx->capture_started = true; @@ -600,6 +606,8 @@ enum ggml_status ggml_metal_graph_compute(ggml_metal_t ctx, struct ggml_cgraph * // enter here only when capturing in order to wait for all computation to finish // otherwise, we leave the graph to compute asynchronously if (use_capture && ctx->capture_started) { + enum ggml_status res = GGML_STATUS_SUCCESS; + // wait for completion and check status of each command buffer // needed to detect if the device ran out-of-memory for example (#1881) { @@ -613,11 +621,11 @@ enum ggml_status ggml_metal_graph_compute(ggml_metal_t ctx, struct ggml_cgraph * GGML_LOG_INFO("error: %s\n", [[cmd_buf error].localizedDescription UTF8String]); } - return GGML_STATUS_FAILED; + res = GGML_STATUS_FAILED; } } - for (int i = 0; i < n_cb; ++i) { + for (int i = 0; i < n_cb && res == GGML_STATUS_SUCCESS; ++i) { id cmd_buf = ctx->cmd_bufs[i].obj; [cmd_buf waitUntilCompleted]; @@ -627,8 +635,8 @@ enum ggml_status ggml_metal_graph_compute(ggml_metal_t ctx, struct ggml_cgraph * if (status == MTLCommandBufferStatusError) { GGML_LOG_INFO("error: %s\n", [[cmd_buf error].localizedDescription UTF8String]); } - - return GGML_STATUS_FAILED; + res = GGML_STATUS_FAILED; + break; } id next_buffer = (i + 1 < n_cb ? ctx->cmd_bufs[i + 1].obj : nil); @@ -643,7 +651,8 @@ enum ggml_status ggml_metal_graph_compute(ggml_metal_t ctx, struct ggml_cgraph * if (ctx->abort_callback && ctx->abort_callback(ctx->abort_callback_data)) { GGML_LOG_INFO("%s: command buffer %d aborted", __func__, i); - return GGML_STATUS_ABORTED; + res = GGML_STATUS_ABORTED; + break; } [next_buffer commit]; @@ -652,7 +661,14 @@ enum ggml_status ggml_metal_graph_compute(ggml_metal_t ctx, struct ggml_cgraph * [ctx->capture_scope endScope]; [[MTLCaptureManager sharedCaptureManager] stopCapture]; + [ctx->capture_scope release]; + ctx->capture_scope = nil; + ctx->capture_started = false; + + if (res != GGML_STATUS_SUCCESS) { + return res; + } } } From eb182d85c0e304a69ecc0be0c5e12884691c116a Mon Sep 17 00:00:00 2001 From: Aman Gupta Date: Wed, 21 Oct 2026 14:02:29 +0530 Subject: [PATCH 44/69] llama : place d2t by its set_rows use so it can stay on the GPU (#30912) --- src/llama-arch.cpp | 2 +- src/llama-model-loader.cpp | 7 +++++++ 2 files changed, 8 insertions(+), 1 deletion(-) diff --git a/src/llama-arch.cpp b/src/llama-arch.cpp index 1b954f176cd2..4534be22f37b 100644 --- a/src/llama-arch.cpp +++ b/src/llama-arch.cpp @@ -1110,7 +1110,7 @@ static const std::map LLM_TENSOR_INFOS = { {LLM_TENSOR_MASKED_EMBD_ORDERING, {LLM_TENSOR_LAYER_INPUT, GGML_OP_NONE}}, // eagle3 {LLM_TENSOR_FC, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL_MAT}}, - {LLM_TENSOR_D2T, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_GET_ROWS}}, + {LLM_TENSOR_D2T, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_SET_ROWS}}, // dspark {LLM_TENSOR_DSPARK_MARKOV_W1, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_GET_ROWS}}, {LLM_TENSOR_DSPARK_MARKOV_W2, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL_MAT}}, diff --git a/src/llama-model-loader.cpp b/src/llama-model-loader.cpp index bc93c6f9e62d..9dc366fda5a0 100644 --- a/src/llama-model-loader.cpp +++ b/src/llama-model-loader.cpp @@ -951,6 +951,13 @@ static bool weight_buft_supported(const llama_hparams & hparams, ggml_tensor * w ggml_tensor * b = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 512); op_tensor = ggml_get_rows(ctx, w, b); } break; + case GGML_OP_SET_ROWS: + { + // w holds the row indices (e.g. d2t) + ggml_tensor * a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, 1, w->ne[0]); + ggml_tensor * b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, 1, w->ne[0]); + op_tensor = ggml_set_rows(ctx, a, b, w); + } break; case GGML_OP_MUL_MAT: { ggml_tensor * b = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, w->ne[0], 512, w->ne[2], w->ne[3]); From 824eaecf486f5ce0070f71501094b60f3cd70864 Mon Sep 17 00:00:00 2001 From: Evgen Mykhailenko Date: Wed, 21 Oct 2026 05:03:27 -0400 Subject: [PATCH 45/69] llama : optimize vocabulary loading (#30679) * llama : optimize vocabulary loading * Update src/unicode.h Co-authored-by: Georgi Gerganov * CR : added reserve in gguf_kv_to_str * CR : trailing space fix --------- Co-authored-by: Georgi Gerganov --- src/llama-adapter.cpp | 4 ++-- src/llama-impl.cpp | 47 +++++++++++++++++++++++--------------- src/llama-impl.h | 3 ++- src/llama-model-loader.cpp | 2 +- src/llama-vocab.cpp | 32 ++++++++++++++------------ src/unicode.cpp | 21 +++++++++++++++++ src/unicode.h | 1 + 7 files changed, 73 insertions(+), 37 deletions(-) diff --git a/src/llama-adapter.cpp b/src/llama-adapter.cpp index 6a7ffd6a61da..5b342acf604d 100644 --- a/src/llama-adapter.cpp +++ b/src/llama-adapter.cpp @@ -179,13 +179,13 @@ static void llama_adapter_lora_init_impl(llama_model & model, FILE * file, llama ? format("%s[%s,%zu]", gguf_type_name(type), gguf_type_name(gguf_get_arr_type(gguf_ctx, i)), gguf_get_arr_n(gguf_ctx, i)) : gguf_type_name(type); const char * name = gguf_get_key(gguf_ctx, i); - const std::string value = gguf_kv_to_str(gguf_ctx, i); + const size_t MAX_VALUE_LEN = 40; + const std::string value = gguf_kv_to_str(gguf_ctx, i, MAX_VALUE_LEN); if (type != GGUF_TYPE_ARRAY) { adapter.gguf_kv.emplace(name, value); } - const size_t MAX_VALUE_LEN = 40; std::string print_value = value.size() > MAX_VALUE_LEN ? format("%s...", value.substr(0, MAX_VALUE_LEN - 3).c_str()) : value; replace_all(print_value, "\n", "\\n"); diff --git a/src/llama-impl.cpp b/src/llama-impl.cpp index c216a310b25d..4ca836ca66c3 100644 --- a/src/llama-impl.cpp +++ b/src/llama-impl.cpp @@ -11,7 +11,6 @@ #include #include #include -#include struct llama_logger_state { ggml_log_callback log_callback = llama_log_callback_default; @@ -149,7 +148,7 @@ std::string llama_format_tensor_shape(const struct ggml_tensor * t) { return buf; } -static std::string gguf_data_to_str(enum gguf_type type, const void * data, int i) { +static std::string gguf_data_to_str(enum gguf_type type, const void * data, size_t i) { switch (type) { case GGUF_TYPE_UINT8: return std::to_string(((const uint8_t *)data)[i]); case GGUF_TYPE_INT8: return std::to_string(((const int8_t *)data)[i]); @@ -166,7 +165,7 @@ static std::string gguf_data_to_str(enum gguf_type type, const void * data, int } } -std::string gguf_kv_to_str(const struct gguf_context * ctx_gguf, int i) { +std::string gguf_kv_to_str(const struct gguf_context * ctx_gguf, int i, size_t max_len) { const enum gguf_type type = gguf_get_kv_type(ctx_gguf, i); switch (type) { @@ -175,28 +174,38 @@ std::string gguf_kv_to_str(const struct gguf_context * ctx_gguf, int i) { case GGUF_TYPE_ARRAY: { const enum gguf_type arr_type = gguf_get_arr_type(ctx_gguf, i); - int arr_n = gguf_get_arr_n(ctx_gguf, i); + const size_t arr_n = gguf_get_arr_n(ctx_gguf, i); const void * data = arr_type == GGUF_TYPE_STRING ? nullptr : gguf_get_arr_data(ctx_gguf, i); - std::stringstream ss; - ss << "["; - for (int j = 0; j < arr_n; j++) { + std::string res = "["; + size_t j = 0; + for (; j < arr_n && res.size() <= max_len; j++) { + if (j > 0) { + res += ", "; + } if (arr_type == GGUF_TYPE_STRING) { - std::string val = gguf_get_arr_str(ctx_gguf, i, j); - // escape quotes - replace_all(val, "\\", "\\\\"); - replace_all(val, "\"", "\\\""); - ss << '"' << val << '"'; + const char * s = gguf_get_arr_str(ctx_gguf, i, j); + const size_t need = res.size() + 2 * strlen(s) + 2; + if (res.capacity() < need) { + res.reserve(std::max(need, 2 * res.capacity())); + } + res += '"'; + for (; *s; s++) { + if (*s == '\\' || *s == '"') { + res += '\\'; + } + res += *s; + } + res += '"'; } else if (arr_type == GGUF_TYPE_ARRAY) { - ss << "???"; + res += "???"; } else { - ss << gguf_data_to_str(arr_type, data, j); - } - if (j < arr_n - 1) { - ss << ", "; + res += gguf_data_to_str(arr_type, data, j); } } - ss << "]"; - return ss.str(); + if (j == arr_n) { + res += "]"; + } + return res; } default: return gguf_data_to_str(type, gguf_get_val_data(ctx_gguf, i), 0); diff --git a/src/llama-impl.h b/src/llama-impl.h index 7ccd23c86eff..884dca647995 100644 --- a/src/llama-impl.h +++ b/src/llama-impl.h @@ -107,4 +107,5 @@ std::string format(const char * fmt, ...); std::string llama_format_tensor_shape(const std::vector & ne); std::string llama_format_tensor_shape(const struct ggml_tensor * t); -std::string gguf_kv_to_str(const struct gguf_context * ctx_gguf, int i); +// arrays are rendered only until the result is longer than max_len +std::string gguf_kv_to_str(const struct gguf_context * ctx_gguf, int i, size_t max_len = SIZE_MAX); diff --git a/src/llama-model-loader.cpp b/src/llama-model-loader.cpp index 9dc366fda5a0..066e69e91b72 100644 --- a/src/llama-model-loader.cpp +++ b/src/llama-model-loader.cpp @@ -807,8 +807,8 @@ llama_model_loader::llama_model_loader( ? format("%s[%s,%zu]", gguf_type_name(type), gguf_type_name(gguf_get_arr_type(metadata, i)), gguf_get_arr_n(metadata, i)) : gguf_type_name(type); - std::string value = gguf_kv_to_str(metadata, i); const size_t MAX_VALUE_LEN = 40; + std::string value = gguf_kv_to_str(metadata, i, MAX_VALUE_LEN); if (value.size() > MAX_VALUE_LEN) { value = format("%s...", value.substr(0, MAX_VALUE_LEN - 3).c_str()); } diff --git a/src/llama-vocab.cpp b/src/llama-vocab.cpp index 84f50d782681..a3d5dedef332 100644 --- a/src/llama-vocab.cpp +++ b/src/llama-vocab.cpp @@ -2260,21 +2260,22 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) { throw std::runtime_error(format("invalid gguf type for %s", kv(LLM_KV_TOKENIZER_MERGES).c_str())); } const int n_merges = gguf_get_arr_n(ctx, merges_keyidx); + bpe_ranks.reserve(n_merges); for (int i = 0; i < n_merges; i++) { - const std::string word = gguf_get_arr_str(ctx, merges_keyidx, i); + const char * word = gguf_get_arr_str(ctx, merges_keyidx, i); //GGML_ASSERT(unicode_cpts_from_utf8(word).size() > 0); std::string first; std::string second; - const size_t pos = word.find(' ', 1); + const char * sep = *word ? strchr(word + 1, ' ') : nullptr; - if (pos != std::string::npos) { - first = word.substr(0, pos); - second = word.substr(pos + 1); + if (sep) { + first.assign(word, sep - word); + second.assign(sep + 1); } - bpe_ranks.emplace(std::make_pair(first, second), i); + bpe_ranks.emplace(std::make_pair(std::move(first), std::move(second)), i); } } @@ -2371,20 +2372,21 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) { throw std::runtime_error(format("invalid gguf type for %s", kv(LLM_KV_TOKENIZER_MERGES).c_str())); } const int n_merges = gguf_get_arr_n(ctx, merges_keyidx); + bpe_ranks.reserve(n_merges); for (int i = 0; i < n_merges; i++) { - const std::string word = gguf_get_arr_str(ctx, merges_keyidx, i); + const char * word = gguf_get_arr_str(ctx, merges_keyidx, i); std::string first; std::string second; - const size_t pos = word.find(' ', 1); + const char * sep = *word ? strchr(word + 1, ' ') : nullptr; - if (pos != std::string::npos) { - first = word.substr(0, pos); - second = word.substr(pos + 1); + if (sep) { + first.assign(word, sep - word); + second.assign(sep + 1); } - bpe_ranks.emplace(std::make_pair(first, second), i); + bpe_ranks.emplace(std::make_pair(std::move(first), std::move(second)), i); } } @@ -2764,6 +2766,7 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) { } id_to_token.resize(n_tokens); + token_to_id.reserve(n_tokens); for (uint32_t i = 0; i < n_tokens; i++) { std::string word = gguf_get_arr_str(ctx, token_idx, i); @@ -3676,13 +3679,14 @@ static void llama_unescape_whitespace(std::string & word) { static std::string llama_decode_text(const std::string & text) { std::string decoded_text; + decoded_text.reserve(text.size()); const auto cpts = unicode_cpts_from_utf8(text); for (const auto cpt : cpts) { - const auto utf8 = unicode_cpt_to_utf8(cpt); try { - decoded_text += unicode_utf8_to_byte(utf8); + decoded_text += unicode_cpt_to_byte(cpt); } catch (const std::out_of_range & /*e*/) { + const auto utf8 = unicode_cpt_to_utf8(cpt); decoded_text += "[UNK_BYTE_0x"; for (const auto c : utf8) { decoded_text += format("%02x", (uint8_t) c); diff --git a/src/unicode.cpp b/src/unicode.cpp index 6c834cbced3c..796f8e8fb6a9 100644 --- a/src/unicode.cpp +++ b/src/unicode.cpp @@ -202,6 +202,19 @@ static std::unordered_map unicode_utf8_to_byte_map() { return map; } +static std::vector unicode_cpt_to_byte_map() { + std::vector map; + for (int ch = 0; ch < 256; ++ch) { + size_t offset = 0; + const uint32_t cpt = unicode_cpt_from_utf8(unicode_byte_to_utf8(ch), offset); + if (cpt >= map.size()) { + map.resize(cpt + 1, -1); + } + map[cpt] = ch; + } + return map; +} + static std::vector unicode_byte_encoding_process(const std::vector & bpe_words) { std::vector bpe_encoded_words; for (const auto & word : bpe_words) { @@ -1416,6 +1429,14 @@ uint8_t unicode_utf8_to_byte(const std::string & utf8) { return map.at(utf8); } +uint8_t unicode_cpt_to_byte(uint32_t cpt) { + static const std::vector map = unicode_cpt_to_byte_map(); + if (cpt >= map.size() || map[cpt] < 0) { + throw std::out_of_range("unicode_cpt_to_byte: not a byte token code point"); + } + return map[cpt]; +} + uint32_t unicode_tolower(uint32_t cpt) { // binary search auto it = std::lower_bound(unicode_map_lowercase.begin(), unicode_map_lowercase.end(), cpt, diff --git a/src/unicode.h b/src/unicode.h index 19bf15a0a42a..29dbfc70993b 100644 --- a/src/unicode.h +++ b/src/unicode.h @@ -104,6 +104,7 @@ unicode_cpt_flags unicode_cpt_flags_from_utf8(const std::string & utf8); std::string unicode_byte_to_utf8(uint8_t byte); uint8_t unicode_utf8_to_byte(const std::string & utf8); +uint8_t unicode_cpt_to_byte (uint32_t cpt); uint32_t unicode_tolower(uint32_t cpt); From f849095365d5ac1f560886a4e6574531d559e96f Mon Sep 17 00:00:00 2001 From: "Alessandro de Oliveira Faria (A.K.A.CABELO)" Date: Wed, 21 Oct 2026 06:15:54 -0300 Subject: [PATCH 46/69] vendor: update BoringSSL to 0.20261020.0 (#30889) [no ci] --- vendor/cpp-httplib/CMakeLists.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/vendor/cpp-httplib/CMakeLists.txt b/vendor/cpp-httplib/CMakeLists.txt index d173798a282c..4bf567e6dbab 100644 --- a/vendor/cpp-httplib/CMakeLists.txt +++ b/vendor/cpp-httplib/CMakeLists.txt @@ -43,7 +43,7 @@ if (LLAMA_BUILD_BORINGSSL) set(FIPS OFF CACHE BOOL "Enable FIPS (BoringSSL)") set(BORINGSSL_GIT "https://boringssl.googlesource.com/boringssl" CACHE STRING "BoringSSL git repository") - set(BORINGSSL_VERSION "0.20260929.0" CACHE STRING "BoringSSL version") + set(BORINGSSL_VERSION "0.20261020.0" CACHE STRING "BoringSSL version") message(STATUS "Fetching BoringSSL version ${BORINGSSL_VERSION}") From 261700b896b9745976e23287b4f71e98c5b03686 Mon Sep 17 00:00:00 2001 From: David Garcia <87497958+Dgarc359@users.noreply.github.com> Date: Wed, 21 Oct 2026 06:58:52 -0400 Subject: [PATCH 47/69] server : forward llama.cpp sampling params on /v1/messages (#30856) * server : forward llama.cpp sampling params on /v1/messages * server : pass the request through on /v1/messages instead of copying listed keys * NIT: Change comment from multiline to single line * server : accept llama.cpp params under extra_params on /v1/messages * server : remove test case for trivial functionality * server : show extra_params in the /v1/messages README example --- tools/server/README.md | 5 ++++- tools/server/server-chat.cpp | 7 +++++++ 2 files changed, 11 insertions(+), 1 deletion(-) diff --git a/tools/server/README.md b/tools/server/README.md index 2886bca15877..71310d9030ae 100644 --- a/tools/server/README.md +++ b/tools/server/README.md @@ -1719,6 +1719,8 @@ See [Anthropic Messages API documentation](https://docs.anthropic.com/en/api/mes `top_k`: Top-k sampling +`extra_params`: llama.cpp parameters that the Anthropic API has no field for, such as `seed`, `min_p` or `repeat_penalty`, as an object (e.g. `{"seed": 42, "min_p": 0.05}`). Its keys are merged into the converted request and behave as on `/v1/chat/completions`. + `stop_sequences`: Array of stop sequences `stream`: Enable streaming (default: false) @@ -1739,7 +1741,8 @@ curl http://localhost:9931/v1/messages \ "system": "You are a helpful assistant.", "messages": [ {"role": "user", "content": "Hello!"} - ] + ], + "extra_params": {"seed": 42, "min_p": 0.05} }' ``` diff --git a/tools/server/server-chat.cpp b/tools/server/server-chat.cpp index c5473417c301..1c2326f33ca7 100644 --- a/tools/server/server-chat.cpp +++ b/tools/server/server-chat.cpp @@ -615,6 +615,13 @@ json server_chat_convert_anthropic_to_oai(const json & body) { } } + // llama.cpp params (e.g. seed, min_p) go under extra_params and are merged into the converted body + if (body.contains("extra_params") && body.at("extra_params").is_object()) { + for (const auto & [key, value] : body.at("extra_params").items()) { + oai_body[key] = value; + } + } + return oai_body; } From 0a1ef309cdbb9f2024f58a38fb4c42352c5db569 Mon Sep 17 00:00:00 2001 From: Xuan-Son Nguyen Date: Wed, 21 Oct 2026 12:59:54 +0200 Subject: [PATCH 48/69] mtmd: add state object for mtmd_gen (#30902) --- tools/mtmd/README-dev.md | 2 +- tools/mtmd/clip-impl.h | 10 ++++ tools/mtmd/clip.cpp | 101 ++++++++++++++++++++++----------- tools/mtmd/clip.h | 13 ++++- tools/mtmd/mtmd-helper-gen.cpp | 50 ++++++++-------- tools/mtmd/mtmd.cpp | 93 ++++++++++++++++++++---------- tools/mtmd/mtmd.h | 34 ++++++++--- 7 files changed, 204 insertions(+), 99 deletions(-) diff --git a/tools/mtmd/README-dev.md b/tools/mtmd/README-dev.md index 70d5c5da051f..5dad86100f44 100644 --- a/tools/mtmd/README-dev.md +++ b/tools/mtmd/README-dev.md @@ -55,7 +55,7 @@ For example, Qwen3-TTS: Due to wide variety of audio generation pipelines, the `mtmd_gen_audio` system is designed to be flexible and reusable by new models. `mtmd_gen_audio` is split into 2 main API: -- Core API `mtmd.h`: handles main inference. Important: the API surface must be stateless; caller must handle state management and audio frame accumulation. +- Core API `mtmd.h`: handles main inference. Important: `mtmd_gen_state` holds only the per-sequence data that the mmproj itself writes and reads back (RNG, decoder KV / conv state, output buffers). Data that goes to or comes from the backbone (hidden states, codes, PCM) must stay explicit in `mtmd_gen_inp` / `mtmd_gen_out`; the caller drives the backbone and handles audio frame accumulation. - Helper API `mtmd-helper.h`: provides a model-agnostic stateful API. Usage example can be found in the `tools/tts` directory. ### Checklist for porting new audio generation models to mtmd diff --git a/tools/mtmd/clip-impl.h b/tools/mtmd/clip-impl.h index 8c4ffc9bb284..71d349cc5981 100644 --- a/tools/mtmd/clip-impl.h +++ b/tools/mtmd/clip-impl.h @@ -16,6 +16,7 @@ #include #include #include +#include #ifdef _WIN32 #ifndef NOMINMAX @@ -463,6 +464,15 @@ struct clip_enc_state { std::vector v; }; +// per-sequence state of audio generation, see clip_gen_state_init() +struct clip_gen_state { + std::mt19937 rng; + + ggml_context_ptr ctx_data; + ggml_backend_buffer_ptr buf; + std::vector slots; // same order as list_gen_state_slots(), F32 [ne0, ne1] +}; + // forward declaration // TODO: improve this later struct clip_ctx; diff --git a/tools/mtmd/clip.cpp b/tools/mtmd/clip.cpp index e4750e52eca9..359ec5ac36b0 100644 --- a/tools/mtmd/clip.cpp +++ b/tools/mtmd/clip.cpp @@ -174,10 +174,6 @@ struct clip_ctx { bool support_batch = false; - // for audio gen, reseeded only when the caller asks for another seed - std::mt19937 rng{std::random_device{}()}; - uint32_t rng_seed = UINT32_MAX; - clip_ctx(clip_context_params & ctx_params) { flash_attn_type = ctx_params.flash_attn_type; no_alloc = ctx_params.no_alloc; @@ -4717,6 +4713,59 @@ static std::vector list_gen_state_slots(const clip_hparams & hpa } } +// state_out can have another shape than its slot, only the byte size must match +static void copy_gen_state_out(ggml_tensor * src, ggml_tensor * dst) { + GGML_ASSERT(ggml_nbytes(src) == ggml_nbytes(dst)); + if (src->type == dst->type && ggml_are_same_shape(src, dst) && ggml_is_contiguous(src)) { + ggml_backend_tensor_copy(src, dst); + return; + } + std::vector tmp(ggml_nbytes(src)); + ggml_backend_tensor_get(src, tmp.data(), 0, tmp.size()); + ggml_backend_tensor_set(dst, tmp.data(), 0, tmp.size()); +} + +clip_gen_state * clip_gen_state_init(clip_ctx * ctx, uint32_t seed) { + auto st = std::make_unique(); + st->rng.seed(seed == UINT32_MAX ? std::random_device{}() : seed); + + const auto slots = list_gen_state_slots(ctx->model.hparams, ctx->model); + if (slots.empty()) { + return st.release(); + } + + ggml_init_params params = { + /*.mem_size =*/ ggml_tensor_overhead() * slots.size(), + /*.mem_buffer =*/ nullptr, + /*.no_alloc =*/ true, + }; + st->ctx_data.reset(ggml_init(params)); + for (const auto & slot : slots) { + ggml_tensor * t = ggml_new_tensor_2d(st->ctx_data.get(), GGML_TYPE_F32, slot.ne0, slot.ne1); + ggml_format_name(t, "gen_state_%s", slot.name.c_str()); + st->slots.push_back(t); + } + st->buf.reset(ggml_backend_alloc_ctx_tensors(st->ctx_data.get(), ctx->backend)); + if (!st->buf) { + LOG_ERR("%s: failed to allocate the gen state\n", __func__); + return nullptr; + } + // a zero state is the cold start + ggml_backend_buffer_clear(st->buf.get(), 0); + + return st.release(); +} + +void clip_gen_state_free(clip_gen_state * state) { + delete state; +} + +void clip_gen_state_reset(clip_gen_state * state) { + if (state->buf) { + ggml_backend_buffer_clear(state->buf.get(), 0); + } +} + clip_enc_state * clip_enc_state_init(clip_ctx * ctx, int32_t n_frames_max) { const auto & hparams = ctx->model.hparams; if (ctx->proj_type() != PROJECTOR_TYPE_VOXTRAL_RT || hparams.attn_window_size <= 0) { @@ -4782,9 +4831,9 @@ bool clip_encode(struct clip_ctx * ctx, struct clip_encode_params * params) { clip_model_loader::warmup(*ctx, *params->imgs); } - if (params->seed != ctx->rng_seed) { - ctx->rng_seed = params->seed; - ctx->rng.seed(params->seed == UINT32_MAX ? std::random_device{}() : params->seed); + if (params->gen_process != CLIP_GEN_PROCESS_GEN_UNKNOWN && params->gen_state == nullptr) { + LOG_ERR("%s: audio gen requires a gen state\n", __func__); + return false; } // build the inference graph @@ -4848,19 +4897,13 @@ bool clip_encode(struct clip_ctx * ctx, struct clip_encode_params * params) { ggml_backend_tensor_set(cur, values.data(), 0, ggml_nbytes(cur)); }; - // upload the decoder state from the previous call, or zero-fill on a cold start + // state of the previous call, zero on a cold start auto set_gen_state_in = [&]() { - size_t offset = 0; + const auto & slots = params->gen_state->slots; + size_t i = 0; for (const auto & slot : list_gen_state_slots(hparams, model)) { - ggml_tensor * t = get_inp_tensor(("state_in_" + slot.name).c_str()); - const size_t nb = ggml_nbytes(t); - if (params->state_in && params->state_in->size() >= offset + nb) { - ggml_backend_tensor_set(t, params->state_in->data() + offset, 0, nb); - } else { - std::vector zeros(nb, 0); - ggml_backend_tensor_set(t, zeros.data(), 0, nb); - } - offset += nb; + GGML_ASSERT(i < slots.size()); + ggml_backend_tensor_copy(slots[i++], get_inp_tensor(("state_in_" + slot.name).c_str())); } }; @@ -5584,7 +5627,7 @@ bool clip_encode(struct clip_ctx * ctx, struct clip_encode_params * params) { std::normal_distribution dist(0.0f, std::sqrt(temp)); std::vector noise(ggml_nelements(t)); for (auto & v : noise) { - v = dist(ctx->rng); + v = dist(params->gen_state->rng); } set_input_f32("inp_noise", noise); } @@ -5732,7 +5775,7 @@ bool clip_encode(struct clip_ctx * ctx, struct clip_encode_params * params) { std::uniform_real_distribution dist(0.0f, 1.0f); const int64_t n_acoustic = model.gen_code_head_w->ne[2]; for (int64_t g = 0; g < n_acoustic; g++) { - std::vector r = { dist(ctx->rng) }; + std::vector r = { dist(params->gen_state->rng) }; set_input_f32(("inp_rand_" + std::to_string(g)).c_str(), r); } } @@ -6262,22 +6305,16 @@ bool clip_encode(struct clip_ctx * ctx, struct clip_encode_params * params) { st->n_past += n_new; } - if (params->state_out != nullptr) { - auto & state_out = *params->state_out; - size_t total = 0; - for (const auto & slot : list_gen_state_slots(hparams, model)) { - total += (size_t) (slot.ne0 * slot.ne1) * sizeof(float); - } - state_out.resize(total); - size_t offset = 0; + if (params->gen_process == CLIP_GEN_PROCESS_GEN_WAV) { + const auto & slots = params->gen_state->slots; + size_t i = 0; for (const auto & slot : list_gen_state_slots(hparams, model)) { ggml_tensor * t = ggml_graph_get_tensor(gf, ("state_out_" + slot.name).c_str()); if (t == nullptr) { - GGML_ABORT("state_out requested but graph has no \"state_out_%s\" tensor", slot.name.c_str()); + GGML_ABORT("graph has no \"state_out_%s\" tensor", slot.name.c_str()); } - const size_t nb = ggml_nbytes(t); - ggml_backend_tensor_get(t, state_out.data() + offset, 0, nb); - offset += nb; + GGML_ASSERT(i < slots.size()); + copy_gen_state_out(t, slots[i++]); } } diff --git a/tools/mtmd/clip.h b/tools/mtmd/clip.h index cdbaeae0d59a..cd3f434da583 100644 --- a/tools/mtmd/clip.h +++ b/tools/mtmd/clip.h @@ -14,6 +14,7 @@ struct clip_ctx; struct clip_enc_state; +struct clip_gen_state; struct clip_image_size { int width; @@ -110,7 +111,6 @@ struct clip_encode_params { float top_p = 1.0f; std::vector * out_codes = nullptr; // this frame's 16 sampled codes std::vector * out_feats = nullptr; // continuous counterpart of out_codes - uint32_t seed = UINT32_MAX; // UINT32_MAX for random float temp = 0.0f; // sampling temperature, noise scale for flow-matching decoders bool * out_is_eos = nullptr; @@ -118,8 +118,9 @@ struct clip_encode_params { const std::vector * codes = nullptr; // this frame's 16 RVQ codes const std::vector * feats = nullptr; // continuous counterpart of codes std::vector * out_audio = nullptr; // decoded PCM samples, F32 - const std::vector * state_in = nullptr; // state from previous call, null or wrong size means cold start - std::vector * state_out = nullptr; // state for the next call + + // audio gen: state of one sequence, clip_encode() updates it in place + struct clip_gen_state * gen_state = nullptr; // streaming audio input, updated in place by clip_encode() struct clip_enc_state * enc_state = nullptr; @@ -132,6 +133,12 @@ bool clip_encode(struct clip_ctx * ctx, struct clip_encode_params * params); struct clip_enc_state * clip_enc_state_init(struct clip_ctx * ctx, int32_t n_frames_max); void clip_enc_state_free(struct clip_enc_state * state); +// per-sequence state of audio generation: RNG and the slots kept between GEN_WAV calls +// seed UINT32_MAX means random; reset() clears the slots and keeps the RNG stream +struct clip_gen_state * clip_gen_state_init(struct clip_ctx * ctx, uint32_t seed); +void clip_gen_state_free(struct clip_gen_state * state); +void clip_gen_state_reset(struct clip_gen_state * state); + bool clip_is_llava(const struct clip_ctx * ctx); // note for contributor: this clip_is_(model) pattern is deprecated // do NOT add new functions like this diff --git a/tools/mtmd/mtmd-helper-gen.cpp b/tools/mtmd/mtmd-helper-gen.cpp index 47493d9069b8..d8299ba442df 100644 --- a/tools/mtmd/mtmd-helper-gen.cpp +++ b/tools/mtmd/mtmd-helper-gen.cpp @@ -105,12 +105,25 @@ class mtmd_gen_audio_pipeline { virtual int32_t flush() = 0; protected: + // one state per generation, so a fixed seed always gives the same output + bool init_gen_state(uint32_t seed) { + mtmd_gen_state_params params = mtmd_gen_state_params_default(); + params.seed = seed; + gen_state.reset(mtmd_gen_state_init(mctx, params)); + if (!gen_state) { + LOG_ERR("mtmd_helper_gen_audio: failed to create the gen state\n"); + return false; + } + return true; + } + llama_context * lctx; mtmd_context * mctx; const llama_model * model; const llama_vocab * vocab; int n_embd; mtmd_gen_audio_info info; + mtmd::gen_state_ptr gen_state; }; // Qwen3-TTS: backbone samples codec_0, code_predictor gives the other 15 codebooks, @@ -123,7 +136,7 @@ class qwen3tts_gen_audio_pipeline : public mtmd_gen_audio_pipeline { seq_id = 0; pos = 0; codes_buf.clear(); - c2w_state.clear(); + gen_state.reset(); audio_pcm.clear(); overlay.clear(); h_state_buf.clear(); @@ -220,9 +233,11 @@ class qwen3tts_gen_audio_pipeline : public mtmd_gen_audio_pipeline { const mtmd_gen_inp def = mtmd_gen_inp_default(mctx); top_k = inp->top_k > 0 ? inp->top_k : def.top_k; top_p = inp->top_p > 0 ? inp->top_p : def.top_p; - seed = inp->seed; out_type = inp->out_type; stream = inp->stream; + if (!init_gen_state(inp->seed)) { + return 1; + } // the prompt above holds the whole text stream up to tts_eos, so every generated // frame adds tts_pad on top of the codes embedding @@ -279,9 +294,8 @@ class qwen3tts_gen_audio_pipeline : public mtmd_gen_audio_pipeline { inp.embd = const_cast(h_state_in); inp.top_k = top_k; inp.top_p = top_p; - inp.seed = seed; mtmd_gen_out out{}; - if (mtmd_gen_audio_process(mctx, &inp, &out) != 0) { + if (mtmd_gen_audio_process(mctx, gen_state.get(), &inp, &out) != 0) { LOG_ERR("mtmd_helper_gen_audio: gen_code process failed\n"); return 1; } @@ -449,16 +463,12 @@ class qwen3tts_gen_audio_pipeline : public mtmd_gen_audio_pipeline { inp.type = MTMD_GEN_PROCESS_TYPE_GEN_WAV; inp.codes = codes_buf.data(); inp.n_codes = codes_buf.size(); - inp.seed = seed; // same seed as gen_code, else clip reseeds mid-generation - inp.state_data = c2w_state.empty() ? nullptr : (const char *) c2w_state.data(); - inp.state_size = c2w_state.size(); mtmd_gen_out out{}; - if (mtmd_gen_audio_process(mctx, &inp, &out) != 0) { + if (mtmd_gen_audio_process(mctx, gen_state.get(), &inp, &out) != 0) { LOG_ERR("mtmd_helper_gen_audio: gen_wav process failed\n"); return false; } audio_pcm.insert(audio_pcm.end(), out.audio, out.audio + out.n_samples); - c2w_state.assign(out.state_data, out.state_data + out.state_size); codes_buf.clear(); return true; } @@ -491,9 +501,7 @@ class qwen3tts_gen_audio_pipeline : public mtmd_gen_audio_pipeline { int prompt_pos = 0; int32_t top_k = 50; float top_p = 1.0f; - uint32_t seed = UINT32_MAX; std::vector codes_buf; - std::vector c2w_state; std::vector audio_pcm; std::vector overlay; std::vector h_state_buf; @@ -539,7 +547,7 @@ class pockettts_gen_audio_pipeline : public mtmd_gen_audio_pipeline { seq_id = 0; pos = 0; feats_buf.clear(); - dec_state.clear(); + gen_state.reset(); audio_pcm.clear(); h_state_buf.clear(); out_buf.clear(); @@ -623,9 +631,11 @@ class pockettts_gen_audio_pipeline : public mtmd_gen_audio_pipeline { prompt_batch->set_position_normal(0, seq_id); prompt_pos = 0; - seed = inp->seed; out_type = inp->out_type; stream = inp->stream; + if (!init_gen_state(inp->seed)) { + return 1; + } return 0; } @@ -663,13 +673,11 @@ class pockettts_gen_audio_pipeline : public mtmd_gen_audio_pipeline { mtmd_gen_inp inp = mtmd_gen_inp_default(mctx); inp.type = MTMD_GEN_PROCESS_TYPE_GEN_CODE; inp.embd = const_cast(h_state_in); - // clip only reseeds when the seed changes, so pass the same one on every step - inp.seed = seed; if (pack.temp > 0.0f) { inp.temp = pack.temp; } mtmd_gen_out out{}; - if (mtmd_gen_audio_process(mctx, &inp, &out) != 0) { + if (mtmd_gen_audio_process(mctx, gen_state.get(), &inp, &out) != 0) { LOG_ERR("mtmd_helper_gen_audio: flow decode failed\n"); return 1; } @@ -895,7 +903,7 @@ class pockettts_gen_audio_pipeline : public mtmd_gen_audio_pipeline { return 1; } // the decoder restarts too, the next chunk's audio is not continuous with this one - dec_state.clear(); + mtmd_gen_state_reset(gen_state.get()); if (chunk_idx + 1 >= chunks.size()) { *out_stop = true; @@ -1023,16 +1031,12 @@ class pockettts_gen_audio_pipeline : public mtmd_gen_audio_pipeline { inp.type = MTMD_GEN_PROCESS_TYPE_GEN_WAV; inp.feats = feats_buf.data(); inp.n_feats = feats_buf.size(); - inp.seed = seed; - inp.state_data = dec_state.empty() ? nullptr : (const char *) dec_state.data(); - inp.state_size = dec_state.size(); mtmd_gen_out out{}; - if (mtmd_gen_audio_process(mctx, &inp, &out) != 0) { + if (mtmd_gen_audio_process(mctx, gen_state.get(), &inp, &out) != 0) { LOG_ERR("mtmd_helper_gen_audio: mimi decode failed\n"); return false; } audio_pcm.insert(audio_pcm.end(), out.audio, out.audio + out.n_samples); - dec_state.assign(out.state_data, out.state_data + out.state_size); feats_buf.clear(); return true; } @@ -1049,7 +1053,6 @@ class pockettts_gen_audio_pipeline : public mtmd_gen_audio_pipeline { std::unique_ptr prompt_batch; int n_prompt = 0; int prompt_pos = 0; - uint32_t seed = UINT32_MAX; // end-of-speech is latched, then a few more frames are generated as tail padding int step_idx = 0; int eos_step = -1; @@ -1064,7 +1067,6 @@ class pockettts_gen_audio_pipeline : public mtmd_gen_audio_pipeline { // latents are decoded a window at a time, the decoder state bridges the windows size_t window_frames = 8; std::vector feats_buf; - std::vector dec_state; std::vector audio_pcm; std::vector h_state_buf; mtmd_helper_gen_audio_outtype out_type = MTMD_HELPER_GEN_AUDIO_OUTTYPE_WAV; diff --git a/tools/mtmd/mtmd.cpp b/tools/mtmd/mtmd.cpp index ffd35b2bb3c5..9e2c5f3c92b6 100644 --- a/tools/mtmd/mtmd.cpp +++ b/tools/mtmd/mtmd.cpp @@ -523,11 +523,6 @@ struct mtmd_context { // generation context struct clip_ctx * ctx_gen_a; // audio - std::vector gen_out_codes; // this frame's 16 sampled codes (GEN_CODE) - std::vector gen_out_feats; // this frame's continuous features, if any (GEN_CODE) - std::vector gen_out_embd; // next-step hidden state fed back to backbone (GEN_CODE) - std::vector gen_out_audio; // decoded PCM samples for the current frame (GEN_WAV) - std::vector gen_out_state; // state to feed into the next GEN_WAV call bool print_timings; int n_threads; @@ -2080,7 +2075,6 @@ mtmd_gen_audio_info mtmd_gen_audio_get_info(const mtmd_context * ctx) { mtmd_gen_inp mtmd_gen_inp_default(const mtmd_context * ctx) { mtmd_gen_inp inp{}; inp.type = MTMD_GEN_PROCESS_TYPE_GEN_CODE; - inp.seed = UINT32_MAX; if (!ctx->ctx_gen_a) { return inp; } @@ -2104,12 +2098,58 @@ mtmd_gen_inp mtmd_gen_inp_default(const mtmd_context * ctx) { return inp; } -static int32_t mtmd_gen_audio_process_impl(mtmd_context * ctx, const mtmd_gen_inp * inp, mtmd_gen_out * out) { +struct mtmd_gen_state { + clip_ctx * ctx_clip = nullptr; + clip_gen_state * clip_state = nullptr; + + std::vector out_codes; // this frame's 16 sampled codes (GEN_CODE) + std::vector out_feats; // this frame's continuous features, if any (GEN_CODE) + std::vector out_embd; // next-step hidden state fed back to backbone (GEN_CODE) + std::vector out_audio; // decoded PCM samples for the current frame (GEN_WAV) + + ~mtmd_gen_state() { + clip_gen_state_free(clip_state); + } +}; + +mtmd_gen_state_params mtmd_gen_state_params_default(void) { + mtmd_gen_state_params params{}; + params.seed = UINT32_MAX; + return params; +} + +mtmd_gen_state * mtmd_gen_state_init(mtmd_context * ctx, mtmd_gen_state_params params) { + if (!ctx->ctx_gen_a) { + LOG_ERR("%s: model does not support audio generation\n", __func__); + return nullptr; + } + auto st = std::make_unique(); + st->ctx_clip = ctx->ctx_gen_a; + st->clip_state = clip_gen_state_init(ctx->ctx_gen_a, params.seed); + if (!st->clip_state) { + return nullptr; + } + return st.release(); +} + +void mtmd_gen_state_free(mtmd_gen_state * state) { + delete state; +} + +void mtmd_gen_state_reset(mtmd_gen_state * state) { + clip_gen_state_reset(state->clip_state); +} + +static int32_t mtmd_gen_audio_process_impl(mtmd_context * ctx, mtmd_gen_state * state, const mtmd_gen_inp * inp, mtmd_gen_out * out) { clip_ctx * ctx_clip = ctx->ctx_gen_a; if (!ctx_clip) { LOG_ERR("%s: model does not support audio generation\n", __func__); return 1; } + if (!state || state->ctx_clip != ctx_clip) { + LOG_ERR("%s: invalid gen state\n", __func__); + return 1; + } *out = {}; @@ -2139,24 +2179,24 @@ static int32_t mtmd_gen_audio_process_impl(mtmd_context * ctx, const mtmd_gen_in params.code0 = inp->code0; params.top_k = inp->top_k; params.top_p = inp->top_p; - params.seed = inp->seed; params.temp = inp->temp; params.out_is_eos = &is_eos; + params.gen_state = state->clip_state; if (!clip_encode(ctx_clip, ¶ms)) { LOG_ERR("%s: clip_encode failed (gen_code)\n", __func__); return 1; } - ctx->gen_out_embd = std::move(out_embd); - ctx->gen_out_codes = std::move(out_codes); - ctx->gen_out_feats = std::move(out_feats); + state->out_embd = std::move(out_embd); + state->out_codes = std::move(out_codes); + state->out_feats = std::move(out_feats); - out->embd = ctx->gen_out_embd.data(); - out->codes = ctx->gen_out_codes.data(); - out->n_codes = ctx->gen_out_codes.size(); - out->feats = ctx->gen_out_feats.data(); - out->n_feats = ctx->gen_out_feats.size(); + out->embd = state->out_embd.data(); + out->codes = state->out_codes.data(); + out->n_codes = state->out_codes.size(); + out->feats = state->out_feats.data(); + out->n_feats = state->out_feats.size(); out->is_eos = is_eos; return 0; } @@ -2175,10 +2215,6 @@ static int32_t mtmd_gen_audio_process_impl(mtmd_context * ctx, const mtmd_gen_in } else { in_feats.assign(inp->feats, inp->feats + inp->n_feats); } - std::vector in_state; - if (inp->state_data) { - in_state.assign(inp->state_data, inp->state_data + inp->state_size); - } // gen_wav has no hidden-state input, the batch entry is an unused placeholder // TODO @ngxson : some models in the future may require hidden-state input, need to update this code later @@ -2194,30 +2230,25 @@ static int32_t mtmd_gen_audio_process_impl(mtmd_context * ctx, const mtmd_gen_in params.imgs = &batch; params.n_threads = ctx->n_threads; params.gen_process = CLIP_GEN_PROCESS_GEN_WAV; - // gen_wav draws no randomness, but keep the seed so it does not reseed mid-generation - params.seed = inp->seed; params.codes = has_codes ? &in_codes : nullptr; params.feats = has_feats ? &in_feats : nullptr; - params.out_audio = &ctx->gen_out_audio; - params.state_in = inp->state_data ? &in_state : nullptr; - params.state_out = &ctx->gen_out_state; + params.out_audio = &state->out_audio; + params.gen_state = state->clip_state; if (!clip_encode(ctx_clip, ¶ms)) { LOG_ERR("%s: clip_encode failed (code2wav)\n", __func__); return 1; } - out->audio = ctx->gen_out_audio.data(); - out->n_samples = ctx->gen_out_audio.size(); - out->state_data = (const char *) ctx->gen_out_state.data(); - out->state_size = ctx->gen_out_state.size(); + out->audio = state->out_audio.data(); + out->n_samples = state->out_audio.size(); return 0; } -int32_t mtmd_gen_audio_process(mtmd_context * ctx, const struct mtmd_gen_inp * inp, struct mtmd_gen_out * out) { +int32_t mtmd_gen_audio_process(mtmd_context * ctx, mtmd_gen_state * state, const struct mtmd_gen_inp * inp, struct mtmd_gen_out * out) { try { - return mtmd_gen_audio_process_impl(ctx, inp, out); + return mtmd_gen_audio_process_impl(ctx, state, inp, out); } catch (const std::exception & e) { LOG_ERR("%s: error: %s\n", __func__, e.what()); return 1; diff --git a/tools/mtmd/mtmd.h b/tools/mtmd/mtmd.h index c740f1e9fd2f..f248ed7390ad 100644 --- a/tools/mtmd/mtmd.h +++ b/tools/mtmd/mtmd.h @@ -65,6 +65,7 @@ struct mtmd_image_tokens; struct mtmd_input_chunk; struct mtmd_input_chunks; struct mtmd_batch; +struct mtmd_gen_state; struct mtmd_input_text { const char * text; @@ -79,6 +80,7 @@ struct mtmd_input_part { const struct mtmd_bitmap * bitmap; }; + // // C API // @@ -91,6 +93,7 @@ typedef struct mtmd_input_chunks mtmd_input_chunks; typedef struct mtmd_input_text mtmd_input_text; typedef struct mtmd_input_part mtmd_input_part; typedef struct mtmd_batch mtmd_batch; +typedef struct mtmd_gen_state mtmd_gen_state; typedef bool (*mtmd_progress_callback)(float progress, void * user_data); @@ -443,8 +446,7 @@ struct mtmd_gen_inp { float * embd; // the hidden state from backbone, must have n_text_embd elements int32_t top_k; float top_p; - uint32_t seed; // UINT32_MAX for random - float temp; // sampling temperature, or noise scale for flow-matching decoders + float temp; // sampling temperature, or noise scale for flow-matching decoders // for MTMD_GEN_PROCESS_TYPE_GEN_WAV // pass either codes (discrete) or feats (continuous), depending on the pipeline @@ -452,12 +454,10 @@ struct mtmd_gen_inp { size_t n_codes; const float * feats; size_t n_feats; - const char * state_data; - size_t state_size; }; struct mtmd_gen_out { - // note: output memory is allocated by the context, valid until next process() call + // note: output memory is owned by the state, valid until the next process() call with this state // for MTMD_GEN_PROCESS_TYPE_GEN_CODE const int32_t * codes; @@ -471,15 +471,28 @@ struct mtmd_gen_out { // for MTMD_GEN_PROCESS_TYPE_GEN_WAV const float * audio; size_t n_samples; - const char * state_data; - size_t state_size; }; +struct mtmd_gen_state_params { + uint32_t seed; // UINT32_MAX for random +}; + +MTMD_API struct mtmd_gen_state_params mtmd_gen_state_params_default(void); + +// returns nullptr if the model does not support audio generation +MTMD_API mtmd_gen_state * mtmd_gen_state_init(mtmd_context * ctx, struct mtmd_gen_state_params params); +MTMD_API void mtmd_gen_state_free(mtmd_gen_state * state); + +// clear the decoder state (cold start), keep the RNG stream +MTMD_API void mtmd_gen_state_reset(mtmd_gen_state * state); + // defaults tuned for the loaded pipeline, callers override only what they care about MTMD_API struct mtmd_gen_inp mtmd_gen_inp_default(const mtmd_context * ctx); -// note: this API is stateless, caller must handle state management and audio frame accumulation +// run one step and update the state in place +// the caller drives the backbone and collects the audio frames MTMD_API int32_t mtmd_gen_audio_process(mtmd_context * ctx, + mtmd_gen_state * state, const struct mtmd_gen_inp * inp, struct mtmd_gen_out * out); @@ -544,6 +557,11 @@ struct mtmd_audio_stream_deleter { }; using audio_stream_ptr = std::unique_ptr; +struct mtmd_gen_state_deleter { + void operator()(mtmd_gen_state * val) { mtmd_gen_state_free(val); } +}; +using gen_state_ptr = std::unique_ptr; + struct bitmap { bitmap_ptr ptr; bitmap() : ptr(nullptr) {} From 9b56ccf00d755b0bf780140f6352a67db1ac0378 Mon Sep 17 00:00:00 2001 From: kurquhar Date: Wed, 21 Oct 2026 07:59:55 -0700 Subject: [PATCH 49/69] ggml-opencl : add IM2COL_3D support (#30895) * ggml-opencl : add IM2COL_3D support Assisted-by: OpenCode * opencl: use same type for kernel and host * opencl: fix for older adreno --------- Co-authored-by: Li He --- ggml/src/ggml-opencl/CMakeLists.txt | 2 + ggml/src/ggml-opencl/ggml-opencl.cpp | 138 ++++++++++++++++++ ggml/src/ggml-opencl/kernels/im2col_3d_f16.cl | 90 ++++++++++++ ggml/src/ggml-opencl/kernels/im2col_3d_f32.cl | 90 ++++++++++++ 4 files changed, 320 insertions(+) create mode 100644 ggml/src/ggml-opencl/kernels/im2col_3d_f16.cl create mode 100644 ggml/src/ggml-opencl/kernels/im2col_3d_f32.cl diff --git a/ggml/src/ggml-opencl/CMakeLists.txt b/ggml/src/ggml-opencl/CMakeLists.txt index 84087162d602..917578c9ff0c 100644 --- a/ggml/src/ggml-opencl/CMakeLists.txt +++ b/ggml/src/ggml-opencl/CMakeLists.txt @@ -80,6 +80,8 @@ set(GGML_OPENCL_KERNELS solve_tri im2col_f32 im2col_f16 + im2col_3d_f32 + im2col_3d_f16 mean mul_mv_f16_f16 mul_mv_f16_f32_1row diff --git a/ggml/src/ggml-opencl/ggml-opencl.cpp b/ggml/src/ggml-opencl/ggml-opencl.cpp index 6071b6e58256..2312c6c7cd5c 100644 --- a/ggml/src/ggml-opencl/ggml-opencl.cpp +++ b/ggml/src/ggml-opencl/ggml-opencl.cpp @@ -971,6 +971,7 @@ struct ggml_backend_opencl_context { cl_kernel kernel_mul_mv_iq4_nl_f32_flat; cl_kernel kernel_solve_tri_f32; cl_kernel kernel_im2col_f32, kernel_im2col_f16; + cl_kernel kernel_im2col_3d_f32, kernel_im2col_3d_f16; cl_kernel kernel_argsort_f32_i32; cl_kernel kernel_sum_rows_f32, kernel_sum_rows_f32_4; cl_kernel kernel_cumsum_blk, kernel_cumsum_add; @@ -2101,6 +2102,40 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { GGML_LOG_CONT("."); } + // im2col_3d_f32 + { +#ifdef GGML_OPENCL_EMBED_KERNELS + const std::string kernel_src { + #include "im2col_3d_f32.cl.h" + }; +#else + const std::string kernel_src = read_file("im2col_3d_f32.cl"); +#endif + cl_program prog = + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); + + CL_CHECK((backend_ctx->kernel_im2col_3d_f32 = clCreateKernel(prog, "kernel_im2col_3d_f32", &err), err)); + CL_CHECK(clReleaseProgram(prog)); + GGML_LOG_CONT("."); + } + + // im2col_3d_f16 + { +#ifdef GGML_OPENCL_EMBED_KERNELS + const std::string kernel_src { + #include "im2col_3d_f16.cl.h" + }; +#else + const std::string kernel_src = read_file("im2col_3d_f16.cl"); +#endif + cl_program prog = + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); + + CL_CHECK((backend_ctx->kernel_im2col_3d_f16 = clCreateKernel(prog, "kernel_im2col_3d_f16", &err), err)); + CL_CHECK(clReleaseProgram(prog)); + GGML_LOG_CONT("."); + } + // mul_mv_q4_0_f32 { #ifdef GGML_OPENCL_EMBED_KERNELS @@ -9656,6 +9691,8 @@ static bool ggml_opencl_supports_op(ggml_backend_dev_t dev, const struct ggml_te return op->src[0]->type == GGML_TYPE_F32 && ggml_is_contiguous(op->src[0]); case GGML_OP_IM2COL: return true; + case GGML_OP_IM2COL_3D: + return true; case GGML_OP_ARGSORT: { load_cl_kernels_argsort(backend_ctx); @@ -30032,6 +30069,101 @@ static void ggml_cl_im2col(ggml_backend_t backend, const ggml_tensor * src0, con backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst); } +static void ggml_cl_im2col_3d(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_ASSERT(src0); + GGML_ASSERT(src1); + GGML_ASSERT(src1->extra); + GGML_ASSERT(dst); + GGML_ASSERT(dst->extra); + + GGML_ASSERT(src1->type == GGML_TYPE_F32); + GGML_ASSERT(dst->type == GGML_TYPE_F16 || dst->type == GGML_TYPE_F32); + + ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context; + + ggml_tensor_extra_cl * extra1 = (ggml_tensor_extra_cl *)src1->extra; + ggml_tensor_extra_cl * extrad = (ggml_tensor_extra_cl *)dst->extra; + + cl_ulong offset1 = extra1->offset + src1->view_offs; + cl_ulong offsetd = extrad->offset + dst->view_offs; + + const int32_t s0 = ((const int32_t*)(dst->op_params))[0]; + const int32_t s1 = ((const int32_t*)(dst->op_params))[1]; + const int32_t s2 = ((const int32_t*)(dst->op_params))[2]; + const int32_t p0 = ((const int32_t*)(dst->op_params))[3]; + const int32_t p1 = ((const int32_t*)(dst->op_params))[4]; + const int32_t p2 = ((const int32_t*)(dst->op_params))[5]; + const int32_t d0 = ((const int32_t*)(dst->op_params))[6]; + const int32_t d1 = ((const int32_t*)(dst->op_params))[7]; + const int32_t d2 = ((const int32_t*)(dst->op_params))[8]; + const cl_long IC = ((const int32_t*)(dst->op_params))[9]; + + const cl_long N = src1->ne[3] / IC; + const cl_long ID = src1->ne[2]; + const cl_long IH = src1->ne[1]; + const cl_long IW = src1->ne[0]; + + const cl_long KD = src0->ne[2]; + const cl_long KH = src0->ne[1]; + const cl_long KW = src0->ne[0]; + + const cl_long OD = dst->ne[3] / N; + const cl_long OH = dst->ne[2]; + const cl_long OW = dst->ne[1]; + + const size_t es = ggml_element_size(src1); + const cl_long stride_x = src1->nb[0] / es; + const cl_long stride_y = src1->nb[1] / es; + const cl_long stride_z = src1->nb[2] / es; + const cl_long stride_q = src1->nb[3] / es; + + const cl_long IC_KD_KH_KW = IC * KD * KH * KW; + + cl_kernel kernel; + + if (dst->type == GGML_TYPE_F16) { + kernel = backend_ctx->kernel_im2col_3d_f16; + } else { + kernel = backend_ctx->kernel_im2col_3d_f32; + } + + CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra1->data_device)); + CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_ulong), &offset1)); + CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extrad->data_device)); + CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_ulong), &offsetd)); + CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_long), &IC)); + CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_long), &ID)); + CL_CHECK(clSetKernelArg(kernel, 6, sizeof(cl_long), &IH)); + CL_CHECK(clSetKernelArg(kernel, 7, sizeof(cl_long), &IW)); + CL_CHECK(clSetKernelArg(kernel, 8, sizeof(cl_long), &KD)); + CL_CHECK(clSetKernelArg(kernel, 9, sizeof(cl_long), &KH)); + CL_CHECK(clSetKernelArg(kernel, 10, sizeof(cl_long), &KW)); + CL_CHECK(clSetKernelArg(kernel, 11, sizeof(cl_long), &OD)); + CL_CHECK(clSetKernelArg(kernel, 12, sizeof(cl_long), &OH)); + CL_CHECK(clSetKernelArg(kernel, 13, sizeof(cl_long), &OW)); + CL_CHECK(clSetKernelArg(kernel, 14, sizeof(cl_long), &N)); + CL_CHECK(clSetKernelArg(kernel, 15, sizeof(cl_long), &stride_x)); + CL_CHECK(clSetKernelArg(kernel, 16, sizeof(cl_long), &stride_y)); + CL_CHECK(clSetKernelArg(kernel, 17, sizeof(cl_long), &stride_z)); + CL_CHECK(clSetKernelArg(kernel, 18, sizeof(cl_long), &stride_q)); + CL_CHECK(clSetKernelArg(kernel, 19, sizeof(int), &s0)); + CL_CHECK(clSetKernelArg(kernel, 20, sizeof(int), &s1)); + CL_CHECK(clSetKernelArg(kernel, 21, sizeof(int), &s2)); + CL_CHECK(clSetKernelArg(kernel, 22, sizeof(int), &p0)); + CL_CHECK(clSetKernelArg(kernel, 23, sizeof(int), &p1)); + CL_CHECK(clSetKernelArg(kernel, 24, sizeof(int), &p2)); + CL_CHECK(clSetKernelArg(kernel, 25, sizeof(int), &d0)); + CL_CHECK(clSetKernelArg(kernel, 26, sizeof(int), &d1)); + CL_CHECK(clSetKernelArg(kernel, 27, sizeof(int), &d2)); + + const size_t local_col = 64; + const size_t num_blocks = (IC_KD_KH_KW + local_col - 1) / local_col; + size_t global_work_size[] = { num_blocks * local_col, (size_t)OW, (size_t)(N * OD * OH) }; + size_t local_work_size[] = { local_col, 1, 1 }; + + backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst); +} + static void ggml_cl_argsort(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_ASSERT(src0); GGML_ASSERT(src0->extra); @@ -30933,6 +31065,12 @@ bool ggml_cl_compute_forward(ggml_backend_t backend, struct ggml_tensor * tensor } func = ggml_cl_im2col; break; + case GGML_OP_IM2COL_3D: + if (!any_on_device) { + return false; + } + func = ggml_cl_im2col_3d; + break; case GGML_OP_ARGSORT: if (!any_on_device) { return false; diff --git a/ggml/src/ggml-opencl/kernels/im2col_3d_f16.cl b/ggml/src/ggml-opencl/kernels/im2col_3d_f16.cl new file mode 100644 index 000000000000..a165e4460460 --- /dev/null +++ b/ggml/src/ggml-opencl/kernels/im2col_3d_f16.cl @@ -0,0 +1,90 @@ +#pragma OPENCL EXTENSION cl_khr_fp16 : enable + +kernel void kernel_im2col_3d_f16( + global float * src1, + ulong offset1, + global half * dst, + ulong offsetd, + long IC, + long ID, + long IH, + long IW, + long KD, + long KH, + long KW, + long OD, + long OH, + long OW, + long N, + long stride_x, + long stride_y, + long stride_z, + long stride_q, + int s0, + int s1, + int s2, + int p0, + int p1, + int p2, + int d0, + int d1, + int d2 +) { + const int ic = (int)IC; + const int kd = (int)KD; + const int kh = (int)KH; + const int kw = (int)KW; + const int od = (int)OD; + const int oh = (int)OH; + const int ow = (int)OW; + const int n = (int)N; + + const int KD_KH_KW = kd * kh * kw; + const int KH_KW = kh * kw; + const int IC_KD_KH_KW = ic * KD_KH_KW; + + const int i = (int)get_global_id(0); + if (i >= IC_KD_KH_KW) { + return; + } + + const int iow = (int)get_global_id(1); + if (iow >= ow) { + return; + } + + const int OD_OH = od * oh; + const int iz = (int)get_global_id(2); + if (iz >= n * OD_OH) { + return; + } + + src1 = (global float*)((global char*)src1 + offset1); + dst = (global half*)((global char*)dst + offsetd); + + const int iic = i / KD_KH_KW; + const int ikd = (i - iic * KD_KH_KW) / KH_KW; + const int ikh = (i - iic * KD_KH_KW - ikd * KH_KW) / kw; + const int ikw = i % kw; + + const int in = iz / OD_OH; + const int iod = (iz - in * OD_OH) / oh; + const int ioh = iz % oh; + + const int iiw = iow * s0 + ikw * d0 - p0; + const int iih = ioh * s1 + ikh * d1 - p1; + const int iid = iod * s2 + ikd * d2 - p2; + + const long offset_dst = + (long)(iz * ow + iow) * (long)IC_KD_KH_KW + + (long)(iic * KD_KH_KW + ikd * KH_KW + ikh * kw + ikw); + + if (iih < 0 || iih >= IH || iiw < 0 || iiw >= IW || iid < 0 || iid >= ID) { + dst[offset_dst] = 0.0f; + } else { + const long offset_src = + (long)(in * ic + iic) * stride_q + (long)iid * stride_z + + (long)iih * stride_y + (long)iiw * stride_x; + dst[offset_dst] = convert_half_rte(src1[offset_src]); + } +} diff --git a/ggml/src/ggml-opencl/kernels/im2col_3d_f32.cl b/ggml/src/ggml-opencl/kernels/im2col_3d_f32.cl new file mode 100644 index 000000000000..ea94d5a5d083 --- /dev/null +++ b/ggml/src/ggml-opencl/kernels/im2col_3d_f32.cl @@ -0,0 +1,90 @@ +#pragma OPENCL EXTENSION cl_khr_fp16 : enable + +kernel void kernel_im2col_3d_f32( + global float * src1, + ulong offset1, + global float * dst, + ulong offsetd, + long IC, + long ID, + long IH, + long IW, + long KD, + long KH, + long KW, + long OD, + long OH, + long OW, + long N, + long stride_x, + long stride_y, + long stride_z, + long stride_q, + int s0, + int s1, + int s2, + int p0, + int p1, + int p2, + int d0, + int d1, + int d2 +) { + const int ic = (int)IC; + const int kd = (int)KD; + const int kh = (int)KH; + const int kw = (int)KW; + const int od = (int)OD; + const int oh = (int)OH; + const int ow = (int)OW; + const int n = (int)N; + + const int KD_KH_KW = kd * kh * kw; + const int KH_KW = kh * kw; + const int IC_KD_KH_KW = ic * KD_KH_KW; + + const int i = (int)get_global_id(0); + if (i >= IC_KD_KH_KW) { + return; + } + + const int iow = (int)get_global_id(1); + if (iow >= ow) { + return; + } + + const int OD_OH = od * oh; + const int iz = (int)get_global_id(2); + if (iz >= n * OD_OH) { + return; + } + + src1 = (global float*)((global char*)src1 + offset1); + dst = (global float*)((global char*)dst + offsetd); + + const int iic = i / KD_KH_KW; + const int ikd = (i - iic * KD_KH_KW) / KH_KW; + const int ikh = (i - iic * KD_KH_KW - ikd * KH_KW) / kw; + const int ikw = i % kw; + + const int in = iz / OD_OH; + const int iod = (iz - in * OD_OH) / oh; + const int ioh = iz % oh; + + const int iiw = iow * s0 + ikw * d0 - p0; + const int iih = ioh * s1 + ikh * d1 - p1; + const int iid = iod * s2 + ikd * d2 - p2; + + const long offset_dst = + (long)(iz * ow + iow) * (long)IC_KD_KH_KW + + (long)(iic * KD_KH_KW + ikd * KH_KW + ikh * kw + ikw); + + if (iih < 0 || iih >= IH || iiw < 0 || iiw >= IW || iid < 0 || iid >= ID) { + dst[offset_dst] = 0.0f; + } else { + const long offset_src = + (long)(in * ic + iic) * stride_q + (long)iid * stride_z + + (long)iih * stride_y + (long)iiw * stride_x; + dst[offset_dst] = src1[offset_src]; + } +} From 9febe63d5a379f8568bae7116a5b2ba844a4a4a6 Mon Sep 17 00:00:00 2001 From: alexey-rsi Date: Wed, 21 Oct 2026 18:04:36 +0200 Subject: [PATCH 50/69] jinja: support hexadecimal string escapes (#30929) --- common/jinja/lexer.cpp | 20 +++++++++++++++++ tests/test-jinja.cpp | 50 ++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 70 insertions(+) diff --git a/common/jinja/lexer.cpp b/common/jinja/lexer.cpp index 598982c2fe4a..bf2dfacdadd2 100644 --- a/common/jinja/lexer.cpp +++ b/common/jinja/lexer.cpp @@ -1,7 +1,9 @@ #include "lexer.h" #include "runtime.h" +#include "unicode.h" #include +#include #include #include #include @@ -75,6 +77,24 @@ lexer_result lexer::tokenize(const std::string & source) { } // add escaped char char escaped_char = src[pos++]; + if (escaped_char == 'x') { + if (pos + 1 >= src.size()) { + throw lexer_exception("incomplete hexadecimal escape", source, pos); + } + + unsigned int value = 0; + const char * first = &src[pos]; + const char * last = first + 2; + + auto [ptr, ec] = std::from_chars(first, last, value, 16); + if (ec != std::errc{} || ptr != last) { + throw lexer_exception("invalid hexadecimal escape", source, pos); + } + + str += common_unicode_cpt_to_utf8(value); + pos += 2; + continue; + } if (escape_chars.find(escaped_char) == escape_chars.end()) { throw lexer_exception(std::string("unknown escape character \\") + escaped_char, source, pos); } diff --git a/tests/test-jinja.cpp b/tests/test-jinja.cpp index ddeaa1fe9a79..327ab365c44e 100644 --- a/tests/test-jinja.cpp +++ b/tests/test-jinja.cpp @@ -25,6 +25,7 @@ static void test_expressions(testing & t); static void test_set_statement(testing & t); static void test_filters(testing & t); static void test_literals(testing & t); +static void test_hexadecimal_escapes(testing & t); static void test_comments(testing & t); static void test_macros(testing & t); static void test_namespace(testing & t); @@ -65,6 +66,7 @@ int main(int argc, char *argv[]) { t.test("set statement", test_set_statement); t.test("filters", test_filters); t.test("literals", test_literals); + t.test("hexadecimal escapes", test_hexadecimal_escapes); t.test("comments", test_comments); t.test("macros", test_macros); t.test("namespace", test_namespace); @@ -1171,6 +1173,54 @@ static void test_literals(testing & t) { ); } +static void test_hexadecimal_escapes(testing & t) { + test_template(t, "remove NUL from message content", + R"({{ message.content | replace("\x00", "") }})", + {{"message", {{"content", std::string("\0a\0b\0", 5)}}}}, + "ab" + ); + + test_template(t, "non-ASCII hex escape produces UTF-8", + R"({{ '\xE9' }})", + json::object(), + "\xc3\xa9" + ); + + test_template(t, "consume exactly two hex digits", + R"({{ '\x414' }})", + json::object(), + "A4" + ); + + test_template(t, "escaped backslashes remain literal", + R"({{ '\\xE9' }})", + json::object(), + R"(\xE9)" + ); + + if (!g_python_mode) { + const char * invalid_templates[] = { + R"({{ '\x' }})", + R"({{ '\x0' }})", + R"({{ '\xG1' }})", + R"({{ '\x1G' }})", + R"({{ '\x)", + R"({{ '\x0)", + }; + for (const auto * tmpl : invalid_templates) { + t.test("reject " + json(tmpl).dump(), [&](testing & t) { + bool rejected = false; + try { + jinja::lexer().tokenize(tmpl); + } catch (const jinja::lexer_exception &) { + rejected = true; + } + t.assert_true("malformed hex escape must raise a lexer error", rejected); + }); + } + } +} + static void test_comments(testing & t) { test_template(t, "inline comment", "before{# comment #}after", From 0756e83fc1529e207af783782905bc98a8ef0770 Mon Sep 17 00:00:00 2001 From: 92es <76127081+92es@users.noreply.github.com> Date: Wed, 21 Oct 2026 10:01:03 -0700 Subject: [PATCH 51/69] sycl: q8 fast dequant (#30775) --- ggml/src/ggml-sycl/convert.cpp | 13 +++++-------- ggml/src/ggml-sycl/dequantize.hpp | 20 ++++++++++++++++++++ 2 files changed, 25 insertions(+), 8 deletions(-) diff --git a/ggml/src/ggml-sycl/convert.cpp b/ggml/src/ggml-sycl/convert.cpp index bc1b74f3497c..bcd0dd6b34bc 100644 --- a/ggml/src/ggml-sycl/convert.cpp +++ b/ggml/src/ggml-sycl/convert.cpp @@ -177,15 +177,12 @@ static void dequantize_row_q8_0_sycl_reorder(const void *vx, dst_t *y, const int dpct::has_capability_or_fail(stream->get_device(), {sycl::aspect::fp16}); + // flat range<1>, two pairs (4 elements) per item: no nd_range/group math, one vectorized store. + // k % QK8_0 == 0 implies k % 4 == 0, so the wide launch covers the row exactly (no tail). GGML_ASSERT(k % QK8_0 == 0); - const int64_t n_groups = ceil_div(k / 2, SYCL_DEQUANTIZE_BLOCK_SIZE); - // decrease global range when it exceeds the max int; the kernel loops to cover the rest - const int64_t wg_size = downsample_sycl_global_range(n_groups, SYCL_DEQUANTIZE_BLOCK_SIZE); - stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, n_groups) * - sycl::range<3>(1, 1, wg_size), - sycl::range<3>(1, 1, wg_size)), - [=](sycl::nd_item<3> item_ct1) { - dequantize_block_q8_0_reorder(vx, y, k, item_ct1); + stream->parallel_for(sycl::range<1>((size_t) (k / 4)), + [=](sycl::id<1> idx) { + dequantize_block_q8_0_reorder_wide(vx, y, k, idx); }); } diff --git a/ggml/src/ggml-sycl/dequantize.hpp b/ggml/src/ggml-sycl/dequantize.hpp index 40f5cd646014..ae709efa1fba 100644 --- a/ggml/src/ggml-sycl/dequantize.hpp +++ b/ggml/src/ggml-sycl/dequantize.hpp @@ -857,6 +857,26 @@ static void dequantize_block_q8_0_reorder(const void * __restrict__ vx, dst_t * } } +template +static void dequantize_block_q8_0_reorder_wide(const void * __restrict__ vx, dst_t * __restrict__ yy, int64_t k, + const sycl::id<1> idx) { + const int64_t i = 4 * (int64_t) idx[0]; + + const int8_t * qs = (const int8_t *) vx; + const sycl::half * d_ptr = (const sycl::half *) ((const uint8_t *) vx + k); + + const float d0 = float(d_ptr[i / QK8_0]); + const float d1 = float(d_ptr[(i + 2) / QK8_0]); + + sycl::vec p; + p[0] = d0 * (float) qs[i + 0]; + p[1] = d0 * (float) qs[i + 1]; + p[2] = d1 * (float) qs[i + 2]; + p[3] = d1 * (float) qs[i + 3]; + + *reinterpret_cast *>(yy + i) = p.convert(); +} + template static void dequantize_block_q4_1(const void * __restrict__ vx, dst_t * __restrict__ yy, int64_t nb32, const sycl::nd_item<3> &item_ct1) { From d454a204ef2b63af357744809c05af897611a72e Mon Sep 17 00:00:00 2001 From: Aman Gupta Date: Thu, 22 Oct 2026 01:02:22 +0800 Subject: [PATCH 52/69] llama: create new fused op for QSA mask (#30640) * llama: create new fused op for QSA scoring * enroll glm5.3-next too * tests : initialize the remaining tensors in test_kpool_mask The harness sentinels were left uninitialized, so leftover memory could read as NaN and fail the NaN check. * ggml: generalize the k-pool mask op into ggml_top_k_mask The k-pool mask op only covered the pooled QSA selection of qwen4exp and glm5-next, while the DSA models and the DeepSeek V4 CSA build the same top-k mask by hand with fill, set_rows and add. ggml_top_k_mask(mask, top_k, score, blocks, extra) keeps the mask cells of the live picks and the extra cells, and sets the rest to -inf. Every row tensor is 4D in the layout of top_k, and the mask is broadcast to those rows, so a selection can also be made per head. score, blocks and extra are optional: without score every pick is live, without blocks a pick is a cell, and extra holds the cells a row keeps without a pick. llm_graph_context::build_fused_top_k_mask views the row tensors in the layout of kq_mask and registers the fused node. The k-pool models, the DSA attention of build_attn and the DeepSeek V4 CSA mask use it when the probe enables the op, and keep their current path on backends without it. --------- Co-authored-by: Pascal --- ggml/include/ggml-rpc.h | 4 +- ggml/include/ggml.h | 21 ++++ ggml/src/ggml-backend-meta.cpp | 3 +- ggml/src/ggml-cpu/ggml-cpu.c | 5 + ggml/src/ggml-cpu/ops.cpp | 86 +++++++++++++++ ggml/src/ggml-cpu/ops.h | 1 + ggml/src/ggml-cuda/ggml-cuda.cu | 6 ++ ggml/src/ggml-cuda/top-k-mask.cu | 141 +++++++++++++++++++++++++ ggml/src/ggml-cuda/top-k-mask.cuh | 3 + ggml/src/ggml.c | 48 ++++++++- src/llama-context.cpp | 16 +++ src/llama-cparams.h | 2 + src/llama-graph.cpp | 64 +++++++---- src/llama-graph.h | 10 ++ src/models/deepseek4.cpp | 7 ++ src/models/glm5-next.cpp | 27 +++-- src/models/qwen4exp.cpp | 10 ++ tests/test-backend-ops.cpp | 169 ++++++++++++++++++++++++++++++ 18 files changed, 592 insertions(+), 31 deletions(-) create mode 100644 ggml/src/ggml-cuda/top-k-mask.cu create mode 100644 ggml/src/ggml-cuda/top-k-mask.cuh diff --git a/ggml/include/ggml-rpc.h b/ggml/include/ggml-rpc.h index 482bd3666d1c..ecf0a3a6dc25 100644 --- a/ggml/include/ggml-rpc.h +++ b/ggml/include/ggml-rpc.h @@ -8,10 +8,10 @@ extern "C" { #define RPC_PROTO_MAJOR_VERSION 8 #define RPC_PROTO_MINOR_VERSION 0 -#define RPC_PROTO_PATCH_VERSION 0 +#define RPC_PROTO_PATCH_VERSION 1 #ifdef __cplusplus -static_assert(GGML_OP_COUNT == 101, "GGML_OP_COUNT has changed - update RPC_PROTO_PATCH_VERSION"); +static_assert(GGML_OP_COUNT == 102, "GGML_OP_COUNT has changed - update RPC_PROTO_PATCH_VERSION"); #endif #define GGML_RPC_MAX_SERVERS 16 diff --git a/ggml/include/ggml.h b/ggml/include/ggml.h index c37ba947140e..3c2e6734b748 100644 --- a/ggml/include/ggml.h +++ b/ggml/include/ggml.h @@ -585,6 +585,7 @@ extern "C" { GGML_OP_DSV4_HC_COMB, GGML_OP_DSV4_HC_PRE, GGML_OP_DSV4_HC_POST, + GGML_OP_TOP_K_MASK, GGML_OP_UNARY, @@ -2732,6 +2733,26 @@ extern "C" { struct ggml_tensor * post, struct ggml_tensor * comb); + // top-k sparse attention mask: keep the mask cells of the live picks and the extra cells, set the rest to -inf + // + // mask: [n_kv, m1, m2, m3] f16 or f32, broadcast to the rows of top_k + // top_k: [n_top, ne1, ne2, ne3] i32, picks of each row + // score: [n_pick, ne1, ne2, ne3] f32, can be NULL: a pick is live when its score is above -inf, else every pick is live + // blocks: [n_blk, n_pick] i32, can be NULL: the member cells of each pick, else a pick is a cell + // extra: [n_extra, ne1, ne2, ne3] i32, can be NULL: cells of each row kept without a pick + // res: [n_kv, ne1, ne2, ne3] same type as mask + // + // res[c, i] = mask[c, i % m] when c is a cell of a live pick of row i or an extra cell of row i, else -inf + // picks and cells out of range are skipped + // + GGML_API struct ggml_tensor * ggml_top_k_mask( + struct ggml_context * ctx, + struct ggml_tensor * mask, + struct ggml_tensor * top_k, + struct ggml_tensor * score, + struct ggml_tensor * blocks, + struct ggml_tensor * extra); + // custom operators typedef void (*ggml_custom1_op_t)(struct ggml_tensor * dst , const struct ggml_tensor * a, int ith, int nth, void * userdata); diff --git a/ggml/src/ggml-backend-meta.cpp b/ggml/src/ggml-backend-meta.cpp index edbe4da41abd..17f871c897fe 100644 --- a/ggml/src/ggml-backend-meta.cpp +++ b/ggml/src/ggml-backend-meta.cpp @@ -1055,7 +1055,8 @@ static struct ggml_backend_meta_split_state ggml_backend_meta_get_split_state( } break; case GGML_OP_DSV4_HC_COMB: case GGML_OP_DSV4_HC_PRE: - case GGML_OP_DSV4_HC_POST: { + case GGML_OP_DSV4_HC_POST: + case GGML_OP_TOP_K_MASK: { split_state = handle_generic(src_ss, /*scalar_only =*/ true); } break; case GGML_OP_UNARY: { diff --git a/ggml/src/ggml-cpu/ggml-cpu.c b/ggml/src/ggml-cpu/ggml-cpu.c index ab82e3d6c043..4c33369eefa3 100644 --- a/ggml/src/ggml-cpu/ggml-cpu.c +++ b/ggml/src/ggml-cpu/ggml-cpu.c @@ -2105,6 +2105,10 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm { ggml_compute_forward_dsv4_hc_post(params, tensor); } break; + case GGML_OP_TOP_K_MASK: + { + ggml_compute_forward_top_k_mask(params, tensor); + } break; case GGML_OP_MAP_CUSTOM1: { ggml_compute_forward_map_custom1(params, tensor); @@ -2288,6 +2292,7 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) { case GGML_OP_DSV4_HC_COMB: case GGML_OP_DSV4_HC_PRE: case GGML_OP_DSV4_HC_POST: + case GGML_OP_TOP_K_MASK: { n_tasks = n_threads; } break; diff --git a/ggml/src/ggml-cpu/ops.cpp b/ggml/src/ggml-cpu/ops.cpp index 7dff1d11b1b9..02d5efffaca7 100644 --- a/ggml/src/ggml-cpu/ops.cpp +++ b/ggml/src/ggml-cpu/ops.cpp @@ -11736,6 +11736,92 @@ void ggml_compute_forward_dsv4_hc_post( } } +// ggml_compute_forward_top_k_mask + +void ggml_compute_forward_top_k_mask( + const ggml_compute_params * params, + ggml_tensor * dst) { + const ggml_tensor * mask = dst->src[0]; + const ggml_tensor * top_k = dst->src[1]; + const ggml_tensor * score = dst->src[2]; + const ggml_tensor * blocks = dst->src[3]; + const ggml_tensor * extra = dst->src[4]; + + GGML_ASSERT(dst->type == mask->type); + GGML_ASSERT(dst->nb[0] == ggml_type_size(dst->type)); + + const int64_t n_kv = mask->ne[0]; + const int64_t ne1 = dst->ne[1]; + const int64_t ne2 = dst->ne[2]; + const int64_t n_rows = ggml_nrows(dst); + const int64_t n_top = top_k->ne[0]; + const int64_t n_blk = blocks ? blocks->ne[0] : 1; + const int64_t n_pick = blocks ? blocks->ne[1] : score ? score->ne[0] : n_kv; + const int64_t n_extra = extra ? extra->ne[0] : 0; + + const size_t ts = ggml_type_size(dst->type); + + const int ith = params->ith; + const int nth = params->nth; + + for (int64_t ir = ith; ir < n_rows; ir += nth) { + const int64_t i1 = ir % ne1; + const int64_t i2 = (ir / ne1) % ne2; + const int64_t i3 = ir / (ne1*ne2); + + const char * mask_row = (const char *) mask->data + + (i1 % mask->ne[1])*mask->nb[1] + (i2 % mask->ne[2])*mask->nb[2] + (i3 % mask->ne[3])*mask->nb[3]; + char * dst_row = (char *) dst->data + i1*dst->nb[1] + i2*dst->nb[2] + i3*dst->nb[3]; + + if (dst->type == GGML_TYPE_F16) { + const ggml_fp16_t ninf = GGML_CPU_FP32_TO_FP16(-INFINITY); + for (int64_t c = 0; c < n_kv; ++c) { + ((ggml_fp16_t *) dst_row)[c] = ninf; + } + } else { + for (int64_t c = 0; c < n_kv; ++c) { + ((float *) dst_row)[c] = -INFINITY; + } + } + + auto keep = [&](int32_t c) { + if (c >= 0 && c < n_kv) { + memcpy(dst_row + c*ts, mask_row + c*ts, ts); + } + }; + + const char * top_k_row = (const char *) top_k->data + i1*top_k->nb[1] + i2*top_k->nb[2] + i3*top_k->nb[3]; + + for (int64_t j = 0; j < n_top; ++j) { + const int32_t p = *(const int32_t *) (top_k_row + j*top_k->nb[0]); + if (p < 0 || p >= n_pick) { + continue; + } + if (score) { + const float s = *(const float *) ((const char *) score->data + + p*score->nb[0] + i1*score->nb[1] + i2*score->nb[2] + i3*score->nb[3]); + if (!(s > -INFINITY)) { + continue; + } + } + if (!blocks) { + keep(p); + continue; + } + for (int64_t k = 0; k < n_blk; ++k) { + keep(*(const int32_t *) ((const char *) blocks->data + k*blocks->nb[0] + p*blocks->nb[1])); + } + } + + if (extra) { + const char * extra_row = (const char *) extra->data + i1*extra->nb[1] + i2*extra->nb[2] + i3*extra->nb[3]; + for (int64_t j = 0; j < n_extra; ++j) { + keep(*(const int32_t *) (extra_row + j*extra->nb[0])); + } + } + } +} + // ggml_compute_forward_rwkv_wkv7 static void ggml_compute_forward_rwkv_wkv7_f32( diff --git a/ggml/src/ggml-cpu/ops.h b/ggml/src/ggml-cpu/ops.h index 2728b08b6c97..00a2ea7dc2de 100644 --- a/ggml/src/ggml-cpu/ops.h +++ b/ggml/src/ggml-cpu/ops.h @@ -108,6 +108,7 @@ void ggml_compute_forward_lightning_indexer(const struct ggml_compute_params * p void ggml_compute_forward_dsv4_hc_comb(const struct ggml_compute_params * params, struct ggml_tensor * dst); void ggml_compute_forward_dsv4_hc_pre(const struct ggml_compute_params * params, struct ggml_tensor * dst); void ggml_compute_forward_dsv4_hc_post(const struct ggml_compute_params * params, struct ggml_tensor * dst); +void ggml_compute_forward_top_k_mask(const struct ggml_compute_params * params, struct ggml_tensor * dst); void ggml_compute_forward_map_custom1(const struct ggml_compute_params * params, struct ggml_tensor * dst); void ggml_compute_forward_map_custom2(const struct ggml_compute_params * params, struct ggml_tensor * dst); void ggml_compute_forward_map_custom3(const struct ggml_compute_params * params, struct ggml_tensor * dst); diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu index f6ff9f893aee..e43807392bba 100644 --- a/ggml/src/ggml-cuda/ggml-cuda.cu +++ b/ggml/src/ggml-cuda/ggml-cuda.cu @@ -70,6 +70,7 @@ #include "ggml-cuda/cumsum.cuh" #include "ggml-cuda/fill.cuh" #include "ggml-cuda/lightning-indexer.cuh" +#include "ggml-cuda/top-k-mask.cuh" #include "ggml.h" #include @@ -2570,6 +2571,9 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg case GGML_OP_LIGHTNING_INDEXER: ggml_cuda_lightning_indexer(ctx, dst); break; + case GGML_OP_TOP_K_MASK: + ggml_cuda_op_top_k_mask(ctx, dst); + break; default: return false; } @@ -6003,6 +6007,8 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g return true; case GGML_OP_LIGHTNING_INDEXER: return ggml_cuda_lightning_indexer_supported(dev_ctx->device, op); + case GGML_OP_TOP_K_MASK: + return op->type == GGML_TYPE_F16 || op->type == GGML_TYPE_F32; default: return false; diff --git a/ggml/src/ggml-cuda/top-k-mask.cu b/ggml/src/ggml-cuda/top-k-mask.cu new file mode 100644 index 000000000000..9ff995240085 --- /dev/null +++ b/ggml/src/ggml-cuda/top-k-mask.cu @@ -0,0 +1,141 @@ +#include "top-k-mask.cuh" +#include "convert.cuh" + +#define CUDA_TOP_K_MASK_BLOCK_SIZE 512 + +// one block per row: fill the row with -inf, then copy the mask cells of the live picks and the extra cells +template +static __global__ void top_k_mask_kernel( + const T * mask, const int32_t * top_k, const float * score, const int32_t * blocks, const int32_t * extra, T * dst, + const int64_t n_kv, const int64_t ne1, const int64_t ne2, + const int64_t m1, const int64_t m2, const int64_t m3, + const int64_t n_top, const int64_t n_pick, const int64_t n_blk, const int64_t n_extra, + const int64_t sm1, const int64_t sm2, const int64_t sm3, + const int64_t sd1, const int64_t sd2, const int64_t sd3, + const int64_t sk0, const int64_t sk1, const int64_t sk2, const int64_t sk3, + const int64_t ss0, const int64_t ss1, const int64_t ss2, const int64_t ss3, + const int64_t sb0, const int64_t sb1, + const int64_t se0, const int64_t se1, const int64_t se2, const int64_t se3, + const bool vec) { + ggml_cuda_pdl_lc(); + + const int64_t ir = blockIdx.x; + const int64_t i1 = ir % ne1; + const int64_t i2 = (ir / ne1) % ne2; + const int64_t i3 = ir / (ne1*ne2); + + const T * mask_row = mask + (i1 % m1)*sm1 + (i2 % m2)*sm2 + (i3 % m3)*sm3; + T * dst_row = dst + i1*sd1 + i2*sd2 + i3*sd3; + const int32_t * top_k_row = top_k + i1*sk1 + i2*sk2 + i3*sk3; + + ggml_cuda_pdl_sync(); + + const T ninf = ggml_cuda_cast(-INFINITY); + if (vec) { + constexpr int nv = sizeof(int4)/sizeof(T); + T fill[nv]; +#pragma unroll + for (int j = 0; j < nv; ++j) { + fill[j] = ninf; + } + const int4 fill4 = *(const int4 *) fill; + for (int64_t i = threadIdx.x; i < n_kv/nv; i += blockDim.x) { + ((int4 *) dst_row)[i] = fill4; + } + } else { + for (int64_t i = threadIdx.x; i < n_kv; i += blockDim.x) { + dst_row[i] = ninf; + } + } + + __syncthreads(); + + // one thread per pick or extra cell; cells kept twice get the same value + for (int64_t i = threadIdx.x; i < n_top + n_extra; i += blockDim.x) { + if (i >= n_top) { + const int32_t c = extra[(i - n_top)*se0 + i1*se1 + i2*se2 + i3*se3]; + if (c >= 0 && c < n_kv) { + dst_row[c] = mask_row[c]; + } + continue; + } + + const int32_t p = top_k_row[i*sk0]; + if (p < 0 || p >= n_pick) { + continue; + } + if (score && !(score[p*ss0 + i1*ss1 + i2*ss2 + i3*ss3] > -INFINITY)) { + continue; + } + if (!blocks) { + if (p < n_kv) { + dst_row[p] = mask_row[p]; + } + continue; + } + for (int64_t k = 0; k < n_blk; ++k) { + const int32_t c = blocks[k*sb0 + p*sb1]; + if (c >= 0 && c < n_kv) { + dst_row[c] = mask_row[c]; + } + } + } +} + +template +static void top_k_mask_cuda(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { + const ggml_tensor * mask = dst->src[0]; + const ggml_tensor * top_k = dst->src[1]; + const ggml_tensor * score = dst->src[2]; + const ggml_tensor * blocks = dst->src[3]; + const ggml_tensor * extra = dst->src[4]; + + const int64_t n_kv = mask->ne[0]; + + GGML_ASSERT(dst->nb[0] == sizeof(T) && mask->nb[0] == sizeof(T)); + GGML_ASSERT(dst->nb[1] % sizeof(T) == 0 && mask->nb[1] % sizeof(T) == 0); + + // 16-byte stores need every dst row aligned + const bool vec = (n_kv*sizeof(T)) % sizeof(int4) == 0 && + dst->nb[1] % sizeof(int4) == 0 && dst->nb[2] % sizeof(int4) == 0 && dst->nb[3] % sizeof(int4) == 0 && + (uintptr_t) dst->data % sizeof(int4) == 0; + + const int64_t n_pick = blocks ? blocks->ne[1] : score ? score->ne[0] : n_kv; + + const size_t si = sizeof(int32_t); + const size_t sf = sizeof(float); + + const dim3 block_dims(CUDA_TOP_K_MASK_BLOCK_SIZE, 1, 1); + const dim3 grid_dims(ggml_nrows(dst), 1, 1); + const ggml_cuda_kernel_launch_params launch_params = ggml_cuda_kernel_launch_params(grid_dims, block_dims, 0, ctx.stream()); + + ggml_cuda_kernel_launch(top_k_mask_kernel, launch_params, + (const T *) mask->data, (const int32_t *) top_k->data, + score ? (const float *) score->data : nullptr, + blocks ? (const int32_t *) blocks->data : nullptr, + extra ? (const int32_t *) extra->data : nullptr, + (T *) dst->data, + n_kv, dst->ne[1], dst->ne[2], + mask->ne[1], mask->ne[2], mask->ne[3], + top_k->ne[0], n_pick, blocks ? blocks->ne[0] : 1, extra ? extra->ne[0] : 0, + mask->nb[1]/sizeof(T), mask->nb[2]/sizeof(T), mask->nb[3]/sizeof(T), + dst->nb[1]/sizeof(T), dst->nb[2]/sizeof(T), dst->nb[3]/sizeof(T), + top_k->nb[0]/si, top_k->nb[1]/si, top_k->nb[2]/si, top_k->nb[3]/si, + score ? score->nb[0]/sf : 0, score ? score->nb[1]/sf : 0, score ? score->nb[2]/sf : 0, score ? score->nb[3]/sf : 0, + blocks ? blocks->nb[0]/si : 0, blocks ? blocks->nb[1]/si : 0, + extra ? extra->nb[0]/si : 0, extra ? extra->nb[1]/si : 0, extra ? extra->nb[2]/si : 0, extra ? extra->nb[3]/si : 0, + vec); +} + +void ggml_cuda_op_top_k_mask(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { + switch (dst->type) { + case GGML_TYPE_F16: + top_k_mask_cuda(ctx, dst); + break; + case GGML_TYPE_F32: + top_k_mask_cuda(ctx, dst); + break; + default: + GGML_ABORT("unsupported type"); + } +} diff --git a/ggml/src/ggml-cuda/top-k-mask.cuh b/ggml/src/ggml-cuda/top-k-mask.cuh new file mode 100644 index 000000000000..930d6667bbe1 --- /dev/null +++ b/ggml/src/ggml-cuda/top-k-mask.cuh @@ -0,0 +1,3 @@ +#include "common.cuh" + +void ggml_cuda_op_top_k_mask(ggml_backend_cuda_context & ctx, ggml_tensor * dst); diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c index 01dc8d05c316..004970db0540 100644 --- a/ggml/src/ggml.c +++ b/ggml/src/ggml.c @@ -1095,6 +1095,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = { "DSV4_HC_COMB", "DSV4_HC_PRE", "DSV4_HC_POST", + "TOP_K_MASK", "UNARY", @@ -1112,7 +1113,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = { "GLU", }; -static_assert(GGML_OP_COUNT == 101, "GGML_OP_COUNT != 101"); +static_assert(GGML_OP_COUNT == 102, "GGML_OP_COUNT != 102"); static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = { "none", @@ -1210,6 +1211,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = { "dsv4_hc_comb(mixes, scale, base)", "dsv4_hc_pre(x, weights)", "dsv4_hc_post(x, residual, post, comb)", + "top_k_mask(mask, top_k, score, blocks, extra)", "unary(x)", @@ -1227,7 +1229,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = { "glu(x)", }; -static_assert(GGML_OP_COUNT == 101, "GGML_OP_COUNT != 101"); +static_assert(GGML_OP_COUNT == 102, "GGML_OP_COUNT != 102"); static_assert(GGML_OP_POOL_COUNT == 2, "GGML_OP_POOL_COUNT != 2"); @@ -6630,6 +6632,48 @@ struct ggml_tensor * ggml_dsv4_hc_post( return result; } +// ggml_top_k_mask + +struct ggml_tensor * ggml_top_k_mask( + struct ggml_context * ctx, + struct ggml_tensor * mask, + struct ggml_tensor * top_k, + struct ggml_tensor * score, + struct ggml_tensor * blocks, + struct ggml_tensor * extra) { + GGML_ASSERT(mask->type == GGML_TYPE_F16 || mask->type == GGML_TYPE_F32); + GGML_ASSERT(mask->nb[0] == ggml_type_size(mask->type)); + GGML_ASSERT(top_k->type == GGML_TYPE_I32); + GGML_ASSERT(top_k->ne[1] % mask->ne[1] == 0 && top_k->ne[2] % mask->ne[2] == 0 && top_k->ne[3] % mask->ne[3] == 0); + + if (score) { + GGML_ASSERT(score->type == GGML_TYPE_F32); + GGML_ASSERT(score->ne[1] == top_k->ne[1] && score->ne[2] == top_k->ne[2] && score->ne[3] == top_k->ne[3]); + } + + if (blocks) { + GGML_ASSERT(blocks->type == GGML_TYPE_I32); + GGML_ASSERT(blocks->ne[2] == 1 && blocks->ne[3] == 1); + GGML_ASSERT(!score || blocks->ne[1] == score->ne[0]); + } + + if (extra) { + GGML_ASSERT(extra->type == GGML_TYPE_I32); + GGML_ASSERT(extra->ne[1] == top_k->ne[1] && extra->ne[2] == top_k->ne[2] && extra->ne[3] == top_k->ne[3]); + } + + struct ggml_tensor * result = ggml_new_tensor_4d(ctx, mask->type, mask->ne[0], top_k->ne[1], top_k->ne[2], top_k->ne[3]); + + result->op = GGML_OP_TOP_K_MASK; + result->src[0] = mask; + result->src[1] = top_k; + result->src[2] = score; + result->src[3] = blocks; + result->src[4] = extra; + + return result; +} + //////////////////////////////////////////////////////////////////////////////// struct ggml_hash_set ggml_hash_set_new(size_t size) { diff --git a/src/llama-context.cpp b/src/llama-context.cpp index 9ee64b566779..ca84fc405bce 100644 --- a/src/llama-context.cpp +++ b/src/llama-context.cpp @@ -165,6 +165,12 @@ static const llm_fused_op_probe llm_fused_op_dsv4_hc_post_probe = { /*.n_tokens_per_seq =*/ 1, }; +static const llm_fused_op_probe llm_fused_op_top_k_mask_probe = { + /*.op =*/ LLM_FUSED_OP_TOP_K_MASK, + /*.name =*/ "fused top-k mask", + /*.n_tokens_per_seq =*/ 1, +}; + llama_context::llama_context( const llama_model & model, llama_context_params params) : @@ -328,6 +334,10 @@ llama_context::llama_context( cparams.fused_dsv4_hc_post = true; cparams.auto_fhc = true; + // only the indexer models build this mask, skip the probe for the others + cparams.fused_top_k_mask = true; + cparams.auto_ftkm = hparams.indexer_top_k > 0; + // with causal attention, the batch size is limited by the context size cparams.n_batch = cparams.causal_attn ? std::min(cparams.n_ctx, params.n_batch) : params.n_batch; @@ -671,6 +681,12 @@ void llama_context::resolve_fused_ops(const llama_memory_context_i * mctx, uint3 resolve(llm_fused_op_dsv4_hc_post_probe, cparams.fused_dsv4_hc_post); cparams.auto_fhc = false; } + + if (cparams.auto_ftkm) { + LLAMA_LOG_INFO("%s: resolving fused top-k mask support:\n", func); + resolve(llm_fused_op_top_k_mask_probe, cparams.fused_top_k_mask); + cparams.auto_ftkm = false; + } } static int llama_graph_n_input_tensors(ggml_cgraph * gf) { diff --git a/src/llama-cparams.h b/src/llama-cparams.h index f4eb181a753e..0c260f7b6285 100644 --- a/src/llama-cparams.h +++ b/src/llama-cparams.h @@ -48,6 +48,8 @@ struct llama_cparams { bool fused_dsv4_hc_comb; bool fused_dsv4_hc_post; bool auto_fhc; + bool fused_top_k_mask; // use fused top-k sparse attention mask + bool auto_ftkm; bool no_perf; bool warmup; // TODO: remove [TAG_LLAMA_GRAPH_NO_WARMUP] bool op_offload; diff --git a/src/llama-graph.cpp b/src/llama-graph.cpp index 59caffe87fce..8c31b513074a 100644 --- a/src/llama-graph.cpp +++ b/src/llama-graph.cpp @@ -3375,6 +3375,27 @@ ggml_tensor * llm_graph_context::build_attn( return cur; } +ggml_tensor * llm_graph_context::build_fused_top_k_mask( + ggml_tensor * kq_mask, + ggml_tensor * top_k, + ggml_tensor * score, + ggml_tensor * blocks, + ggml_tensor * extra, + int il) const { + const int64_t m1 = kq_mask->ne[1]; + const int64_t m2 = kq_mask->ne[2]; + const int64_t m3 = kq_mask->ne[3]; + + auto rows = [&](ggml_tensor * t) { + return t ? ggml_reshape_4d(ctx0, t, t->ne[0], m1, m2, m3) : nullptr; + }; + + ggml_tensor * cur = ggml_top_k_mask(ctx0, kq_mask, rows(top_k), rows(score), blocks, rows(extra)); + res->add_fused_node({LLM_FUSED_OP_TOP_K_MASK, cur, il}); + + return cur; +} + ggml_tensor * llm_graph_context::build_attn( llm_graph_input_attn_k_dsa * inp, ggml_tensor * wo, @@ -3407,31 +3428,36 @@ ggml_tensor * llm_graph_context::build_attn( const auto & kq_mask = inp->get_kq_mask_mla(); - // prepare new kq mask - starts filled with -INFINITY - ggml_tensor * kq_mask_all = ggml_fill(ctx0, kq_mask, -INFINITY); + ggml_tensor * kq_mask_top_k = nullptr; + if (cparams.fused_top_k_mask) { + kq_mask_top_k = build_fused_top_k_mask(kq_mask, top_k, nullptr, nullptr, nullptr, il); + } else { + // prepare new kq mask - starts filled with -INFINITY + ggml_tensor * kq_mask_all = ggml_fill(ctx0, kq_mask, -INFINITY); - // reshape KQ mask into tensor with rows of size 1: - // [n_kv, n_batch, 1, n_stream] -> [1, n_kv, n_batch, n_stream] - kq_mask_all = ggml_view_4d(ctx0, kq_mask_all, 1, kq_mask_all->ne[0], kq_mask_all->ne[1], kq_mask_all->ne[3], kq_mask_all->nb[0], kq_mask_all->nb[1], kq_mask_all->nb[2], 0); + // reshape KQ mask into tensor with rows of size 1: + // [n_kv, n_batch, 1, n_stream] -> [1, n_kv, n_batch, n_stream] + kq_mask_all = ggml_view_4d(ctx0, kq_mask_all, 1, kq_mask_all->ne[0], kq_mask_all->ne[1], kq_mask_all->ne[3], kq_mask_all->nb[0], kq_mask_all->nb[1], kq_mask_all->nb[2], 0); - // reshape top_k indices: [n_top_k, n_batch, 1, n_stream] -> [n_top_k, n_batch, n_stream, 1] - ggml_tensor * top_k_3d = ggml_view_4d(ctx0, top_k, top_k->ne[0], top_k->ne[1], top_k->ne[3], 1, top_k->nb[1], top_k->nb[2], top_k->ne[3]*top_k->nb[3], 0); + // reshape top_k indices: [n_top_k, n_batch, 1, n_stream] -> [n_top_k, n_batch, n_stream, 1] + ggml_tensor * top_k_3d = ggml_view_4d(ctx0, top_k, top_k->ne[0], top_k->ne[1], top_k->ne[3], 1, top_k->nb[1], top_k->nb[2], top_k->ne[3]*top_k->nb[3], 0); - // prepare zero-filled tensor with rows of size 1: [1, n_top_k, n_batch, n_stream] - // this will be our source of zero values for unmasking top k mask elements - ggml_tensor * zeros = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, 1, top_k_3d->ne[0], top_k_3d->ne[1], top_k_3d->ne[2]); - zeros = ggml_fill(ctx0, zeros, 0.0f); + // prepare zero-filled tensor with rows of size 1: [1, n_top_k, n_batch, n_stream] + // this will be our source of zero values for unmasking top k mask elements + ggml_tensor * zeros = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, 1, top_k_3d->ne[0], top_k_3d->ne[1], top_k_3d->ne[2]); + zeros = ggml_fill(ctx0, zeros, 0.0f); - // modify KQ mask by unmasking elements that are in top_k indices - // ggml_set_rows([1, n_kv, n_batch, n_stream], [1, n_top_k, n_batch, n_stream], [n_top_k, n_batch, n_stream, 1]) - ggml_tensor * kq_mask_top_k = ggml_set_rows(ctx0, kq_mask_all, zeros, top_k_3d); + // modify KQ mask by unmasking elements that are in top_k indices + // ggml_set_rows([1, n_kv, n_batch, n_stream], [1, n_top_k, n_batch, n_stream], [n_top_k, n_batch, n_stream, 1]) + kq_mask_top_k = ggml_set_rows(ctx0, kq_mask_all, zeros, top_k_3d); - // reshape to restore the original shape of KQ mask: - // [1, n_kv, n_batch, n_stream] -> [n_kv, n_batch, 1, n_stream] - kq_mask_top_k = ggml_view_4d(ctx0, kq_mask_top_k, kq_mask_top_k->ne[1], kq_mask_top_k->ne[2], 1, kq_mask_top_k->ne[3], kq_mask_top_k->nb[2], kq_mask_top_k->nb[3], kq_mask_top_k->nb[3], 0); + // reshape to restore the original shape of KQ mask: + // [1, n_kv, n_batch, n_stream] -> [n_kv, n_batch, 1, n_stream] + kq_mask_top_k = ggml_view_4d(ctx0, kq_mask_top_k, kq_mask_top_k->ne[1], kq_mask_top_k->ne[2], 1, kq_mask_top_k->ne[3], kq_mask_top_k->nb[2], kq_mask_top_k->nb[3], kq_mask_top_k->nb[3], 0); - // combine with the original kq mask - kq_mask_top_k = ggml_add(ctx0, kq_mask_top_k, kq_mask); + // combine with the original kq mask + kq_mask_top_k = ggml_add(ctx0, kq_mask_top_k, kq_mask); + } ggml_tensor * q = q_cur; ggml_tensor * k = mctx_cur->get_k(ctx0, il); diff --git a/src/llama-graph.h b/src/llama-graph.h index abe5133c97a2..9c5f3aea9a5e 100644 --- a/src/llama-graph.h +++ b/src/llama-graph.h @@ -53,6 +53,7 @@ enum llm_fused_op { LLM_FUSED_OP_DSV4_HC_PRE, LLM_FUSED_OP_DSV4_HC_COMB, LLM_FUSED_OP_DSV4_HC_POST, + LLM_FUSED_OP_TOP_K_MASK, }; enum llm_ffn_op_type : int { @@ -1335,6 +1336,15 @@ struct llm_graph_context { float kq_scale, int il) const; + // ggml_top_k_mask with top_k, score and extra viewed in the row layout of kq_mask, see ggml.h + ggml_tensor * build_fused_top_k_mask( + ggml_tensor * kq_mask, // [n_kv, n_tokens/n_stream, 1, n_stream] + ggml_tensor * top_k, // [n_top, n_tokens] + ggml_tensor * score, // [n_pick, n_tokens], can be null + ggml_tensor * blocks, // [n_blk, n_pick], can be null + ggml_tensor * extra, // [n_extra, n_tokens], can be null + int il) const; + llm_graph_input_attn_kv_iswa * build_attn_inp_kv_iswa() const; llm_graph_input_dsv4 * build_inp_dsv4() const; diff --git a/src/models/deepseek4.cpp b/src/models/deepseek4.cpp index af04dddd3921..184585a9e81c 100644 --- a/src/models/deepseek4.cpp +++ b/src/models/deepseek4.cpp @@ -683,6 +683,13 @@ ggml_tensor * llama_model_deepseek4::graph::build_top_k_mask( GGML_ASSERT(kq_mask); GGML_ASSERT(top_k); + if (cparams.fused_top_k_mask) { + ggml_tensor * kq_mask_top_k = build_fused_top_k_mask(kq_mask, top_k, nullptr, nullptr, nullptr, il); + cb(kq_mask_top_k, name, il); + + return kq_mask_top_k; + } + ggml_tensor * kq_mask_all = ggml_fill(ctx0, kq_mask, -INFINITY); kq_mask_all = ggml_view_4d(ctx0, kq_mask_all, 1, kq_mask_all->ne[0], kq_mask_all->ne[1], kq_mask_all->ne[3], kq_mask_all->nb[0], kq_mask_all->nb[1], kq_mask_all->nb[2], 0); diff --git a/src/models/glm5-next.cpp b/src/models/glm5-next.cpp index 90870bd9675d..5056c901b7f8 100644 --- a/src/models/glm5-next.cpp +++ b/src/models/glm5-next.cpp @@ -323,13 +323,15 @@ llama_model_glm5_next::llm_graph_input_kpool * llama_model_glm5_next::graph::bui const int64_t n_sel = kpool*n_top_pool + (hparams.indexer_kpool_select_tail ? kpool - 1 : 0); inp->n_sel = (uint32_t) n_sel; - // the scatter maps the dead slots of this mask to dump rows - inp->sel_mask = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, n_sel, 1, 1, n_tokens); - ggml_set_input(inp->sel_mask); - // set_input_kpool always fills the mask, so it stays allocated in every graph - ggml_build_forward_expand(gf, inp->sel_mask); - // one view for all layers: each view of a host input is a separate upload - inp->sel_mask_2d = ggml_reshape_2d(ctx0, inp->sel_mask, n_sel, n_tokens); + // the scatter maps the dead slots of this mask to dump rows, the fused mask op reads liveness from the scores instead + if (!cparams.fused_top_k_mask) { + inp->sel_mask = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, n_sel, 1, 1, n_tokens); + ggml_set_input(inp->sel_mask); + // set_input_kpool always fills the mask, so it stays allocated in every graph + ggml_build_forward_expand(gf, inp->sel_mask); + // one view for all layers: each view of a host input is a separate upload + inp->sel_mask_2d = ggml_reshape_2d(ctx0, inp->sel_mask, n_sel, n_tokens); + } } inp->n_new = n_new; @@ -973,6 +975,17 @@ ggml_tensor * llama_model_glm5_next::graph::build_kpool_select( const int64_t n_top_pool = std::min(n_pool, hparams.indexer_top_k / kpool); ggml_tensor * top_k = ggml_top_k(ctx0, score, n_top_pool); // [n_top_pool, n_tokens], UNORDERED + // keep the kq_mask cells of the live picked pools and the tail, -inf elsewhere + if (cparams.fused_top_k_mask) { + ggml_tensor * tail = hparams.indexer_kpool_select_tail ? inp_kpool->tail_idxs : nullptr; + GGML_ASSERT(kpool*n_top_pool + (tail ? tail->ne[0] : 0) == (int64_t) inp_kpool->n_sel); + + ggml_tensor * sel = build_fused_top_k_mask(kq_mask, top_k, score, inp_kpool->pool_idxs, tail, il); + cb(sel, "indexer_sel", il); + + return sel; + } + // The selection mask marks the first min(nv, n_top_pool) slots as the visible pools, so order the set by descending score. ggml_tensor * sel_score = ggml_get_rows(ctx0, ggml_reshape_3d(ctx0, score, 1, n_pool, n_tokens), top_k); // [1, n_top_pool, n_tokens] diff --git a/src/models/qwen4exp.cpp b/src/models/qwen4exp.cpp index 22e7159840b9..ec0b438fa3d2 100644 --- a/src/models/qwen4exp.cpp +++ b/src/models/qwen4exp.cpp @@ -813,6 +813,16 @@ ggml_tensor * llama_model_qwen4exp::graph::build_qsa_sel( ggml_tensor * top_k = ggml_top_k(ctx0, score, n_top_pool); // [n_top_pool, n_tokens], unordered cb(top_k, "indexer_top_k", il); + // keep the kq_mask cells of the live picked pools and the tail, -inf elsewhere + if (cparams.fused_top_k_mask) { + GGML_ASSERT(kpool*n_top_pool + inp_kpool->tail_idxs->ne[0] == inp_kpool->n_sel); + + ggml_tensor * sel = build_fused_top_k_mask(kq_mask, top_k, score, inp_kpool->pool_idxs, inp_kpool->tail_idxs, il); + cb(sel, "indexer_sel", il); + + return sel; + } + // the top blocks, then the incomplete tail with n_kv for missing cells ggml_tensor * sel_idx = ggml_get_rows(ctx0, inp_kpool->pool_idxs, ggml_reshape_1d(ctx0, top_k, n_top_pool*n_tokens)); // [kpool, n_top_pool*n_tokens] diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index 58f3275da146..9f2d1f54db71 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -9128,6 +9128,148 @@ struct test_lightning_indexer : public test_case { } }; +// GGML_OP_TOP_K_MASK +struct test_top_k_mask : public test_case { + const ggml_type type; + const int64_t n_kv; + const int64_t n_blk; // 0: no blocks, a pick is a cell + const int64_t n_pick; // number of blocks, ignored without blocks + const int64_t n_top; + const int64_t n_extra; // 0: no extra cells + const bool score; // false: every pick is live + const int64_t nb; // rows per stream + const int64_t nh; // rows of top_k per mask row, broadcast over ne2 + const int64_t ns; // streams + const bool real; // blocks of consecutive cells, live picks, as in a real cache + + std::string vars() override { + return VARS_TO_STR11(type, n_kv, n_blk, n_pick, n_top, n_extra, score, nb, nh, ns, real); + } + + // the result is a copy of the mask or -inf, the harness checks the -inf pattern, the finite values must match exactly + double max_err() override { + return 0.0; + } + + double err(const float * a, const float * b, size_t n) override { + double result = 0.0; + for (size_t i = 0; i < n; ++i) { + if (std::isfinite(a[i]) && std::isfinite(b[i])) { + result = std::max(result, (double) fabsf(a[i] - b[i])); + } + } + return result; + } + + size_t op_size(ggml_tensor * t) override { + const size_t ts = ggml_type_size(type); + const size_t nc = std::max(n_blk, 1); + return ggml_nbytes(t) + nb*nh*ns*(n_top*(sizeof(int32_t) + (score ? sizeof(float) : 0) + nc*(sizeof(int32_t) + 2*ts)) + n_extra*(sizeof(int32_t) + 2*ts)); + } + + test_top_k_mask(ggml_type type = GGML_TYPE_F16, int64_t n_kv = 256, int64_t n_blk = 4, int64_t n_pick = 64, + int64_t n_top = 16, int64_t n_extra = 3, bool score = true, int64_t nb = 32, int64_t nh = 1, int64_t ns = 1, bool real = false) + : type(type), n_kv(n_kv), n_blk(n_blk), n_pick(n_blk ? n_pick : n_kv), n_top(n_top), n_extra(n_extra), score(score), + nb(nb), nh(nh), ns(ns), real(real) {} + + ggml_tensor * build_graph(ggml_context * ctx) override { + ggml_tensor * m = ggml_new_tensor_4d(ctx, type, n_kv, nb, 1, ns); + ggml_set_name(m, "m"); + + ggml_tensor * top_k = ggml_new_tensor_4d(ctx, GGML_TYPE_I32, n_top, nb, nh, ns); + ggml_set_name(top_k, "top_k"); + + ggml_tensor * s = nullptr; + if (score) { + s = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, n_pick, nb, nh, ns); + ggml_set_name(s, "score"); + } + + ggml_tensor * blocks = nullptr; + if (n_blk > 0) { + blocks = ggml_new_tensor_2d(ctx, GGML_TYPE_I32, n_blk, n_pick); + ggml_set_name(blocks, "blocks"); + } + + ggml_tensor * extra = nullptr; + if (n_extra > 0) { + extra = ggml_new_tensor_4d(ctx, GGML_TYPE_I32, n_extra, nb, nh, ns); + ggml_set_name(extra, "extra"); + } + + ggml_tensor * out = ggml_top_k_mask(ctx, m, top_k, s, blocks, extra); + ggml_set_name(out, "out"); + + return out; + } + + void initialize_tensors(ggml_context * ctx) override { + std::mt19937 gen(0x4B50); + + // cells and picks sometimes fall out of range, the op skips them + auto init_cells = [&](ggml_tensor * t) { + std::vector data(ggml_nelements(t)); + if (real) { + // block p holds cells p*n_blk..p*n_blk + n_blk - 1, the extra cells are the last cells + const bool is_blocks = strcmp(t->name, "blocks") == 0; + for (int64_t i = 0; i < (int64_t) data.size(); ++i) { + const int64_t c = is_blocks ? i : n_kv - 1 - i % t->ne[0]; + data[i] = c < n_kv ? (int32_t) c : (int32_t) n_kv; + } + } else { + std::uniform_int_distribution cell(0, n_kv - 1); + for (auto & c : data) { + const uint32_t r = gen() % 16; + c = r == 0 ? (int32_t) n_kv : r == 1 ? -1 : cell(gen); + } + } + ggml_backend_tensor_set(t, data.data(), 0, data.size()*sizeof(int32_t)); + }; + + for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) { + if (strcmp(t->name, "m") == 0) { + if (type == GGML_TYPE_F16) { + init_tensor_kq_mask(t); + } else { + std::vector data(ggml_nelements(t)); + std::uniform_real_distribution dis(-1.0f, 1.0f); + for (auto & v : data) { + v = gen() % 4 == 0 ? -INFINITY : dis(gen); + } + ggml_backend_tensor_set(t, data.data(), 0, data.size()*sizeof(float)); + } + } else if (strcmp(t->name, "score") == 0) { + // -inf marks the picks a row cannot see + std::vector data(ggml_nelements(t)); + std::uniform_real_distribution dis(-1.0f, 1.0f); + for (auto & v : data) { + v = !real && gen() % 4 == 0 ? -INFINITY : dis(gen); + } + ggml_backend_tensor_set(t, data.data(), 0, data.size()*sizeof(float)); + } else if (strcmp(t->name, "top_k") == 0) { + std::vector data(ggml_nelements(t)); + std::vector order(n_pick); + for (int64_t r = 0; r < ggml_nrows(t); ++r) { + for (int64_t i = 0; i < n_pick; ++i) { + order[i] = i; + } + std::shuffle(order.begin(), order.end(), gen); + for (int64_t j = 0; j < n_top; ++j) { + const bool out = !real && j == 0 && r % 5 == 0; + data[r*n_top + j] = out ? (r % 2 ? -1 : (int32_t) n_pick) : order[j % n_pick]; + } + } + ggml_backend_tensor_set(t, data.data(), 0, data.size()*sizeof(int32_t)); + } else if (strcmp(t->name, "blocks") == 0 || strcmp(t->name, "extra") == 0) { + init_cells(t); + } else { + // the harness sentinels and the output, the callback checks the sentinels for NaN + init_tensor_uniform(t); + } + } + } +}; + // Deserializable generic test case struct input_tensor { ggml_type type; @@ -9912,6 +10054,25 @@ static std::vector> make_test_cases_eval() { test_cases.emplace_back(new test_dsv4_hc_post(2560, 21, true, true)); test_cases.emplace_back(new test_dsv4_hc_post(31, 17, false, true)); + for (ggml_type type : {GGML_TYPE_F16, GGML_TYPE_F32}) { + // blocks with extra cells and scores (k-pool) + test_cases.emplace_back(new test_top_k_mask(type)); + test_cases.emplace_back(new test_top_k_mask(type, 1001, 4, 70, 16, 3, true, 7)); + test_cases.emplace_back(new test_top_k_mask(type, 256, 4, 64, 64, 0, true, 33)); + test_cases.emplace_back(new test_top_k_mask(type, 512, 8, 16, 32, 7, true, 5, 1, 4)); + test_cases.emplace_back(new test_top_k_mask(type, 4096, 4, 1024, 512, 3, true, 1)); + test_cases.emplace_back(new test_top_k_mask(type, 4096, 4, 1024, 512, 3, true, 64, 1, 2)); + // cells without scores (DSA) + test_cases.emplace_back(new test_top_k_mask(type, 256, 0, 0, 16, 0, false, 32)); + test_cases.emplace_back(new test_top_k_mask(type, 1001, 0, 0, 64, 0, false, 7, 1, 3)); + test_cases.emplace_back(new test_top_k_mask(type, 4096, 0, 0, 2048, 0, false, 5)); + // cells with scores and extra cells + test_cases.emplace_back(new test_top_k_mask(type, 512, 0, 0, 32, 5, true, 9)); + // blocks picked per head, the mask broadcast over the heads + test_cases.emplace_back(new test_top_k_mask(type, 512, 16, 32, 4, 0, true, 6, 4, 1)); + test_cases.emplace_back(new test_top_k_mask(type, 1024, 8, 128, 16, 8, false, 3, 2, 2)); + } + // glu ops for (ggml_type type : {GGML_TYPE_F16, GGML_TYPE_F32, GGML_TYPE_BF16}) { for (int v : {0, 1}) { @@ -12745,6 +12906,14 @@ static std::vector> make_test_cases_perf() { } } + // top_k_mask, Flash-Next QSA shapes: 512 pools of 4 + a 3-cell tail, and DSA shapes: 2048 cells + for (int64_t n_kv : { 18432, 131072 }) { + for (int64_t nb : { 1, 2048 }) { + test_cases.emplace_back(new test_top_k_mask(GGML_TYPE_F16, n_kv, 4, n_kv/4, 512, 3, true, nb, 1, 1, true)); + test_cases.emplace_back(new test_top_k_mask(GGML_TYPE_F16, n_kv, 0, 0, 2048, 0, false, nb, 1, 1, true)); + } + } + // launch-overhead isolation: single L2_NORM launch vs batched siblings at the GDN // production shape (strided qkv views) -- perf-mode only, the eval list has its own // 2/4-wide coverage From 7775ffb7e48fa0c7dcffd5815743b3d2fb3b6761 Mon Sep 17 00:00:00 2001 From: Maphist0 <28743569+Maphist0@users.noreply.github.com> Date: Thu, 22 Oct 2026 03:54:16 +0800 Subject: [PATCH 53/69] model: Add support for SenseNova U1 model (#28919) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * model : add SenseNova U1 understanding support Add support for the dense SenseNova U1 understanding branch, including the U1.5-8B-MoT model. - Add HF-to-GGUF conversion for NEOChatModel, excluding MoT generation tensors and concatenating temporal and spatial Q/K normalization weights. - Register the sensenova_u1 GGUF architecture and custom spatial RoPE metadata. - Implement temporal/spatial Q/K normalization with 4-axis RoPE. - Keep U1-specific text position handling in the model implementation. - Add synthetic architecture coverage for sensenova_u1. Only the dense understanding branch is supported; generation branches are intentionally rejected during conversion. * conversion : treat one-expert SenseNova U1 configs as dense * mtmd : add SenseNova U1 vision support Add conversion support for the SenseNova U1 vision encoder and implement its CPU mtmd projector graph. The graph applies patch embedding, GELU, adjacent-pair 2D RoPE, spatial downsampling, and dense projection for dynamic-resolution image inputs. * mtmd : fix SenseNova U1 vision token layout * model : clean up SenseNova U1 RoPE metadata Replace the hard-coded spatial RoPE key with shared GGUF and LLM_KV definitions used by conversion, model loading, and architecture tests. Clarify why SenseNova U1 retains its custom text-position input: U1 uses [t, 0, 0, 0], while the generic M-RoPE input uses [t, t, t, 0]. * mtmd : simplify SenseNova U1 projector integration Map the SenseNova U1 dense projection to the standard mm.0 weight and bias tensors and reuse the shared projector fields in the loader and graph. Read vision parameters from the checkpoint configuration and remove the U1-specific tensor names and model fields while preserving its strided projection, bias, and row-major token layout. * mtmd : fix SenseNova U1 image positions and attention * fix spacing * mtmd : restore generic test input chunks * mtmd : restore SenseNova U1 integration after upstream merge * Update conversion/sensenova_u1.py Co-authored-by: Sigbjørn Skjæret * Update src/models/sensenova-u1.cpp Co-authored-by: Sigbjørn Skjæret * model : remove SenseNova U1 BF16 activation casts Use standard backend precision handling for QKV, FFN, and output projections instead of casting inputs based on the output tensor type. Assisted-by: Codex * Update src/models/sensenova-u1.cpp Co-authored-by: Sigbjørn Skjæret --------- Co-authored-by: Sigbjørn Skjæret --- conversion/__init__.py | 2 + conversion/base.py | 2 +- conversion/sensenova_u1.py | 114 +++++++++++++++++ gguf-py/gguf/constants.py | 22 ++++ gguf-py/gguf/gguf_writer.py | 3 + src/llama-arch.cpp | 2 + src/llama-arch.h | 2 + src/llama-model-saver.cpp | 1 + src/llama-model.cpp | 3 + src/models/models.h | 15 +++ src/models/sensenova-u1.cpp | 190 +++++++++++++++++++++++++++++ tests/test-llama-archs.cpp | 3 + tools/mtmd/CMakeLists.txt | 1 + tools/mtmd/clip-impl.h | 2 + tools/mtmd/clip.cpp | 41 +++++++ tools/mtmd/models/models.h | 5 + tools/mtmd/models/sensenova-u1.cpp | 40 ++++++ tools/mtmd/mtmd.cpp | 24 +++- 18 files changed, 470 insertions(+), 2 deletions(-) create mode 100644 conversion/sensenova_u1.py create mode 100644 src/models/sensenova-u1.cpp create mode 100644 tools/mtmd/models/sensenova-u1.cpp diff --git a/conversion/__init__.py b/conversion/__init__.py index bddfe3c1f531..369ce7afdab5 100644 --- a/conversion/__init__.py +++ b/conversion/__init__.py @@ -17,6 +17,7 @@ TEXT_MODEL_MAP: dict[str, str] = { + "NEOChatModel": "sensenova_u1", "AfmoeForCausalLM": "afmoe", "LagunaForCausalLM": "laguna", "ApertusForCausalLM": "llama", @@ -367,6 +368,7 @@ "MuseSparkForConditionalGeneration": "muse_spark", "PaddleOCRVisionModel": "ernie", "Phi4ForCausalLMV": "phi", + "NEOVisionModel": "sensenova_u1", "Qwen2AudioForConditionalGeneration": "ultravox", "Qwen2VLForConditionalGeneration": "qwenvl", "Qwen2VLModel": "qwenvl", diff --git a/conversion/base.py b/conversion/base.py index dec1d43222ea..2cbaed700f13 100644 --- a/conversion/base.py +++ b/conversion/base.py @@ -3143,7 +3143,7 @@ def get_model_architecture(hparams: dict[str, Any], model_type: ModelType) -> st # Kimi-K3's text_config reports "KimiLinearForCausalLM", which is the older # Kimi-Linear-48B architecture and cannot load K3 (no attention residuals, # latent MoE, situ, ...). Route on the top-level architecture instead. - if model_type == ModelType.TEXT and arch in ("StepVLForConditionalGeneration", "Sarashina2VisionForCausalLM", "Exaone4_5_ForConditionalGeneration", "Step3p7ForConditionalGeneration", "KimiK3ForConditionalGeneration"): + if model_type == ModelType.TEXT and arch in ("NEOChatModel", "StepVLForConditionalGeneration", "Sarashina2VisionForCausalLM", "Exaone4_5_ForConditionalGeneration", "Step3p7ForConditionalGeneration", "KimiK3ForConditionalGeneration"): return arch # if "architectures" is found in the sub-config, use that instead diff --git a/conversion/sensenova_u1.py b/conversion/sensenova_u1.py new file mode 100644 index 000000000000..bde5f9d9b388 --- /dev/null +++ b/conversion/sensenova_u1.py @@ -0,0 +1,114 @@ +from __future__ import annotations + +from pathlib import Path + +import torch + +from .base import MmprojModel, ModelBase, gguf +from .qwen import Qwen2Model + + +@ModelBase.example("sensenova/SenseNova-U1.5-8B-MoT") +@ModelBase.register("NEOChatModel") +class SenseNovaU1Model(Qwen2Model): + model_arch = gguf.MODEL_ARCH.SENSENOVA_U1 + + @classmethod + def filter_tensors(cls, item): + name, _ = item + if not name.startswith("language_model.") or "_mot_gen" in name: + return None + return super().filter_tensors(item) + + def set_gguf_parameters(self): + if int(self.hparams.get("num_experts") or 0) > 1: + raise ValueError("SenseNova U1 conversion currently supports the dense understanding branch") + if self.rope_parameters.get("rope_type", "default") not in ("default", None): + raise ValueError("SenseNova U1 scaled RoPE is not supported") + if self.hparams.get("attention_bias", False): + raise ValueError("SenseNova U1 attention bias is not supported") + if self.hparams.get("use_sliding_window", False): + raise ValueError("SenseNova U1 sliding attention is not supported") + super().set_gguf_parameters() + self.gguf_writer.add_rope_freq_base_spatial(self.hparams["rope_theta_hw"]) + + def get_tensors(self): + for name, gen in self.model_tensors.items(): + if name.endswith((".q_norm_hw.weight", ".k_norm_hw.weight")): + continue + data = gen() + if name.endswith((".q_norm.weight", ".k_norm.weight")): + spatial = self.model_tensors[name.replace("_norm.weight", "_norm_hw.weight")]() + # Keep one weight vector; the graph normalizes its two halves independently. + data = torch.cat((data, spatial), dim=0) + yield name, data + + +@ModelBase.register("NEOVisionModel") +@ModelBase.example("sensenova/SenseNova-U1.5-8B-MoT") +class SenseNovaU1VisionModel(MmprojModel): + def __init__(self, dir_model: Path, *args, **kwargs): + hparams = kwargs.pop("hparams", None) + if hparams is None: + hparams = ModelBase.load_hparams(dir_model, is_mistral_format=False) + else: + hparams = dict(hparams) + + vision_config = dict(hparams["vision_config"]) + # NEOVisionModel has no transformer blocks + # these defaults satisfy the generic mmproj metadata contract without replacing checkpoint values + vision_config.setdefault("image_size", 0) + vision_config.setdefault("intermediate_size", 0) + vision_config.setdefault("num_hidden_layers", 0) + vision_config.setdefault("num_attention_heads", 1) + hparams["vision_config"] = vision_config + super().__init__(dir_model, *args, hparams=hparams, **kwargs) + + self.preprocessor_config.setdefault("image_mean", [0.485, 0.456, 0.406]) + self.preprocessor_config.setdefault("image_std", [0.229, 0.224, 0.225]) + + @classmethod + def filter_tensors(cls, item): + name, gen = item + if not name.startswith("vision_model."): + return None + return super().filter_tensors((name, gen)) + + def set_gguf_parameters(self): + super().set_gguf_parameters() + self.gguf_writer.add_clip_projector_type("sensenova_u1") + vision_config = self.hparams["vision_config"] + # These defaults match the known U1.5 checkpoint when older configs omit fields. + hidden_size = int(vision_config.get("hidden_size", 1024)) + min_pixels = int(vision_config.get("min_pixels", 65536)) + max_pixels = int(vision_config.get("max_pixels", 16777216)) + downsample_ratio = float(vision_config.get("downsample_ratio", 0.5)) + if downsample_ratio <= 0: + raise ValueError(f"SenseNova U1 downsample_ratio must be positive, got {downsample_ratio}") + merge_size = round(1.0 / downsample_ratio) + if abs((1.0 / downsample_ratio) - merge_size) > 1e-6: + raise ValueError(f"SenseNova U1 downsample_ratio must have an integer reciprocal, got {downsample_ratio}") + norm_eps = float(vision_config.get("layer_norm_eps", vision_config.get("rms_norm_eps", 1e-6))) + self.gguf_writer.add_vision_head_dim(hidden_size) + self.gguf_writer.add_vision_attention_layernorm_eps(norm_eps) + self.gguf_writer.add_vision_min_pixels(min_pixels) + self.gguf_writer.add_vision_max_pixels(max_pixels) + self.gguf_writer.add_vision_spatial_merge_size(merge_size) + + def modify_tensors(self, data_torch, name, bid): + tensor_names = { + "vision_model.embeddings.patch_embedding.weight": "v.patch_embd.weight", + "vision_model.embeddings.patch_embedding.bias": "v.patch_embd.bias", + "vision_model.embeddings.dense_embedding.weight": self.format_tensor_name(gguf.MODEL_TENSOR.V_MMPROJ, 0), + "vision_model.embeddings.dense_embedding.bias": self.format_tensor_name( + gguf.MODEL_TENSOR.V_MMPROJ, 0, suffix=".bias" + ), + } + if name not in tensor_names: + raise ValueError(f"Unexpected SenseNova U1 vision tensor: {name!r}") + return [(tensor_names[name], data_torch)] + + def tensor_force_quant(self, name, new_name, bid, n_dims): + if new_name == self.format_tensor_name(gguf.MODEL_TENSOR.V_MMPROJ, 0): + return gguf.GGMLQuantizationType.F16 if self.ftype == gguf.LlamaFileType.MOSTLY_F16 else gguf.GGMLQuantizationType.F32 + return super().tensor_force_quant(name, new_name, bid, n_dims) diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index 2840348cba24..619a15fc96d9 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -263,6 +263,7 @@ class Rope: DIMENSION_SECTIONS = "{arch}.rope.dimension_sections" SECTION_ORDER = "{arch}.rope.section_order" FREQ_BASE = "{arch}.rope.freq_base" + FREQ_BASE_SPATIAL = "{arch}.rope.freq_base_spatial" FREQ_BASE_SWA = "{arch}.rope.freq_base_swa" SCALING_TYPE = "{arch}.rope.scaling.type" SCALING_FACTOR = "{arch}.rope.scaling.factor" @@ -573,6 +574,7 @@ class MODEL_ARCH(IntEnum): QWEN2 = auto() QWEN2MOE = auto() QWEN2VL = auto() + SENSENOVA_U1 = auto() QWEN3 = auto() QWEN3MOE = auto() QWEN3NEXT = auto() @@ -1390,6 +1392,7 @@ class MODEL_TENSOR(IntEnum): MODEL_ARCH.QWEN2: "qwen2", MODEL_ARCH.QWEN2MOE: "qwen2moe", MODEL_ARCH.QWEN2VL: "qwen2vl", + MODEL_ARCH.SENSENOVA_U1: "sensenova_u1", MODEL_ARCH.QWEN3: "qwen3", MODEL_ARCH.QWEN3MOE: "qwen3moe", MODEL_ARCH.QWEN3NEXT: "qwen3next", @@ -2916,6 +2919,24 @@ class MODEL_TENSOR(IntEnum): MODEL_TENSOR.FFN_DOWN_SHEXP, MODEL_TENSOR.FFN_UP_SHEXP, ], + MODEL_ARCH.SENSENOVA_U1: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_QKV, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_Q_NORM, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_K_NORM, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], MODEL_ARCH.QWEN3: [ MODEL_TENSOR.TOKEN_EMBD, MODEL_TENSOR.OUTPUT_NORM, @@ -6485,6 +6506,7 @@ class VisionProjectorType: # RoPE KEY_ROPE_DIMENSION_COUNT = Keys.Rope.DIMENSION_COUNT KEY_ROPE_FREQ_BASE = Keys.Rope.FREQ_BASE +KEY_ROPE_FREQ_BASE_SPATIAL = Keys.Rope.FREQ_BASE_SPATIAL KEY_ROPE_SCALING_TYPE = Keys.Rope.SCALING_TYPE KEY_ROPE_SCALING_FACTOR = Keys.Rope.SCALING_FACTOR KEY_ROPE_SCALING_ORIG_CTX_LEN = Keys.Rope.SCALING_ORIG_CTX_LEN diff --git a/gguf-py/gguf/gguf_writer.py b/gguf-py/gguf/gguf_writer.py index e8a96e57102e..d00392211619 100644 --- a/gguf-py/gguf/gguf_writer.py +++ b/gguf-py/gguf/gguf_writer.py @@ -1167,6 +1167,9 @@ def add_rope_section_order(self, value: RopeSectionOrder) -> None: def add_rope_freq_base(self, value: float) -> None: self.add_float32(Keys.Rope.FREQ_BASE.format(arch=self.arch), value) + def add_rope_freq_base_spatial(self, value: float) -> None: + self.add_float32(Keys.Rope.FREQ_BASE_SPATIAL.format(arch=self.arch), value) + def add_rope_scaling_type(self, value: RopeScalingType) -> None: self.add_string(Keys.Rope.SCALING_TYPE.format(arch=self.arch), value.value) diff --git a/src/llama-arch.cpp b/src/llama-arch.cpp index 4534be22f37b..1c11536b51c5 100644 --- a/src/llama-arch.cpp +++ b/src/llama-arch.cpp @@ -33,6 +33,7 @@ static const std::map LLM_ARCH_NAMES = { { LLM_ARCH_QWEN2, "qwen2" }, { LLM_ARCH_QWEN2MOE, "qwen2moe" }, { LLM_ARCH_QWEN2VL, "qwen2vl" }, + { LLM_ARCH_SENSENOVA_U1, "sensenova_u1" }, { LLM_ARCH_QWEN3, "qwen3" }, { LLM_ARCH_QWEN3MOE, "qwen3moe" }, { LLM_ARCH_QWEN3NEXT, "qwen3next" }, @@ -339,6 +340,7 @@ static const std::map LLM_KV_NAMES = { { LLM_KV_ROPE_DIMENSION_SECTIONS, "%s.rope.dimension_sections" }, { LLM_KV_ROPE_SECTION_ORDER, "%s.rope.section_order" }, { LLM_KV_ROPE_FREQ_BASE, "%s.rope.freq_base" }, + { LLM_KV_ROPE_FREQ_BASE_SPATIAL, "%s.rope.freq_base_spatial" }, { LLM_KV_ROPE_FREQ_BASE_SWA, "%s.rope.freq_base_swa" }, { LLM_KV_ROPE_SCALE_LINEAR, "%s.rope.scale_linear" }, { LLM_KV_ROPE_SCALING_TYPE, "%s.rope.scaling.type" }, diff --git a/src/llama-arch.h b/src/llama-arch.h index 5a8ca425001c..afceb8ea6f92 100644 --- a/src/llama-arch.h +++ b/src/llama-arch.h @@ -38,6 +38,7 @@ enum llm_arch { LLM_ARCH_QWEN2, LLM_ARCH_QWEN2MOE, LLM_ARCH_QWEN2VL, + LLM_ARCH_SENSENOVA_U1, LLM_ARCH_QWEN3, LLM_ARCH_QWEN3MOE, LLM_ARCH_QWEN3NEXT, @@ -344,6 +345,7 @@ enum llm_kv { LLM_KV_ROPE_DIMENSION_SECTIONS, LLM_KV_ROPE_SECTION_ORDER, LLM_KV_ROPE_FREQ_BASE, + LLM_KV_ROPE_FREQ_BASE_SPATIAL, LLM_KV_ROPE_FREQ_BASE_SWA, LLM_KV_ROPE_SCALE_LINEAR, LLM_KV_ROPE_SCALING_TYPE, diff --git a/src/llama-model-saver.cpp b/src/llama-model-saver.cpp index 46b1d2ae2744..d620fcd9c82e 100644 --- a/src/llama-model-saver.cpp +++ b/src/llama-model-saver.cpp @@ -15,6 +15,7 @@ bool llama_model_saver_supports_arch(llm_arch arch) { switch (arch) { + case LLM_ARCH_SENSENOVA_U1: case LLM_ARCH_GEMMA3N: case LLM_ARCH_BITNET: case LLM_ARCH_T5: diff --git a/src/llama-model.cpp b/src/llama-model.cpp index 6712f0f22f2a..3797c567dc47 100644 --- a/src/llama-model.cpp +++ b/src/llama-model.cpp @@ -110,6 +110,8 @@ static llama_model * llama_model_mapping(llm_arch arch, const llama_model_params return new llama_model_qwen2vl(params); case LLM_ARCH_QWEN2MOE: return new llama_model_qwen2moe(params); + case LLM_ARCH_SENSENOVA_U1: + return new llama_model_sensenova_u1(params); case LLM_ARCH_QWEN3: return new llama_model_qwen3(params); case LLM_ARCH_QWEN3MOE: @@ -3640,6 +3642,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) { // DSV4 DSpark drafters use DeepSeek-V4's normal RoPE; legacy DFlash backbones are NeoX return model->hparams.dsv4_hc_mult > 0 ? LLAMA_ROPE_TYPE_NORM : LLAMA_ROPE_TYPE_NEOX; + case LLM_ARCH_SENSENOVA_U1: case LLM_ARCH_QWEN2VL: case LLM_ARCH_PADDLEOCR: return LLAMA_ROPE_TYPE_MROPE; diff --git a/src/models/models.h b/src/models/models.h index 9aa450deabff..47b0203da192 100644 --- a/src/models/models.h +++ b/src/models/models.h @@ -614,6 +614,21 @@ struct llama_model_qwen2moe : public llama_model_base { }; +struct llama_model_sensenova_u1 : public llama_model_base { + llama_model_sensenova_u1(const struct llama_model_params & params) : llama_model_base(params) {} + void load_arch_hparams(llama_model_loader & ml) override; + void load_arch_tensors(llama_model_loader & ml) override; + + float rope_freq_base_spatial = 10000.0f; + + struct graph : public llm_graph_context { + graph(const llama_model_sensenova_u1 & model, const llm_graph_params & params); + }; + + std::unique_ptr build_arch_graph(const llm_graph_params & params) const override; +}; + + struct llama_model_qwen3 : public llama_model_base { llama_model_qwen3(const struct llama_model_params & params) : llama_model_base(params) {} void load_arch_hparams(llama_model_loader & ml) override; diff --git a/src/models/sensenova-u1.cpp b/src/models/sensenova-u1.cpp new file mode 100644 index 000000000000..c42c604e8ecd --- /dev/null +++ b/src/models/sensenova-u1.cpp @@ -0,0 +1,190 @@ +#include "models.h" + +namespace { +class llm_graph_input_pos_sensenova_u1 : public llm_graph_input_pos { +public: + llm_graph_input_pos_sensenova_u1() : llm_graph_input_pos(4) {} + + void set_input(const llama_ubatch * ubatch) override { + if (ubatch->pos && pos) { + const int64_t n_tokens = ubatch->n_tokens; + + // U1 expects text-token positions in the temporal plane only: [t, 0, 0, 0]. + // The generic M-RoPE input uses [t, t, t, 0], so U1 needs a custom input class. + if (ubatch->token) { + std::vector pos_data(n_tokens*n_pos_per_embd, 0); + for (int i = 0; i < n_tokens; ++i) { + pos_data[i] = ubatch->pos[i]; + } + ggml_backend_tensor_set(pos, pos_data.data(), 0, pos_data.size()*ggml_element_size(pos)); + } else { + ggml_backend_tensor_set(pos, ubatch->pos, 0, n_tokens*n_pos_per_embd*ggml_element_size(pos)); + } + } + } +}; +} + +void llama_model_sensenova_u1::load_arch_hparams(llama_model_loader & ml) { + ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); + + ml.get_key(LLM_KV_ROPE_FREQ_BASE_SPATIAL, rope_freq_base_spatial); + if (hparams.n_embd_head_k() % 4 != 0 || hparams.n_embd_head_v() != hparams.n_embd_head_k()) { + throw std::runtime_error("SenseNova U1 requires equal Q/K/V head sizes divisible by four"); + } + switch (hparams.n_layer()) { + case 42: type = LLM_TYPE_8B; break; + default: type = LLM_TYPE_UNKNOWN; + } +} + +void llama_model_sensenova_u1::load_arch_tensors(llama_model_loader &) { + LLAMA_LOAD_LOCALS; + + tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); + + // output + output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); + output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED); + // if output is NULL, init from the input tok embed + if (output == NULL) { + output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED); + } + + for (int i = 0; i < n_layer; ++i) { + auto & layer = layers[i]; + + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); + + create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_gqa, n_embd_gqa, 0); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0); + + layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, 0); + layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, 0); + + layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0); + layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); + } +} + +std::unique_ptr llama_model_sensenova_u1::build_arch_graph(const llm_graph_params & params) const { + return std::make_unique(*this, params); +} + +llama_model_sensenova_u1::graph::graph(const llama_model_sensenova_u1 & model, const llm_graph_params & params) : llm_graph_context(params) { + const int64_t n_embd_head = hparams.n_embd_head_v(); + + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + + ggml_tensor * cur; + ggml_tensor * inpL; + + auto normalize = [&](ggml_tensor * x, ggml_tensor * weight) { + x = ggml_rms_norm(ctx0, x, hparams.f_norm_rms_eps); + + return ggml_mul(ctx0, x, weight); + }; + + inpL = build_inp_embd(model.tok_embd); + + ggml_tensor * pos[3]{}; + auto inp = std::make_unique(); + auto & inp_pos = inp->pos; + inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, (int64_t)n_tokens*hparams.n_pos_per_embd()); + ggml_set_input(inp_pos); + res->add_input(std::move(inp)); + + for (int axis = 0; axis < 3; ++axis) { + pos[axis] = ggml_view_1d(ctx0, inp_pos, n_tokens, axis*n_tokens*sizeof(int32_t)); + } + + auto normalize_and_rotate = [&](ggml_tensor * x, ggml_tensor * weight) { + const int64_t heads = x->ne[1]; + x = ggml_reshape_4d(ctx0, x, n_embd_head/2, 2, heads, n_tokens); + x = normalize(x, ggml_reshape_2d(ctx0, weight, n_embd_head/2, 2)); + x = ggml_reshape_3d(ctx0, x, n_embd_head, heads, n_tokens); + + // Normalize temporal and spatial halves separately; rotate time, height, then width. + for (int axis = 0; axis < 3; ++axis) { + const int dims = axis == 0 ? n_embd_head/2 : n_embd_head/4; + const int offset = axis == 0 ? 0 : n_embd_head/2 + (axis - 1)*n_embd_head/4; + const float base = axis == 0 ? freq_base : model.rope_freq_base_spatial; + x = ggml_rope_ext(ctx0, x, pos[axis], nullptr, dims, GGML_ROPE_TYPE_NEOX, + n_ctx_orig, base, 1.0f, 0.0f, 1.0f, 32.0f, 1.0f); + ggml_rope_set_offset(x, offset); + } + return x; + }; + + auto * inp_attn = build_attn_inp_kv(); + + ggml_tensor * inp_out_ids = build_inp_out_ids(); + + for (int il = 0; il < n_layer; ++il) { + res->t_layer_inp[il] = inpL; + + ggml_tensor * inpSA = inpL; + + // norm + cur = normalize(inpL, model.layers[il].attn_norm); + cb(cur, "attn_norm", il); + + // self-attention + { + // compute Q and K and RoPE them + auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur, + n_embd_head, n_head, n_head_kv, il); + + Qcur = normalize_and_rotate(Qcur, model.layers[il].attn_q_norm); + Kcur = normalize_and_rotate(Kcur, model.layers[il].attn_k_norm); + + cb(Qcur, "Qcur", il); + cb(Kcur, "Kcur", il); + cb(Vcur, "Vcur", il); + + cur = build_attn(inp_attn, + model.layers[il].wo, model.layers[il].wo_b, model.layers[il].wo_s, + Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il); + } + if (il == n_layer - 1 && inp_out_ids) { + cur = ggml_get_rows(ctx0, cur, inp_out_ids); + inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); + } + ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); + cb(ffn_inp, "ffn_inp", il); + + // feed-forward network + cur = normalize(ffn_inp, model.layers[il].ffn_norm); + cb(cur, "ffn_norm", il); + + cur = build_ffn(cur, + model.layers[il].ffn_up, NULL, model.layers[il].ffn_up_s, + model.layers[il].ffn_gate, NULL, model.layers[il].ffn_gate_s, + model.layers[il].ffn_down, NULL, model.layers[il].ffn_down_s, + NULL, + LLM_FFN_SILU, LLM_FFN_PAR, il); + cb(cur, "ffn_out", il); + + cur = ggml_add(ctx0, cur, ffn_inp); + + cur = build_cvec(cur, il); + cb(cur, "l_out", il); + + // input for next layer + inpL = cur; + } + cur = inpL; + + cur = normalize(cur, model.output_norm); + + cb(cur, "result_norm", -1); + res->t_embd = cur; + + // lm_head + cur = build_lora_mm(model.output, cur, model.output_s); + + cb(cur, "result_output", -1); + res->t_logits = cur; +} diff --git a/tests/test-llama-archs.cpp b/tests/test-llama-archs.cpp index 21d27e17d930..c86b1315a660 100644 --- a/tests/test-llama-archs.cpp +++ b/tests/test-llama-archs.cpp @@ -329,6 +329,9 @@ static gguf_context_ptr get_gguf_ctx(const llm_arch arch, const bool moe) { // partial rotary: n_rot must not exceed the indexer key length (64) ms.add_kv(LLM_KV_ROPE_DIMENSION_COUNT, uint32_t(64)); } + if (arch == LLM_ARCH_SENSENOVA_U1) { + ms.add_kv(LLM_KV_ROPE_FREQ_BASE_SPATIAL, 10000.0f); + } ms.add_kv(LLM_KV_ATTENTION_CLAMP_KQV, 1.0f); ms.add_kv(LLM_KV_ATTENTION_LAYERNORM_EPS, 1e-5f); ms.add_kv(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, 1e-5f); diff --git a/tools/mtmd/CMakeLists.txt b/tools/mtmd/CMakeLists.txt index 398b73904f5c..bd7fb735665e 100644 --- a/tools/mtmd/CMakeLists.txt +++ b/tools/mtmd/CMakeLists.txt @@ -55,6 +55,7 @@ add_library(mtmd models/paddleocr.cpp models/pixtral.cpp models/qwen2vl.cpp + models/sensenova-u1.cpp models/minimax-m3.cpp models/qwen3vl.cpp models/ling3vl.cpp diff --git a/tools/mtmd/clip-impl.h b/tools/mtmd/clip-impl.h index 71d349cc5981..6ca465280830 100644 --- a/tools/mtmd/clip-impl.h +++ b/tools/mtmd/clip-impl.h @@ -547,6 +547,7 @@ enum projector_type { PROJECTOR_TYPE_POCKETTTS_SPKENC, PROJECTOR_TYPE_POCKETTTS_GEN, PROJECTOR_TYPE_MUSE_GLIMMER, + PROJECTOR_TYPE_SENSENOVA_U1, PROJECTOR_TYPE_COHERE2V, PROJECTOR_TYPE_MUSE_SPARK_AUDIO, PROJECTOR_TYPE_UNKNOWN, @@ -621,6 +622,7 @@ static std::map PROJECTOR_TYPE_NAMES = { { PROJECTOR_TYPE_POCKETTTS_SPKENC, "pockettts_spkenc"}, { PROJECTOR_TYPE_POCKETTTS_GEN, "pockettts_gen"}, { PROJECTOR_TYPE_MUSE_GLIMMER, "muse-glimmer"}, + { PROJECTOR_TYPE_SENSENOVA_U1, "sensenova_u1"}, { PROJECTOR_TYPE_COHERE2V, "cohere2v"}, { PROJECTOR_TYPE_MUSE_SPARK_AUDIO, "muse-spark-audio"}, }; diff --git a/tools/mtmd/clip.cpp b/tools/mtmd/clip.cpp index 359ec5ac36b0..869e3fff8260 100644 --- a/tools/mtmd/clip.cpp +++ b/tools/mtmd/clip.cpp @@ -997,6 +997,10 @@ static std::unique_ptr clip_get_graph_builder(clip_ctx * ctx, const { builder = std::make_unique(ctx, img); } break; + case PROJECTOR_TYPE_SENSENOVA_U1: + { + builder = std::make_unique(ctx, img); + } break; case PROJECTOR_TYPE_LING3VL: { builder = std::make_unique(ctx, img); @@ -1795,6 +1799,20 @@ struct clip_model_loader { LOG_WRN("%s: more info: https://github.com/ggml-org/llama.cpp/issues/16842\n\n", __func__); } } break; + case PROJECTOR_TYPE_SENSENOVA_U1: + { + hparams.n_merge = 2; + hparams.image_resize_algo = RESIZE_ALGO_BICUBIC; + get_u32(KEY_SPATIAL_MERGE_SIZE, hparams.n_merge, false); + get_u32(KEY_IMAGE_MIN_PIXELS, hparams.image_min_pixels); + get_u32(KEY_IMAGE_MAX_PIXELS, hparams.image_max_pixels); + hparams.rope_theta = 10000.0f; + hparams.set_limit_image_tokens(16, 4096); + if (hparams.n_merge != 2) { + throw std::runtime_error(string_format("%s: SenseNova U1 requires spatial_merge_size=2, got %u", __func__, hparams.n_merge)); + } + hparams.set_warmup_n_tokens(16*16); + } break; case PROJECTOR_TYPE_MINIMAX_M3: { hparams.n_merge = 2; // spatial_merge_size @@ -3837,6 +3855,11 @@ struct clip_model_loader { } } break; + case PROJECTOR_TYPE_SENSENOVA_U1: + { + model.mm_0_w = get_tensor(string_format(TN_LLAVA_PROJ, 0, "weight")); + model.mm_0_b = get_tensor(string_format(TN_LLAVA_PROJ, 0, "bias")); + } break; default: GGML_ASSERT(false && "unknown projector type"); } @@ -4311,6 +4334,7 @@ int clip_n_output_tokens_x(const clip_ctx * ctx, const clip_image_f32 * img) { case PROJECTOR_TYPE_QWEN2VL: case PROJECTOR_TYPE_QWEN25VL: case PROJECTOR_TYPE_QWEN3VL: + case PROJECTOR_TYPE_SENSENOVA_U1: case PROJECTOR_TYPE_LING3VL: case PROJECTOR_TYPE_EXAONE4_5: case PROJECTOR_TYPE_MIMOVL: @@ -4341,6 +4365,7 @@ int clip_n_output_tokens_y(const clip_ctx * ctx, const clip_image_f32 * img) { case PROJECTOR_TYPE_QWEN2VL: case PROJECTOR_TYPE_QWEN25VL: case PROJECTOR_TYPE_QWEN3VL: + case PROJECTOR_TYPE_SENSENOVA_U1: case PROJECTOR_TYPE_LING3VL: case PROJECTOR_TYPE_EXAONE4_5: case PROJECTOR_TYPE_MIMOVL: @@ -4426,6 +4451,7 @@ int clip_n_output_tokens(const clip_ctx * ctx, const clip_image_f32 * img) { case PROJECTOR_TYPE_QWEN2VL: case PROJECTOR_TYPE_QWEN25VL: case PROJECTOR_TYPE_QWEN3VL: + case PROJECTOR_TYPE_SENSENOVA_U1: case PROJECTOR_TYPE_LING3VL: case PROJECTOR_TYPE_EXAONE4_5: case PROJECTOR_TYPE_MIMOVL: @@ -5525,6 +5551,19 @@ bool clip_encode(struct clip_ctx * ctx, struct clip_encode_params * params) { } set_input_i32("pos_w", pos_data); } break; + case PROJECTOR_TYPE_SENSENOVA_U1: + { + const int n_patches_per_row = image_size_width / patch_size; + std::vector pos_data(n_pos); + for (int i = 0; i < n_pos; i++) { + pos_data[i] = i / n_patches_per_row; + } + set_input_i32("pos_y", pos_data); + for (int i = 0; i < n_pos; i++) { + pos_data[i] = i % n_patches_per_row; + } + set_input_i32("pos_x", pos_data); + } break; case PROJECTOR_TYPE_DEEPSEEK4V: { // set the 2D positions (mrope layout, only the first 2 channels are used) @@ -6411,6 +6450,8 @@ int clip_n_mmproj_embd(const struct clip_ctx * ctx) { case PROJECTOR_TYPE_JANUS_PRO: case PROJECTOR_TYPE_YOUTUVL: return ctx->model.mm_1_b->ne[0]; + case PROJECTOR_TYPE_SENSENOVA_U1: + return ctx->model.hparams.projection_dim; case PROJECTOR_TYPE_QWEN3VL: // main path + deepstack paths return ctx->model.mm_1_b->ne[0] * (1 + ctx->model.n_deepstack_layers); diff --git a/tools/mtmd/models/models.h b/tools/mtmd/models/models.h index 294448605972..55e525cdc0f5 100644 --- a/tools/mtmd/models/models.h +++ b/tools/mtmd/models/models.h @@ -50,6 +50,11 @@ struct clip_graph_qwen3vl : clip_graph_qwen2vl { ggml_cgraph * build() override; }; +struct clip_graph_sensenova_u1 : clip_graph { + clip_graph_sensenova_u1(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {} + ggml_cgraph * build() override; +}; + struct clip_graph_ling3vl : clip_graph_qwen3vl { clip_graph_ling3vl(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph_qwen3vl(ctx, img) {} ggml_cgraph * build() override; diff --git a/tools/mtmd/models/sensenova-u1.cpp b/tools/mtmd/models/sensenova-u1.cpp new file mode 100644 index 000000000000..57875c6edbc8 --- /dev/null +++ b/tools/mtmd/models/sensenova-u1.cpp @@ -0,0 +1,40 @@ +#include "models.h" + +ggml_cgraph * clip_graph_sensenova_u1::build() { + GGML_ASSERT(model.patch_embeddings_0 != nullptr); + GGML_ASSERT(model.patch_bias != nullptr); + GGML_ASSERT(model.mm_0_w != nullptr); + GGML_ASSERT(model.mm_0_b != nullptr); + GGML_ASSERT(hparams.n_merge == 2); + + ggml_tensor * pos_x = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_patches); + ggml_set_name(pos_x, "pos_x"); + ggml_set_input(pos_x); + + ggml_tensor * pos_y = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_patches); + ggml_set_name(pos_y, "pos_y"); + ggml_set_input(pos_y); + + ggml_tensor * inp = build_inp(); + inp = ggml_gelu(ctx0, inp); + + // Apply adjacent-pair 2D RoPE to patch embeddings before downsampling. + inp = ggml_reshape_4d(ctx0, inp, n_embd, 1, n_patches, n_batch); + inp = build_rope_2d(ctx0, inp, pos_x, pos_y, hparams.rope_theta, false); + inp = ggml_reshape_3d(ctx0, inp, n_embd, n_patches, n_batch); + cb(inp, "patch_embd_rope", -1); + + inp = ggml_reshape_4d(ctx0, inp, n_embd, n_patches_x, n_patches_y, n_batch); + inp = ggml_cont(ctx0, ggml_permute(ctx0, inp, 2, 0, 1, 3)); + inp = ggml_conv_2d(ctx0, model.mm_0_w, inp, + hparams.n_merge, hparams.n_merge, 0, 0, 1, 1); + + const int n_output_patches = n_patches / (hparams.n_merge * hparams.n_merge); + inp = ggml_cont(ctx0, ggml_permute(ctx0, inp, 1, 2, 0, 3)); + inp = ggml_reshape_3d(ctx0, inp, n_mmproj_embd, n_output_patches, n_batch); + inp = ggml_add(ctx0, inp, model.mm_0_b); + cb(inp, "proj_out", -1); + + ggml_build_forward_expand(gf, inp); + return gf; +} diff --git a/tools/mtmd/mtmd.cpp b/tools/mtmd/mtmd.cpp index 9e2c5f3c92b6..9460f15fe2e7 100644 --- a/tools/mtmd/mtmd.cpp +++ b/tools/mtmd/mtmd.cpp @@ -225,6 +225,7 @@ enum mtmd_pos_type { MTMD_POS_TYPE_NORMAL, // number of positions equals to number of tokens MTMD_POS_TYPE_MROPE, // qwen-vl mrope style, each image takes max(t,h,w) position indexes MTMD_POS_TYPE_HUNYUANVL, // HunyuanVL mrope + BOI/EOI/newline layout with XD-RoPE dim-3 + MTMD_POS_TYPE_SENSENOVA_U1, // U1 uses zero-based H/W and one temporal position per image MTMD_POS_TYPE_CANVAS, // MiniCPM-V 4.7: overview + slices in one chunk, sharing one 2D canvas (see mtmd_image_tokens::canvas_tile_grid) MTMD_POS_TYPE_COUNT, // for validation }; @@ -751,6 +752,12 @@ struct mtmd_context { img_end = "]<]end of image[>["; image_preproc = std::make_unique(ctx_v); } break; + case PROJECTOR_TYPE_SENSENOVA_U1: + { + img_beg = ""; + img_end = ""; + image_preproc = std::make_unique(ctx_v); + } break; case PROJECTOR_TYPE_MUSE_GLIMMER: { img_beg = "<|image_start|>"; @@ -1615,10 +1622,15 @@ struct mtmd_tokenizer { image_tokens->ny = 1; } image_tokens->pos = ctx->pos_type; + // U1 uses M-RoPE, but unlike Qwen its image H/W coordinates are + // zero-based and the whole image occupies one temporal position. + if (ctx->proj_type_v() == PROJECTOR_TYPE_SENSENOVA_U1) { + image_tokens->pos = MTMD_POS_TYPE_SENSENOVA_U1; + } // HunyuanVL wraps the image grid with BOI/EOI and adds one newline per row, // and uses XD-RoPE (dim-3 = image index). Override the position type so that // n_tokens() and mtmd_image_tokens_get_decoder_pos pick the HunyuanVL layout. - if (ctx->proj_type_v() == PROJECTOR_TYPE_HUNYUANVL) { + else if (ctx->proj_type_v() == PROJECTOR_TYPE_HUNYUANVL) { image_tokens->pos = MTMD_POS_TYPE_HUNYUANVL; image_tokens->image_idx = n_images_added; GGML_ASSERT(n_tokens == (size_t)image_tokens->n_tokens()); @@ -2398,6 +2410,7 @@ bool mtmd_decode_use_non_causal(const mtmd_context * ctx, const mtmd_input_chunk } case PROJECTOR_TYPE_GEMMA4UV: case PROJECTOR_TYPE_GEMMA3: + case PROJECTOR_TYPE_SENSENOVA_U1: case PROJECTOR_TYPE_DEEPSEEK4V: return true; default: @@ -2761,6 +2774,13 @@ mtmd_decoder_pos mtmd_image_tokens_get_decoder_pos(const mtmd_image_tokens * ima pos.y = pos_0 + i; pos.z = pos_0 + i; } break; + case MTMD_POS_TYPE_SENSENOVA_U1: + { + pos.t = pos_0; + pos.x = i % image_tokens->nx; + pos.y = i / image_tokens->nx; + pos.z = 0; + } break; case MTMD_POS_TYPE_HUNYUANVL: { // HunyuanVL layout: [BOI] [row0 tokens + newline] ... [row(ny-1) tokens + newline] [EOI] @@ -2813,6 +2833,8 @@ llama_pos mtmd_image_tokens_get_n_pos(const mtmd_image_tokens * image_tokens) { return std::max(image_tokens->nx, image_tokens->ny); case MTMD_POS_TYPE_NORMAL: return image_tokens->n_tokens(); + case MTMD_POS_TYPE_SENSENOVA_U1: + return 1; case MTMD_POS_TYPE_HUNYUANVL: // HunyuanVL: the sequential (dim-0) position advances by the full token count // (includes BOI/EOI and row newline tokens), not by max(nx, ny) From 99fb997184c5598b965a704575ca06ed4ed3ae05 Mon Sep 17 00:00:00 2001 From: Tarek Dakhran Date: Wed, 21 Oct 2026 23:06:56 +0200 Subject: [PATCH 54/69] jinja : fix namespace attributes shadowed by dict methods (#30941) --- common/jinja/value.cpp | 7 ++++++- tests/test-jinja.cpp | 12 ++++++++++++ 2 files changed, 18 insertions(+), 1 deletion(-) diff --git a/common/jinja/value.cpp b/common/jinja/value.cpp index 5ecff52a1861..0840e05b8285 100644 --- a/common/jinja/value.cpp +++ b/common/jinja/value.cpp @@ -425,7 +425,12 @@ const func_builtins & global_builtins() { throw raised_exception("Jinja Exception: " + msg); }}, {"dict", toobject}, - {"namespace", toobject}, + {"namespace", [](const func_args & args) -> value { + // unlike a dict, a namespace has no methods: ns.items is the attribute, as in jinja2 + value out = toobject(args); + cast_val(out)->has_builtins = false; + return out; + }}, {"strftime_now", [](const func_args & args) -> value { args.ensure_vals(); std::string format = args.get_pos(0)->as_string().str(); diff --git a/tests/test-jinja.cpp b/tests/test-jinja.cpp index 327ab365c44e..99552a8f7881 100644 --- a/tests/test-jinja.cpp +++ b/tests/test-jinja.cpp @@ -1299,6 +1299,18 @@ static void test_namespace(testing & t) { json::object(), "3" ); + + test_template(t, "namespace attribute named like a dict method", + "{% set ns = namespace(items=[]) %}{{ 'yes' if ns.items else 'no' }}{% set ns.items = ns.items + [1] %}{{ ns.items | length }}{{ 'yes' if ns.items else 'no' }}", + json::object(), + "no1yes" + ); + + test_template(t, "dict keeps its methods", + "{% set d = dict(items=1) %}{% for k, v in d.items() %}{{ k }}={{ v }}{% endfor %}", + json::object(), + "items=1" + ); } static void test_tests(testing & t) { From a37cc46104eb50900d5ab4a9ba02a0abdac039db Mon Sep 17 00:00:00 2001 From: alexey-rsi Date: Wed, 21 Oct 2026 23:26:22 +0200 Subject: [PATCH 55/69] models : add Flint model (FlintForCausalLM) (#30928) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * Add Flint model (FlintForCausalLM). Assisted by Codex * cleanup * one more --------- Co-authored-by: Sigbjørn Skjæret --- conversion/__init__.py | 1 + conversion/base.py | 5 +- conversion/flint.py | 38 +++++++ convert_hf_to_gguf_update.py | 1 + gguf-py/gguf/constants.py | 29 +++++ gguf-py/gguf/tensor_mapping.py | 1 + src/llama-arch.cpp | 1 + src/llama-arch.h | 1 + src/llama-graph.cpp | 7 +- src/llama-graph.h | 2 +- src/llama-kv-cache.cpp | 5 +- src/llama-model.cpp | 3 + src/llama-vocab.cpp | 12 +- src/llama-vocab.h | 1 + src/models/flint.cpp | 194 +++++++++++++++++++++++++++++++++ src/models/models.h | 13 +++ tests/fusion/MTL.csv | 3 + tests/test-llama-archs.cpp | 9 +- 18 files changed, 319 insertions(+), 7 deletions(-) create mode 100644 conversion/flint.py create mode 100644 src/models/flint.cpp diff --git a/conversion/__init__.py b/conversion/__init__.py index 369ce7afdab5..8921f53e446d 100644 --- a/conversion/__init__.py +++ b/conversion/__init__.py @@ -20,6 +20,7 @@ "NEOChatModel": "sensenova_u1", "AfmoeForCausalLM": "afmoe", "LagunaForCausalLM": "laguna", + "FlintForCausalLM": "flint", "ApertusForCausalLM": "llama", "ArceeForCausalLM": "llama", "ArcticForCausalLM": "arctic", diff --git a/conversion/base.py b/conversion/base.py index 2cbaed700f13..f3bbe7b0ff08 100644 --- a/conversion/base.py +++ b/conversion/base.py @@ -1701,7 +1701,7 @@ def set_gguf_parameters(self): self.gguf_writer.add_expert_group_used_count(n_group_used) logger.info(f"gguf: expert groups used count = {n_group_used}") - if (score_func := self.find_hparam(["score_function", "scoring_func", "score_func", "moe_router_activation", "moe_router_activation_func", "expert_selection_fn", "router_score_func"], optional=True)) is not None: + if (score_func := self.find_hparam(["score_function", "scoring_func", "score_func", "moe_router_activation", "moe_router_activation_func", "expert_selection_fn", "router_score_func", "router_score_function"], optional=True)) is not None: if score_func == "sigmoid": self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SIGMOID) elif score_func == "softmax": @@ -1939,6 +1939,9 @@ def get_vocab_base_pre(self, tokenizer) -> str: if chkhsh == "52df12b4c8d4176e7481aab4b6e8454d1fd0a210a04a574f6d4e067d10e23c3e": # ref: https://huggingface.co/CohereLabs/North-Mini-Code-1.0 res = "cohere2moe" + if chkhsh == "d5420e601e979078c670bc503d9ac411c311fa76b76a76267c31e0d6ba816637": + # ref: https://huggingface.co/RecursiveSI/flint + res = "flint" if chkhsh == "e636dc30a262dcc0d8c323492e32ae2b70728f4df7dfe9737d9f920a282b8aea": # ref: https://huggingface.co/Qwen/Qwen1.5-7B res = "qwen2" diff --git a/conversion/flint.py b/conversion/flint.py new file mode 100644 index 000000000000..a69314a9bcc8 --- /dev/null +++ b/conversion/flint.py @@ -0,0 +1,38 @@ +from __future__ import annotations + +from .base import ModelBase, gguf +from .qwen import Qwen2MoeModel + + +@ModelBase.register("FlintForCausalLM") +@ModelBase.example("RecursiveSI/flint") +class FlintModel(Qwen2MoeModel): + model_arch = gguf.MODEL_ARCH.FLINT + + def set_gguf_parameters(self): + super().set_gguf_parameters() + dense = self.hparams["mlp_only_layers"] + if dense != list(range(len(dense))): + raise ValueError("Flint dense layers must precede the MoE layers") + self.gguf_writer.add_leading_dense_block_count(len(dense)) + self.gguf_writer.add_expert_shared_count( + self.hparams["shared_expert_intermediate_size"] // self.hparams["moe_intermediate_size"] + ) + self.gguf_writer.add_expert_weights_norm(self.hparams["norm_topk_prob"]) + self.gguf_writer.add_expert_weights_scale(1.0) + self.gguf_writer.add_sliding_window(self.hparams["sliding_window"]) + self.gguf_writer.add_sliding_window_pattern([ + kind == "sliding_attention" for kind in self.hparams["layer_types"] + ]) + self.gguf_writer.add_rope_pattern([ + i not in self.hparams["no_rope_layer_indices"] for i in range(self.block_count) + ]) + + def modify_tensors(self, data_torch, name, bid): + if name.endswith(".softmax_offset"): + name += ".weight" + if name.endswith(".gate.expert_bias"): + if not self.hparams["router_expert_bias"]: + return + name += ".bias" + yield from super().modify_tensors(data_torch, name, bid) diff --git a/convert_hf_to_gguf_update.py b/convert_hf_to_gguf_update.py index d3aa3c3807f0..7d8c4fda6412 100755 --- a/convert_hf_to_gguf_update.py +++ b/convert_hf_to_gguf_update.py @@ -101,6 +101,7 @@ class TOKENIZER_TYPE(IntEnum): {"name": "command-r", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/CohereForAI/c4ai-command-r-v01", }, {"name": "tiny_aya", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/CohereLabs/tiny-aya-base", }, {"name": "cohere2moe", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/CohereLabs/North-Mini-Code-1.0", }, + {"name": "flint", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/RecursiveSI/flint", }, {"name": "qwen2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/Qwen/Qwen1.5-7B", }, {"name": "olmo", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/allenai/OLMo-1.7-7B-hf", }, {"name": "dbrx", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/databricks/dbrx-base", }, diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index 619a15fc96d9..51a16ef52b0b 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -577,6 +577,7 @@ class MODEL_ARCH(IntEnum): SENSENOVA_U1 = auto() QWEN3 = auto() QWEN3MOE = auto() + FLINT = auto() QWEN3NEXT = auto() HRM_TEXT = auto() BEAM = auto() @@ -1394,6 +1395,7 @@ class MODEL_TENSOR(IntEnum): MODEL_ARCH.QWEN2VL: "qwen2vl", MODEL_ARCH.SENSENOVA_U1: "sensenova_u1", MODEL_ARCH.QWEN3: "qwen3", + MODEL_ARCH.FLINT: "flint", MODEL_ARCH.QWEN3MOE: "qwen3moe", MODEL_ARCH.QWEN3NEXT: "qwen3next", MODEL_ARCH.HRM_TEXT: "hrm_text", @@ -2974,6 +2976,33 @@ class MODEL_TENSOR(IntEnum): MODEL_TENSOR.FFN_DOWN_EXP, MODEL_TENSOR.FFN_UP_EXP, ], + + MODEL_ARCH.FLINT: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_QKV, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_Q_NORM, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_K_NORM, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE_INP, + MODEL_TENSOR.FFN_GATE_EXP, + MODEL_TENSOR.FFN_DOWN_EXP, + MODEL_TENSOR.FFN_UP_EXP, + MODEL_TENSOR.ATTN_SINKS, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_GATE_SHEXP, + MODEL_TENSOR.FFN_UP_SHEXP, + MODEL_TENSOR.FFN_DOWN_SHEXP, + MODEL_TENSOR.FFN_EXP_PROBS_B, + ], MODEL_ARCH.QWEN3NEXT: [ MODEL_TENSOR.TOKEN_EMBD, MODEL_TENSOR.OUTPUT_NORM, diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py index 3586d65285ca..79c9add78a13 100644 --- a/gguf-py/gguf/tensor_mapping.py +++ b/gguf-py/gguf/tensor_mapping.py @@ -429,6 +429,7 @@ class TensorNameMap: ), MODEL_TENSOR.ATTN_SINKS: ( + "model.layers.{bid}.self_attn.softmax_offset", # flint "model.layers.{bid}.self_attn.sinks", # openai-moe "model.layers.{bid}.self_attn.attention_sink_bias", # mimov2 "model.layers.{bid}.self_attn.learnable_sink_param", # hy-v4 diff --git a/src/llama-arch.cpp b/src/llama-arch.cpp index 1c11536b51c5..62b012c5c3f0 100644 --- a/src/llama-arch.cpp +++ b/src/llama-arch.cpp @@ -35,6 +35,7 @@ static const std::map LLM_ARCH_NAMES = { { LLM_ARCH_QWEN2VL, "qwen2vl" }, { LLM_ARCH_SENSENOVA_U1, "sensenova_u1" }, { LLM_ARCH_QWEN3, "qwen3" }, + { LLM_ARCH_FLINT, "flint" }, { LLM_ARCH_QWEN3MOE, "qwen3moe" }, { LLM_ARCH_QWEN3NEXT, "qwen3next" }, { LLM_ARCH_QWEN3VL, "qwen3vl" }, diff --git a/src/llama-arch.h b/src/llama-arch.h index afceb8ea6f92..6713db6b5906 100644 --- a/src/llama-arch.h +++ b/src/llama-arch.h @@ -41,6 +41,7 @@ enum llm_arch { LLM_ARCH_SENSENOVA_U1, LLM_ARCH_QWEN3, LLM_ARCH_QWEN3MOE, + LLM_ARCH_FLINT, LLM_ARCH_QWEN3NEXT, LLM_ARCH_QWEN3VL, LLM_ARCH_QWEN3VLMOE, diff --git a/src/llama-graph.cpp b/src/llama-graph.cpp index 8c31b513074a..f19368693f6e 100644 --- a/src/llama-graph.cpp +++ b/src/llama-graph.cpp @@ -1684,7 +1684,10 @@ llm_graph_amax_src1 llm_graph_context::build_amax_src1_f16( constexpr float k_ln_2 = 0.6931471805599453f; ggml_tensor * e; - e = ggml_scale(ctx0, amax, 1.0f/32768.0f); + // note: this can technically be: + // e = ggml_scale(ctx0, amax, 1.0f/32768.0f); + // but we leave x32 headroom for the F16 sums of 32 values in Q8 activation blocks + e = ggml_scale(ctx0, amax, 1.0f/1024.0f); e = ggml_log (ctx0, e); e = ggml_scale(ctx0, e, k_log2_e); e = ggml_ceil (ctx0, e); @@ -2505,7 +2508,7 @@ ggml_tensor * llm_graph_context::build_moe_ffn( // ref: https://github.com/ggml-org/llama.cpp/pull/30656#issuecomment-6269597922 llm_graph_amax_src1 amax_src1 = { nullptr, nullptr }; - if (arch == LLM_ARCH_MISTRAL4 || arch == LLM_ARCH_MELLUM) { + if (arch == LLM_ARCH_MISTRAL4 || arch == LLM_ARCH_MELLUM || arch == LLM_ARCH_FLINT) { amax_src1 = build_amax_src1_f16(cur); cur = amax_src1.res; } diff --git a/src/llama-graph.h b/src/llama-graph.h index 9c5f3aea9a5e..a49f5432d2b9 100644 --- a/src/llama-graph.h +++ b/src/llama-graph.h @@ -1122,7 +1122,7 @@ struct llm_graph_context { ggml_tensor * slots = nullptr) const; // rescale `cur` (the src1 of a matmul) by a power of two, computed from its maximum absolute - // when the values already fit in F16, the factors are exactly 1.0f + // keep both individual values and Q8 activation block sums within F16 range llm_graph_amax_src1 build_amax_src1_f16( ggml_tensor * cur) const; diff --git a/src/llama-kv-cache.cpp b/src/llama-kv-cache.cpp index b7dfadef4638..6f0e3f7f796f 100644 --- a/src/llama-kv-cache.cpp +++ b/src/llama-kv-cache.cpp @@ -882,7 +882,10 @@ bool llama_kv_cache::update(llama_context * lctx, bool do_shift, const stream_co LLAMA_LOG_DEBUG("%s: applying K-shift\n", __func__); // apply K-shift if needed - if (hparams.rope_type != LLAMA_ROPE_TYPE_NONE) { + if (hparams.rope_type != LLAMA_ROPE_TYPE_NONE && + std::any_of(layers.begin(), layers.end(), [&](const auto & layer) { + return hparams.has_rope(layer.il); + })) { ggml_backend_sched_reset(sched); auto * res = lctx->get_gf_res_reserve(); diff --git a/src/llama-model.cpp b/src/llama-model.cpp index 3797c567dc47..60199d6d9941 100644 --- a/src/llama-model.cpp +++ b/src/llama-model.cpp @@ -114,6 +114,8 @@ static llama_model * llama_model_mapping(llm_arch arch, const llama_model_params return new llama_model_sensenova_u1(params); case LLM_ARCH_QWEN3: return new llama_model_qwen3(params); + case LLM_ARCH_FLINT: + return new llama_model_flint(params); case LLM_ARCH_QWEN3MOE: return new llama_model_qwen3moe(params); case LLM_ARCH_QWEN3VL: @@ -3571,6 +3573,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) { case LLM_ARCH_QWEN2MOE: case LLM_ARCH_QWEN3: case LLM_ARCH_VLLM_DECISION2: + case LLM_ARCH_FLINT: case LLM_ARCH_QWEN3MOE: case LLM_ARCH_LLADA_MOE: case LLM_ARCH_RND1: diff --git a/src/llama-vocab.cpp b/src/llama-vocab.cpp index a3d5dedef332..05c0f96a29cc 100644 --- a/src/llama-vocab.cpp +++ b/src/llama-vocab.cpp @@ -281,6 +281,13 @@ struct llm_tokenizer_bpe : llm_tokenizer { llm_tokenizer_bpe(const llama_vocab & vocab) { GGML_ASSERT(vocab.get_type() == LLAMA_VOCAB_TYPE_BPE); switch (vocab.get_pre_type()) { + case LLAMA_VOCAB_PRE_TYPE_FLINT: + regex_exprs = { + "\\p{N}{1,3}", + "[一-龥぀-ゟ゠-ヿ]+", + "[^\\r\\n\\p{L}\\p{N}]?[\\p{Lu}\\p{Lt}\\p{Lm}\\p{Lo}\\p{M}]*[\\p{Ll}\\p{Lm}\\p{Lo}\\p{M}]+(?:'[sS]|'[tT]|'[rR][eE]|'[vV][eE]|'[mM]|'[lL][lL]|'[dD])?|[^\\r\\n\\p{L}\\p{N}]?[\\p{Lu}\\p{Lt}\\p{Lm}\\p{Lo}\\p{M}]+[\\p{Ll}\\p{Lm}\\p{Lo}\\p{M}]*(?:'[sS]|'[tT]|'[rR][eE]|'[vV][eE]|'[mM]|'[lL][lL]|'[dD])?| ?[\\p{P}\\p{S}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+", + }; + break; case LLAMA_VOCAB_PRE_TYPE_LLAMA3: regex_exprs = { // original regex from tokenizer.json @@ -2417,6 +2424,9 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) { LLAMA_LOG_WARN("%s: ************************************ \n", __func__); LLAMA_LOG_WARN("%s: \n", __func__); pre_type = LLAMA_VOCAB_PRE_TYPE_DEFAULT; + } else if (tokenizer_pre == "flint") { + pre_type = LLAMA_VOCAB_PRE_TYPE_FLINT; + clean_spaces = false; } else if (tokenizer_pre == "default") { pre_type = LLAMA_VOCAB_PRE_TYPE_DEFAULT; } else if (tokenizer_pre == "minicpm5") { @@ -3147,7 +3157,6 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) { // this is currently determined based on the token text, which is obviously not ideal // ref: https://github.com/ggml-org/llama.cpp/issues/9606 special_eog_ids.clear(); - if (special_fim_pad_id != LLAMA_TOKEN_NULL && special_eog_ids.count(special_fim_pad_id) == 0) { special_eog_ids.insert(special_fim_pad_id); } @@ -3181,6 +3190,7 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) { || t.first == "[EOT]" // Kimi-K2 || t.first == "[EOS]" // Kimi-K2 || t.first == "<|end_of_text|>" + || (pre_type == LLAMA_VOCAB_PRE_TYPE_FLINT && (t.first == "<|user|>" || t.first == "<|observation|>")) || t.first == "" // smoldocling || t.first == "" // gemma4 || t.first == "" // gemma4 diff --git a/src/llama-vocab.h b/src/llama-vocab.h index 415acd22bf40..a87c0de42b33 100644 --- a/src/llama-vocab.h +++ b/src/llama-vocab.h @@ -70,6 +70,7 @@ enum llama_vocab_pre_type { LLAMA_VOCAB_PRE_TYPE_UFAKZEKA = 59, LLAMA_VOCAB_PRE_TYPE_MMBERT = 60, LLAMA_VOCAB_PRE_TYPE_K2_HORIZON = 61, + LLAMA_VOCAB_PRE_TYPE_FLINT = 62, }; struct LLM_KV; diff --git a/src/models/flint.cpp b/src/models/flint.cpp new file mode 100644 index 000000000000..e7e6f6b792df --- /dev/null +++ b/src/models/flint.cpp @@ -0,0 +1,194 @@ +#include "models.h" + +void llama_model_flint::load_arch_hparams(llama_model_loader & ml) { + ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); + ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead); + ml.get_key_or_arr(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp_arr, hparams.n_layer_all); + ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared); + ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale, false); + ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM, hparams.expert_weights_norm); + ml.get_key(LLM_KV_EXPERT_GATING_FUNC, hparams.expert_gating_func); + + ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp); + ml.get_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, hparams.is_swa_impl); + ml.get_arr(LLM_KV_ATTENTION_ROPE_PATTERN, hparams.rope_pattern); + ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa); + hparams.swa_type = LLAMA_SWA_TYPE_STANDARD; + type = LLM_TYPE_UNKNOWN; +} + +void llama_model_flint::load_arch_tensors(llama_model_loader &) { + LLAMA_LOAD_LOCALS; + + const int64_t n_ff_exp = hparams.n_ff_exp(); + + tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); + + output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); + output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0); + + for (int i = 0; i < n_layer; ++i) { + auto & layer = layers[i]; + + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); + + create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, 0); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0); + + layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, 0); + layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, 0); + + layer.attn_sinks = create_tensor(tn(LLM_TENSOR_ATTN_SINKS, "weight", i), {n_head}, 0); + + layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0); + + if (i < (int) hparams.n_layer_dense_lead) { + layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); + } else { + layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0); + layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, TENSOR_NOT_REQUIRED); + + if (n_expert == 0) { + throw std::runtime_error("n_expert must be > 0"); + } + if (n_expert_used == 0) { + throw std::runtime_error("n_expert_used must be > 0"); + } + + // MoE branch + layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert}, 0); + layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert}, 0); + layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert}, 0); + + // Shared expert branch + layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, hparams.n_ff_shexp}, 0); + layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), { hparams.n_ff_shexp, n_embd}, 0); + layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), {n_embd, hparams.n_ff_shexp}, 0); + } + } +} + +std::unique_ptr llama_model_flint::build_arch_graph(const llm_graph_params & params) const { + return std::make_unique(*this, params); +} + +llama_model_flint::graph::graph(const llama_model & model, const llm_graph_params & params) : + llm_graph_context(params) { + const int64_t n_embd_head = hparams.n_embd_head_v(); + + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); + + ggml_tensor * cur; + ggml_tensor * inpL; + + inpL = build_inp_embd(model.tok_embd); + + // inp_pos - contains the positions + ggml_tensor * inp_pos = build_inp_pos(); + + auto * inp_attn = build_attn_inp_kv_iswa(); + + ggml_tensor * inp_out_ids = build_inp_out_ids(); + + for (int il = 0; il < n_layer; ++il) { + res->t_layer_inp[il] = inpL; + ggml_tensor * inpSA = inpL; + + // norm + cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il); + cb(cur, "attn_norm", il); + + // self_attention + { + // compute Q and K and RoPE them + auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur, + n_embd_head, n_head, n_head_kv, il); + + Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, nullptr, LLM_NORM_RMS, il); + Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, nullptr, LLM_NORM_RMS, il); + if (hparams.has_rope(il)) { + Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, + freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow); + Kcur = ggml_rope_ext(ctx0, Kcur, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, + freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow); + } + cb(Qcur, "Qcur", il); + cb(Kcur, "Kcur", il); + cb(Vcur, "Vcur", il); + + cur = build_attn(inp_attn, + model.layers[il].wo, model.layers[il].wo_b, model.layers[il].wo_s, + Qcur, Kcur, Vcur, nullptr, model.layers[il].attn_sinks, nullptr, 1.0f / sqrtf(float(n_embd_head)), il); + } + if (il == n_layer - 1 && inp_out_ids) { + cur = ggml_get_rows(ctx0, cur, inp_out_ids); + inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); + } + ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); + cb(ffn_inp, "ffn_inp", il); + + // MoE branch + cur = build_norm(ffn_inp, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il); + cb(cur, "ffn_norm", il); + + if ((uint32_t) il < hparams.n_layer_dense_lead) { + cur = build_ffn(cur, + model.layers[il].ffn_up, NULL, model.layers[il].ffn_up_s, + model.layers[il].ffn_gate, NULL, model.layers[il].ffn_gate_s, + model.layers[il].ffn_down, NULL, model.layers[il].ffn_down_s, + NULL, LLM_FFN_SILU, LLM_FFN_PAR, il); + cb(cur, "ffn_out", il); + } else { + ggml_tensor * moe_out = build_moe_ffn(cur, + model.layers[il].ffn_gate_inp, + model.layers[il].ffn_up_exps, + model.layers[il].ffn_gate_exps, + model.layers[il].ffn_down_exps, + model.layers[il].ffn_exp_probs_b, + n_expert, n_expert_used, + LLM_FFN_SILU, hparams.expert_weights_norm, + hparams.expert_weights_scale, + (llama_expert_gating_func_type) hparams.expert_gating_func, + il, nullptr, nullptr, + model.layers[il].ffn_up_exps_s, + model.layers[il].ffn_gate_exps_s, + model.layers[il].ffn_down_exps_s); + cb(moe_out, "ffn_moe_out", il); + + { + ggml_tensor * ffn_shexp = + build_ffn(cur, + model.layers[il].ffn_up_shexp, NULL, model.layers[il].ffn_up_shexp_s, + model.layers[il].ffn_gate_shexp, NULL, model.layers[il].ffn_gate_shexp_s, + model.layers[il].ffn_down_shexp, NULL, model.layers[il].ffn_down_shexp_s, + NULL, LLM_FFN_SILU, LLM_FFN_PAR, il); + cb(ffn_shexp, "ffn_shexp", il); + + cur = ggml_add(ctx0, moe_out, ffn_shexp); + cb(cur, "ffn_out", il); + } + } + cur = ggml_add(ctx0, cur, ffn_inp); + + cur = build_cvec(cur, il); + cb(cur, "l_out", il); + + // input for next layer + inpL = cur; + } + cur = inpL; + + cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1); + + cb(cur, "result_norm", -1); + res->t_embd = cur; + + // lm_head + cur = build_lora_mm(model.output, cur, model.output_s); + + cb(cur, "result_output", -1); + res->t_logits = cur; +} diff --git a/src/models/models.h b/src/models/models.h index 47b0203da192..bdcc1a4faef9 100644 --- a/src/models/models.h +++ b/src/models/models.h @@ -693,6 +693,19 @@ struct llama_model_qwen3moe : public llama_model_base { }; +struct llama_model_flint : public llama_model_base { + llama_model_flint(const struct llama_model_params & params) : llama_model_base(params) {} + void load_arch_hparams(llama_model_loader & ml) override; + void load_arch_tensors(llama_model_loader & ml) override; + + struct graph : public llm_graph_context { + graph(const llama_model & model, const llm_graph_params & params); + }; + + std::unique_ptr build_arch_graph(const llm_graph_params & params) const override; +}; + + struct llama_model_qwen3vl : public llama_model_base { llama_model_qwen3vl(const struct llama_model_params & params) : llama_model_base(params) {} void load_arch_hparams(llama_model_loader & ml) override; diff --git a/tests/fusion/MTL.csv b/tests/fusion/MTL.csv index e6cd43a82b9b..56044b350e45 100644 --- a/tests/fusion/MTL.csv +++ b/tests/fusion/MTL.csv @@ -71,6 +71,9 @@ falcon ,0 ,any ,ADD+ADD , 2 falcon ,0 ,any ,NORM+MUL+ADD , 5 falcon-h1 ,0 ,any ,ADD+ADD , 2 falcon-h1 ,0 ,any ,RMS_NORM+MUL , 9 +flint ,0 ,any ,ADD+ADD , 1 +flint ,0 ,any ,MUL+ADD , 2 +flint ,0 ,any ,RMS_NORM+MUL , 9 gemma ,0 ,any ,RMS_NORM+MUL , 5 gemma2 ,0 ,any ,RMS_NORM+MUL , 5 gemma2 ,0 ,any ,RMS_NORM+MUL+ADD , 4 diff --git a/tests/test-llama-archs.cpp b/tests/test-llama-archs.cpp index c86b1315a660..1e0b75511241 100644 --- a/tests/test-llama-archs.cpp +++ b/tests/test-llama-archs.cpp @@ -361,13 +361,16 @@ static gguf_context_ptr get_gguf_ctx(const llm_arch arch, const bool moe) { ms.add_kv(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, uint32_t(5)); } else if (arch == LLM_ARCH_COHERE2MOE || arch == LLM_ARCH_MIMO2 || arch == LLM_ARCH_STEP35 || arch == LLM_ARCH_SPARK2_5 || arch == LLM_ARCH_MUSE_GLIMMER || arch == LLM_ARCH_MUSE_SPARK || arch == LLM_ARCH_GRANITE_SWA || arch == LLM_ARCH_DOTS3NOTE || - arch == LLM_ARCH_KOLIBRI1 || arch == LLM_ARCH_MAPLE) { + arch == LLM_ARCH_KOLIBRI1 || arch == LLM_ARCH_MAPLE || arch == LLM_ARCH_FLINT) { std::vector pattern; pattern.reserve(n_layer); for (uint32_t il = 0; il < n_layer; il++) { pattern.push_back(il % 2); } ms.add_kv(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, pattern); + if (arch == LLM_ARCH_FLINT) { + ms.add_kv(LLM_KV_ATTENTION_ROPE_PATTERN, pattern); + } } else { ms.add_kv(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, uint32_t(2)); } @@ -525,6 +528,9 @@ static gguf_context_ptr get_gguf_ctx(const llm_arch arch, const bool moe) { ms.add_kv(LLM_KV_EXPERT_GATING_FUNC, gating_func); // sigmoid : softmax : sqrtsoftplus ms.add_kv(LLM_KV_EXPERT_GROUP_SCALE, 1.0f); ms.add_kv(LLM_KV_EXPERTS_PER_GROUP, uint32_t(1)); + if (arch == LLM_ARCH_FLINT) { + ms.add_kv(LLM_KV_EXPERT_WEIGHTS_NORM, true); + } if (arch == LLM_ARCH_K2_HORIZON) { ms.add_kv(LLM_KV_ATTENTION_VALUE_EXPERT_COUNT, uint32_t(2)); ms.add_kv(LLM_KV_ATTENTION_VALUE_EXPERT_USED_COUNT, uint32_t(2)); @@ -754,6 +760,7 @@ static bool check_causal_attn_toggle( static bool moe_mandatory(const llm_arch arch) { switch (arch) { + case LLM_ARCH_FLINT: case LLM_ARCH_LLAMA4: case LLM_ARCH_MAION_CODER: case LLM_ARCH_COHERE2MOE: From c7fc5094179bf8a9c73c18593db714f8da41a402 Mon Sep 17 00:00:00 2001 From: kurquhar Date: Wed, 21 Oct 2026 18:02:50 -0700 Subject: [PATCH 56/69] opencl: add F32-to-I32 ARGMAX (#30897) * opencl: add F32-to-I32 ARGMAX Assisted-by: OpenCode * opencl: init to 0 to be consistent with cpu --------- Co-authored-by: Li He --- ggml/src/ggml-opencl/CMakeLists.txt | 1 + ggml/src/ggml-opencl/ggml-opencl.cpp | 72 +++++++++++++++++++++++++- ggml/src/ggml-opencl/kernels/argmax.cl | 55 ++++++++++++++++++++ 3 files changed, 126 insertions(+), 2 deletions(-) create mode 100644 ggml/src/ggml-opencl/kernels/argmax.cl diff --git a/ggml/src/ggml-opencl/CMakeLists.txt b/ggml/src/ggml-opencl/CMakeLists.txt index 917578c9ff0c..977013b94e3c 100644 --- a/ggml/src/ggml-opencl/CMakeLists.txt +++ b/ggml/src/ggml-opencl/CMakeLists.txt @@ -64,6 +64,7 @@ set(GGML_OPENCL_KERNELS add add_id moe_add_id_glu + argmax argsort tri fill diff --git a/ggml/src/ggml-opencl/ggml-opencl.cpp b/ggml/src/ggml-opencl/ggml-opencl.cpp index 2312c6c7cd5c..d56b3488dd07 100644 --- a/ggml/src/ggml-opencl/ggml-opencl.cpp +++ b/ggml/src/ggml-opencl/ggml-opencl.cpp @@ -646,8 +646,8 @@ struct ggml_backend_opencl_context { ggml_cl_version platform_version; ggml_cl_version opencl_c_version; - // argsort is loaded in supports_op because its availability depends on how - // many workgroups are allowed, which requires kernel compilation. + // argmax and argsort are loaded in supports_op; argsort availability depends on the compiled kernel's workgroup limit. + bool kernels_loaded_argmax = false; bool kernels_loaded_argsort = false; // rest of the kernels are currently always loaded in alloc_buffer. bool kernels_loaded = false; @@ -972,6 +972,7 @@ struct ggml_backend_opencl_context { cl_kernel kernel_solve_tri_f32; cl_kernel kernel_im2col_f32, kernel_im2col_f16; cl_kernel kernel_im2col_3d_f32, kernel_im2col_3d_f16; + cl_kernel kernel_argmax_f32_i32; cl_kernel kernel_argsort_f32_i32; cl_kernel kernel_sum_rows_f32, kernel_sum_rows_f32_4; cl_kernel kernel_cumsum_blk, kernel_cumsum_add; @@ -1485,6 +1486,27 @@ static cl_program build_program_from_binary(cl_context ctx, cl_device_id dev, co return p; } +static void load_cl_kernels_argmax(ggml_backend_opencl_context *backend_ctx) { + auto opencl_c_std = + std::string("CL") + std::to_string(backend_ctx->opencl_c_version.major) + "." + std::to_string(backend_ctx->opencl_c_version.minor); + std::string compile_opts = std::string("-cl-std=") + opencl_c_std; + + if (!backend_ctx->kernels_loaded_argmax) { + cl_int err; +#ifdef GGML_OPENCL_EMBED_KERNELS + const std::string kernel_src { + #include "argmax.cl.h" + }; +#else + const std::string kernel_src = read_file("argmax.cl"); +#endif + cl_program prog = build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); + CL_CHECK((backend_ctx->kernel_argmax_f32_i32 = clCreateKernel(prog, "kernel_argmax_f32_i32", &err), err)); + CL_CHECK(clReleaseProgram(prog)); + backend_ctx->kernels_loaded_argmax = true; + } +} + static void load_cl_kernels_argsort(ggml_backend_opencl_context *backend_ctx) { // compiler options for general kernels auto opencl_c_std = @@ -9693,6 +9715,11 @@ static bool ggml_opencl_supports_op(ggml_backend_dev_t dev, const struct ggml_te return true; case GGML_OP_IM2COL_3D: return true; + case GGML_OP_ARGMAX: + load_cl_kernels_argmax(backend_ctx); + return op->src[0]->type == GGML_TYPE_F32 && op->type == GGML_TYPE_I32 && + ggml_is_contiguous(op->src[0]) && ggml_is_contiguous(op) && + backend_ctx->get_kernel_workgroup_size(backend_ctx->kernel_argmax_f32_i32) >= 64; case GGML_OP_ARGSORT: { load_cl_kernels_argsort(backend_ctx); @@ -30164,6 +30191,41 @@ static void ggml_cl_im2col_3d(ggml_backend_t backend, const ggml_tensor * src0, backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst); } +static void ggml_cl_argmax(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_ASSERT(src0); + GGML_ASSERT(src0->extra); + GGML_ASSERT(dst); + GGML_ASSERT(dst->extra); + GGML_UNUSED(src1); + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT(dst->type == GGML_TYPE_I32); + GGML_ASSERT(ggml_is_contiguous(src0)); + GGML_ASSERT(ggml_is_contiguous(dst)); + + ggml_backend_opencl_context * backend_ctx = (ggml_backend_opencl_context *) backend->context; + ggml_tensor_extra_cl * extra0 = (ggml_tensor_extra_cl *) src0->extra; + ggml_tensor_extra_cl * extrad = (ggml_tensor_extra_cl *) dst->extra; + + const cl_ulong offset0 = extra0->offset + src0->view_offs; + const cl_ulong offsetd = extrad->offset + dst->view_offs; + const int ne00 = src0->ne[0]; + const int nrows = ggml_nrows(src0); + constexpr int nth = 64; + + cl_kernel kernel = backend_ctx->kernel_argmax_f32_i32; + CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra0->data_device)); + CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_ulong), &offset0)); + CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extrad->data_device)); + CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_ulong), &offsetd)); + CL_CHECK(clSetKernelArg(kernel, 4, sizeof(int), &ne00)); + CL_CHECK(clSetKernelArg(kernel, 5, sizeof(int), &nrows)); + + size_t global_work_size[] = { (size_t) nth, (size_t) CEIL_DIV(nrows, 2), 1 }; + size_t local_work_size[] = { (size_t) nth, 1, 1 }; + backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst); +} + static void ggml_cl_argsort(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_ASSERT(src0); GGML_ASSERT(src0->extra); @@ -31071,6 +31133,12 @@ bool ggml_cl_compute_forward(ggml_backend_t backend, struct ggml_tensor * tensor } func = ggml_cl_im2col_3d; break; + case GGML_OP_ARGMAX: + if (!any_on_device) { + return false; + } + func = ggml_cl_argmax; + break; case GGML_OP_ARGSORT: if (!any_on_device) { return false; diff --git a/ggml/src/ggml-opencl/kernels/argmax.cl b/ggml/src/ggml-opencl/kernels/argmax.cl new file mode 100644 index 000000000000..24ed0e68bcd1 --- /dev/null +++ b/ggml/src/ggml-opencl/kernels/argmax.cl @@ -0,0 +1,55 @@ +#define ARGMAX_LANES_PER_ROW 32 +#define ARGMAX_ROWS_PER_GROUP 2 +#define ARGMAX_BLOCK_SIZE (ARGMAX_LANES_PER_ROW * ARGMAX_ROWS_PER_GROUP) + +kernel void kernel_argmax_f32_i32( + global const char * src0, + ulong offset0, + global char * dst, + ulong offsetd, + int ne00, + int nrows) { + const int local_id = get_local_id(0); + const int col = local_id % ARGMAX_LANES_PER_ROW; + const int row = get_group_id(1) * ARGMAX_ROWS_PER_GROUP + local_id / ARGMAX_LANES_PER_ROW; + + float max_value = -INFINITY; + int max_index = 0; + + if (row < nrows) { + global const float * src_row = (global const float *) (src0 + offset0) + row * ne00; + for (int i = col; i < ne00; i += ARGMAX_LANES_PER_ROW) { + const float value = src_row[i]; + if (value > max_value || (value == max_value && i > max_index)) { + max_value = value; + max_index = i; + } + } + } + + local float max_values[ARGMAX_BLOCK_SIZE]; + local int max_indices[ARGMAX_BLOCK_SIZE]; + + max_values[local_id] = max_value; + max_indices[local_id] = max_index; + + barrier(CLK_LOCAL_MEM_FENCE); + + const int row_offset = local_id / ARGMAX_LANES_PER_ROW * ARGMAX_LANES_PER_ROW; + for (int stride = ARGMAX_LANES_PER_ROW / 2; stride > 0; stride /= 2) { + if (col < stride) { + const float value = max_values[row_offset + col + stride]; + const int index = max_indices[row_offset + col + stride]; + if (value > max_values[row_offset + col] || (value == max_values[row_offset + col] && index > max_indices[row_offset + col])) { + max_values[row_offset + col] = value; + max_indices[row_offset + col] = index; + } + } + barrier(CLK_LOCAL_MEM_FENCE); + } + + if (col == 0 && row < nrows) { + global int * dst_row = (global int *) (dst + offsetd) + row; + dst_row[0] = max_indices[row_offset]; + } +} From a6b83d426f90d65be8f0c44145437cf42d10e442 Mon Sep 17 00:00:00 2001 From: Aleksander Grygier Date: Thu, 22 Oct 2026 07:11:45 +0200 Subject: [PATCH 57/69] ui : key draft sidecar badges by model instead of repo (#30919) Two quants of one repo pulled each other's draft: the badge read every --model-draft arg of the repo, so each row showed both drafts. Assisted-by: DeepSeek-V4.1-Flash-1017 --- .../ui/src/lib/stores/models/index.svelte.ts | 3 +- .../ui/src/lib/stores/models/status.svelte.ts | 31 ++++++----- .../models-sidecar-badges.svelte.test.ts | 55 +++++++++++-------- 3 files changed, 49 insertions(+), 40 deletions(-) diff --git a/tools/ui/src/lib/stores/models/index.svelte.ts b/tools/ui/src/lib/stores/models/index.svelte.ts index 38d734f4ee6e..3008eb521416 100644 --- a/tools/ui/src/lib/stores/models/index.svelte.ts +++ b/tools/ui/src/lib/stores/models/index.svelte.ts @@ -578,7 +578,6 @@ class ModelsStore implements ModelPropsHost, ModelStatusHost { const displayNameSource = details?.name && details.name.trim().length > 0 ? details.name : item.id; const modelId = details?.model || item.id; - const repo = repoOf(modelId); return { aliases: item.aliases ?? [], @@ -594,7 +593,7 @@ class ModelsStore implements ModelPropsHost, ModelStatusHost { : undefined), description: details?.description, details: details?.details, - draftSidecars: this._status.getDraftSidecars(repo), + draftSidecars: this._status.getDraftSidecars(modelId), id: item.id, meta: item.meta ?? null, modalities: this.props.buildArchitectureModalities(item.architecture), diff --git a/tools/ui/src/lib/stores/models/status.svelte.ts b/tools/ui/src/lib/stores/models/status.svelte.ts index 4c00adbd7800..a2e3b275220c 100644 --- a/tools/ui/src/lib/stores/models/status.svelte.ts +++ b/tools/ui/src/lib/stores/models/status.svelte.ts @@ -97,10 +97,11 @@ export class ModelStatusManager { /** Sidecar kinds each in-flight or paused download pulls with its model. */ private downloadSidecars = new SvelteMap(); /** - * Draft sidecars the registered models pull with them, keyed by the repo of the model - * that pulls them: the router records them in the model's `--model-draft` args. + * Draft sidecars the registered models pull with them, keyed by the model that pulls + * them: the router records them in the model's `--model-draft` args, and each quant + * of a repo can pull a different draft. */ - private draftSidecarsByRepo = $derived.by(() => { + private draftSidecarsByModel = $derived.by(() => { const result = new SvelteMap(); for (const m of this.host.routerModels) { @@ -108,8 +109,6 @@ export class ModelStatusManager { if (!args) continue; - const baseRepo = repoOf(m.id); - for (let i = 0; i < args.length - 1; i++) { if (args[i] !== CLI_FLAGS.MODEL_DRAFT && args[i] !== CLI_FLAGS.MODEL_DRAFT_SHORT) { continue; @@ -128,7 +127,7 @@ export class ModelStatusManager { quant: meta.quant, repo: parsed.repo }; - const kinds = result.get(baseRepo); + const kinds = result.get(m.id); if (kinds) { if ( @@ -139,7 +138,7 @@ export class ModelStatusManager { kinds.push(badge); } } else { - result.set(baseRepo, [badge]); + result.set(m.id, [badge]); } } } @@ -404,17 +403,19 @@ export class ModelStatusManager { } /** - * Draft sidecars a repo pulls with its model, e.g. `mtp` at `Q8_0`: the - * router records loaded models' sidecars in their `--model-draft` args, and - * lists the sidecars each cached model has on disk. + * Draft sidecars a model pulls with it, e.g. `mtp` at `Q8_0`: the router + * records each model's sidecars in its `--model-draft` args, and lists the + * sidecars each cached repo has on disk. */ - getDraftSidecars(repoId: string): ModelSidecarBadge[] { - // one badge per kind: the args know the quant, the listing carries kinds only, - // and a draft still coming down is pending rather than on disk - const merged = [...(this.draftSidecarsByRepo.get(repoId) ?? [])]; + getDraftSidecars(modelId: string): ModelSidecarBadge[] { + const repo = repoOf(modelId); + // one badge per kind: the args know the quant of this model's draft, the + // listing carries repo-wide kinds only, and a draft still coming down is + // pending rather than on disk + const merged = [...(this.draftSidecarsByModel.get(modelId) ?? [])]; for (const source of [this.listedSidecarsByRepo, this.pendingSidecarsByRepo]) { - for (const badge of source.get(repoId) ?? []) { + for (const badge of source.get(repo) ?? []) { if (!merged.some((b) => b.kind === badge.kind && b.repo === badge.repo)) { merged.push(badge); } diff --git a/tools/ui/tests/client/models-sidecar-badges.svelte.test.ts b/tools/ui/tests/client/models-sidecar-badges.svelte.test.ts index a5754abc1f4b..03a402b74d51 100644 --- a/tools/ui/tests/client/models-sidecar-badges.svelte.test.ts +++ b/tools/ui/tests/client/models-sidecar-badges.svelte.test.ts @@ -1,6 +1,7 @@ -// Guards the draft badges a repo row shows: a loaded model records its draft in -// the `--model-draft` args, which know the file's quant, and the listing reports -// the kinds of every cached model, which do not. The row shows each kind once. +// Guards the draft badges a model row shows: a registered model records its draft +// in its `--model-draft` args, which know the file's quant, and the listing reports +// the kinds every cached model of the repo has on disk, which do not. The row shows +// each kind once, and two quants of one repo keep their own drafts apart. import { ModelAuxSidecar, ModelDraftSidecar, ServerModelStatus } from '$lib/enums'; import { modelsStore } from '$lib/stores'; @@ -9,6 +10,11 @@ import { beforeEach, describe, expect, it } from 'vitest'; const REPO = 'ggml-org/Qwen3.8-Flash-Next-GGUF'; +/** Draft file of a cached model, as a preset records it in `--model-draft`. */ +function draftPath(quant: string): string { + return `/cache/models--ggml-org--Qwen3.8-Flash-Next-GGUF/snapshots/${'a'.repeat(40)}/mtp-Qwen3.8-Flash-Next-${quant}.gguf`; +} + /** A router entry; `args` stand in for the args of a loaded instance. */ function entry( id: string, @@ -36,18 +42,28 @@ describe('draft sidecar badges', () => { it('shows one badge for a draft the args and the listing both report', () => { modelsStore.routerModels = [ entry( - `${REPO}:IQ4_NL`, - [], - [ - '--model-draft', - `/cache/models--ggml-org--Qwen3.8-Flash-Next-GGUF/snapshots/${'a'.repeat(40)}/mtp-Qwen3.8-Flash-Next-Q8_0.gguf` - ] - ), - entry(`${REPO}:Q8_0`, [ModelAuxSidecar.MMPROJ, ModelDraftSidecar.MTP]) + `${REPO}:Q8_0`, + [ModelAuxSidecar.MMPROJ, ModelDraftSidecar.MTP], + ['--model-draft', draftPath('Q8_0')] + ) ]; // the args badge wins: it knows the quant the listing does not - expect(modelsStore.status.getDraftSidecars(REPO)).toEqual([ + expect(modelsStore.status.getDraftSidecars(`${REPO}:Q8_0`)).toEqual([ + { kind: ModelDraftSidecar.MTP, quant: 'Q8_0', repo: REPO } + ]); + }); + + it('gives each quant of a repo the draft it pulls itself', () => { + modelsStore.routerModels = [ + entry(`${REPO}:IQ4_NL`, [], ['--model-draft', draftPath('Q4_0')]), + entry(`${REPO}:Q8_0`, [], ['--model-draft', draftPath('Q8_0')]) + ]; + + expect(modelsStore.status.getDraftSidecars(`${REPO}:IQ4_NL`)).toEqual([ + { kind: ModelDraftSidecar.MTP, quant: 'Q4_0', repo: REPO } + ]); + expect(modelsStore.status.getDraftSidecars(`${REPO}:Q8_0`)).toEqual([ { kind: ModelDraftSidecar.MTP, quant: 'Q8_0', repo: REPO } ]); }); @@ -58,7 +74,7 @@ describe('draft sidecar badges', () => { entry(`${REPO}:Q8_0`, [ModelDraftSidecar.MTP]) ]; - expect(modelsStore.status.getDraftSidecars(REPO)).toEqual([ + expect(modelsStore.status.getDraftSidecars(`${REPO}:IQ4_NL`)).toEqual([ { kind: ModelDraftSidecar.MTP, quant: null, repo: REPO } ]); }); @@ -74,7 +90,7 @@ describe('draft sidecar badges', () => { ]; // the kinds are not on disk yet, so the row reads them as in flight - expect(modelsStore.status.getDraftSidecars(REPO)).toEqual([ + expect(modelsStore.status.getDraftSidecars(`${REPO}:Q4_0`)).toEqual([ { kind: ModelDraftSidecar.MTP, pending: true, quant: null, repo: REPO } ]); }); @@ -82,17 +98,10 @@ describe('draft sidecar badges', () => { it('keeps the on-disk badge of a kind a download also pulls', () => { modelsStore.routerModels = [ entry(`${REPO}:IQ4_NL`, [ModelDraftSidecar.MTP], [], ServerModelStatus.INCOMPLETE), - entry( - `${REPO}:Q8_0`, - [], - [ - '--model-draft', - `/cache/models--ggml-org--Qwen3.8-Flash-Next-GGUF/snapshots/${'a'.repeat(40)}/mtp-Qwen3.8-Flash-Next-Q8_0.gguf` - ] - ) + entry(`${REPO}:Q8_0`, [], ['--model-draft', draftPath('Q8_0')]) ]; - expect(modelsStore.status.getDraftSidecars(REPO)).toEqual([ + expect(modelsStore.status.getDraftSidecars(`${REPO}:Q8_0`)).toEqual([ { kind: ModelDraftSidecar.MTP, quant: 'Q8_0', repo: REPO } ]); }); From 0682a576a772de436caa5d1c1e94036486d6ebd0 Mon Sep 17 00:00:00 2001 From: Hoang Vien Duy Date: Thu, 22 Oct 2026 12:12:17 +0700 Subject: [PATCH 58/69] lora : support prism.hadamard folded weights (#30910) * lora : support prism.hadamard folded weights * tests : remove LoRA test from test-llama-archs --- src/llama-adapter.cpp | 7 ++++--- src/llama-graph.cpp | 12 +++++++----- 2 files changed, 11 insertions(+), 8 deletions(-) diff --git a/src/llama-adapter.cpp b/src/llama-adapter.cpp index 5b342acf604d..824601ed6f6a 100644 --- a/src/llama-adapter.cpp +++ b/src/llama-adapter.cpp @@ -334,9 +334,10 @@ static void llama_adapter_lora_init_impl(llama_model & model, FILE * file, llama if (!model_tensor) { throw std::runtime_error("LoRA tensor '" + name + "' does not exist in base model (hint: maybe wrong base model?)"); } - // the LoRA delta of a Hadamard-folded weight reads the transformed input, so the result is wrong - if (model.hdmd.weight_blocks.count(name) || model.hdmd.inverse_blocks.count(name)) { - throw std::runtime_error("LoRA tensor '" + name + "' targets a prism.hadamard folded weight, which is not supported"); + // build_lora_mm feeds a folded weight's LoRA delta the primal input, so folded matmul weights are fine; + // a latent lookup table (token_embd, also the head when the output is tied) stores rotated rows, refuse it + if (model.hdmd.inverse_blocks.count(name)) { + throw std::runtime_error("LoRA tensor '" + name + "' targets a prism.hadamard latent lookup table, which is not supported"); } auto * buft = ggml_backend_buffer_get_type(model_tensor->buffer); diff --git a/src/llama-graph.cpp b/src/llama-graph.cpp index f19368693f6e..415dff33e9d3 100644 --- a/src/llama-graph.cpp +++ b/src/llama-graph.cpp @@ -1630,8 +1630,9 @@ ggml_tensor * llm_graph_context::build_lora_mm( ggml_tensor * w, ggml_tensor * cur, ggml_tensor * w_s) const { - cur = build_hadamard_input(w, cur); - ggml_tensor * res = ggml_mul_mat(ctx0, w, cur); + // a Hadamard-folded weight reads the transformed input; a LoRA delta lives in the primal basis and reads `cur` + ggml_tensor * cur_mm = build_hadamard_input(w, cur); + ggml_tensor * res = ggml_mul_mat(ctx0, w, cur_mm); if (prec_policy) { prec_policy->apply(res); @@ -1709,11 +1710,12 @@ ggml_tensor * llm_graph_context::build_lora_mm_id( ggml_tensor * ids, ggml_tensor * w_s, ggml_tensor * slots) const { - cur = build_hadamard_input(w, cur); + // as in build_lora_mm: the folded weight reads the transformed input, the LoRA delta reads `cur` + ggml_tensor * cur_mm = build_hadamard_input(w, cur); // the experts in the MoE cache are selected by their slots ggml_tensor * res = slots == nullptr ? - ggml_mul_mat_id(ctx0, w, cur, ids) : - ggml_mul_mat_id(ctx0, moe_cache->get_experts(w), cur, slots); + ggml_mul_mat_id(ctx0, w, cur_mm, ids) : + ggml_mul_mat_id(ctx0, moe_cache->get_experts(w), cur_mm, slots); if (prec_policy) { prec_policy->apply(res); From 84746c5c94c229af4bd90070faba495cb07c3278 Mon Sep 17 00:00:00 2001 From: Aleksander Grygier Date: Thu, 22 Oct 2026 07:14:47 +0200 Subject: [PATCH 59/69] ui: Add "Show tool call in progress" setting (#30681) * feat(ui): add Show tool call in progress display setting Add the showToolCallInProgress key and its checkbox to Settings > Display, right next to its "always show" counterpart. Upstream dropped a setting of this exact name in 43b5e63589, folding it into alwaysShowToolCallContent because the old name promised expansion that outlives the call; this brings back the narrower, in-progress-only meaning. The entry is registry-driven, so SETTING_CONFIG_DEFAULT, the dialog field and the ui_settings admin baseline pick it up without extra wiring. It ships enabled, mirroring Show thought in progress, and the always-show help text is reworded so the two toggles read as a pair rather than contradicting each other. Assisted by: llama-ui (ggml-org/Qwen3.8-Flash-Next:Q8_0) * feat(ui): expand tool call blocks while they are in progress Drive the default expansion of TOOL_CALL_STREAMING and TOOL_CALL_PENDING from showToolCallInProgress and leave TOOL_CALL, the terminal state, to alwaysShowToolCallContent. A finished call now folds back on its own once its result lands, which is exactly how the reasoning block behaves when the thought completes. Manual toggling keeps precedence over both defaults in either direction: isExpanded() only consults the settings while expandedStates holds no entry for the section. Client tests pin the truth table for the two toggles, including the automatic fold-back and both manual overrides. Assisted by: llama-ui (ggml-org/Qwen3.8-Flash-Next:Q8_0) * fix(ui): keep running tool calls expanded A call the agentic loop reports as running now outranks its section type. exec_shell_command persists an empty result before reading any output, so a live call is already TOOL_CALL and folded back, which hid the streaming output the setting is meant to show. Move the expansion default to isSectionExpandedByDefault so the running-id rule is covered by a truth table, and test the pending -> finished transition instead of only the finished state: a block that only ever got mounted finished cannot prove it folds back on its own. Assisted by: llama-ui (ggml-org/Qwen3.8-Flash-Next:Q8_0) * feat(ui): stream the exec command from the first character The exec parser read its args strictly, so while the model was still typing the call the block title stayed empty and the body claimed "Running...". Parse the args partially (the read_file / edit_file / write_file pattern), keep a terminal cursor while the args JSON is incomplete, and stand the conversation cwd in for the x-tool-cwd that only arrives with the result message, so the prompt is there from the first character. The body stays empty while the args stream, shows "Executing command..." once the call is executing, and the wrapper drops its padding for empty phases so the title sits flush with the frame. Assisted by: Delta (zai-org/GLM-5.3) * fix(ui): stream file tool bodies even before a path arrives write_file and edit_file refused to render until their args carried a path, so a call that streams its body first (or a same-named tool with other arg names) showed an empty block. Meta now carries optional fileName/filePath, path alias reading moves to shared helpers, and the blocks fall back to the raw args JSON when no known content key is present. Assisted by: Delta (zai-org/GLM-5.3) * fix(ui): keep an empty write_file body instead of the raw args A call carrying {"content":""} fell through to the raw-args fallback, so a finished write of an empty file showed the args JSON. The parser now leaves content undefined when the key is missing and the block tests !== undefined, so an empty write renders as an empty code block with its byte count. Assisted by: DeepSeek-V4.1-Flash (deepseek-ai) --- .../ChatMessageToolCallBlock.svelte | 5 +- .../ChatMessageToolCallBlockEditFile.svelte | 38 ++- ...essageToolCallBlockExecShellCommand.svelte | 71 +++++- .../ChatMessageToolCallBlockWriteFile.svelte | 39 +++- .../ChatMessageToolCall/parsers/_shared.ts | 38 ++- .../ChatMessageToolCall/parsers/edit-file.ts | 40 ++-- .../parsers/exec-shell-command.ts | 4 +- .../ChatMessageToolCall/parsers/write-file.ts | 63 ++--- .../ChatMessageAgenticContent.svelte | 37 ++- .../content/CollapsibleTerminalBlock.svelte | 10 +- .../lib/constants/settings-keys.constants.ts | 1 + .../src/lib/constants/settings.constants.ts | 9 +- tools/ui/src/lib/types/tools.d.ts | 21 +- tools/ui/src/lib/utils/agentic.ts | 43 ++++ tools/ui/src/lib/utils/index.ts | 1 + ...xec-shell-command-streaming.svelte.test.ts | 216 ++++++++++++++++++ ...ol-call-block-streamed-body.svelte.test.ts | 82 +++++++ .../tool-call-in-progress.svelte.test.ts | 212 +++++++++++++++++ .../unit/agentic-section-expansion.test.ts | 122 ++++++++++ .../settings-tool-call-in-progress.test.ts | 45 ++++ tools/ui/tests/unit/tool-calls.test.ts | 128 +++++++++-- 21 files changed, 1098 insertions(+), 127 deletions(-) create mode 100644 tools/ui/tests/client/exec-shell-command-streaming.svelte.test.ts create mode 100644 tools/ui/tests/client/tool-call-block-streamed-body.svelte.test.ts create mode 100644 tools/ui/tests/client/tool-call-in-progress.svelte.test.ts create mode 100644 tools/ui/tests/unit/agentic-section-expansion.test.ts create mode 100644 tools/ui/tests/unit/settings-tool-call-in-progress.test.ts diff --git a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/ChatMessageToolCallBlock.svelte b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/ChatMessageToolCallBlock.svelte index 6b0b573288d1..57e49f9ee2d6 100644 --- a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/ChatMessageToolCallBlock.svelte +++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/ChatMessageToolCallBlock.svelte @@ -23,13 +23,15 @@ interface Props { section: AgenticSection; attachments?: DatabaseMessageExtra[]; + conversationCwd?: string; open: boolean; isStreaming: boolean; isExecuting?: boolean; onToggle?: () => void; } - let { attachments, isExecuting, isStreaming, onToggle, open, section }: Props = $props(); + let { attachments, conversationCwd, isExecuting, isStreaming, onToggle, open, section }: Props = + $props(); // Runs for every tool block on mount, before the body renders: the cheap // content prefilter and the tool-name allow-list come first so blobs from @@ -60,6 +62,7 @@ {:else if section.toolName === BuiltInTool.SERVER_EXEC_SHELL_COMMAND} {/snippet} - {#snippet children(meta, _ctx)} - {#if meta?.errorMessage} + {#snippet children(_meta, ctx)} + {#if editFileBody?.errorMessage}
- {meta.errorMessage} + {editFileBody.errorMessage}
- {:else if meta && editFileBody && editFileBody.edits.length > 0} + {:else if editFileBody?.edits.length} {#each editDiffs as diffLines, ei (ei)}
@@ -79,14 +81,28 @@ {/each}
- {#if meta.resultMessage} - {meta.resultMessage}{meta.editsApplied != null ? RESULT_STAT_SEPARATOR : ''}{/if} - - {#if meta.editsApplied != null} - {meta.editsApplied} - {meta.editsApplied === 1 ? 'edit' : 'edits'} applied + {#if editFileBody.resultMessage} + {editFileBody.resultMessage}{editFileBody.editsApplied != null + ? RESULT_STAT_SEPARATOR + : ''}{/if} + + {#if editFileBody.editsApplied != null} + {editFileBody.editsApplied} + {editFileBody.editsApplied === 1 ? 'edit' : 'edits'} applied {/if}
+ {:else if section.toolArgs} + + + {:else if ctx.isStreaming} +
+ Receiving arguments... +
{:else}
No edits
{/if} diff --git a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/ChatMessageToolCallBlockExecShellCommand.svelte b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/ChatMessageToolCallBlockExecShellCommand.svelte index 075cde6796ad..de72054430c6 100644 --- a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/ChatMessageToolCallBlockExecShellCommand.svelte +++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/ChatMessageToolCallBlockExecShellCommand.svelte @@ -11,7 +11,7 @@ import { AlertTriangle, Check, Loader2, XCircle } from '@lucide/svelte'; import { CollapsibleTerminalBlock } from '$lib/components/app'; import { SETTINGS_KEYS, TOOL_RUNTIME_SCROLL_AT_BOTTOM_THRESHOLD_PX } from '$lib/constants'; - import { AttachmentType } from '$lib/enums'; + import { AgenticSectionType, AttachmentType } from '$lib/enums'; import { settingsStore, toolsStore } from '$lib/stores'; import type { AgenticSection, DatabaseMessageExtra, ToolResultLine } from '$lib/types'; import { @@ -32,17 +32,40 @@ * tool call. Drives max-height + auto-scroll while true; releases * them when the loop reports this call as done. */ isExecuting?: boolean; + /** Working directory of the conversation this call belongs to; stands + * in for `section.toolCwd` until the result message persists the + * real one. */ + conversationCwd?: string; attachments?: DatabaseMessageExtra[]; onToggle?: () => void; } - let { attachments, isExecuting = false, isStreaming, onToggle, open, section }: Props = $props(); + let { + attachments, + conversationCwd, + isExecuting = false, + isStreaming, + onToggle, + open, + section + }: Props = $props(); // `isLive` covers all in-flight phases: pre-chunk spinner and // streaming itself. Frozen output (tool done while agent continues) // is not live. const isLive = $derived(isExecuting); + const isPreResult = $derived( + section.type === AgenticSectionType.TOOL_CALL_PENDING || + section.type === AgenticSectionType.TOOL_CALL_STREAMING + ); + + // The model is still typing the call while the args JSON doesn't parse; + // the title keeps a terminal cursor until it does. + const commandStreaming = $derived( + isStreaming && isPreResult && !isParsableJson(section.toolArgs) + ); + const execShellMeta = $derived(parseExecShellCommandMeta(section)); const execShellError = $derived(parseExecShellCommandError(section.toolResult)); const execShellExitStatus: ExecShellExitStatus | undefined = $derived( @@ -76,9 +99,11 @@ // The working directory the command ran with, persisted per call on the // tool result message (it travels via the x-tool-cwd header, not the tool - // args). Reading it from the section keeps it accurate even if the - // conversation cwd changes later. - const cwd = $derived(section.toolCwd); + // args). Until that message exists, the conversation cwd is the best known + // value - it is what the agentic loop passes when executing the call. + const cwd = $derived( + section.toolCwd ?? (isStreaming && isPreResult ? conversationCwd : undefined) + ); const home = $derived(toolsStore.serverHome); const wdDisplay = $derived(abbreviateHome(cwd ?? '', home)); @@ -112,6 +137,18 @@ ); } + function isParsableJson(value: string | undefined): boolean { + if (!value) return false; + + try { + JSON.parse(value); + + return true; + } catch { + return false; + } + } + function scrollToBottomOnFrame() { if (pendingFrame !== null || !scrollEl || userScrolledUp) return; @@ -185,6 +222,10 @@ {:else} {execShellMeta?.command} {/if} + + {#if commandStreaming} + + {/if} {/snippet} - Running... + Executing command...
{:else if execShellError}
@@ -277,6 +318,22 @@ margin-right: var(--exec-wd-margin); } + .exec-cursor { + display: inline-block; + width: 0.5em; + height: 1.05em; + margin-left: 0.15rem; + vertical-align: text-bottom; + background: var(--foreground); + animation: exec-cursor-blink 1.1s steps(1) infinite; + } + + @keyframes exec-cursor-blink { + 50% { + opacity: 0; + } + } + .terminal-output { overscroll-behavior: contain; } diff --git a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/ChatMessageToolCallBlockWriteFile.svelte b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/ChatMessageToolCallBlockWriteFile.svelte index ac2a8e6576c6..f3f676017e7b 100644 --- a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/ChatMessageToolCallBlockWriteFile.svelte +++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/ChatMessageToolCallBlockWriteFile.svelte @@ -4,9 +4,10 @@ import { XCircle } from '@lucide/svelte'; import { SyntaxHighlightedCode } from '$lib/components/app'; import { MAX_HEIGHT_CODE_BLOCK, RESULT_STAT_SEPARATOR } from '$lib/constants'; + import { FileTypeText } from '$lib/enums'; import { toolsStore } from '$lib/stores'; import type { AgenticSection } from '$lib/types'; - import { abbreviateHome } from '$lib/utils'; + import { abbreviateHome, formatJsonPretty } from '$lib/utils'; interface Props { section: AgenticSection; @@ -42,32 +43,48 @@ {/snippet} - {#snippet children(meta, ctx)} - {#if meta?.errorMessage} + {#snippet children(_meta, ctx)} + {#if writeFileBody?.errorMessage}
- {meta.errorMessage} + {writeFileBody.errorMessage}
- {:else if meta} + {:else if writeFileBody?.content !== undefined}
- {#if meta.resultMessage} - {meta.resultMessage}{meta.bytesWritten != null ? RESULT_STAT_SEPARATOR : ''}{/if} + {#if writeFileBody.resultMessage} + {writeFileBody.resultMessage}{writeFileBody.bytesWritten != null + ? RESULT_STAT_SEPARATOR + : ''}{/if} - {#if meta.bytesWritten != null} - {meta.bytesWritten} + {#if writeFileBody.bytesWritten != null} + {writeFileBody.bytesWritten} bytes {/if}
+ {:else if section.toolArgs} + + + {:else if ctx.isStreaming} +
+ Receiving arguments... +
+ {:else} +
No content
{/if} {/snippet} diff --git a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/parsers/_shared.ts b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/parsers/_shared.ts index 79d7c2e27e6a..039b2634a4dc 100644 --- a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/parsers/_shared.ts +++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/parsers/_shared.ts @@ -1,10 +1,14 @@ // Helpers shared by the per-tool meta parsers under // `src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/parsers/`. // Each tool needs the same first three steps (tool-name check, -// args-present check, JSON parse) - keeping them here lets each parser -// stay focused on its own format quirks. +// args-present check, JSON parse), plus the same path aliases - keeping +// them here lets each parser stay focused on its own format quirks. -import { TOOL_ARG_STRING_FIELD_PATTERN_TEMPLATE } from '$lib/constants'; +import { + FILE_PATH_SEPARATOR_REGEX, + TOOL_ARG_PATH_KEYS, + TOOL_ARG_STRING_FIELD_PATTERN_TEMPLATE +} from '$lib/constants'; import { BuiltInTool } from '$lib/enums'; import type { AgenticSection } from '$lib/types/agentic'; import { parsePartialJsonArgs } from '$lib/utils/parse-partial-json-args'; @@ -68,6 +72,34 @@ export function extractToolArgString( return undefined; } +/** + * File name shown in a block title, from a tool-call path. Undefined when + * the call carries no path; the title then shows the tool label alone. + */ +export function fileNameFromPath(filePath: string | undefined): string | undefined { + if (!filePath) return undefined; + + return filePath.split(FILE_PATH_SEPARATOR_REGEX).pop() || filePath; +} + +/** + * Read the path argument out of an already parsed args object, checking + * the aliases in `TOOL_ARG_PATH_KEYS`. Returns undefined when the call + * carries no usable path - the path is display-only, so callers must + * still render whatever else the call streamed. + */ +export function extractToolArgPath(args?: Record | null): string | undefined { + if (!args) return undefined; + + for (const key of TOOL_ARG_PATH_KEYS) { + const value = args[key]; + + if (typeof value === 'string' && value) return value; + } + + return undefined; +} + /** * Parse a section's toolArgs against an expected tool name. Returns * `null` when: diff --git a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/parsers/edit-file.ts b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/parsers/edit-file.ts index d711466cb27c..2bbb6bab52eb 100644 --- a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/parsers/edit-file.ts +++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/parsers/edit-file.ts @@ -1,10 +1,16 @@ // Meta parser for `edit_file` tool calls. Reads the file path and the // array of edits from the streamed args (partial JSON for incremental // rendering), plus the result blob for `result` / `edits_applied` / -// `error` fields. - -import { extractToolArgString, parseToolArgs } from './_shared'; -import { FILE_PATH_SEPARATOR_REGEX, TOOL_ARG_PATH_KEYS } from '$lib/constants'; +// `error` fields. The path is display-only (block title), so a call +// that has not streamed one still shows its diffs. + +import { + extractToolArgPath, + extractToolArgString, + fileNameFromPath, + parseToolArgs +} from './_shared'; +import { TOOL_ARG_PATH_KEYS } from '$lib/constants'; import { BuiltInTool } from '$lib/enums'; import type { AgenticSection, EditFileEdit, EditFileMeta, EditFileTitleMeta } from '$lib/types'; import { tryParseToolResultObject } from '$lib/utils'; @@ -14,11 +20,8 @@ export function parseEditFileMeta(section: AgenticSection): EditFileMeta | null if (!args) return null; - const rawPath = args.path ?? args.file_path ?? args.filePath; - - if (typeof rawPath !== 'string' || !rawPath) return null; - - const fileName = rawPath.split(FILE_PATH_SEPARATOR_REGEX).pop() || rawPath; + const filePath = extractToolArgPath(args); + const fileName = fileNameFromPath(filePath); // Filter the streamed edits array strictly: each entry must be an // object with a non-empty `old_text`. Edits without an old_text // would diff against empty and render as a full re-write. @@ -61,7 +64,7 @@ export function parseEditFileMeta(section: AgenticSection): EditFileMeta | null editsApplied, errorMessage, fileName, - filePath: rawPath, + filePath, resultMessage }; } @@ -75,18 +78,15 @@ export function parseEditFileMeta(section: AgenticSection): EditFileMeta | null export function parseEditFileTitleMeta(section: AgenticSection): EditFileTitleMeta | null { if (section.toolName !== BuiltInTool.SERVER_EDIT_FILE || !section.toolArgs) return null; - let rawPath: string | undefined = extractToolArgString(section.toolArgs, TOOL_ARG_PATH_KEYS); + let filePath = extractToolArgString(section.toolArgs, TOOL_ARG_PATH_KEYS); - if (!rawPath) { - const args = parseToolArgs(BuiltInTool.SERVER_EDIT_FILE, section, { partial: true }); - const fallbackPath = args?.path ?? args?.file_path ?? args?.filePath; - - if (typeof fallbackPath === 'string' && fallbackPath) rawPath = fallbackPath; + if (!filePath) { + filePath = extractToolArgPath( + parseToolArgs(BuiltInTool.SERVER_EDIT_FILE, section, { partial: true }) + ); } - if (!rawPath) return null; - - const fileName = rawPath.split(FILE_PATH_SEPARATOR_REGEX).pop() || rawPath; + const fileName = fileNameFromPath(filePath); const resultObj = tryParseToolResultObject(section.toolResult); let resultMessage: string | undefined; @@ -105,5 +105,5 @@ export function parseEditFileTitleMeta(section: AgenticSection): EditFileTitleMe } } - return { editsApplied, errorMessage, fileName, filePath: rawPath, resultMessage }; + return { editsApplied, errorMessage, fileName, filePath, resultMessage }; } diff --git a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/parsers/exec-shell-command.ts b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/parsers/exec-shell-command.ts index 7cf767535099..2371c11e5a74 100644 --- a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/parsers/exec-shell-command.ts +++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/parsers/exec-shell-command.ts @@ -1,5 +1,7 @@ // Meta parser for `exec_shell_command` tool calls. Surfaces the // command text from args `command` / `cmd` / `shell_command` aliases. +// Args are parsed partially so the title can render the command while +// it is still streaming. // The exit-status and error parsing live in their own utilities // (`parse-exec-shell-status.ts` / `parse-exec-shell-error.ts`) - this // file only deals with what's strictly about *calling* the tool, since @@ -14,7 +16,7 @@ export type ExecShellCommandMeta = { }; export function parseExecShellCommandMeta(section: AgenticSection): ExecShellCommandMeta | null { - const args = parseToolArgs(BuiltInTool.SERVER_EXEC_SHELL_COMMAND, section); + const args = parseToolArgs(BuiltInTool.SERVER_EXEC_SHELL_COMMAND, section, { partial: true }); if (!args) return null; diff --git a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/parsers/write-file.ts b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/parsers/write-file.ts index 4a8e1a9c980d..e8917845a739 100644 --- a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/parsers/write-file.ts +++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/parsers/write-file.ts @@ -1,30 +1,40 @@ // Meta parser for `write_file` tool calls. Reads the path/content from // the streamed args (partial JSON so we can render before the call // finishes) and surfaces `bytes`, `result`, and `error` from the -// result blob. +// result blob. The path is display-only (title + highlight language), +// so a call that has not streamed one still shows its content. -import { extractToolArgString, parseToolArgs } from './_shared'; -import { CODE_BLOCK, FILE_PATH_SEPARATOR_REGEX, TOOL_ARG_PATH_KEYS } from '$lib/constants'; +import { + extractToolArgPath, + extractToolArgString, + fileNameFromPath, + parseToolArgs +} from './_shared'; +import { CODE_BLOCK, TOOL_ARG_PATH_KEYS } from '$lib/constants'; import { BuiltInTool } from '$lib/enums'; import type { AgenticSection, WriteFileMeta, WriteFileTitleMeta } from '$lib/types'; import { getFileTypeByExtension, tryParseToolResultObject } from '$lib/utils'; +// Plain text when the call carries no path, or one with no known extension. +function languageForPath(filePath: string | undefined): string { + if (!filePath) return CODE_BLOCK.DEFAULT_LANGUAGE; + + return ( + getFileTypeByExtension(filePath)?.replace(CODE_BLOCK.TEXT_LANGUAGE_PREFIX_REGEX, '') ?? + CODE_BLOCK.DEFAULT_LANGUAGE + ); +} + export function parseWriteFileMeta(section: AgenticSection): WriteFileMeta | null { const args = parseToolArgs(BuiltInTool.SERVER_WRITE_FILE, section, { partial: true }); if (!args) return null; - // Tool contracts drifted over time: some models emit `path`, - // others `file_path` / `filePath`. Accept all three. - const rawPath = args.path ?? args.file_path ?? args.filePath; - - if (typeof rawPath !== 'string' || !rawPath) return null; - - const fileName = rawPath.split(FILE_PATH_SEPARATOR_REGEX).pop() || rawPath; - const content = typeof args.content === 'string' ? args.content : ''; - const language = - getFileTypeByExtension(rawPath)?.replace(CODE_BLOCK.TEXT_LANGUAGE_PREFIX_REGEX, '') ?? - CODE_BLOCK.DEFAULT_LANGUAGE; + const filePath = extractToolArgPath(args); + const fileName = fileNameFromPath(filePath); + // undefined, not '', so an intentionally empty write still renders + const content = typeof args.content === 'string' ? args.content : undefined; + const language = languageForPath(filePath); const resultObj = tryParseToolResultObject(section.toolResult); const bytesWritten = resultObj && Number.isFinite(Number(resultObj.bytes)) ? Number(resultObj.bytes) : undefined; @@ -36,7 +46,7 @@ export function parseWriteFileMeta(section: AgenticSection): WriteFileMeta | nul content, errorMessage, fileName, - filePath: rawPath, + filePath, language, resultMessage }; @@ -51,21 +61,14 @@ export function parseWriteFileMeta(section: AgenticSection): WriteFileMeta | nul export function parseWriteFileTitleMeta(section: AgenticSection): WriteFileTitleMeta | null { if (section.toolName !== BuiltInTool.SERVER_WRITE_FILE || !section.toolArgs) return null; - let rawPath: string | undefined = extractToolArgString(section.toolArgs, TOOL_ARG_PATH_KEYS); + let filePath = extractToolArgString(section.toolArgs, TOOL_ARG_PATH_KEYS); - if (!rawPath) { - const args = parseToolArgs(BuiltInTool.SERVER_WRITE_FILE, section, { partial: true }); - const fallbackPath = args?.path ?? args?.file_path ?? args?.filePath; - - if (typeof fallbackPath === 'string' && fallbackPath) rawPath = fallbackPath; + if (!filePath) { + filePath = extractToolArgPath( + parseToolArgs(BuiltInTool.SERVER_WRITE_FILE, section, { partial: true }) + ); } - if (!rawPath) return null; - - const fileName = rawPath.split(FILE_PATH_SEPARATOR_REGEX).pop() || rawPath; - const language = - getFileTypeByExtension(rawPath)?.replace(CODE_BLOCK.TEXT_LANGUAGE_PREFIX_REGEX, '') ?? - CODE_BLOCK.DEFAULT_LANGUAGE; const resultObj = tryParseToolResultObject(section.toolResult); const bytesWritten = resultObj && Number.isFinite(Number(resultObj.bytes)) ? Number(resultObj.bytes) : undefined; @@ -75,9 +78,9 @@ export function parseWriteFileTitleMeta(section: AgenticSection): WriteFileTitle return { bytesWritten, errorMessage, - fileName, - filePath: rawPath, - language, + fileName: fileNameFromPath(filePath), + filePath, + language: languageForPath(filePath), resultMessage }; } diff --git a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessageAgenticContent.svelte b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessageAgenticContent.svelte index ea9428e0712a..b6138dfc0879 100644 --- a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessageAgenticContent.svelte +++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessageAgenticContent.svelte @@ -8,14 +8,14 @@ MarkdownContent } from '$lib/components/app'; import { AgenticSectionType, ChatMessageStatsView, ToolPermissionDecision } from '$lib/enums'; - import { agenticStore, settingsStore } from '$lib/stores'; + import { agenticStore, conversationsStore, settingsStore } from '$lib/stores'; import type { AgenticSection, ChatMessageAgenticTimings, ChatMessageAgenticTurnStats, DatabaseMessage } from '$lib/types'; - import { deriveAgenticSections } from '$lib/utils'; + import { deriveAgenticSections, isSectionExpandedByDefault } from '$lib/utils'; interface Props { message: DatabaseMessage; @@ -34,6 +34,7 @@ let expandedStates: Record = $state({}); const showThoughtInProgress = $derived(Boolean(settingsStore.config.showThoughtInProgress)); + const showToolCallInProgress = $derived(Boolean(settingsStore.config.showToolCallInProgress)); const alwaysShowToolCallContent = $derived( Boolean(settingsStore.config.alwaysShowToolCallContent) ); @@ -95,6 +96,14 @@ isStreaming ? agenticStore.getExecutingToolCallId(message.convId) : null ); + // Cwd the agentic loop passes to tool calls in this conversation; the exec + // block shows it until the result message persists the real one. + const conversationCwd = $derived( + conversationsStore.activeConversation?.id === message.convId + ? conversationsStore.activeConversation?.cwd + : undefined + ); + type TurnGroup = { sections: AgenticSection[]; flatIndices: number[]; @@ -132,28 +141,17 @@ return groups; }); - function getDefaultExpanded(section: AgenticSection): boolean { - if ( - section.type === AgenticSectionType.TOOL_CALL || - section.type === AgenticSectionType.TOOL_CALL_PENDING || - section.type === AgenticSectionType.TOOL_CALL_STREAMING - ) { - return alwaysShowToolCallContent; - } - - if (section.type === AgenticSectionType.REASONING_PENDING) { - return showThoughtInProgress; - } - - return false; - } - function isExpanded(index: number, section: AgenticSection): boolean { if (expandedStates[index] !== undefined) { return expandedStates[index]; } - return getDefaultExpanded(section); + return isSectionExpandedByDefault(section, { + alwaysShowToolCallContent, + executingToolCallId: currentlyExecutingToolCallId, + showThoughtInProgress, + showToolCallInProgress + }); } function toggleExpanded(index: number, section: AgenticSection) { @@ -190,6 +188,7 @@ {:else if section.type === AgenticSectionType.TOOL_CALL || section.type === AgenticSectionType.TOOL_CALL_PENDING || section.type === AgenticSectionType.TOOL_CALL_STREAMING} {#if open} -
+
{@render children()}
{/if} + + diff --git a/tools/ui/src/lib/constants/settings-keys.constants.ts b/tools/ui/src/lib/constants/settings-keys.constants.ts index d4ae8318c599..0dcca2ace13b 100644 --- a/tools/ui/src/lib/constants/settings-keys.constants.ts +++ b/tools/ui/src/lib/constants/settings-keys.constants.ts @@ -65,6 +65,7 @@ export const SETTINGS_KEYS = { SHOW_RAW_OUTPUT_SWITCH: 'showRawOutputSwitch', SHOW_SYSTEM_MESSAGE: 'showSystemMessage', SHOW_THOUGHT_IN_PROGRESS: 'showThoughtInProgress', + SHOW_TOOL_CALL_IN_PROGRESS: 'showToolCallInProgress', SYMBOLIC_MATH_ENABLED: 'symbolicMathEnabled', SYSTEM_MESSAGE: 'systemMessage', // Sampling diff --git a/tools/ui/src/lib/constants/settings.constants.ts b/tools/ui/src/lib/constants/settings.constants.ts index 9d00ac9efdbd..4c0d759f64c7 100644 --- a/tools/ui/src/lib/constants/settings.constants.ts +++ b/tools/ui/src/lib/constants/settings.constants.ts @@ -227,9 +227,16 @@ export const SETTINGS_REGISTRY: SettingsSectionEntry[] = [ label: 'Show thought in progress', type: SettingsFieldType.CHECKBOX }, + { + defaultValue: true, + help: 'Expand tool call details while the call is being generated and executed, and collapse them again once its result is in. Mirrors "Show thought in progress". Tool calls you expand or collapse by hand keep your choice.', + key: SETTINGS_KEYS.SHOW_TOOL_CALL_IN_PROGRESS, + label: 'Show tool call in progress', + type: SettingsFieldType.CHECKBOX + }, { defaultValue: false, - help: 'Automatically expand tool call details while executing and keep them expanded after completion.', + help: 'Keep tool call details expanded after completion. Takes precedence over "Show tool call in progress", which only covers the in-flight phases.', key: SETTINGS_KEYS.ALWAYS_SHOW_TOOL_CALL_CONTENT, label: 'Always show tool call content', type: SettingsFieldType.CHECKBOX diff --git a/tools/ui/src/lib/types/tools.d.ts b/tools/ui/src/lib/types/tools.d.ts index fa8963bd1f8f..013e1930c959 100644 --- a/tools/ui/src/lib/types/tools.d.ts +++ b/tools/ui/src/lib/types/tools.d.ts @@ -33,10 +33,12 @@ export interface ToolGroup { } export interface WriteFileMeta { - fileName: string; - filePath: string; + /** Undefined until the call streams a path; content renders without it. */ + fileName?: string; + filePath?: string; language: string; - content: string; + /** Undefined until the args carry a content string; an empty write is ''. */ + content?: string; bytesWritten?: number; resultMessage?: string; errorMessage?: string; @@ -46,8 +48,8 @@ export interface WriteFileMeta { * ( with the embedded file content ) stays body-only so collapsed blocks * never parse the content blob. */ export interface WriteFileTitleMeta { - fileName: string; - filePath: string; + fileName?: string; + filePath?: string; language: string; bytesWritten?: number; resultMessage?: string; @@ -60,8 +62,9 @@ export interface EditFileEdit { } export interface EditFileMeta { - fileName: string; - filePath: string; + /** Undefined until the call streams a path; the edits render without it. */ + fileName?: string; + filePath?: string; edits: EditFileEdit[]; resultMessage?: string; editsApplied?: number; @@ -72,8 +75,8 @@ export interface EditFileMeta { * ( with the embedded edit strings ) stays body-only so collapsed blocks * never parse the args blob. */ export interface EditFileTitleMeta { - fileName: string; - filePath: string; + fileName?: string; + filePath?: string; resultMessage?: string; editsApplied?: number; errorMessage?: string; diff --git a/tools/ui/src/lib/utils/agentic.ts b/tools/ui/src/lib/utils/agentic.ts index 28b3f43ee048..f4d40972158e 100644 --- a/tools/ui/src/lib/utils/agentic.ts +++ b/tools/ui/src/lib/utils/agentic.ts @@ -540,6 +540,49 @@ export function hasAgenticContent( return toolMessages.length > 0; } +/** + * Default expansion of a section, used until the user toggles it by hand. + * + * @param section - The section to judge + * @param flags - Display settings plus the tool call the agentic loop is running now. + * A running call outranks its section type: `exec_shell_command` persists an empty + * result before reading output, so a live call can already be TOOL_CALL. + */ +export function isSectionExpandedByDefault( + section: AgenticSection, + { + alwaysShowToolCallContent, + executingToolCallId, + showThoughtInProgress, + showToolCallInProgress + }: { + alwaysShowToolCallContent: boolean; + executingToolCallId?: string | null; + showThoughtInProgress: boolean; + showToolCallInProgress: boolean; + } +): boolean { + const isRunning = section.toolCallId !== undefined && section.toolCallId === executingToolCallId; + + if ( + isRunning || + section.type === AgenticSectionType.TOOL_CALL_PENDING || + section.type === AgenticSectionType.TOOL_CALL_STREAMING + ) { + return showToolCallInProgress || alwaysShowToolCallContent; + } + + if (section.type === AgenticSectionType.TOOL_CALL) { + return alwaysShowToolCallContent; + } + + if (section.type === AgenticSectionType.REASONING_PENDING) { + return showThoughtInProgress; + } + + return false; +} + /** * Decide how a Continue click on messages[idx] should resume generation. * Pure function over the persisted history snapshot. diff --git a/tools/ui/src/lib/utils/index.ts b/tools/ui/src/lib/utils/index.ts index 5b46bedef5c1..a5f2af55f1fa 100644 --- a/tools/ui/src/lib/utils/index.ts +++ b/tools/ui/src/lib/utils/index.ts @@ -282,6 +282,7 @@ export { parseToolResultWithMedia, splitSearchSummaryList, hasAgenticContent, + isSectionExpandedByDefault, classifyToolResult, classifyContinueIntent } from './agentic'; diff --git a/tools/ui/tests/client/exec-shell-command-streaming.svelte.test.ts b/tools/ui/tests/client/exec-shell-command-streaming.svelte.test.ts new file mode 100644 index 000000000000..87668c0e30b6 --- /dev/null +++ b/tools/ui/tests/client/exec-shell-command-streaming.svelte.test.ts @@ -0,0 +1,216 @@ +// Guards the exec_shell_command title while the call is still being +// generated: the command must stream into the terminal title from the +// first character (args arrive as partial JSON), with a blinking cursor +// while the args are incomplete, and the conversation cwd standing in +// for the x-tool-cwd value that only arrives with the result message. + +import ChatMessageToolCallBlockExecShellCommand from '$lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/ChatMessageToolCallBlockExecShellCommand.svelte'; +import ChatMessageAgenticContent from '$lib/components/app/chat/ChatMessages/ChatMessageAgenticContent.svelte'; +import { SETTINGS_KEYS } from '$lib/constants'; +import { AgenticSectionType, BuiltInTool, MessageRole, MessageType } from '$lib/enums'; +import { conversationsStore, settingsStore } from '$lib/stores'; +import type { AgenticSection, DatabaseMessage } from '$lib/types'; +import { tick } from 'svelte'; +import { afterEach, beforeEach, describe, expect, it } from 'vitest'; +import { render } from 'vitest-browser-svelte'; + +const CONV_ID = 'conv-1'; +const TOOL_CALL_ID = 'call-1'; +const TOOL_NAME = BuiltInTool.SERVER_EXEC_SHELL_COMMAND; +const CWD = '/home/user/project'; + +function messageWithArgs(args: string): DatabaseMessage { + return { + children: [], + content: '', + convId: CONV_ID, + id: 'assistant-1', + parent: null, + role: MessageRole.ASSISTANT, + timestamp: 0, + toolCalls: JSON.stringify([ + { function: { arguments: args, name: TOOL_NAME }, id: TOOL_CALL_ID } + ]), + type: MessageType.TEXT + }; +} + +function toolResultMessage(content: string, toolCwd?: string): DatabaseMessage { + return { + children: [], + content, + convId: CONV_ID, + id: 'tool-1', + parent: null, + role: MessageRole.TOOL, + timestamp: 0, + toolCallId: TOOL_CALL_ID, + toolCalls: '', + toolCwd, + type: MessageType.TEXT + }; +} + +function triggerText(container: Element): string { + return container.querySelector('[data-slot="collapsible-trigger"]')?.textContent ?? ''; +} + +function hasCursor(container: Element): boolean { + return container.querySelector('.exec-cursor') !== null; +} + +describe('exec_shell_command streaming title', () => { + beforeEach(() => { + settingsStore.updateMultipleConfig({ + [SETTINGS_KEYS.ALWAYS_SHOW_TOOL_CALL_CONTENT]: false, + [SETTINGS_KEYS.SHOW_TOOL_CALL_IN_PROGRESS]: true + }); + conversationsStore.activeConversation = { + currNode: null, + cwd: CWD, + id: CONV_ID, + lastModified: 0, + name: 'test' + }; + }); + + afterEach(() => { + conversationsStore.activeConversation = null; + conversationsStore.activeMessages = []; + }); + + it('streams the partial command into the title with a cursor', async () => { + const { container } = render(ChatMessageAgenticContent, { + isStreaming: true, + message: messageWithArgs('{"command":"git sta'), + toolMessages: [] + }); + + await tick(); + + const title = triggerText(container); + + expect(title).toContain('git sta'); + expect(title).toContain('$'); + expect(title).toContain(CWD); + expect(hasCursor(container)).toBe(true); + }); + + it('drops the cursor once the args JSON is complete', async () => { + const screen = render(ChatMessageAgenticContent, { + isStreaming: true, + message: messageWithArgs('{"command":"git sta'), + toolMessages: [] + }); + + await tick(); + + expect(hasCursor(screen.container)).toBe(true); + + screen.rerender({ + isStreaming: true, + message: messageWithArgs('{"command":"git status"}'), + toolMessages: [] + }); + await tick(); + + expect(triggerText(screen.container)).toContain('git status'); + expect(hasCursor(screen.container)).toBe(false); + }); + + it('switches the prompt from the conversation cwd to the persisted one', async () => { + const screen = render(ChatMessageAgenticContent, { + isStreaming: true, + message: messageWithArgs('{"command":"git status"}'), + toolMessages: [] + }); + + await tick(); + + expect(triggerText(screen.container)).toContain(CWD); + + // Result message lands: its x-tool-cwd takes over the prompt. + screen.rerender({ + isStreaming: true, + message: messageWithArgs('{"command":"git status"}'), + toolMessages: [toolResultMessage('', '/other/dir')] + }); + await tick(); + + expect(triggerText(screen.container)).toContain('/other/dir'); + expect(triggerText(screen.container)).not.toContain(CWD); + }); + + it('shows the executing label only after the command finishes streaming', async () => { + const screen = render(ChatMessageAgenticContent, { + isStreaming: true, + message: messageWithArgs('{"command":"git sta'), + toolMessages: [] + }); + + await tick(); + + // While the args stream, the title cursor alone signals activity. + expect(screen.container.textContent).not.toContain('Executing command...'); + + screen.rerender({ + isStreaming: true, + message: messageWithArgs('{"command":"git status"}'), + toolMessages: [] + }); + await tick(); + + expect(screen.container.textContent).toContain('Executing command...'); + }); + + it('shows Executing command... while executing before the first output chunk', async () => { + // The result message is created empty right before execution, so + // the block must not go blank while the process produces nothing. + const section: AgenticSection = { + content: '', + toolArgs: '{"command":"sleep 1"}', + toolCallId: TOOL_CALL_ID, + toolName: TOOL_NAME, + toolResult: '', + type: AgenticSectionType.TOOL_CALL + }; + const { container } = render(ChatMessageToolCallBlockExecShellCommand, { + isExecuting: true, + isStreaming: true, + open: true, + section + }); + + await tick(); + + expect(container.textContent).toContain('Executing command...'); + }); + + it('updates the title when the store mutates toolCalls in place, as the stream does', async () => { + // The live flow never swaps the message object: onToolCallsStreaming + // writes growing toolCalls strings into the same store row per delta. + conversationsStore.activeMessages = [messageWithArgs('')]; + + const screen = render(ChatMessageAgenticContent, { + isStreaming: true, + message: conversationsStore.activeMessages[0]!, + toolMessages: [] + }); + + await tick(); + + const idx = conversationsStore.findMessageIndex('assistant-1'); + + for (const args of ['{"command', '{"command":"git', '{"command":"git sta']) { + conversationsStore.updateMessageAtIndex(idx, { + toolCalls: JSON.stringify([ + { function: { arguments: args, name: TOOL_NAME }, id: TOOL_CALL_ID } + ]) + }); + await tick(); + + expect(triggerText(screen.container)).toContain(args.slice('{"command":"'.length)); + expect(hasCursor(screen.container)).toBe(true); + } + }); +}); diff --git a/tools/ui/tests/client/tool-call-block-streamed-body.svelte.test.ts b/tools/ui/tests/client/tool-call-block-streamed-body.svelte.test.ts new file mode 100644 index 000000000000..1ff0f1fd7fbc --- /dev/null +++ b/tools/ui/tests/client/tool-call-block-streamed-body.svelte.test.ts @@ -0,0 +1,82 @@ +// A file tool call must show what it streamed even when it carries no +// path: the path only names the file in the block title, so a call that +// streams its body first (or a same-named tool with other arg names) used +// to render an empty block. + +import ChatMessageToolCallBlockEditFile from '$lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/ChatMessageToolCallBlockEditFile.svelte'; +import ChatMessageToolCallBlockWriteFile from '$lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/ChatMessageToolCallBlockWriteFile.svelte'; +import { AgenticSectionType, BuiltInTool } from '$lib/enums'; +import type { AgenticSection } from '$lib/types'; +import { tick } from 'svelte'; +import { describe, expect, it } from 'vitest'; +import { render } from 'vitest-browser-svelte'; + +const WRITE_FILE_BODY = 'body streamed without a path'; +const EDIT_FILE_NEW_TEXT = 'replacement text'; + +function makeSection(toolName: BuiltInTool, toolArgs: string): AgenticSection { + return { + content: '', + toolArgs, + toolName, + toolResult: undefined, + type: AgenticSectionType.TOOL_CALL + }; +} + +describe('file tool calls render their body without a path', () => { + it('write_file shows the content it streamed', async () => { + render(ChatMessageToolCallBlockWriteFile, { + isStreaming: true, + open: true, + section: makeSection(BuiltInTool.SERVER_WRITE_FILE, `{"content":"${WRITE_FILE_BODY}"`) + }); + + await tick(); + + expect(document.body.textContent).toContain(WRITE_FILE_BODY); + }); + + it('edit_file shows the diff it streamed', async () => { + render(ChatMessageToolCallBlockEditFile, { + isStreaming: true, + open: true, + section: makeSection( + BuiltInTool.SERVER_EDIT_FILE, + `{"edits":[{"old_text":"stale text","new_text":"${EDIT_FILE_NEW_TEXT}"}]}` + ) + }); + + await tick(); + + expect(document.body.textContent).toContain(EDIT_FILE_NEW_TEXT); + expect(document.body.textContent).toContain('stale text'); + }); + + it('write_file falls back to the raw args when no known content key is present', async () => { + render(ChatMessageToolCallBlockWriteFile, { + isStreaming: false, + open: true, + section: makeSection(BuiltInTool.SERVER_WRITE_FILE, '{"contents":"x"}') + }); + + await tick(); + + expect(document.body.textContent).toContain('contents'); + }); + + it('write_file renders an empty code block for a finished empty write', async () => { + // content of '' used to fall through to the raw-args fallback + const section = makeSection(BuiltInTool.SERVER_WRITE_FILE, '{"content":""}'); + + section.toolResult = '{"result":"File written","bytes":0}'; + + render(ChatMessageToolCallBlockWriteFile, { isStreaming: false, open: true, section }); + + await tick(); + + expect(document.querySelector('pre code.hljs')).toBeTruthy(); + expect(document.body.textContent).not.toContain('"content"'); + expect(document.body.textContent).toMatch(/0\s*bytes/); + }); +}); diff --git a/tools/ui/tests/client/tool-call-in-progress.svelte.test.ts b/tools/ui/tests/client/tool-call-in-progress.svelte.test.ts new file mode 100644 index 000000000000..af32aef3c9a1 --- /dev/null +++ b/tools/ui/tests/client/tool-call-in-progress.svelte.test.ts @@ -0,0 +1,212 @@ +// Guards the two Settings > Display toggles that drive the default expansion of +// a tool call block. +// +// "Show tool call in progress" mirrors "Show thought in progress": a call is +// expanded by default only while it is in flight and folds back on its own once +// the result lands. "Always show tool call content" is the one that keeps +// finished calls open and wins on its own. A manual toggle always overrides the +// default, in both directions. + +import ChatMessageAgenticContent from '$lib/components/app/chat/ChatMessages/ChatMessageAgenticContent.svelte'; +import { SETTINGS_KEYS } from '$lib/constants'; +import { MessageRole, MessageType } from '$lib/enums'; +import { settingsStore } from '$lib/stores'; +import type { DatabaseMessage } from '$lib/types'; +import { tick } from 'svelte'; +import { beforeEach, describe, expect, it } from 'vitest'; +import { userEvent } from 'vitest/browser'; +import { render } from 'vitest-browser-svelte'; + +const TOOL_NAME = 'custom_tool'; +const TOOL_CALL_ID = 'call-1'; +// Both markers sit inside the collapsible body, which CollapsibleContentBlock +// only mounts while open - so their presence is exactly "the block is expanded". +const PENDING_BODY = 'Waiting for result...'; +const RESULT_BODY = 'marker-tool-output'; +const TRIGGER_SELECTOR = '[data-slot="collapsible-trigger"]'; + +function assistantMessage(): DatabaseMessage { + return { + children: [], + content: '', + convId: 'conv-1', + id: 'assistant-1', + parent: null, + role: MessageRole.ASSISTANT, + timestamp: 0, + toolCalls: JSON.stringify([ + { function: { arguments: '{"path":"a.txt"}', name: TOOL_NAME }, id: TOOL_CALL_ID } + ]), + type: MessageType.TEXT + }; +} + +function toolResultMessage(): DatabaseMessage { + return { + ...assistantMessage(), + content: RESULT_BODY, + id: 'tool-1', + role: MessageRole.TOOL, + toolCallId: TOOL_CALL_ID + }; +} + +function setSettings({ + alwaysShow, + inProgress +}: { + alwaysShow: boolean; + inProgress: boolean; +}): void { + settingsStore.updateMultipleConfig({ + [SETTINGS_KEYS.ALWAYS_SHOW_TOOL_CALL_CONTENT]: alwaysShow, + [SETTINGS_KEYS.SHOW_TOOL_CALL_IN_PROGRESS]: inProgress + }); +} + +function textOf(container: Element): string { + return container.textContent ?? ''; +} + +async function collapseBlock(screen: { container: Element }): Promise { + const trigger = screen.container.querySelector(TRIGGER_SELECTOR); + + if (!(trigger instanceof HTMLButtonElement)) throw new Error('trigger not found'); + + await userEvent.click(trigger); + await tick(); +} + +describe('Show tool call in progress', () => { + beforeEach(() => { + setSettings({ alwaysShow: false, inProgress: false }); + }); + + it('expands a call that is still executing when the setting is on', async () => { + setSettings({ alwaysShow: false, inProgress: true }); + + const { container } = render(ChatMessageAgenticContent, { + isStreaming: true, + message: assistantMessage(), + toolMessages: [] + }); + + await tick(); + + expect(textOf(container)).toContain(PENDING_BODY); + }); + + it('keeps the call collapsed while executing when the setting is off', async () => { + const { container } = render(ChatMessageAgenticContent, { + isStreaming: true, + message: assistantMessage(), + toolMessages: [] + }); + + await tick(); + + // The header is still there, only the body stays hidden. + expect(textOf(container)).toContain(TOOL_NAME); + expect(textOf(container)).not.toContain(PENDING_BODY); + }); + + it('folds the call back when its result arrives', async () => { + setSettings({ alwaysShow: false, inProgress: true }); + + const screen = render(ChatMessageAgenticContent, { + isStreaming: true, + message: assistantMessage(), + toolMessages: [] + }); + + await tick(); + + // Open while it waits for the result. + expect(textOf(screen.container)).toContain(PENDING_BODY); + + // Same block, now finished: it must close on its own, no toggle given. + screen.rerender({ + isStreaming: true, + message: assistantMessage(), + toolMessages: [toolResultMessage()] + }); + await tick(); + + expect(textOf(screen.container)).toContain(TOOL_NAME); + expect(textOf(screen.container)).not.toContain(PENDING_BODY); + expect(textOf(screen.container)).not.toContain(RESULT_BODY); + }); + + it('keeps the manual choice once the result arrives', async () => { + setSettings({ alwaysShow: false, inProgress: true }); + + const screen = render(ChatMessageAgenticContent, { + isStreaming: true, + message: assistantMessage(), + toolMessages: [] + }); + + await tick(); + + // Collapsed by hand while running, so the result must not reopen it. + await collapseBlock(screen); + + screen.rerender({ + isStreaming: true, + message: assistantMessage(), + toolMessages: [toolResultMessage()] + }); + await tick(); + + expect(textOf(screen.container)).toContain(TOOL_NAME); + expect(textOf(screen.container)).not.toContain(RESULT_BODY); + }); + + it('still expands a finished call when "always show" is on', async () => { + setSettings({ alwaysShow: true, inProgress: false }); + + const { container } = render(ChatMessageAgenticContent, { + isStreaming: true, + message: assistantMessage(), + toolMessages: [toolResultMessage()] + }); + + await tick(); + + expect(textOf(container)).toContain(RESULT_BODY); + }); + + it('lets a manual collapse win over the in-progress default', async () => { + setSettings({ alwaysShow: false, inProgress: true }); + + const screen = render(ChatMessageAgenticContent, { + isStreaming: true, + message: assistantMessage(), + toolMessages: [] + }); + + await tick(); + + expect(textOf(screen.container)).toContain(PENDING_BODY); + + await collapseBlock(screen); + + expect(textOf(screen.container)).not.toContain(PENDING_BODY); + }); + + it('lets a manual expand win over the collapsed default', async () => { + const screen = render(ChatMessageAgenticContent, { + isStreaming: true, + message: assistantMessage(), + toolMessages: [] + }); + + await tick(); + + expect(textOf(screen.container)).not.toContain(PENDING_BODY); + + await collapseBlock(screen); + + expect(textOf(screen.container)).toContain(PENDING_BODY); + }); +}); diff --git a/tools/ui/tests/unit/agentic-section-expansion.test.ts b/tools/ui/tests/unit/agentic-section-expansion.test.ts new file mode 100644 index 000000000000..1b598f616aab --- /dev/null +++ b/tools/ui/tests/unit/agentic-section-expansion.test.ts @@ -0,0 +1,122 @@ +// Truth table for the default expansion of agentic sections - what a tool call +// block does before the user touches it. +// +// The interesting row is a call the agentic loop reports as running while its +// section is already TOOL_CALL: exec_shell_command persists an empty result +// before any output is read, so the section type alone cannot tell a live call +// from a finished one. + +import { AgenticSectionType } from '$lib/enums'; +import type { AgenticSection } from '$lib/types'; +import { isSectionExpandedByDefault } from '$lib/utils'; +import { describe, expect, it } from 'vitest'; + +const CALL_ID = 'call-1'; + +function section(overrides: Partial = {}): AgenticSection { + return { content: '', type: AgenticSectionType.TOOL_CALL, ...overrides }; +} + +function expanded(section: AgenticSection, flags: Record = {}): boolean { + return isSectionExpandedByDefault(section, { + alwaysShowToolCallContent: false, + executingToolCallId: null, + showThoughtInProgress: false, + showToolCallInProgress: false, + ...flags + }); +} + +describe('isSectionExpandedByDefault', () => { + it('expands a call whose arguments are streaming when the setting is on', () => { + const streaming = section({ + toolCallId: CALL_ID, + toolName: 'read_file', + type: AgenticSectionType.TOOL_CALL_STREAMING + }); + + expect(expanded(streaming, { showToolCallInProgress: true })).toBe(true); + expect(expanded(streaming)).toBe(false); + }); + + it('expands a call waiting for its result when the setting is on', () => { + const pending = section({ toolCallId: CALL_ID, type: AgenticSectionType.TOOL_CALL_PENDING }); + + expect(expanded(pending, { showToolCallInProgress: true })).toBe(true); + expect(expanded(pending)).toBe(false); + }); + + it('collapses a finished call unless "always show" is on', () => { + const finished = section({ toolCallId: CALL_ID, toolResult: 'output' }); + + expect(expanded(finished, { showToolCallInProgress: true })).toBe(false); + expect(expanded(finished, { alwaysShowToolCallContent: true })).toBe(true); + }); + + it('keeps the running call expanded even when its result is already persisted', () => { + // exec_shell_command: empty result created, output still streaming + const live = section({ toolCallId: CALL_ID, toolResult: '' }); + + expect(expanded(live, { executingToolCallId: CALL_ID, showToolCallInProgress: true })).toBe( + true + ); + }); + + it('does not treat another call finishing as the running one', () => { + const other = section({ toolCallId: 'call-2', toolResult: 'output' }); + + expect(expanded(other, { executingToolCallId: CALL_ID, showToolCallInProgress: true })).toBe( + false + ); + }); + + it('still leaves the running call collapsed when both settings are off', () => { + const live = section({ toolCallId: CALL_ID, toolResult: '' }); + + expect(expanded(live, { executingToolCallId: CALL_ID })).toBe(false); + }); + + it('never reads a missing executing id as a running call', () => { + expect(expanded(section({ toolCallId: CALL_ID }), { executingToolCallId: null })).toBe(false); + expect(expanded(section({ type: AgenticSectionType.TEXT }), { executingToolCallId: '' })).toBe( + false + ); + }); + + it('expands a running call when "always show" is on and the in-progress one is off', () => { + const pending = section({ toolCallId: CALL_ID, type: AgenticSectionType.TOOL_CALL_PENDING }); + + expect(expanded(pending, { alwaysShowToolCallContent: true })).toBe(true); + }); + + it('follows the thought setting for reasoning and never for finished thoughts', () => { + expect( + expanded( + { content: 'think', type: AgenticSectionType.REASONING_PENDING }, + { showThoughtInProgress: true } + ) + ).toBe(true); + expect( + expanded( + { content: 'think', type: AgenticSectionType.REASONING }, + { + showThoughtInProgress: true + } + ) + ).toBe(false); + }); + + it('never expands plain text', () => { + expect( + expanded( + { content: 'hello', type: AgenticSectionType.TEXT }, + { + alwaysShowToolCallContent: true, + executingToolCallId: CALL_ID, + showThoughtInProgress: true, + showToolCallInProgress: true + } + ) + ).toBe(false); + }); +}); diff --git a/tools/ui/tests/unit/settings-tool-call-in-progress.test.ts b/tools/ui/tests/unit/settings-tool-call-in-progress.test.ts new file mode 100644 index 000000000000..04651644a5c0 --- /dev/null +++ b/tools/ui/tests/unit/settings-tool-call-in-progress.test.ts @@ -0,0 +1,45 @@ +// Guards the Settings > Display wiring of "Show tool call in progress": the +// flat config default and its presence as a checkbox in the Display section +// come from the settings registry, so the key/label/type/section pairing is the +// contract the dialog renders. + +import { + SETTING_CONFIG_DEFAULT, + SETTINGS_CHAT_SECTIONS, + SETTINGS_KEYS, + SETTINGS_SECTION_SLUGS +} from '$lib/constants'; +import { SettingsFieldType } from '$lib/enums'; +import { describe, expect, it } from 'vitest'; + +function displayField(key: string) { + const section = SETTINGS_CHAT_SECTIONS.find((s) => s.slug === SETTINGS_SECTION_SLUGS.DISPLAY); + + return section?.fields?.find((field) => field?.key === key); +} + +describe('showToolCallInProgress setting', () => { + it('is a checkbox in the Display section', () => { + const field = displayField(SETTINGS_KEYS.SHOW_TOOL_CALL_IN_PROGRESS); + + expect(field).toBeDefined(); + expect(field?.type).toBe(SettingsFieldType.CHECKBOX); + expect(field?.label).toBe('Show tool call in progress'); + }); + + it('ships enabled, like "Show thought in progress"', () => { + expect(SETTING_CONFIG_DEFAULT[SETTINGS_KEYS.SHOW_TOOL_CALL_IN_PROGRESS]).toBe(true); + expect(SETTING_CONFIG_DEFAULT[SETTINGS_KEYS.SHOW_THOUGHT_IN_PROGRESS]).toBe(true); + }); + + it('is listed next to its "always show" counterpart', () => { + const section = SETTINGS_CHAT_SECTIONS.find( + (s) => s.slug === SETTINGS_SECTION_SLUGS.DISPLAY + ) as (typeof SETTINGS_CHAT_SECTIONS)[number]; + const keys = section.fields?.map((field) => field?.key) ?? []; + const inProgressIndex = keys.indexOf(SETTINGS_KEYS.SHOW_TOOL_CALL_IN_PROGRESS); + + expect(inProgressIndex).toBeGreaterThanOrEqual(0); + expect(keys[inProgressIndex + 1]).toBe(SETTINGS_KEYS.ALWAYS_SHOW_TOOL_CALL_CONTENT); + }); +}); diff --git a/tools/ui/tests/unit/tool-calls.test.ts b/tools/ui/tests/unit/tool-calls.test.ts index a2274f9d2803..7b01b7b26b1d 100644 --- a/tools/ui/tests/unit/tool-calls.test.ts +++ b/tools/ui/tests/unit/tool-calls.test.ts @@ -1,4 +1,7 @@ -import { parseToolArgs } from '$lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/parsers/_shared'; +import { + extractToolArgPath, + parseToolArgs +} from '$lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/parsers/_shared'; import { parseEditFileMeta, parseEditFileTitleMeta @@ -12,6 +15,7 @@ import { parseWriteFileMeta, parseWriteFileTitleMeta } from '$lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/parsers/write-file'; +import { CODE_BLOCK } from '$lib/constants'; import { AgenticSectionType, BuiltInTool } from '$lib/enums'; import type { AgenticSection, WriteFileMeta } from '$lib/types'; import { abbreviateHome, formatCwdMessage, lastPathSegment, parseCwdMessage } from '$lib/utils'; @@ -164,6 +168,22 @@ describe('parseToolArgs (shared)', () => { }); }); +describe('extractToolArgPath', () => { + it('reads every path alias', () => { + expect(extractToolArgPath({ path: '/a' })).toBe('/a'); + expect(extractToolArgPath({ file_path: '/b' })).toBe('/b'); + expect(extractToolArgPath({ filePath: '/c' })).toBe('/c'); + }); + + it('returns undefined when no path alias is a usable string', () => { + expect(extractToolArgPath({ content: 'x' })).toBeUndefined(); + expect(extractToolArgPath({ path: '' })).toBeUndefined(); + expect(extractToolArgPath({ path: 7 })).toBeUndefined(); + expect(extractToolArgPath(null)).toBeUndefined(); + expect(extractToolArgPath(undefined)).toBeUndefined(); + }); +}); + describe('parseWriteFileMeta', () => { it('returns null for sections with a different tool name', () => { expect( @@ -176,12 +196,23 @@ describe('parseWriteFileMeta', () => { ).toBeNull(); }); - it('returns null when args have no path-like field', () => { - expect( - parseWriteFileMeta( - makeSection({ toolArgs: '{"content":"x"}', toolName: BuiltInTool.SERVER_WRITE_FILE }) - ) - ).toBeNull(); + it('returns content without a path when args have no path-like field', () => { + const meta = parseWriteFileMeta( + makeSection({ toolArgs: '{"content":"x"}', toolName: BuiltInTool.SERVER_WRITE_FILE }) + ); + + expect(meta?.content).toBe('x'); + expect(meta?.filePath).toBeUndefined(); + expect(meta?.fileName).toBeUndefined(); + expect(meta?.language).toBe(CODE_BLOCK.DEFAULT_LANGUAGE); + }); + + it('returns content streamed before the path has arrived', () => { + const meta = parseWriteFileMeta( + makeSection({ toolArgs: '{"content":"hello ', toolName: BuiltInTool.SERVER_WRITE_FILE }) + ); + + expect(meta?.content).toBe('hello '); }); it('accepts partial args (renders incrementally as content streams in)', () => { @@ -279,6 +310,16 @@ describe('parseWriteFileTitleMeta', () => { expect(parseWriteFileTitleMeta(section)?.filePath).toBe('/foo.t'); }); + it('returns meta without a path when args have no path-like field', () => { + const title = parseWriteFileTitleMeta( + makeSection({ toolArgs: '{"content":"x"}', toolName: BuiltInTool.SERVER_WRITE_FILE }) + ); + + expect(title?.filePath).toBeUndefined(); + expect(title?.fileName).toBeUndefined(); + expect(title?.language).toBe(CODE_BLOCK.DEFAULT_LANGUAGE); + }); + it('returns null for sections with a different tool name', () => { expect( parseWriteFileTitleMeta( @@ -324,12 +365,13 @@ describe('parseEditFileTitleMeta', () => { expect(parseEditFileTitleMeta(section)?.errorMessage).toBe('permission denied'); }); - it('returns null when args have no path-like field', () => { - expect( - parseEditFileTitleMeta( - makeSection({ toolArgs: '{"edits":[]}', toolName: BuiltInTool.SERVER_EDIT_FILE }) - ) - ).toBeNull(); + it('returns meta without a path when args have no path-like field', () => { + const title = parseEditFileTitleMeta( + makeSection({ toolArgs: '{"edits":[]}', toolName: BuiltInTool.SERVER_EDIT_FILE }) + ); + + expect(title?.filePath).toBeUndefined(); + expect(title?.fileName).toBeUndefined(); }); }); @@ -383,6 +425,33 @@ describe('parseEditFileMeta', () => { expect(meta?.errorMessage).toBe('bad path'); expect(meta?.resultMessage).toBeUndefined(); }); + + it('still parses edits when args have no path-like field', () => { + const meta = parseEditFileMeta( + makeSection({ + toolArgs: '{"edits":[{"old_text":"a","new_text":"b"}]}', + toolName: BuiltInTool.SERVER_EDIT_FILE + }) + ); + + expect(meta?.edits).toEqual([{ newText: 'b', oldText: 'a' }]); + expect(meta?.filePath).toBeUndefined(); + }); + + it('parses edits streamed before the path has arrived', () => { + const meta = parseEditFileMeta( + makeSection({ + // second edit is still mid-stream: only its old_text has landed + toolArgs: '{"edits":[{"old_text":"a","new_text":"b"},{"old_text":"c', + toolName: BuiltInTool.SERVER_EDIT_FILE + }) + ); + + expect(meta?.edits).toEqual([ + { newText: 'b', oldText: 'a' }, + { newText: '', oldText: 'c' } + ]); + }); }); describe('parseReadFileMeta', () => { @@ -647,4 +716,37 @@ describe('parseExecShellCommandMeta', () => { ) ).toBeNull(); }); + + it('surfaces a partially streamed command before the JSON closes', () => { + const meta = parseExecShellCommandMeta( + makeSection( + { toolArgs: '{"command":"git sta', toolName: BuiltInTool.SERVER_EXEC_SHELL_COMMAND }, + BuiltInTool.SERVER_EXEC_SHELL_COMMAND + ) + ); + + expect(meta?.command).toBe('git sta'); + }); + + it('keeps a dangling escape from breaking the partial parse', () => { + const meta = parseExecShellCommandMeta( + makeSection( + { toolArgs: '{"command":"echo \\"he', toolName: BuiltInTool.SERVER_EXEC_SHELL_COMMAND }, + BuiltInTool.SERVER_EXEC_SHELL_COMMAND + ) + ); + + expect(meta?.command).toBe('echo "he'); + }); + + it('returns null while the command value is still empty', () => { + expect( + parseExecShellCommandMeta( + makeSection( + { toolArgs: '{"command":"', toolName: BuiltInTool.SERVER_EXEC_SHELL_COMMAND }, + BuiltInTool.SERVER_EXEC_SHELL_COMMAND + ) + ) + ).toBeNull(); + }); }); From 4912ac24f12a22828b1bc3782ccad3183137aecb Mon Sep 17 00:00:00 2001 From: Hrishith Thadicherla <99313418+hthadicherla@users.noreply.github.com> Date: Thu, 22 Oct 2026 10:54:12 +0530 Subject: [PATCH 60/69] dflash : load optional FC input scale (#30945) Assisted-by: Codex --- src/llama-model.h | 5 +++-- src/models/dflash.cpp | 1 + 2 files changed, 4 insertions(+), 2 deletions(-) diff --git a/src/llama-model.h b/src/llama-model.h index 9fe9d77a2350..cdf060db2965 100644 --- a/src/llama-model.h +++ b/src/llama-model.h @@ -779,8 +779,9 @@ struct llama_model { struct ggml_tensor * per_layer_proj_norm = nullptr; // eagle3 / dflash feature fusion layer - struct ggml_tensor * fc = nullptr; - struct ggml_tensor * fc_s = nullptr; + struct ggml_tensor * fc = nullptr; + struct ggml_tensor * fc_s = nullptr; + struct ggml_tensor * fc_in_s = nullptr; struct ggml_tensor * d2t = nullptr; // draft to target vocabulary mapping // dspark diff --git a/src/models/dflash.cpp b/src/models/dflash.cpp index 6f28d2f1db4c..e15af6afdc74 100644 --- a/src/models/dflash.cpp +++ b/src/models/dflash.cpp @@ -238,6 +238,7 @@ void llama_model_dflash::load_arch_tensors(llama_model_loader &) { fc = create_tensor(tn(LLM_TENSOR_FC, "weight"), { n_embd_inp, n_embd }, 0); fc_s = create_tensor(tn(LLM_TENSOR_FC, "scale"), { 1 }, TENSOR_NOT_REQUIRED); + fc_in_s = create_tensor(tn(LLM_TENSOR_FC, "input_scale"), { 1 }, TENSOR_NOT_REQUIRED); output_norm_enc = create_tensor(tn(LLM_TENSOR_ENC_OUTPUT_NORM, "weight"), { n_embd }, 0); // encoder hidden_norm (after fc) output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), { n_embd }, 0); // decoder final norm From 0b1281aeef1838bf5848de7848a17e8d12747770 Mon Sep 17 00:00:00 2001 From: Chanho Lee Date: Thu, 22 Oct 2026 14:30:04 +0900 Subject: [PATCH 61/69] webgpu : add Q2_0 support (#30914) * webgpu : add Q2_0 row dequantization * webgpu : add Q2_0 matrix multiplication --- .../ggml-webgpu/ggml-webgpu-shader-lib.hpp | 3 +- ggml/src/ggml-webgpu/ggml-webgpu.cpp | 2 + .../ggml-webgpu/wgsl-shaders/get_rows.wgsl | 14 +++++++ .../wgsl-shaders/mul_mat_decls.tmpl | 23 +++++++++++ .../wgsl-shaders/mul_mat_vec_acc.tmpl | 40 +++++++++++++++++++ 5 files changed, 81 insertions(+), 1 deletion(-) diff --git a/ggml/src/ggml-webgpu/ggml-webgpu-shader-lib.hpp b/ggml/src/ggml-webgpu/ggml-webgpu-shader-lib.hpp index 6efff9b376a6..e01a417704e5 100644 --- a/ggml/src/ggml-webgpu/ggml-webgpu-shader-lib.hpp +++ b/ggml/src/ggml-webgpu/ggml-webgpu-shader-lib.hpp @@ -1655,6 +1655,7 @@ class ggml_webgpu_shader_lib { switch (key.src_type) { case GGML_TYPE_Q1_0: + case GGML_TYPE_Q2_0: case GGML_TYPE_Q4_0: case GGML_TYPE_Q5_0: case GGML_TYPE_Q8_0: @@ -1699,7 +1700,7 @@ class ggml_webgpu_shader_lib { } else if ((key.src_type >= GGML_TYPE_Q4_0 && key.src_type <= GGML_TYPE_Q8_1) || key.src_type == GGML_TYPE_IQ4_NL || key.src_type == GGML_TYPE_MXFP4) { defines.push_back("BLOCK_SIZE=32u"); - } else if (key.src_type == GGML_TYPE_NVFP4) { + } else if (key.src_type == GGML_TYPE_Q2_0 || key.src_type == GGML_TYPE_NVFP4) { defines.push_back("BLOCK_SIZE=64u"); } else if (key.src_type >= GGML_TYPE_Q2_K) { defines.push_back("BLOCK_SIZE=256u"); diff --git a/ggml/src/ggml-webgpu/ggml-webgpu.cpp b/ggml/src/ggml-webgpu/ggml-webgpu.cpp index 77fbb4f476e9..71d9ae2b92c3 100644 --- a/ggml/src/ggml-webgpu/ggml-webgpu.cpp +++ b/ggml/src/ggml-webgpu/ggml-webgpu.cpp @@ -4395,6 +4395,7 @@ static bool ggml_backend_webgpu_device_supports_buft(ggml_backend_dev_t dev, ggm static bool ggml_webgpu_supported_qtype(ggml_type type) { switch (type) { case GGML_TYPE_Q1_0: + case GGML_TYPE_Q2_0: case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_1: case GGML_TYPE_Q5_0: @@ -4500,6 +4501,7 @@ static bool ggml_backend_webgpu_device_supports_op(ggml_backend_dev_t dev, const case GGML_TYPE_F16: case GGML_TYPE_BF16: case GGML_TYPE_Q1_0: + case GGML_TYPE_Q2_0: case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_1: case GGML_TYPE_Q5_0: diff --git a/ggml/src/ggml-webgpu/wgsl-shaders/get_rows.wgsl b/ggml/src/ggml-webgpu/wgsl-shaders/get_rows.wgsl index a3114a4bea00..4bf3c934718e 100644 --- a/ggml/src/ggml-webgpu/wgsl-shaders/get_rows.wgsl +++ b/ggml/src/ggml-webgpu/wgsl-shaders/get_rows.wgsl @@ -51,6 +51,20 @@ fn copy_elements(src_base: u32, dst_base: u32, offset: u32) { } #endif +#ifdef Q2_0 +fn copy_elements(src_base: u32, dst_base: u32, offset: u32) { + let block_byte_base = (src_base + offset) * 18u; + let d = load_f16_as_f32_at_src(block_byte_base); + for (var j = 0u; j < 4u; j++) { + let q_packed = load_u32_at_src(block_byte_base + 2u + j * 4u); + for (var k = 0u; k < 16u; k++) { + let q = (q_packed >> (2u * k)) & 3u; + dst[dst_base + offset * 64u + j * 16u + k] = (f32(q) - 1.0) * d; + } + } +} +#endif + #ifdef Q4_0 fn copy_elements(src_base: u32, dst_base: u32, offset: u32) { let block_byte_base = (src_base + offset) * 18; // Block stride: 18 bytes diff --git a/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_decls.tmpl b/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_decls.tmpl index 83f6fa172831..1af6286ac486 100644 --- a/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_decls.tmpl +++ b/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_decls.tmpl @@ -111,6 +111,29 @@ fn init_shmem_src0(thread_id: u32, batch_offset: u32, offset_m: u32, k_outer: u3 } #endif // INIT_SRC0_SHMEM_Q1_0 +#ifdef INIT_SRC0_SHMEM_Q2_0 +const BLOCK_SIZE = 64u; +const BLOCK_SIZE_BYTES = 18u; +const NQ = 16u; + +fn init_shmem_src0(thread_id: u32, batch_offset: u32, offset_m: u32, k_outer: u32) { + for (var i = thread_id * NQ; i < TILE_SRC0_SHMEM; i += TOTAL_WORKGROUP_SIZE * NQ) { + let global_m = offset_m + i / TILE_K; + let global_k = k_outer + i % TILE_K; + if (global_m < params.m && global_k < params.k) { + let block_idx = batch_offset + global_m * params.stride_01 + global_k / BLOCK_SIZE; + let block_byte_base = block_idx * BLOCK_SIZE_BYTES; + let d = load_f16_at_src0(block_byte_base); + let q_packed = load_u32_at_src0(block_byte_base + 2u + (global_k % BLOCK_SIZE) / 4u); + for (var k = 0u; k < NQ; k++) { + let q = (q_packed >> (2u * k)) & 3u; + shmem[i + k] = (f16(q) - 1.0) * d; + } + } + } +} +#endif // INIT_SRC0_SHMEM_Q2_0 + // legacy-quants #if defined(INIT_SRC0_SHMEM_Q4_0) || defined(INIT_SRC0_SHMEM_Q4_1) || defined(INIT_SRC0_SHMEM_Q5_0) || defined(INIT_SRC0_SHMEM_Q5_1) || defined(INIT_SRC0_SHMEM_Q8_0) || defined(INIT_SRC0_SHMEM_Q8_1) || defined(INIT_SRC0_SHMEM_MXFP4) const BLOCK_SIZE = 32u; diff --git a/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_vec_acc.tmpl b/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_vec_acc.tmpl index 841777df9c32..2c116aad6c38 100644 --- a/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_vec_acc.tmpl +++ b/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_vec_acc.tmpl @@ -119,6 +119,46 @@ fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src } #endif +#ifdef MUL_ACC_Q2_0 +#define BLOCK_SIZE 64 +#define BLOCK_SIZE_BYTES 18 +#define THREADS_PER_BLOCK 4 +#define ELEMS_PER_THREAD (BLOCK_SIZE/THREADS_PER_BLOCK) +fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array, NUM_COLS> { + var acc: array, NUM_COLS>; + + let num_blocks = params.k / BLOCK_SIZE; + let thread_within_block = thread_id % THREADS_PER_BLOCK; + for (var block = thread_id / THREADS_PER_BLOCK; block < num_blocks; block += WG_SIZE / THREADS_PER_BLOCK) { + let x_base = src1_idx_base + block * BLOCK_SIZE + thread_within_block * ELEMS_PER_THREAD; + var x_block: array, NUM_COLS>; + for (var col = 0u; col < NUM_COLS; col++) { + for (var i = 0u; i < ELEMS_PER_THREAD; i++) { + x_block[col][i] = f32(SRC1[x_base + col * params.stride_11 + i]); + } + } + for (var row = 0u; row < OUTPUTS_PER_WG; row++) { + let output_row = row_base + row; + if (output_row < params.m) { + let block_byte_base = (src0_batch_offset + output_row * params.stride_01 + block) * BLOCK_SIZE_BYTES; + let d = f32(load_f16_at_src0(block_byte_base)); + let q_packed = load_u32_at_src0(block_byte_base + 2u + 4u * thread_within_block); + for (var col = 0u; col < NUM_COLS; col++) { + var row_sum = 0.0; + for (var i = 0u; i < ELEMS_PER_THREAD; i++) { + let q = (q_packed >> (2u * i)) & 3u; + row_sum += (f32(q) - 1.0) * x_block[col][i]; + } + acc[col][row] += d * row_sum; + } + } + } + } + + return acc; +} +#endif + #ifdef MUL_ACC_Q4_0 #define BLOCK_SIZE 32 #define BLOCK_SIZE_BYTES 18 From e3797b184d428c7a019362612b76971f60af6670 Mon Sep 17 00:00:00 2001 From: Titaniumtown Date: Wed, 21 Oct 2026 22:30:21 -0700 Subject: [PATCH 62/69] sycl: merge the fp16 and fp32 dequant switches (#30740) --- ggml/src/ggml-sycl/convert.cpp | 170 +++++++++------------------------ 1 file changed, 46 insertions(+), 124 deletions(-) diff --git a/ggml/src/ggml-sycl/convert.cpp b/ggml/src/ggml-sycl/convert.cpp index bcd0dd6b34bc..02b287ebcaba 100644 --- a/ggml/src/ggml-sycl/convert.cpp +++ b/ggml/src/ggml-sycl/convert.cpp @@ -657,58 +657,68 @@ static void convert_unary_sycl(const void * vx, dst_t * y, const int64_t k, dpct } -to_fp16_sycl_t ggml_get_to_fp16_sycl(ggml_type type, ggml_tensor * dst) { +static bool is_reordered_src0(const ggml_tensor * dst) { + return dst->src[0]->extra && + ((ggml_tensor_extra_gpu *) dst->src[0]->extra)->optimized_feature.reorder; +} + +template +static to_t_sycl_t ggml_get_dequant_sycl(ggml_type type, ggml_tensor * dst) { switch (type) { case GGML_TYPE_Q1_0: - return dequantize_block_sycl; + return dequantize_block_sycl; case GGML_TYPE_Q2_0: - return dequantize_block_sycl; + return dequantize_block_sycl; case GGML_TYPE_Q4_0: - if (dst->src[0]->extra && - ((ggml_tensor_extra_gpu*)dst->src[0]->extra)->optimized_feature.reorder) { + if (is_reordered_src0(dst)) { return dequantize_row_q4_0_sycl_reorder; + } else if constexpr (std::is_same_v) { + return dequantize_row_q4_0_sycl; } else { - return dequantize_block_sycl; + return dequantize_block_sycl; } case GGML_TYPE_Q4_1: - return dequantize_block_sycl; + if constexpr (std::is_same_v) { + return dequantize_row_q4_1_sycl; + } else { + return dequantize_block_sycl; + } case GGML_TYPE_Q5_0: - return dequantize_block_sycl; + return dequantize_block_sycl; case GGML_TYPE_Q5_1: - return dequantize_block_sycl; + return dequantize_block_sycl; case GGML_TYPE_Q8_0: - if (dst->src[0]->extra && - ((ggml_tensor_extra_gpu *) dst->src[0]->extra)->optimized_feature.reorder) { + if (is_reordered_src0(dst)) { return dequantize_row_q8_0_sycl_reorder; } else { - return dequantize_block_sycl; + return dequantize_block_sycl; } case GGML_TYPE_Q2_K: - if (dst->src[0]->extra && ((ggml_tensor_extra_gpu *) dst->src[0]->extra)->optimized_feature.reorder) { + if (is_reordered_src0(dst)) { return dequantize_row_q2_K_sycl_reorder; } else { return dequantize_row_q2_K_sycl; } case GGML_TYPE_Q3_K: - if (dst->src[0]->extra && ((ggml_tensor_extra_gpu *) dst->src[0]->extra)->optimized_feature.reorder) { + if (is_reordered_src0(dst)) { return dequantize_row_q3_K_sycl_reorder; } else { return dequantize_row_q3_K_sycl; } case GGML_TYPE_Q4_K: - if (dst->src[0]->extra && ((ggml_tensor_extra_gpu *) dst->src[0]->extra)->optimized_feature.reorder) { + if (is_reordered_src0(dst)) { return dequantize_row_q4_K_sycl_reorder; } else { return dequantize_row_q4_K_sycl; } case GGML_TYPE_Q5_K: - if (dst->src[0]->extra && ((ggml_tensor_extra_gpu *) dst->src[0]->extra)->optimized_feature.reorder) { + if (is_reordered_src0(dst)) { return dequantize_row_q5_K_sycl_reorder; } else { return dequantize_row_q5_K_sycl; } case GGML_TYPE_Q6_K: - if (dst->src[0]->extra && ((ggml_tensor_extra_gpu *) dst->src[0]->extra)->optimized_feature.reorder) { + if (is_reordered_src0(dst)) { return dequantize_row_q6_K_sycl_reorder; } else { return dequantize_row_q6_K_sycl; @@ -724,15 +734,13 @@ to_fp16_sycl_t ggml_get_to_fp16_sycl(ggml_type type, ggml_tensor * dst) { case GGML_TYPE_IQ2_S: return dequantize_row_iq2_s_sycl; case GGML_TYPE_IQ3_XXS: - if (dst->src[0]->extra && - ((ggml_tensor_extra_gpu *) dst->src[0]->extra)->optimized_feature.reorder) { + if (is_reordered_src0(dst)) { return dequantize_row_iq3_xxs_sycl_reorder; } else { return dequantize_row_iq3_xxs_sycl; } case GGML_TYPE_IQ3_S: - if (dst->src[0]->extra && - ((ggml_tensor_extra_gpu *) dst->src[0]->extra)->optimized_feature.reorder) { + if (is_reordered_src0(dst)) { return dequantize_row_iq3_s_sycl_reorder; } else { return dequantize_row_iq3_s_sycl; @@ -742,124 +750,38 @@ to_fp16_sycl_t ggml_get_to_fp16_sycl(ggml_type type, ggml_tensor * dst) { case GGML_TYPE_IQ4_NL: return dequantize_row_iq4_nl_sycl; case GGML_TYPE_MXFP4: - if (dst->src[0]->extra && ((ggml_tensor_extra_gpu *) dst->src[0]->extra)->optimized_feature.reorder) { + if (is_reordered_src0(dst)) { return dequantize_row_mxfp4_sycl_reorder; } return dequantize_row_mxfp4_sycl; case GGML_TYPE_NVFP4: return dequantize_row_nvfp4_sycl; case GGML_TYPE_F32: - return convert_unary_sycl; + if constexpr (std::is_same_v) { + return convert_unary_sycl; + } + break; + case GGML_TYPE_F16: + if constexpr (std::is_same_v) { + return convert_unary_sycl; + } + break; #ifdef GGML_SYCL_HAS_BF16 case GGML_TYPE_BF16: return convert_unary_sycl; #endif default: - GGML_ABORT("fatal error: unsupport data type=%s\n", ggml_type_name(type)); + break; } + GGML_ABORT("fatal error: unsupport data type=%s\n", ggml_type_name(type)); +} + +to_fp16_sycl_t ggml_get_to_fp16_sycl(ggml_type type, ggml_tensor * dst) { + return ggml_get_dequant_sycl(type, dst); } to_fp32_sycl_t ggml_get_to_fp32_sycl(ggml_type type, ggml_tensor *dst) { - switch (type) { - case GGML_TYPE_Q1_0: - return dequantize_block_sycl; - case GGML_TYPE_Q2_0: - return dequantize_block_sycl; - case GGML_TYPE_Q4_0: - if (dst->src[0]->extra && - ((ggml_tensor_extra_gpu*)dst->src[0]->extra)->optimized_feature.reorder) { - return dequantize_row_q4_0_sycl_reorder; - } else { - return dequantize_row_q4_0_sycl; - } - case GGML_TYPE_Q4_1: - return dequantize_row_q4_1_sycl; - case GGML_TYPE_Q5_0: - return dequantize_block_sycl; - case GGML_TYPE_Q5_1: - return dequantize_block_sycl; - case GGML_TYPE_Q8_0: - if (dst->src[0]->extra && - ((ggml_tensor_extra_gpu*)dst->src[0]->extra)->optimized_feature.reorder) { - return dequantize_row_q8_0_sycl_reorder; - } else { - return dequantize_block_sycl; - } - case GGML_TYPE_Q2_K: - if (dst->src[0]->extra && ((ggml_tensor_extra_gpu *) dst->src[0]->extra)->optimized_feature.reorder) { - return dequantize_row_q2_K_sycl_reorder; - } else { - return dequantize_row_q2_K_sycl; - } - case GGML_TYPE_Q3_K: - if (dst->src[0]->extra && ((ggml_tensor_extra_gpu *) dst->src[0]->extra)->optimized_feature.reorder) { - return dequantize_row_q3_K_sycl_reorder; - } else { - return dequantize_row_q3_K_sycl; - } - case GGML_TYPE_Q4_K: - if (dst->src[0]->extra && - ((ggml_tensor_extra_gpu*)dst->src[0]->extra)->optimized_feature.reorder) { - return dequantize_row_q4_K_sycl_reorder; - } else { - return dequantize_row_q4_K_sycl; - } - case GGML_TYPE_Q5_K: - if (dst->src[0]->extra && ((ggml_tensor_extra_gpu *) dst->src[0]->extra)->optimized_feature.reorder) { - return dequantize_row_q5_K_sycl_reorder; - } else { - return dequantize_row_q5_K_sycl; - } - case GGML_TYPE_Q6_K: - if (dst->src[0]->extra && ((ggml_tensor_extra_gpu *) dst->src[0]->extra)->optimized_feature.reorder) { - return dequantize_row_q6_K_sycl_reorder; - } else { - return dequantize_row_q6_K_sycl; - } - case GGML_TYPE_IQ1_S: - return dequantize_row_iq1_s_sycl; - case GGML_TYPE_IQ1_M: - return dequantize_row_iq1_m_sycl; - case GGML_TYPE_IQ2_XXS: - return dequantize_row_iq2_xxs_sycl; - case GGML_TYPE_IQ2_XS: - return dequantize_row_iq2_xs_sycl; - case GGML_TYPE_IQ2_S: - return dequantize_row_iq2_s_sycl; - case GGML_TYPE_IQ3_XXS: - if (dst->src[0]->extra && - ((ggml_tensor_extra_gpu *) dst->src[0]->extra)->optimized_feature.reorder) { - return dequantize_row_iq3_xxs_sycl_reorder; - } else { - return dequantize_row_iq3_xxs_sycl; - } - case GGML_TYPE_IQ3_S: - if (dst->src[0]->extra && - ((ggml_tensor_extra_gpu *) dst->src[0]->extra)->optimized_feature.reorder) { - return dequantize_row_iq3_s_sycl_reorder; - } else { - return dequantize_row_iq3_s_sycl; - } - case GGML_TYPE_IQ4_XS: - return dequantize_row_iq4_xs_sycl; - case GGML_TYPE_IQ4_NL: - return dequantize_row_iq4_nl_sycl; - case GGML_TYPE_MXFP4: - if (dst->src[0]->extra && ((ggml_tensor_extra_gpu *) dst->src[0]->extra)->optimized_feature.reorder) { - return dequantize_row_mxfp4_sycl_reorder; - } - return dequantize_row_mxfp4_sycl; - case GGML_TYPE_NVFP4: - return dequantize_row_nvfp4_sycl; - case GGML_TYPE_F16: - return convert_unary_sycl; -#ifdef GGML_SYCL_HAS_BF16 - case GGML_TYPE_BF16: - return convert_unary_sycl; -#endif - default: - GGML_ABORT("fatal error: unsupport data type=%s\n", ggml_type_name(type)); - } + return ggml_get_dequant_sycl(type, dst); } From dfeea520058802ef3528d7a262d3fc4a91268cf9 Mon Sep 17 00:00:00 2001 From: Charles Xu Date: Thu, 22 Oct 2026 08:56:21 +0200 Subject: [PATCH 63/69] kleidiai : add Q8_0 SVE dotprod GEMV and SVE i8mm GEMM kernels (#30808) --- ggml/src/ggml-cpu/CMakeLists.txt | 4 +- ggml/src/ggml-cpu/kleidiai/kernels.cpp | 262 ++++++++++++++++-------- ggml/src/ggml-cpu/kleidiai/kernels.h | 3 +- ggml/src/ggml-cpu/kleidiai/kleidiai.cpp | 7 + ggml/src/ggml-feats.h | 11 + 5 files changed, 193 insertions(+), 94 deletions(-) diff --git a/ggml/src/ggml-cpu/CMakeLists.txt b/ggml/src/ggml-cpu/CMakeLists.txt index d347757e1854..8327c52e6dae 100644 --- a/ggml/src/ggml-cpu/CMakeLists.txt +++ b/ggml/src/ggml-cpu/CMakeLists.txt @@ -650,9 +650,9 @@ function(ggml_add_cpu_backend_variant_impl tag_name) # Fetch KleidiAI sources: include(FetchContent) - set(KLEIDIAI_COMMIT_TAG "v1.31.0") + set(KLEIDIAI_COMMIT_TAG "v1.32.0") set(KLEIDIAI_DOWNLOAD_URL "https://github.com/ARM-software/kleidiai/releases/download/${KLEIDIAI_COMMIT_TAG}/kleidiai-${KLEIDIAI_COMMIT_TAG}-src.tar.gz") - set(KLEIDIAI_RELEASE_ARCHIVE_MD5 "930495f3aeb16b25dc18a2bde45924f4") + set(KLEIDIAI_RELEASE_ARCHIVE_MD5 "05f1953428ffe8e8c7f585dc6d508887") set(KLEIDIAI_FETCH_ARGS URL ${KLEIDIAI_DOWNLOAD_URL} diff --git a/ggml/src/ggml-cpu/kleidiai/kernels.cpp b/ggml/src/ggml-cpu/kleidiai/kernels.cpp index 89e95968cd91..24aa2824493f 100644 --- a/ggml/src/ggml-cpu/kleidiai/kernels.cpp +++ b/ggml/src/ggml-cpu/kleidiai/kernels.cpp @@ -201,85 +201,117 @@ static struct kai_matmul_uker_config q4_k_matmul_config() { return config; } -template -static size_t q4_k_get_m_step() { - const struct kai_matmul_uker_api api = GetApi(); - const struct kai_matmul_uker_config config = q4_k_matmul_config(); - return api.get_step(&config).m; +static struct kai_matmul_uker_config default_matmul_config() { + return {}; } -template -static size_t q4_k_get_n_step() { - const struct kai_matmul_uker_api api = GetApi(); - const struct kai_matmul_uker_config config = q4_k_matmul_config(); - return api.get_step(&config).n; -} +template< + struct kai_matmul_uker_api (*GetApi)(void), + struct kai_matmul_uker_config (*GetConfig)(void), + size_t MR, size_t NR, size_t KR, size_t SR> +struct matmul_uker_adapter { + static size_t get_m_step() { + const struct kai_matmul_uker_api api = GetApi(); + const struct kai_matmul_uker_config config = GetConfig(); + return api.get_step(&config).m; + } -template -static size_t q4_k_get_constant() { - return Value; -} + static size_t get_n_step() { + const struct kai_matmul_uker_api api = GetApi(); + const struct kai_matmul_uker_config config = GetConfig(); + return api.get_step(&config).n; + } -template -static size_t q4_k_get_dst_offset(size_t m_idx, size_t n_idx, size_t dst_stride) { - const struct kai_matmul_uker_api api = GetApi(); - const struct kai_matmul_uker_config config = q4_k_matmul_config(); - const struct kai_matmul_uker_dst_dim_args index = { m_idx, n_idx }; - const struct kai_matmul_uker_dst_stride_args stride = { dst_stride }; - return api.get_dst_offset(&config, &index, &stride); -} + static size_t get_mr() { + if constexpr (MR == 0) { + return get_m_step(); + } + return MR; + } -template -static size_t q4_k_get_lhs_packed_offset(size_t m_idx, size_t k, size_t bl) { - GGML_ASSERT(bl == KLEIDIAI_Q4_K_BLOCK_LENGTH); + static size_t get_nr() { + if constexpr (NR == 0) { + return get_n_step(); + } + return NR; + } - const struct kai_matmul_uker_api api = GetApi(); - const struct kai_matmul_uker_config config = q4_k_matmul_config(); - const struct kai_matmul_uker_lhs_dim_args shape = { m_idx, k }; - const struct kai_matmul_uker_lhs_dim_args index = { m_idx, 0 }; - const struct kai_matmul_uker_lhs_stride_args stride = api.get_lhs_stride(&config, &shape); - return api.get_lhs_offset(&config, &index, &stride); -} + static size_t get_kr() { + return KR; + } -template -static size_t q4_k_get_rhs_packed_offset(size_t n_idx, size_t k, size_t bl) { - GGML_ASSERT(bl == KLEIDIAI_Q4_K_BLOCK_LENGTH); + static size_t get_sr() { + return SR; + } - const struct kai_matmul_uker_api api = GetApi(); - const struct kai_matmul_uker_config config = q4_k_matmul_config(); - const struct kai_matmul_uker_rhs_dim_args shape = { n_idx, k }; - const struct kai_matmul_uker_rhs_dim_args index = { n_idx, 0 }; - const struct kai_matmul_uker_rhs_stride_args stride = api.get_rhs_stride(&config, &shape); - return api.get_rhs_offset(&config, &index, &stride); -} + static size_t get_dst_offset(size_t m_idx, size_t n_idx, size_t dst_stride) { + const struct kai_matmul_uker_api api = GetApi(); + const struct kai_matmul_uker_config config = GetConfig(); + const struct kai_matmul_uker_dst_dim_args index = { m_idx, n_idx }; + const struct kai_matmul_uker_dst_stride_args stride = { dst_stride }; + return api.get_dst_offset(&config, &index, &stride); + } -template -static void q4_k_run_matmul( - size_t m, size_t n, size_t k, size_t bl, - const void * lhs_packed, const void * rhs_packed, - void * dst, size_t dst_stride_row, size_t dst_stride_col, - float clamp_min, float clamp_max) { - GGML_ASSERT(bl == KLEIDIAI_Q4_K_BLOCK_LENGTH); - GGML_ASSERT(dst_stride_col == sizeof(float)); - - const struct kai_matmul_uker_api api = GetApi(); - const struct kai_matmul_uker_config config = q4_k_matmul_config(); - const struct kai_matmul_uker_lhs_dim_args lhs_shape = { m, k }; - const struct kai_matmul_uker_rhs_dim_args rhs_shape = { n, k }; - - struct kai_matmul_uker_args args = {}; - args.flags = KAI_MATMUL_UKER_FLAGS_ARGS_CLAMP; - args.shape = { m, n, k }; - args.operand.lhs.ptr = lhs_packed; - args.operand.lhs.stride = api.get_lhs_stride(&config, &lhs_shape); - args.operand.rhs.ptr = rhs_packed; - args.operand.rhs.stride = api.get_rhs_stride(&config, &rhs_shape); - args.operand.dst.ptr = dst; - args.operand.dst.stride.m = dst_stride_row; - args.activation.clamp.min_ptr = &clamp_min; - args.activation.clamp.max_ptr = &clamp_max; - api.run(&config, &args); -} + static size_t get_lhs_packed_offset(size_t m_idx, size_t k, size_t bl) { + const struct kai_matmul_uker_api api = GetApi(); + const struct kai_matmul_uker_config config = GetConfig(); + GGML_ASSERT(bl == config.format.bl); + const struct kai_matmul_uker_lhs_dim_args shape = { m_idx, k }; + const struct kai_matmul_uker_lhs_dim_args index = { m_idx, 0 }; + const struct kai_matmul_uker_lhs_stride_args stride = api.get_lhs_stride(&config, &shape); + return api.get_lhs_offset(&config, &index, &stride); + } + + static size_t get_rhs_packed_offset(size_t n_idx, size_t k, size_t bl) { + const struct kai_matmul_uker_api api = GetApi(); + const struct kai_matmul_uker_config config = GetConfig(); + GGML_ASSERT(bl == config.format.bl); + const struct kai_matmul_uker_rhs_dim_args shape = { n_idx, k }; + const struct kai_matmul_uker_rhs_dim_args index = { n_idx, 0 }; + const struct kai_matmul_uker_rhs_stride_args stride = api.get_rhs_stride(&config, &shape); + return api.get_rhs_offset(&config, &index, &stride); + } + + static void run( + size_t m, size_t n, size_t k, size_t bl, + const void * lhs_packed, const void * rhs_packed, + void * dst, size_t dst_stride_row, size_t dst_stride_col, + float clamp_min, float clamp_max) { + const struct kai_matmul_uker_api api = GetApi(); + const struct kai_matmul_uker_config config = GetConfig(); + GGML_ASSERT(bl == config.format.bl); + GGML_ASSERT(dst_stride_col == sizeof(float)); + const struct kai_matmul_uker_lhs_dim_args lhs_shape = { m, k }; + const struct kai_matmul_uker_rhs_dim_args rhs_shape = { n, k }; + + struct kai_matmul_uker_args args = {}; + args.flags = KAI_MATMUL_UKER_FLAGS_ARGS_CLAMP; + args.shape = { m, n, k }; + args.operand.lhs.ptr = lhs_packed; + args.operand.lhs.stride = api.get_lhs_stride(&config, &lhs_shape); + args.operand.rhs.ptr = rhs_packed; + args.operand.rhs.stride = api.get_rhs_stride(&config, &rhs_shape); + args.operand.dst.ptr = dst; + args.operand.dst.stride.m = dst_stride_row; + args.activation.clamp.min_ptr = &clamp_min; + args.activation.clamp.max_ptr = &clamp_max; + api.run(&config, &args); + } +}; + +using q4_k_gemm_adapter = matmul_uker_adapter< + kai_matmul_clamp_f32_f16p4vsx2_qai4c32p16vsx4s1s0sf16_4vsx16vs_sme2_mopa, + q4_k_matmul_config, 0, 0, 4, 2>; +using q4_k_gemv_adapter = matmul_uker_adapter< + kai_matmul_clamp_f32_qsi8d32p1x4_qai4c32p16vsx4s1s0sf16_1x16vs_sme2_dot, + q4_k_matmul_config, 0, 0, 4, 2>; +// GEMM and GEMV share the RHS layout only at SVE256 (NR=8, KR=8, SR=1). +using sve_q8_gemm_adapter = matmul_uker_adapter< + kai_matmul_clamp_f32_qai8dxp4x8sf32_qsi8cxp4vsx8sf32bf32_16x4vs_sve_i8mm, + default_matmul_config, 4, 0, 8, 1>; +using sve_q8_gemv_adapter = matmul_uker_adapter< + kai_matmul_clamp_f32_qai8dxp1x8_qsi8cxp8x8_1x8_sve_dot, + default_matmul_config, 1, 8, 8, 1>; static struct kai_matmul_pack_lhs_uker_config q4_k_lhs_pack_config() { struct kai_matmul_pack_lhs_uker_config config = {}; @@ -628,7 +660,7 @@ static ggml_kleidiai_kernels gemm_gemv_kernels[] = { /* .packed_size_ex = */ &rhs_ps_fn5, /* .pack_func_ex = */ &rhs_pack_fn12, }, - /* .required_cpu = */ CPU_FEATURE_SVE | CPU_FEATURE_I8MM | CPU_FEATURE_DOTPROD, + /* .required_cpu = */ CPU_FEATURE_SVE | CPU_FEATURE_SVEI8MM | CPU_FEATURE_DOTPROD, /* .lhs_type = */ GGML_TYPE_F32, /* .rhs_type = */ GGML_TYPE_Q4_0, /* .op_type = */ GGML_TYPE_F32, @@ -737,16 +769,16 @@ static ggml_kleidiai_kernels gemm_gemv_kernels_q4_k[] = { { /* SME2 GEMM */ { - /* .get_m_step = */ &q4_k_get_m_step, - /* .get_n_step = */ &q4_k_get_n_step, - /* .get_mr = */ &q4_k_get_m_step, - /* .get_nr = */ &q4_k_get_n_step, - /* .get_kr = */ &q4_k_get_constant<4>, - /* .get_sr = */ &q4_k_get_constant<2>, - /* .get_dst_offset = */ &q4_k_get_dst_offset, - /* .get_lhs_offset_ex = */ &q4_k_get_lhs_packed_offset, - /* .get_rhs_packed_offset_ex = */ &q4_k_get_rhs_packed_offset, - /* .run_kernel_ex = */ &q4_k_run_matmul, + /* .get_m_step = */ q4_k_gemm_adapter::get_m_step, + /* .get_n_step = */ q4_k_gemm_adapter::get_n_step, + /* .get_mr = */ q4_k_gemm_adapter::get_mr, + /* .get_nr = */ q4_k_gemm_adapter::get_nr, + /* .get_kr = */ q4_k_gemm_adapter::get_kr, + /* .get_sr = */ q4_k_gemm_adapter::get_sr, + /* .get_dst_offset = */ q4_k_gemm_adapter::get_dst_offset, + /* .get_lhs_offset_ex = */ q4_k_gemm_adapter::get_lhs_packed_offset, + /* .get_rhs_packed_offset_ex = */ q4_k_gemm_adapter::get_rhs_packed_offset, + /* .run_kernel_ex = */ q4_k_gemm_adapter::run, }, { /* .get_offset = */ kai_get_lhs_offset_lhs_pack_f16pmrx2_f32_neon, @@ -756,16 +788,16 @@ static ggml_kleidiai_kernels gemm_gemv_kernels_q4_k[] = { }, /* SME2 GEMV */ { - /* .get_m_step = */ &q4_k_get_m_step, - /* .get_n_step = */ &q4_k_get_n_step, - /* .get_mr = */ &q4_k_get_m_step, - /* .get_nr = */ &q4_k_get_n_step, - /* .get_kr = */ &q4_k_get_constant<4>, - /* .get_sr = */ &q4_k_get_constant<2>, - /* .get_dst_offset = */ &q4_k_get_dst_offset, - /* .get_lhs_offset_ex = */ &q4_k_get_lhs_packed_offset, - /* .get_rhs_packed_offset_ex = */ &q4_k_get_rhs_packed_offset, - /* .run_kernel_ex = */ &q4_k_run_matmul, + /* .get_m_step = */ q4_k_gemv_adapter::get_m_step, + /* .get_n_step = */ q4_k_gemv_adapter::get_n_step, + /* .get_mr = */ q4_k_gemv_adapter::get_mr, + /* .get_nr = */ q4_k_gemv_adapter::get_nr, + /* .get_kr = */ q4_k_gemv_adapter::get_kr, + /* .get_sr = */ q4_k_gemv_adapter::get_sr, + /* .get_dst_offset = */ q4_k_gemv_adapter::get_dst_offset, + /* .get_lhs_offset_ex = */ q4_k_gemv_adapter::get_lhs_packed_offset, + /* .get_rhs_packed_offset_ex = */ q4_k_gemv_adapter::get_rhs_packed_offset, + /* .run_kernel_ex = */ q4_k_gemv_adapter::run, }, { /* .get_offset = */ q4_k_lhs_get_offset, @@ -883,6 +915,54 @@ static ggml_kleidiai_kernels gemm_gemv_kernels_q8[] = { /* .rhs_type = */ GGML_TYPE_Q8_0, /* .op_type = */ GGML_TYPE_F32, }, + { + /* SVE I8MM GEMM */ + { + /* .get_m_step = */ sve_q8_gemm_adapter::get_m_step, + /* .get_n_step = */ sve_q8_gemm_adapter::get_n_step, + /* .get_mr = */ sve_q8_gemm_adapter::get_mr, + /* .get_nr = */ sve_q8_gemm_adapter::get_nr, + /* .get_kr = */ sve_q8_gemm_adapter::get_kr, + /* .get_sr = */ sve_q8_gemm_adapter::get_sr, + /* .get_dst_offset = */ sve_q8_gemm_adapter::get_dst_offset, + /* .get_lhs_offset_ex = */ sve_q8_gemm_adapter::get_lhs_packed_offset, + /* .get_rhs_packed_offset_ex = */ sve_q8_gemm_adapter::get_rhs_packed_offset, + /* .run_kernel_ex = */ sve_q8_gemm_adapter::run, + }, + /* .gemm_lhs_info = */ { + /* .get_offset = */ kai_get_lhs_offset_lhs_quant_pack_qai8dxp_f32, + /* .get_packed_offset_ex = */ &lhs_offs_fn5, + /* .packed_size_ex = */ &lhs_ps_fn5, + /* .pack_func_ex = */ &lhs_pack_float_fn9_no_bl, + }, + /* SVE DOT GEMV */ + { + /* .get_m_step = */ sve_q8_gemv_adapter::get_m_step, + /* .get_n_step = */ sve_q8_gemv_adapter::get_n_step, + /* .get_mr = */ sve_q8_gemv_adapter::get_mr, + /* .get_nr = */ sve_q8_gemv_adapter::get_nr, + /* .get_kr = */ sve_q8_gemv_adapter::get_kr, + /* .get_sr = */ sve_q8_gemv_adapter::get_sr, + /* .get_dst_offset = */ sve_q8_gemv_adapter::get_dst_offset, + /* .get_lhs_offset_ex = */ sve_q8_gemv_adapter::get_lhs_packed_offset, + /* .get_rhs_packed_offset_ex = */ sve_q8_gemv_adapter::get_rhs_packed_offset, + /* .run_kernel_ex = */ sve_q8_gemv_adapter::run, + }, + /* .gemv_lhs_info = */ { + /* .get_offset = */ kai_get_lhs_offset_lhs_quant_pack_qai8dxp_f32, + /* .get_packed_offset_ex = */ &lhs_offs_fn5, + /* .packed_size_ex = */ &lhs_ps_fn5, + /* .pack_func_ex = */ &lhs_pack_float_fn9_no_bl, + }, + /* .rhs_info = */ { + /* .packed_size_ex = */ &rhs_ps_fn5, + /* .pack_func_ex = */ &rhs_pack_scale_fn12, + }, + /* .required_cpu = */ CPU_FEATURE_SVE | CPU_FEATURE_SVEI8MM | CPU_FEATURE_DOTPROD, + /* .lhs_type = */ GGML_TYPE_F32, + /* .rhs_type = */ GGML_TYPE_Q8_0, + /* .op_type = */ GGML_TYPE_F32, + }, { /* I8MM GEMM */ { diff --git a/ggml/src/ggml-cpu/kleidiai/kernels.h b/ggml/src/ggml-cpu/kleidiai/kernels.h index 308ea945182f..ac5d5d302e88 100644 --- a/ggml/src/ggml-cpu/kleidiai/kernels.h +++ b/ggml/src/ggml-cpu/kleidiai/kernels.h @@ -13,7 +13,8 @@ enum cpu_feature { CPU_FEATURE_SVE = 4, CPU_FEATURE_SME = 8, CPU_FEATURE_SME2 = 16, - CPU_FEATURE_FP16 = 32 + CPU_FEATURE_FP16 = 32, + CPU_FEATURE_SVEI8MM = 64 }; inline cpu_feature& operator|=(cpu_feature& lhs, cpu_feature rhs) { diff --git a/ggml/src/ggml-cpu/kleidiai/kleidiai.cpp b/ggml/src/ggml-cpu/kleidiai/kleidiai.cpp index dd9bcbf6347a..57a4a5662e89 100644 --- a/ggml/src/ggml-cpu/kleidiai/kleidiai.cpp +++ b/ggml/src/ggml-cpu/kleidiai/kleidiai.cpp @@ -319,8 +319,10 @@ static void init_kleidiai_context(void) { size_t detected_smcus = 0; + // The fixed-width SVE kernels require 32-byte vectors, including the Q8 shared RHS layout. ctx.features = (runtime_feat.has_dotprod ? CPU_FEATURE_DOTPROD : CPU_FEATURE_NONE) | (runtime_feat.has_i8mm ? CPU_FEATURE_I8MM : CPU_FEATURE_NONE) | + (runtime_feat.has_svei8mm ? CPU_FEATURE_SVEI8MM : CPU_FEATURE_NONE) | (runtime_feat.has_fp16 ? CPU_FEATURE_FP16 : CPU_FEATURE_NONE) | (runtime_feat.sve_cnt == QK8_0 ? CPU_FEATURE_SVE : CPU_FEATURE_NONE); @@ -1616,6 +1618,11 @@ class tensor_traits : public ggml::cpu::tensor_traits { const size_t kr = kernel->get_kr(); const size_t sr = kernel->get_sr(); const ggml_type rhs_type = kernels->rhs_type; + if (rhs_type == GGML_TYPE_Q8_0 && (kernels->required_cpu & CPU_FEATURE_SVE)) { + GGML_ASSERT(nr == kernels->gemv.get_nr()); + GGML_ASSERT(kr == kernels->gemv.get_kr()); + GGML_ASSERT(sr == kernels->gemv.get_sr()); + } const size_t block_len = rhs_type == GGML_TYPE_Q8_0 ? QK8_0 : rhs_type == GGML_TYPE_Q4_0 ? QK4_0 : rhs_type == GGML_TYPE_Q4_K ? GGML_KLEIDIAI_Q4_K_BL : diff --git a/ggml/src/ggml-feats.h b/ggml/src/ggml-feats.h index 79a0afd87a87..c45e4ad082fc 100644 --- a/ggml/src/ggml-feats.h +++ b/ggml/src/ggml-feats.h @@ -22,6 +22,10 @@ #define HWCAP2_I8MM (1ULL << 13) #endif +#if !defined(HWCAP2_SVEI8MM) +#define HWCAP2_SVEI8MM (1ULL << 9) +#endif + #if !defined(HWCAP_ASIMDDP) #define HWCAP_ASIMDDP (1 << 20) #endif @@ -63,6 +67,10 @@ #define PF_ARM_SVE2_INSTRUCTIONS_AVAILABLE 47 #endif +#if !defined(PF_ARM_SVE_I8MM_INSTRUCTIONS_AVAILABLE) +#define PF_ARM_SVE_I8MM_INSTRUCTIONS_AVAILABLE 57 +#endif + #if !defined(PF_ARM_V82_I8MM_INSTRUCTIONS_AVAILABLE) #define PF_ARM_V82_I8MM_INSTRUCTIONS_AVAILABLE 66 #endif @@ -87,6 +95,7 @@ typedef struct ggml_feats_arch64_runtime { bool has_sve; bool has_sve2; bool has_i8mm; + bool has_svei8mm; bool has_sme; bool has_sme2; int sve_cnt; @@ -104,6 +113,7 @@ static inline ggml_feats_arch64_runtime_t ggml_feats_get_arch64_runtime(void) { runtime_feat.has_sve = !!(hwcap & HWCAP_SVE); runtime_feat.has_sve2 = !!(hwcap2 & HWCAP2_SVE2); runtime_feat.has_i8mm = !!(hwcap2 & HWCAP2_I8MM); + runtime_feat.has_svei8mm = !!(hwcap2 & HWCAP2_SVEI8MM); runtime_feat.has_sme = !!(hwcap2 & HWCAP2_SME); runtime_feat.has_sme2 = !!(hwcap2 & HWCAP2_SME2); @@ -153,6 +163,7 @@ static inline ggml_feats_arch64_runtime_t ggml_feats_get_arch64_runtime(void) { runtime_feat.has_sve = IsProcessorFeaturePresent(PF_ARM_SVE_INSTRUCTIONS_AVAILABLE) != 0; runtime_feat.has_sve2 = IsProcessorFeaturePresent(PF_ARM_SVE2_INSTRUCTIONS_AVAILABLE) != 0; runtime_feat.has_i8mm = IsProcessorFeaturePresent(PF_ARM_V82_I8MM_INSTRUCTIONS_AVAILABLE) != 0; + runtime_feat.has_svei8mm = IsProcessorFeaturePresent(PF_ARM_SVE_I8MM_INSTRUCTIONS_AVAILABLE) != 0; runtime_feat.has_sme = IsProcessorFeaturePresent(PF_ARM_SME_INSTRUCTIONS_AVAILABLE) != 0; runtime_feat.has_sme2 = IsProcessorFeaturePresent(PF_ARM_SME2_INSTRUCTIONS_AVAILABLE) != 0; From bf039c84969bfca70ba2814c9e5e95752559b4bc Mon Sep 17 00:00:00 2001 From: Gabe Goodhart Date: Thu, 22 Oct 2026 03:02:41 -0400 Subject: [PATCH 64/69] models : Record layer inputs for all granite models (#30957) Branch: GraniteSpeculatorSupport AI-usage: full (Claude + Sonnet 5.5) Signed-off-by: Gabe Goodhart --- src/models/granite-hybrid.cpp | 2 ++ src/models/granite-swa.cpp | 2 ++ src/models/granite-switch.cpp | 2 ++ src/models/granite.cpp | 2 ++ 4 files changed, 8 insertions(+) diff --git a/src/models/granite-hybrid.cpp b/src/models/granite-hybrid.cpp index fb3bf7024be3..a752a365cfd1 100644 --- a/src/models/granite-hybrid.cpp +++ b/src/models/granite-hybrid.cpp @@ -153,6 +153,8 @@ llama_model_granite_hybrid::graph::graph(const llama_model & model, const llm_gr } for (int il = 0; il < n_layer; ++il) { + res->t_layer_inp[il] = inpL; + struct ggml_tensor * inpSA = inpL; // norm diff --git a/src/models/granite-swa.cpp b/src/models/granite-swa.cpp index f683ef6e8eed..60c1eb8ba821 100644 --- a/src/models/granite-swa.cpp +++ b/src/models/granite-swa.cpp @@ -154,6 +154,8 @@ llama_model_granite_swa::graph::graph( cb(inpL, "deepstack_in", il); } + res->t_layer_inp[il] = inpL; + ggml_tensor * inpSA = inpL; // norm diff --git a/src/models/granite-switch.cpp b/src/models/granite-switch.cpp index a73fcdaff52e..f67bf6055f76 100644 --- a/src/models/granite-switch.cpp +++ b/src/models/granite-switch.cpp @@ -293,6 +293,8 @@ llama_model_granite_switch::graph::graph( ggml_tensor * cur; for (int il = 0; il < n_layer; ++il) { + res->t_layer_inp[il] = inpL; + ggml_tensor * inpSA = inpL; cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il); diff --git a/src/models/granite.cpp b/src/models/granite.cpp index 4cac49595327..aa4d01095675 100644 --- a/src/models/granite.cpp +++ b/src/models/granite.cpp @@ -159,6 +159,8 @@ llama_model_granite::graph::graph( cb(inpL, "deepstack_in", il); } + res->t_layer_inp[il] = inpL; + ggml_tensor * inpSA = inpL; // norm From e197cef4554d466e0becac20e453a355daba8e5d Mon Sep 17 00:00:00 2001 From: tkran2 <157059462+tkran2@users.noreply.github.com> Date: Thu, 22 Oct 2026 02:06:47 -0500 Subject: [PATCH 65/69] vocab : use Tekken pre-tokenizer for pixtral rather than the Llama 3 one (#30960) --- src/llama-vocab.cpp | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/llama-vocab.cpp b/src/llama-vocab.cpp index 05c0f96a29cc..d25c4ceec5ba 100644 --- a/src/llama-vocab.cpp +++ b/src/llama-vocab.cpp @@ -2438,7 +2438,6 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) { tokenizer_pre == "llama-bpe"|| tokenizer_pre == "falcon3" || tokenizer_pre == "falcon-h1" || - tokenizer_pre == "pixtral" || tokenizer_pre == "midm-2.0" || tokenizer_pre == "lfm2" || tokenizer_pre == "jina-v5-nano") { @@ -2566,7 +2565,8 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) { tokenizer_pre == "jais") { pre_type = LLAMA_VOCAB_PRE_TYPE_JAIS; } else if ( - tokenizer_pre == "tekken") { + tokenizer_pre == "tekken" || + tokenizer_pre == "pixtral") { pre_type = LLAMA_VOCAB_PRE_TYPE_TEKKEN; clean_spaces = false; ignore_merges = true; From d88040c704c8855c497d46c20675a3d492f70f94 Mon Sep 17 00:00:00 2001 From: tkran2 <157059462+tkran2@users.noreply.github.com> Date: Thu, 22 Oct 2026 02:58:08 -0500 Subject: [PATCH 66/69] vocab : add clean_spaces GGUF key (#30940) --- gguf-py/gguf/constants.py | 1 + gguf-py/gguf/gguf_writer.py | 3 +++ gguf-py/gguf/vocab.py | 10 ++++++++++ src/llama-arch.cpp | 1 + src/llama-arch.h | 1 + src/llama-model-saver.cpp | 1 + src/llama-vocab.cpp | 15 ++++++++++++++- tests/CMakeLists.txt | 2 +- tests/test-tokenizer-0.cpp | 19 ++++++++++++++++--- 9 files changed, 48 insertions(+), 5 deletions(-) diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index 51a16ef52b0b..4482f7f335f6 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -371,6 +371,7 @@ class Tokenizer: DNA_AUTO_TAGS = "tokenizer.ggml.dna_auto_tags" ADD_PREFIX = "tokenizer.ggml.add_space_prefix" REMOVE_EXTRA_WS = "tokenizer.ggml.remove_extra_whitespaces" + CLEAN_SPACES = "tokenizer.ggml.clean_spaces" PRECOMPILED_CHARSMAP = "tokenizer.ggml.precompiled_charsmap" SUPPRESS_TOKENS = "tokenizer.ggml.suppress_tokens" HF_JSON = "tokenizer.huggingface.json" diff --git a/gguf-py/gguf/gguf_writer.py b/gguf-py/gguf/gguf_writer.py index d00392211619..2faaefa59df7 100644 --- a/gguf-py/gguf/gguf_writer.py +++ b/gguf-py/gguf/gguf_writer.py @@ -1326,6 +1326,9 @@ def add_add_space_prefix(self, value: bool) -> None: def add_remove_extra_whitespaces(self, value: bool) -> None: self.add_bool(Keys.Tokenizer.REMOVE_EXTRA_WS, value) + def add_clean_spaces(self, value: bool) -> None: + self.add_bool(Keys.Tokenizer.CLEAN_SPACES, value) + def add_precompiled_charsmap(self, charsmap: bytes) -> None: self.add_array(Keys.Tokenizer.PRECOMPILED_CHARSMAP, charsmap) diff --git a/gguf-py/gguf/vocab.py b/gguf-py/gguf/vocab.py index 78e90eddc4b3..6383178ea7be 100644 --- a/gguf-py/gguf/vocab.py +++ b/gguf-py/gguf/vocab.py @@ -55,6 +55,7 @@ class SpecialVocab: normalizer_nfc: bool | None normalizer_lowercase: bool | None normalizer_strip_accents: bool | None + clean_spaces: bool | None def __init__( self, path: str | os.PathLike[str], load_merges: bool = False, @@ -70,6 +71,7 @@ def __init__( self.normalizer_nfc = None self.normalizer_lowercase = None self.normalizer_strip_accents = None + self.clean_spaces = None if special_token_types is not None: self.special_token_types = special_token_types else: @@ -120,6 +122,10 @@ def add_to_gguf(self, gw: GGUFWriter, quiet: bool = False) -> None: if not quiet: logger.info(f'Setting normalizer_strip_accents to {self.normalizer_strip_accents}') gw.add_normalizer_strip_accents(self.normalizer_strip_accents) + if self.clean_spaces is not None: + if not quiet: + logger.info(f'Setting clean_spaces to {self.clean_spaces}') + gw.add_clean_spaces(self.clean_spaces) def _load(self, path: Path) -> None: self._try_load_from_tokenizer_json(path) @@ -345,6 +351,10 @@ def _try_load_from_tokenizer_json(self, path: Path) -> bool: self.chat_template = chat_template else: logger.warning(f'Bad type for chat_template field in {tokenizer_config_file!r} - ignoring') + # decode clean-up of spaces before punctuation (transformers clean_up_tokenization_spaces) + clean_spaces = tokenizer_config.get('clean_up_tokenization_spaces') + if isinstance(clean_spaces, bool): + self.clean_spaces = clean_spaces for typ in self.special_token_types: add_entry = tokenizer_config.get(f'add_{typ}_token') if isinstance(add_entry, bool): diff --git a/src/llama-arch.cpp b/src/llama-arch.cpp index 62b012c5c3f0..d79b18dec95a 100644 --- a/src/llama-arch.cpp +++ b/src/llama-arch.cpp @@ -437,6 +437,7 @@ static const std::map LLM_KV_NAMES = { { LLM_KV_TOKENIZER_DNA_AUTO_TAGS, "tokenizer.ggml.dna_auto_tags" }, { LLM_KV_TOKENIZER_ADD_PREFIX, "tokenizer.ggml.add_space_prefix" }, { LLM_KV_TOKENIZER_REMOVE_EXTRA_WS, "tokenizer.ggml.remove_extra_whitespaces" }, + { LLM_KV_TOKENIZER_CLEAN_SPACES, "tokenizer.ggml.clean_spaces" }, { LLM_KV_TOKENIZER_PRECOMPILED_CHARSMAP, "tokenizer.ggml.precompiled_charsmap" }, { LLM_KV_TOKENIZER_HF_JSON, "tokenizer.huggingface.json" }, { LLM_KV_TOKENIZER_RWKV, "tokenizer.rwkv.world" }, diff --git a/src/llama-arch.h b/src/llama-arch.h index 6713db6b5906..12d8fe392529 100644 --- a/src/llama-arch.h +++ b/src/llama-arch.h @@ -400,6 +400,7 @@ enum llm_kv { LLM_KV_TOKENIZER_DNA_AUTO_TAGS, LLM_KV_TOKENIZER_ADD_PREFIX, LLM_KV_TOKENIZER_REMOVE_EXTRA_WS, + LLM_KV_TOKENIZER_CLEAN_SPACES, LLM_KV_TOKENIZER_PRECOMPILED_CHARSMAP, LLM_KV_TOKENIZER_HF_JSON, LLM_KV_TOKENIZER_RWKV, diff --git a/src/llama-model-saver.cpp b/src/llama-model-saver.cpp index d620fcd9c82e..2135b82b159a 100644 --- a/src/llama-model-saver.cpp +++ b/src/llama-model-saver.cpp @@ -424,6 +424,7 @@ void llama_model_saver::add_kv_from_model() { add_kv(LLM_KV_TOKENIZER_ADD_SEP, vocab.get_add_sep()); add_kv(LLM_KV_TOKENIZER_ADD_PREFIX, vocab.get_add_space_prefix()); add_kv(LLM_KV_TOKENIZER_REMOVE_EXTRA_WS, vocab.get_remove_extra_whitespaces()); + add_kv(LLM_KV_TOKENIZER_CLEAN_SPACES, vocab.get_clean_spaces()); add_kv(LLM_KV_TOKENIZER_PRECOMPILED_CHARSMAP, vocab.get_precompiled_charsmap()); // add_kv(LLM_KV_TOKENIZER_HF_JSON, ???); // add_kv(LLM_KV_TOKENIZER_RWKV, ???); diff --git a/src/llama-vocab.cpp b/src/llama-vocab.cpp index d25c4ceec5ba..80a5fcdff870 100644 --- a/src/llama-vocab.cpp +++ b/src/llama-vocab.cpp @@ -2432,6 +2432,7 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) { } else if (tokenizer_pre == "minicpm5") { pre_type = LLAMA_VOCAB_PRE_TYPE_MINICPM5; ignore_merges = true; + clean_spaces = false; } else if ( tokenizer_pre == "llama3" || tokenizer_pre == "llama-v3" || @@ -2439,11 +2440,16 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) { tokenizer_pre == "falcon3" || tokenizer_pre == "falcon-h1" || tokenizer_pre == "midm-2.0" || - tokenizer_pre == "lfm2" || tokenizer_pre == "jina-v5-nano") { pre_type = LLAMA_VOCAB_PRE_TYPE_LLAMA3; ignore_merges = true; add_bos = true; + } else if ( + tokenizer_pre == "lfm2") { + pre_type = LLAMA_VOCAB_PRE_TYPE_LLAMA3; + ignore_merges = true; + add_bos = true; + clean_spaces = false; } else if ( tokenizer_pre == "deepseek-llm") { pre_type = LLAMA_VOCAB_PRE_TYPE_DEEPSEEK_LLM; @@ -2494,6 +2500,7 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) { tokenizer_pre == "granite-embed-multi-311m") { pre_type = LLAMA_VOCAB_PRE_TYPE_GEMMA4; escape_whitespaces = true; + clean_spaces = false; } else if ( tokenizer_pre == "mmbert") { pre_type = LLAMA_VOCAB_PRE_TYPE_MMBERT; @@ -2540,6 +2547,7 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) { } else if ( tokenizer_pre == "dbrx") { pre_type = LLAMA_VOCAB_PRE_TYPE_DBRX; + clean_spaces = false; } else if ( tokenizer_pre == "smaug-bpe") { pre_type = LLAMA_VOCAB_PRE_TYPE_SMAUG; @@ -2551,12 +2559,14 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) { tokenizer_pre == "chatglm-bpe") { pre_type = LLAMA_VOCAB_PRE_TYPE_CHATGLM4; special_bos_id = LLAMA_TOKEN_NULL; + clean_spaces = false; } else if ( tokenizer_pre == "glm4" || tokenizer_pre == "glm5") { pre_type = LLAMA_VOCAB_PRE_TYPE_CHATGLM4; special_bos_id = LLAMA_TOKEN_NULL; ignore_merges = true; + clean_spaces = false; } else if ( tokenizer_pre == "viking") { pre_type = LLAMA_VOCAB_PRE_TYPE_VIKING; @@ -2590,6 +2600,7 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) { } else if ( tokenizer_pre == "exaone4") { pre_type = LLAMA_VOCAB_PRE_TYPE_GPT2; + clean_spaces = false; } else if ( tokenizer_pre == "exaone-moe") { pre_type = LLAMA_VOCAB_PRE_TYPE_EXAONE_MOE; @@ -2690,6 +2701,7 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) { } else if ( tokenizer_pre == "mellum2") { pre_type = LLAMA_VOCAB_PRE_TYPE_MELLUM2; + clean_spaces = false; } else if ( tokenizer_pre == "k2-horizon") { pre_type = LLAMA_VOCAB_PRE_TYPE_K2_HORIZON; @@ -2726,6 +2738,7 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) { ml.get_key(LLM_KV_TOKENIZER_ADD_PREFIX, add_space_prefix, false); ml.get_key(LLM_KV_TOKENIZER_REMOVE_EXTRA_WS, remove_extra_whitespaces, false); + ml.get_key(LLM_KV_TOKENIZER_CLEAN_SPACES, clean_spaces, false); } const int token_idx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_LIST).c_str()); diff --git a/tests/CMakeLists.txt b/tests/CMakeLists.txt index 24e6ba609a21..815cc15a3219 100644 --- a/tests/CMakeLists.txt +++ b/tests/CMakeLists.txt @@ -142,7 +142,7 @@ llama_test(test-tokenizer-0 NAME test-tokenizer-0-command-r ARGS ${PROJE llama_test(test-tokenizer-0 NAME test-tokenizer-0-deepseek-coder ARGS ${PROJECT_SOURCE_DIR}/models/ggml-vocab-deepseek-coder.gguf) llama_test(test-tokenizer-0 NAME test-tokenizer-0-deepseek-llm ARGS ${PROJECT_SOURCE_DIR}/models/ggml-vocab-deepseek-llm.gguf) llama_test(test-tokenizer-0 NAME test-tokenizer-0-falcon ARGS ${PROJECT_SOURCE_DIR}/models/ggml-vocab-falcon.gguf) -llama_test(test-tokenizer-0 NAME test-tokenizer-0-gemma-4 ARGS ${PROJECT_SOURCE_DIR}/models/ggml-vocab-gemma-4.gguf) +llama_test(test-tokenizer-0 NAME test-tokenizer-0-gemma-4 ARGS ${PROJECT_SOURCE_DIR}/models/ggml-vocab-gemma-4.gguf --check-decode) llama_test(test-tokenizer-0 NAME test-tokenizer-0-gpt-2 ARGS ${PROJECT_SOURCE_DIR}/models/ggml-vocab-gpt-2.gguf) llama_test(test-tokenizer-0 NAME test-tokenizer-0-llama-bpe ARGS ${PROJECT_SOURCE_DIR}/models/ggml-vocab-llama-bpe.gguf) llama_test(test-tokenizer-0 NAME test-tokenizer-0-llama-spm ARGS ${PROJECT_SOURCE_DIR}/models/ggml-vocab-llama-spm.gguf) diff --git a/tests/test-tokenizer-0.cpp b/tests/test-tokenizer-0.cpp index 37f8312c46fe..5253a46a612c 100644 --- a/tests/test-tokenizer-0.cpp +++ b/tests/test-tokenizer-0.cpp @@ -125,7 +125,7 @@ static llama_tests read_tests(const std::string & fname_inp, const std::string & int main(int argc, char **argv) { if (argc < 2) { - fprintf(stderr, "Usage: %s vocab-file [text-file]\n", argv[0]); + fprintf(stderr, "Usage: %s vocab-file [text-file] [--check-decode]\n", argv[0]); return 1; } @@ -135,8 +135,13 @@ int main(int argc, char **argv) { const std::string fname_out = fname + ".out"; std::string fname_text; - if (argc > 2) { - fname_text = argv[2]; + bool check_decode = false; // also check that the expected tokens detokenize to the input text + for (int i = 2; i < argc; i++) { + if (std::string(argv[i]) == "--check-decode") { + check_decode = true; + } else { + fname_text = argv[i]; + } } fprintf(stderr, "%s : reading vocab from: '%s'\n", __func__, fname.c_str()); @@ -244,6 +249,14 @@ int main(int argc, char **argv) { success = false; } + + if (check_decode) { + const std::string dec = common_detokenize(ctx, test_kv.second); + if (dec != test_kv.first) { + fprintf(stderr, "%s : failed decode: '%s' detokenized to '%s'\n", __func__, test_kv.first.c_str(), dec.c_str()); + success = false; + } + } } }); } From 7170cad4d02f7907517613931840cbb2367399a3 Mon Sep 17 00:00:00 2001 From: Michael Taylor <162068037+mctylr-gh@users.noreply.github.com> Date: Thu, 22 Oct 2026 05:19:58 -0300 Subject: [PATCH 67/69] sycl: fix CONV_2D wrong results for non-contiguous input (#30363) * sycl: fix CONV_2D wrong results for non-contiguous input The kernel indexes the `input` as a contiguous [W, H, C, N] tensor, but supports_op claimed the op for any layout. A non-contiguous (e.g. `CWHN` permuted) input was read with the wrong element offsets, silently producing wrong results. Gate the CONV_2D op on `input` and `kernel` contiguity, matching the CUDA backend contract, and assert input contiguity in the op. `test-backend-ops -b SYCL0 -o CONV_2D`: goes from 16 failures to 0, none. ```text 2006/2006 tests passed Backend SYCL0: OK Backend 2/2: CPU Skipping 2/2 backends passed OK ``` Assisted-by: pi (unsloth/Qwen3.8-27B-GGUF:Q4_K_M via llama.cpp) * sycl : CONV_2D cwhn=1 support Make the SYCL backend compute `GGML_OP_CONV_2D` correctly for `cwhn=1` (CWHN channel-order) inputs and kernels, so the previously failing test- backend-ops CONV_2D cases pass on SYCL instead of being skipped or miscomputed. - Implement CWHN layout support in `ggml/src/ggml-sycl/conv2d.cpp` (`ggml_sycl_op_conv2d`). - Update SYCL `supports_op` for `GGML_OP_CONV_2D` in `ggml/src/ggml-sycl/ggml-sycl.cpp` to accept both layouts. Assisted-by: pi (Greatjedi/Qwen3.8-27B-Intel-Arc-Tuned-GGUF via llama.cpp) * sycl docs : update ops for SYCL --- docs/ops.md | 10 +- docs/ops/SYCL.csv | 13840 ++++++++++++++++------------- ggml/src/ggml-sycl/conv2d.cpp | 45 +- ggml/src/ggml-sycl/ggml-sycl.cpp | 5 + 4 files changed, 7836 insertions(+), 6064 deletions(-) diff --git a/docs/ops.md b/docs/ops.md index 581db6495718..6ffc642399e3 100644 --- a/docs/ops.md +++ b/docs/ops.md @@ -27,7 +27,7 @@ Legend: | COL2IM_1D | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | | CONCAT | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ✅ | 🟡 | ❌ | ❌ | | CONT | ❌ | 🟡 | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ✅ | 🟡 | ❌ | ❌ | -| CONV_1D_DW | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | +| CONV_1D_DW | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ✅ | | CONV_2D | ❌ | ❌ | 🟡 | ✅ | ❌ | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ | | CONV_2D_DW | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | | CONV_3D | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | @@ -76,10 +76,10 @@ Legend: | MUL_MAT | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | | MUL_MAT_HADAMARD | ❌ | ❌ | ✅ | ❌ | ✅ | 🟡 | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | 🟡 | | MUL_MAT_ID | ❌ | 🟡 | ✅ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | 🟡 | ❌ | -| MUL_MAT_ID_W4A4 | ❌ | ❌ | ✅ | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | -| MUL_MAT_ID_W4A8 | ❌ | ❌ | ✅ | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | -| MUL_MAT_W4A4 | ❌ | ❌ | ✅ | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | -| MUL_MAT_W4A8 | ❌ | ❌ | ✅ | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | +| MUL_MAT_ID_W4A4 | ❌ | ❌ | ✅ | ❌ | ❌ | 🟡 | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | +| MUL_MAT_ID_W4A8 | ❌ | ❌ | ✅ | ❌ | ❌ | 🟡 | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | +| MUL_MAT_W4A4 | ❌ | ❌ | ✅ | ❌ | ❌ | 🟡 | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | +| MUL_MAT_W4A8 | ❌ | ❌ | ✅ | ❌ | ❌ | 🟡 | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | | NEG | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | | NORM | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ✅ | ✅ | 🟡 | ❌ | ❌ | | OPT_STEP_ADAMW | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | diff --git a/docs/ops/SYCL.csv b/docs/ops/SYCL.csv index 09e698abace2..3ef1ca1d7718 100644 --- a/docs/ops/SYCL.csv +++ b/docs/ops/SYCL.csv @@ -167,6 +167,90 @@ "SYCL0","ROUND","type=f32,ne_a=[5,7,11,13],v=1","support","1","yes","SYCL" "SYCL0","TRUNC","type=f32,ne_a=[128,2,2,2],v=1","support","1","yes","SYCL" "SYCL0","TRUNC","type=f32,ne_a=[5,7,11,13],v=1","support","1","yes","SYCL" +"SYCL0","ABS","type=bf16,ne_a=[128,2,2,2],v=0","support","1","yes","SYCL" +"SYCL0","ABS","type=bf16,ne_a=[5,7,11,13],v=0","support","1","yes","SYCL" +"SYCL0","SGN","type=bf16,ne_a=[128,2,2,2],v=0","support","1","yes","SYCL" +"SYCL0","SGN","type=bf16,ne_a=[5,7,11,13],v=0","support","1","yes","SYCL" +"SYCL0","NEG","type=bf16,ne_a=[128,2,2,2],v=0","support","1","yes","SYCL" +"SYCL0","NEG","type=bf16,ne_a=[5,7,11,13],v=0","support","1","yes","SYCL" +"SYCL0","STEP","type=bf16,ne_a=[128,2,2,2],v=0","support","1","yes","SYCL" +"SYCL0","STEP","type=bf16,ne_a=[5,7,11,13],v=0","support","1","yes","SYCL" +"SYCL0","TANH","type=bf16,ne_a=[128,2,2,2],v=0","support","1","yes","SYCL" +"SYCL0","TANH","type=bf16,ne_a=[5,7,11,13],v=0","support","1","yes","SYCL" +"SYCL0","ELU","type=bf16,ne_a=[128,2,2,2],v=0","support","1","yes","SYCL" +"SYCL0","ELU","type=bf16,ne_a=[5,7,11,13],v=0","support","1","yes","SYCL" +"SYCL0","RELU","type=bf16,ne_a=[128,2,2,2],v=0","support","1","yes","SYCL" +"SYCL0","RELU","type=bf16,ne_a=[5,7,11,13],v=0","support","1","yes","SYCL" +"SYCL0","SIGMOID","type=bf16,ne_a=[128,2,2,2],v=0","support","1","yes","SYCL" +"SYCL0","SIGMOID","type=bf16,ne_a=[5,7,11,13],v=0","support","1","yes","SYCL" +"SYCL0","GELU","type=bf16,ne_a=[128,2,2,2],v=0","support","1","yes","SYCL" +"SYCL0","GELU","type=bf16,ne_a=[5,7,11,13],v=0","support","1","yes","SYCL" +"SYCL0","GELU_QUICK","type=bf16,ne_a=[128,2,2,2],v=0","support","1","yes","SYCL" +"SYCL0","GELU_QUICK","type=bf16,ne_a=[5,7,11,13],v=0","support","1","yes","SYCL" +"SYCL0","SILU","type=bf16,ne_a=[128,2,2,2],v=0","support","1","yes","SYCL" +"SYCL0","SILU","type=bf16,ne_a=[5,7,11,13],v=0","support","1","yes","SYCL" +"SYCL0","HARDSWISH","type=bf16,ne_a=[128,2,2,2],v=0","support","1","yes","SYCL" +"SYCL0","HARDSWISH","type=bf16,ne_a=[5,7,11,13],v=0","support","1","yes","SYCL" +"SYCL0","HARDSIGMOID","type=bf16,ne_a=[128,2,2,2],v=0","support","1","yes","SYCL" +"SYCL0","HARDSIGMOID","type=bf16,ne_a=[5,7,11,13],v=0","support","1","yes","SYCL" +"SYCL0","EXP","type=bf16,ne_a=[128,2,2,2],v=0","support","1","yes","SYCL" +"SYCL0","EXP","type=bf16,ne_a=[5,7,11,13],v=0","support","1","yes","SYCL" +"SYCL0","EXPM1","type=bf16,ne_a=[128,2,2,2],v=0","support","1","yes","SYCL" +"SYCL0","EXPM1","type=bf16,ne_a=[5,7,11,13],v=0","support","1","yes","SYCL" +"SYCL0","SOFTPLUS","type=bf16,ne_a=[128,2,2,2],v=0","support","1","yes","SYCL" +"SYCL0","SOFTPLUS","type=bf16,ne_a=[5,7,11,13],v=0","support","1","yes","SYCL" +"SYCL0","GELU_ERF","type=bf16,ne_a=[128,2,2,2],v=0","support","1","yes","SYCL" +"SYCL0","GELU_ERF","type=bf16,ne_a=[5,7,11,13],v=0","support","1","yes","SYCL" +"SYCL0","FLOOR","type=bf16,ne_a=[128,2,2,2],v=0","support","1","yes","SYCL" +"SYCL0","FLOOR","type=bf16,ne_a=[5,7,11,13],v=0","support","1","yes","SYCL" +"SYCL0","CEIL","type=bf16,ne_a=[128,2,2,2],v=0","support","1","yes","SYCL" +"SYCL0","CEIL","type=bf16,ne_a=[5,7,11,13],v=0","support","1","yes","SYCL" +"SYCL0","ROUND","type=bf16,ne_a=[128,2,2,2],v=0","support","1","yes","SYCL" +"SYCL0","ROUND","type=bf16,ne_a=[5,7,11,13],v=0","support","1","yes","SYCL" +"SYCL0","TRUNC","type=bf16,ne_a=[128,2,2,2],v=0","support","1","yes","SYCL" +"SYCL0","TRUNC","type=bf16,ne_a=[5,7,11,13],v=0","support","1","yes","SYCL" +"SYCL0","ABS","type=bf16,ne_a=[128,2,2,2],v=1","support","1","yes","SYCL" +"SYCL0","ABS","type=bf16,ne_a=[5,7,11,13],v=1","support","1","yes","SYCL" +"SYCL0","SGN","type=bf16,ne_a=[128,2,2,2],v=1","support","1","yes","SYCL" +"SYCL0","SGN","type=bf16,ne_a=[5,7,11,13],v=1","support","1","yes","SYCL" +"SYCL0","NEG","type=bf16,ne_a=[128,2,2,2],v=1","support","1","yes","SYCL" +"SYCL0","NEG","type=bf16,ne_a=[5,7,11,13],v=1","support","1","yes","SYCL" +"SYCL0","STEP","type=bf16,ne_a=[128,2,2,2],v=1","support","1","yes","SYCL" +"SYCL0","STEP","type=bf16,ne_a=[5,7,11,13],v=1","support","1","yes","SYCL" +"SYCL0","TANH","type=bf16,ne_a=[128,2,2,2],v=1","support","1","yes","SYCL" +"SYCL0","TANH","type=bf16,ne_a=[5,7,11,13],v=1","support","1","yes","SYCL" +"SYCL0","ELU","type=bf16,ne_a=[128,2,2,2],v=1","support","1","yes","SYCL" +"SYCL0","ELU","type=bf16,ne_a=[5,7,11,13],v=1","support","1","yes","SYCL" +"SYCL0","RELU","type=bf16,ne_a=[128,2,2,2],v=1","support","1","yes","SYCL" +"SYCL0","RELU","type=bf16,ne_a=[5,7,11,13],v=1","support","1","yes","SYCL" +"SYCL0","SIGMOID","type=bf16,ne_a=[128,2,2,2],v=1","support","1","yes","SYCL" +"SYCL0","SIGMOID","type=bf16,ne_a=[5,7,11,13],v=1","support","1","yes","SYCL" +"SYCL0","GELU","type=bf16,ne_a=[128,2,2,2],v=1","support","1","yes","SYCL" +"SYCL0","GELU","type=bf16,ne_a=[5,7,11,13],v=1","support","1","yes","SYCL" +"SYCL0","GELU_QUICK","type=bf16,ne_a=[128,2,2,2],v=1","support","1","yes","SYCL" +"SYCL0","GELU_QUICK","type=bf16,ne_a=[5,7,11,13],v=1","support","1","yes","SYCL" +"SYCL0","SILU","type=bf16,ne_a=[128,2,2,2],v=1","support","1","yes","SYCL" +"SYCL0","SILU","type=bf16,ne_a=[5,7,11,13],v=1","support","1","yes","SYCL" +"SYCL0","HARDSWISH","type=bf16,ne_a=[128,2,2,2],v=1","support","1","yes","SYCL" +"SYCL0","HARDSWISH","type=bf16,ne_a=[5,7,11,13],v=1","support","1","yes","SYCL" +"SYCL0","HARDSIGMOID","type=bf16,ne_a=[128,2,2,2],v=1","support","1","yes","SYCL" +"SYCL0","HARDSIGMOID","type=bf16,ne_a=[5,7,11,13],v=1","support","1","yes","SYCL" +"SYCL0","EXP","type=bf16,ne_a=[128,2,2,2],v=1","support","1","yes","SYCL" +"SYCL0","EXP","type=bf16,ne_a=[5,7,11,13],v=1","support","1","yes","SYCL" +"SYCL0","EXPM1","type=bf16,ne_a=[128,2,2,2],v=1","support","1","yes","SYCL" +"SYCL0","EXPM1","type=bf16,ne_a=[5,7,11,13],v=1","support","1","yes","SYCL" +"SYCL0","SOFTPLUS","type=bf16,ne_a=[128,2,2,2],v=1","support","1","yes","SYCL" +"SYCL0","SOFTPLUS","type=bf16,ne_a=[5,7,11,13],v=1","support","1","yes","SYCL" +"SYCL0","GELU_ERF","type=bf16,ne_a=[128,2,2,2],v=1","support","1","yes","SYCL" +"SYCL0","GELU_ERF","type=bf16,ne_a=[5,7,11,13],v=1","support","1","yes","SYCL" +"SYCL0","FLOOR","type=bf16,ne_a=[128,2,2,2],v=1","support","1","yes","SYCL" +"SYCL0","FLOOR","type=bf16,ne_a=[5,7,11,13],v=1","support","1","yes","SYCL" +"SYCL0","CEIL","type=bf16,ne_a=[128,2,2,2],v=1","support","1","yes","SYCL" +"SYCL0","CEIL","type=bf16,ne_a=[5,7,11,13],v=1","support","1","yes","SYCL" +"SYCL0","ROUND","type=bf16,ne_a=[128,2,2,2],v=1","support","1","yes","SYCL" +"SYCL0","ROUND","type=bf16,ne_a=[5,7,11,13],v=1","support","1","yes","SYCL" +"SYCL0","TRUNC","type=bf16,ne_a=[128,2,2,2],v=1","support","1","yes","SYCL" +"SYCL0","TRUNC","type=bf16,ne_a=[5,7,11,13],v=1","support","1","yes","SYCL" "SYCL0","DSV4_HC_COMB","n_tokens=1,n_iter=1,eps=0.000001","support","1","yes","SYCL" "SYCL0","DSV4_HC_COMB","n_tokens=17,n_iter=4,eps=0.000001","support","1","yes","SYCL" "SYCL0","DSV4_HC_COMB","n_tokens=257,n_iter=8,eps=0.000001","support","1","yes","SYCL" @@ -196,12 +280,12 @@ "SYCL0","DSV4_HC_PRE","n_embd=128,n_hc=8,n_tokens=17,gated=1","support","1","yes","SYCL" "SYCL0","DSV4_HC_PRE","n_embd=128,n_hc=65,n_tokens=17,gated=0","support","1","yes","SYCL" "SYCL0","DSV4_HC_PRE","n_embd=128,n_hc=65,n_tokens=17,gated=1","support","1","yes","SYCL" -"SYCL0","DSV4_HC_POST","n_embd=1,n_tokens=1,identity=0","support","1","yes","SYCL" -"SYCL0","DSV4_HC_POST","n_embd=31,n_tokens=17,identity=0","support","1","yes","SYCL" -"SYCL0","DSV4_HC_POST","n_embd=128,n_tokens=257,identity=0","support","1","yes","SYCL" -"SYCL0","DSV4_HC_POST","n_embd=4096,n_tokens=21,identity=0","support","1","yes","SYCL" -"SYCL0","DSV4_HC_POST","n_embd=31,n_tokens=17,identity=1","support","1","yes","SYCL" -"SYCL0","DSV4_HC_POST","n_embd=4096,n_tokens=21,identity=1","support","1","yes","SYCL" +"SYCL0","DSV4_HC_POST","n_embd=1,n_tokens=1,identity=0,gated=0","support","1","yes","SYCL" +"SYCL0","DSV4_HC_POST","n_embd=31,n_tokens=17,identity=0,gated=0","support","1","yes","SYCL" +"SYCL0","DSV4_HC_POST","n_embd=128,n_tokens=257,identity=0,gated=0","support","1","yes","SYCL" +"SYCL0","DSV4_HC_POST","n_embd=4096,n_tokens=21,identity=0,gated=0","support","1","yes","SYCL" +"SYCL0","DSV4_HC_POST","n_embd=31,n_tokens=17,identity=1,gated=0","support","1","yes","SYCL" +"SYCL0","DSV4_HC_POST","n_embd=4096,n_tokens=21,identity=1,gated=0","support","1","yes","SYCL" "SYCL0","REGLU","type=f16,ne_a=[128,2,2,2],v=0,swapped=0","support","1","yes","SYCL" "SYCL0","REGLU","type=f16,ne_a=[5,7,11,13],v=0,swapped=0","support","1","yes","SYCL" "SYCL0","REGLU","type=f16,ne_a=[128,2,2,2],v=0,swapped=1","support","1","yes","SYCL" @@ -322,6 +406,66 @@ "SYCL0","GEGLU_QUICK","type=f32,ne_a=[5,7,11,13],v=1,swapped=1","support","1","yes","SYCL" "SYCL0","GEGLU_QUICK","type=f32,ne_a=[128,2,2,2],v=1,split","support","1","yes","SYCL" "SYCL0","GEGLU_QUICK","type=f32,ne_a=[5,7,11,13],v=1,split","support","1","yes","SYCL" +"SYCL0","REGLU","type=bf16,ne_a=[128,2,2,2],v=0,swapped=0","support","1","yes","SYCL" +"SYCL0","REGLU","type=bf16,ne_a=[5,7,11,13],v=0,swapped=0","support","1","yes","SYCL" +"SYCL0","REGLU","type=bf16,ne_a=[128,2,2,2],v=0,swapped=1","support","1","yes","SYCL" +"SYCL0","REGLU","type=bf16,ne_a=[5,7,11,13],v=0,swapped=1","support","1","yes","SYCL" +"SYCL0","REGLU","type=bf16,ne_a=[128,2,2,2],v=0,split","support","1","yes","SYCL" +"SYCL0","REGLU","type=bf16,ne_a=[5,7,11,13],v=0,split","support","1","yes","SYCL" +"SYCL0","GEGLU","type=bf16,ne_a=[128,2,2,2],v=0,swapped=0","support","1","yes","SYCL" +"SYCL0","GEGLU","type=bf16,ne_a=[5,7,11,13],v=0,swapped=0","support","1","yes","SYCL" +"SYCL0","GEGLU","type=bf16,ne_a=[128,2,2,2],v=0,swapped=1","support","1","yes","SYCL" +"SYCL0","GEGLU","type=bf16,ne_a=[5,7,11,13],v=0,swapped=1","support","1","yes","SYCL" +"SYCL0","GEGLU","type=bf16,ne_a=[128,2,2,2],v=0,split","support","1","yes","SYCL" +"SYCL0","GEGLU","type=bf16,ne_a=[5,7,11,13],v=0,split","support","1","yes","SYCL" +"SYCL0","SWIGLU","type=bf16,ne_a=[128,2,2,2],v=0,swapped=0","support","1","yes","SYCL" +"SYCL0","SWIGLU","type=bf16,ne_a=[5,7,11,13],v=0,swapped=0","support","1","yes","SYCL" +"SYCL0","SWIGLU","type=bf16,ne_a=[128,2,2,2],v=0,swapped=1","support","1","yes","SYCL" +"SYCL0","SWIGLU","type=bf16,ne_a=[5,7,11,13],v=0,swapped=1","support","1","yes","SYCL" +"SYCL0","SWIGLU","type=bf16,ne_a=[128,2,2,2],v=0,split","support","1","yes","SYCL" +"SYCL0","SWIGLU","type=bf16,ne_a=[5,7,11,13],v=0,split","support","1","yes","SYCL" +"SYCL0","GEGLU_ERF","type=bf16,ne_a=[128,2,2,2],v=0,swapped=0","support","1","yes","SYCL" +"SYCL0","GEGLU_ERF","type=bf16,ne_a=[5,7,11,13],v=0,swapped=0","support","1","yes","SYCL" +"SYCL0","GEGLU_ERF","type=bf16,ne_a=[128,2,2,2],v=0,swapped=1","support","1","yes","SYCL" +"SYCL0","GEGLU_ERF","type=bf16,ne_a=[5,7,11,13],v=0,swapped=1","support","1","yes","SYCL" +"SYCL0","GEGLU_ERF","type=bf16,ne_a=[128,2,2,2],v=0,split","support","1","yes","SYCL" +"SYCL0","GEGLU_ERF","type=bf16,ne_a=[5,7,11,13],v=0,split","support","1","yes","SYCL" +"SYCL0","GEGLU_QUICK","type=bf16,ne_a=[128,2,2,2],v=0,swapped=0","support","1","yes","SYCL" +"SYCL0","GEGLU_QUICK","type=bf16,ne_a=[5,7,11,13],v=0,swapped=0","support","1","yes","SYCL" +"SYCL0","GEGLU_QUICK","type=bf16,ne_a=[128,2,2,2],v=0,swapped=1","support","1","yes","SYCL" +"SYCL0","GEGLU_QUICK","type=bf16,ne_a=[5,7,11,13],v=0,swapped=1","support","1","yes","SYCL" +"SYCL0","GEGLU_QUICK","type=bf16,ne_a=[128,2,2,2],v=0,split","support","1","yes","SYCL" +"SYCL0","GEGLU_QUICK","type=bf16,ne_a=[5,7,11,13],v=0,split","support","1","yes","SYCL" +"SYCL0","REGLU","type=bf16,ne_a=[128,2,2,2],v=1,swapped=0","support","1","yes","SYCL" +"SYCL0","REGLU","type=bf16,ne_a=[5,7,11,13],v=1,swapped=0","support","1","yes","SYCL" +"SYCL0","REGLU","type=bf16,ne_a=[128,2,2,2],v=1,swapped=1","support","1","yes","SYCL" +"SYCL0","REGLU","type=bf16,ne_a=[5,7,11,13],v=1,swapped=1","support","1","yes","SYCL" +"SYCL0","REGLU","type=bf16,ne_a=[128,2,2,2],v=1,split","support","1","yes","SYCL" +"SYCL0","REGLU","type=bf16,ne_a=[5,7,11,13],v=1,split","support","1","yes","SYCL" +"SYCL0","GEGLU","type=bf16,ne_a=[128,2,2,2],v=1,swapped=0","support","1","yes","SYCL" +"SYCL0","GEGLU","type=bf16,ne_a=[5,7,11,13],v=1,swapped=0","support","1","yes","SYCL" +"SYCL0","GEGLU","type=bf16,ne_a=[128,2,2,2],v=1,swapped=1","support","1","yes","SYCL" +"SYCL0","GEGLU","type=bf16,ne_a=[5,7,11,13],v=1,swapped=1","support","1","yes","SYCL" +"SYCL0","GEGLU","type=bf16,ne_a=[128,2,2,2],v=1,split","support","1","yes","SYCL" +"SYCL0","GEGLU","type=bf16,ne_a=[5,7,11,13],v=1,split","support","1","yes","SYCL" +"SYCL0","SWIGLU","type=bf16,ne_a=[128,2,2,2],v=1,swapped=0","support","1","yes","SYCL" +"SYCL0","SWIGLU","type=bf16,ne_a=[5,7,11,13],v=1,swapped=0","support","1","yes","SYCL" +"SYCL0","SWIGLU","type=bf16,ne_a=[128,2,2,2],v=1,swapped=1","support","1","yes","SYCL" +"SYCL0","SWIGLU","type=bf16,ne_a=[5,7,11,13],v=1,swapped=1","support","1","yes","SYCL" +"SYCL0","SWIGLU","type=bf16,ne_a=[128,2,2,2],v=1,split","support","1","yes","SYCL" +"SYCL0","SWIGLU","type=bf16,ne_a=[5,7,11,13],v=1,split","support","1","yes","SYCL" +"SYCL0","GEGLU_ERF","type=bf16,ne_a=[128,2,2,2],v=1,swapped=0","support","1","yes","SYCL" +"SYCL0","GEGLU_ERF","type=bf16,ne_a=[5,7,11,13],v=1,swapped=0","support","1","yes","SYCL" +"SYCL0","GEGLU_ERF","type=bf16,ne_a=[128,2,2,2],v=1,swapped=1","support","1","yes","SYCL" +"SYCL0","GEGLU_ERF","type=bf16,ne_a=[5,7,11,13],v=1,swapped=1","support","1","yes","SYCL" +"SYCL0","GEGLU_ERF","type=bf16,ne_a=[128,2,2,2],v=1,split","support","1","yes","SYCL" +"SYCL0","GEGLU_ERF","type=bf16,ne_a=[5,7,11,13],v=1,split","support","1","yes","SYCL" +"SYCL0","GEGLU_QUICK","type=bf16,ne_a=[128,2,2,2],v=1,swapped=0","support","1","yes","SYCL" +"SYCL0","GEGLU_QUICK","type=bf16,ne_a=[5,7,11,13],v=1,swapped=0","support","1","yes","SYCL" +"SYCL0","GEGLU_QUICK","type=bf16,ne_a=[128,2,2,2],v=1,swapped=1","support","1","yes","SYCL" +"SYCL0","GEGLU_QUICK","type=bf16,ne_a=[5,7,11,13],v=1,swapped=1","support","1","yes","SYCL" +"SYCL0","GEGLU_QUICK","type=bf16,ne_a=[128,2,2,2],v=1,split","support","1","yes","SYCL" +"SYCL0","GEGLU_QUICK","type=bf16,ne_a=[5,7,11,13],v=1,split","support","1","yes","SYCL" "SYCL0","SWIGLU_OAI","type=f32,ne_a=[128,2,2,2],v=0,alpha=0.500000,limit=2.000000","support","1","yes","SYCL" "SYCL0","SWIGLU_OAI","type=f32,ne_a=[128,2,2,2],v=0,alpha=0.500000,limit=7.000000","support","1","yes","SYCL" "SYCL0","SWIGLU_OAI","type=f32,ne_a=[128,2,2,2],v=0,alpha=1.702000,limit=2.000000","support","1","yes","SYCL" @@ -1429,6 +1573,10 @@ "SYCL0","POOL_2D","pool_type=avg,type_input=f32,ne_input=[10,10,3,1],k0=3,k1=3,s0=2,s1=2,p0=0,p1=1","support","1","yes","SYCL" "SYCL0","POOL_2D","pool_type=avg,type_input=f32,ne_input=[10,10,3,1],k0=3,k1=3,s0=2,s1=2,p0=1,p1=0","support","1","yes","SYCL" "SYCL0","POOL_2D","pool_type=avg,type_input=f32,ne_input=[10,10,3,1],k0=3,k1=3,s0=2,s1=2,p0=1,p1=1","support","1","yes","SYCL" +"SYCL0","POOL_2D","pool_type=avg,type_input=f32,ne_input=[4096,2,1,1],k0=32,k1=1,s0=32,s1=1,p0=0,p1=0","support","1","yes","SYCL" +"SYCL0","POOL_2D","pool_type=avg,type_input=f32,ne_input=[4096,2,1,1],k0=64,k1=1,s0=64,s1=1,p0=0,p1=0","support","1","yes","SYCL" +"SYCL0","POOL_2D","pool_type=avg,type_input=f32,ne_input=[4160,2,1,1],k0=32,k1=1,s0=32,s1=1,p0=0,p1=0","support","1","yes","SYCL" +"SYCL0","POOL_2D","pool_type=avg,type_input=f32,ne_input=[4160,2,1,1],k0=64,k1=1,s0=64,s1=1,p0=0,p1=0","support","1","yes","SYCL" "SYCL0","POOL_2D","pool_type=max,type_input=f32,ne_input=[10,10,3,1],k0=1,k1=1,s0=1,s1=1,p0=0,p1=0","support","1","yes","SYCL" "SYCL0","POOL_2D","pool_type=max,type_input=f32,ne_input=[10,10,3,1],k0=1,k1=1,s0=1,s1=1,p0=0,p1=1","support","1","yes","SYCL" "SYCL0","POOL_2D","pool_type=max,type_input=f32,ne_input=[10,10,3,1],k0=1,k1=1,s0=1,s1=1,p0=1,p1=0","support","1","yes","SYCL" @@ -1493,6 +1641,10 @@ "SYCL0","POOL_2D","pool_type=max,type_input=f32,ne_input=[10,10,3,1],k0=3,k1=3,s0=2,s1=2,p0=0,p1=1","support","1","yes","SYCL" "SYCL0","POOL_2D","pool_type=max,type_input=f32,ne_input=[10,10,3,1],k0=3,k1=3,s0=2,s1=2,p0=1,p1=0","support","1","yes","SYCL" "SYCL0","POOL_2D","pool_type=max,type_input=f32,ne_input=[10,10,3,1],k0=3,k1=3,s0=2,s1=2,p0=1,p1=1","support","1","yes","SYCL" +"SYCL0","POOL_2D","pool_type=max,type_input=f32,ne_input=[4096,2,1,1],k0=32,k1=1,s0=32,s1=1,p0=0,p1=0","support","1","yes","SYCL" +"SYCL0","POOL_2D","pool_type=max,type_input=f32,ne_input=[4096,2,1,1],k0=64,k1=1,s0=64,s1=1,p0=0,p1=0","support","1","yes","SYCL" +"SYCL0","POOL_2D","pool_type=max,type_input=f32,ne_input=[4160,2,1,1],k0=32,k1=1,s0=32,s1=1,p0=0,p1=0","support","1","yes","SYCL" +"SYCL0","POOL_2D","pool_type=max,type_input=f32,ne_input=[4160,2,1,1],k0=64,k1=1,s0=64,s1=1,p0=0,p1=0","support","1","yes","SYCL" "SYCL0","POOL_1D","pool_type=avg,type_input=f32,ne_input=[10,3,2,1],k0=1,s0=1,p0=0","support","1","yes","SYCL" "SYCL0","POOL_1D","pool_type=avg,type_input=f32,ne_input=[11,1,3,2],k0=1,s0=1,p0=0","support","1","yes","SYCL" "SYCL0","POOL_1D","pool_type=avg,type_input=f32,ne_input=[128,2,1,3],k0=1,s0=1,p0=0","support","1","yes","SYCL" @@ -1801,6 +1953,16 @@ "SYCL0","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[2,2,1536,729],ne_kernel=[2,2,1536,4096],s0=1,s1=1,p0=0,p1=0,d0=1,d1=1,is_2D=1","support","1","yes","SYCL" "SYCL0","IM2COL","type_input=f32,type_kernel=f16,dst_type=f16,ne_input=[128,128,1,2],ne_kernel=[32,33,1,2],s0=1,s1=1,p0=1,p1=1,d0=1,d1=1,is_2D=1","support","1","yes","SYCL" "SYCL0","IM2COL","type_input=f32,type_kernel=f16,dst_type=f16,ne_input=[128,128,2,1],ne_kernel=[33,34,2,1],s0=1,s1=1,p0=1,p1=1,d0=1,d1=1,is_2D=1","support","1","yes","SYCL" +"SYCL0","IM2COL","type_input=f32,type_kernel=f16,dst_type=f16,ne_input=[64,64,320,1],ne_kernel=[1,1,320,1],s0=1,s1=1,p0=0,p1=0,d0=1,d1=1,is_2D=1","support","1","yes","SYCL" +"SYCL0","IM2COL","type_input=f32,type_kernel=f16,dst_type=f16,ne_input=[16,16,1280,2],ne_kernel=[1,1,1280,1],s0=1,s1=1,p0=0,p1=0,d0=1,d1=1,is_2D=1","support","1","yes","SYCL" +"SYCL0","IM2COL","type_input=f32,type_kernel=f16,dst_type=f16,ne_input=[8,8,2560,1],ne_kernel=[1,1,2560,1],s0=1,s1=1,p0=0,p1=0,d0=1,d1=1,is_2D=1","support","1","yes","SYCL" +"SYCL0","IM2COL","type_input=f32,type_kernel=f16,dst_type=f16,ne_input=[32,32,192,1],ne_kernel=[2,2,192,1],s0=2,s1=2,p0=0,p1=0,d0=1,d1=1,is_2D=1","support","1","yes","SYCL" +"SYCL0","IM2COL","type_input=f32,type_kernel=f16,dst_type=f16,ne_input=[64,512,1,1],ne_kernel=[2,512,1,1],s0=2,s1=0,p0=0,p1=0,d0=1,d1=0,is_2D=0","support","1","yes","SYCL" +"SYCL0","IM2COL","type_input=f32,type_kernel=f16,dst_type=f32,ne_input=[64,64,320,1],ne_kernel=[1,1,320,1],s0=1,s1=1,p0=0,p1=0,d0=1,d1=1,is_2D=1","support","1","yes","SYCL" +"SYCL0","IM2COL","type_input=f32,type_kernel=f16,dst_type=f32,ne_input=[16,16,1280,2],ne_kernel=[1,1,1280,1],s0=1,s1=1,p0=0,p1=0,d0=1,d1=1,is_2D=1","support","1","yes","SYCL" +"SYCL0","IM2COL","type_input=f32,type_kernel=f16,dst_type=f32,ne_input=[8,8,2560,1],ne_kernel=[1,1,2560,1],s0=1,s1=1,p0=0,p1=0,d0=1,d1=1,is_2D=1","support","1","yes","SYCL" +"SYCL0","IM2COL","type_input=f32,type_kernel=f16,dst_type=f32,ne_input=[32,32,192,1],ne_kernel=[2,2,192,1],s0=2,s1=2,p0=0,p1=0,d0=1,d1=1,is_2D=1","support","1","yes","SYCL" +"SYCL0","IM2COL","type_input=f32,type_kernel=f16,dst_type=f32,ne_input=[64,512,1,1],ne_kernel=[2,512,1,1],s0=2,s1=0,p0=0,p1=0,d0=1,d1=0,is_2D=0","support","1","yes","SYCL" "SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[10,10,10,9],ne_kernel=[3,3,3,1],IC=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL" "SYCL0","IM2COL_3D","type_input=f32,type_kernel=f16,dst_type=f32,ne_input=[10,10,10,9],ne_kernel=[3,3,3,1],IC=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL" "SYCL0","IM2COL_3D","type_input=f32,type_kernel=f16,dst_type=f16,ne_input=[10,10,10,9],ne_kernel=[3,3,3,1],IC=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL" @@ -3852,3149 +4014,3159 @@ "SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=3,p2=3,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL" "SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=3,p2=3,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL" "SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=3,p2=3,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL" -"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[1,1,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","1","yes","SYCL" -"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[1,1,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=1","support","1","yes","SYCL" -"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[1,1,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","1","yes","SYCL" -"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[1,1,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=1","support","1","yes","SYCL" -"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[1,1,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","1","yes","SYCL" -"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[1,1,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=1","support","1","yes","SYCL" -"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[1,1,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","1","yes","SYCL" -"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[1,1,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=1","support","1","yes","SYCL" -"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,1,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","1","yes","SYCL" -"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,1,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=1","support","1","yes","SYCL" -"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,1,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","1","yes","SYCL" -"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,1,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=1","support","1","yes","SYCL" -"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,1,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","1","yes","SYCL" -"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,1,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=1","support","1","yes","SYCL" -"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,1,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","1","yes","SYCL" -"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,1,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=1","support","1","yes","SYCL" -"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[2,1,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","1","yes","SYCL" -"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[2,1,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=1","support","1","yes","SYCL" -"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[2,1,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","1","yes","SYCL" -"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[2,1,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=1","support","1","yes","SYCL" -"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[2,1,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","1","yes","SYCL" -"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[2,1,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=1","support","1","yes","SYCL" -"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[2,1,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","1","yes","SYCL" -"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[2,1,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=1","support","1","yes","SYCL" -"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,1,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","1","yes","SYCL" -"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,1,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=1","support","1","yes","SYCL" -"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,1,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","1","yes","SYCL" -"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,1,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=1","support","1","yes","SYCL" -"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,1,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","1","yes","SYCL" -"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,1,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=1","support","1","yes","SYCL" -"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,1,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","1","yes","SYCL" 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-7003,264 +7175,282 @@ "SYCL0","CONV_2D_DW","ne_input=[17,34,9,1],ne_kernel=[3,3,1,9],type_kernel=f16,stride=1,padding=0,dilation=1,cwhn=1","support","1","yes","SYCL" "SYCL0","CONV_2D_DW","ne_input=[32,8,64,1],ne_kernel=[3,3,1,64],type_kernel=f16,stride=2,padding=1,dilation=1,cwhn=0","support","1","yes","SYCL" "SYCL0","CONV_2D_DW","ne_input=[32,8,64,1],ne_kernel=[3,3,1,64],type_kernel=f16,stride=2,padding=1,dilation=1,cwhn=1","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f32","support","1","yes","SYCL" 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-"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=1,IC=4,ID=8,IH=8,IW=8,OC=8,KD=1,KH=1,KW=1,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f32","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f16","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f16","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f16","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","1","yes","SYCL" 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-"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f16","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f16","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f16","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","1","yes","SYCL" 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-"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f16","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f16","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f16","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f16","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f16","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f16","support","1","yes","SYCL" -"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","1","yes","SYCL" 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+"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16,kernel_offset=0","support","1","yes","SYCL" +"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f16,kernel_offset=0","support","1","yes","SYCL" +"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16,kernel_offset=0","support","1","yes","SYCL" +"SYCL0","CONV_3D","N=1,IC=4,ID=8,IH=8,IW=8,OC=8,KD=1,KH=1,KW=1,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f16,kernel_offset=0","support","1","yes","SYCL" +"SYCL0","CONV_3D","N=2,IC=8,ID=5,IH=11,IW=9,OC=65,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f16,kernel_offset=0","support","1","yes","SYCL" +"SYCL0","CONV_3D","N=2,IC=5,ID=7,IH=9,IW=13,OC=17,KD=2,KH=3,KW=4,s0=2,s1=1,s2=3,p0=3,p1=2,p2=2,d0=2,d1=1,d2=2,type_kernel=f16,kernel_offset=0","support","1","yes","SYCL" +"SYCL0","CONV_3D","N=3,IC=16,ID=3,IH=7,IW=9,OC=33,KD=1,KH=1,KW=1,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f16,kernel_offset=0","support","1","yes","SYCL" +"SYCL0","CONV_3D","N=2,IC=8,ID=7,IH=5,IW=9,OC=33,KD=3,KH=1,KW=1,s0=1,s1=1,s2=2,p0=0,p1=0,p2=2,d0=1,d1=1,d2=2,type_kernel=f16,kernel_offset=0","support","1","yes","SYCL" +"SYCL0","CONV_3D","N=2,IC=3,ID=1,IH=2,IW=1,OC=7,KD=1,KH=1,KW=1,s0=1,s1=1,s2=1,p0=3,p1=4,p2=2,d0=1,d1=1,d2=1,type_kernel=f16,kernel_offset=0","support","1","yes","SYCL" +"SYCL0","CONV_3D","N=2,IC=8,ID=5,IH=7,IW=9,OC=17,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f16,kernel_offset=1","support","1","yes","SYCL" +"SYCL0","CONV_3D","N=1,IC=1,ID=2,IH=2,IW=2,OC=1,KD=1,KH=1,KW=0,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f16,kernel_offset=0","support","1","yes","SYCL" +"SYCL0","CONV_3D","N=1,IC=1,ID=2,IH=2,IW=2,OC=1,KD=1,KH=0,KW=1,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f16,kernel_offset=0","support","1","yes","SYCL" +"SYCL0","CONV_3D","N=1,IC=1,ID=2,IH=2,IW=2,OC=1,KD=0,KH=1,KW=1,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f16,kernel_offset=0","support","1","yes","SYCL" "SYCL0","CONV_TRANSPOSE_1D","ne_input=[1,1,1,1],ne_kernel=[1,1,1,1],s0=1,p0=0,d0=1","support","1","yes","SYCL" "SYCL0","CONV_TRANSPOSE_1D","ne_input=[1,1,1,1],ne_kernel=[1,1,1,1],s0=2,p0=0,d0=1","support","1","yes","SYCL" "SYCL0","CONV_TRANSPOSE_1D","ne_input=[1,1,1,1],ne_kernel=[1,1,1,1],s0=3,p0=0,d0=1","support","1","yes","SYCL" @@ -7977,6 +8167,8 @@ "SYCL0","CPY","type_src=f32,type_dst=f32,ne_src=[2,2,524281,1],permute_src=[1,0,2,3],permute_dst=[0,0,0,0],_src_transpose=0","support","1","yes","SYCL" "SYCL0","CPY","type_src=f32,type_dst=f32,ne_src=[128,2,3,1],ne_dst=[128,2,3,1],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0,dst_alloc=[128,4,3,1]","support","1","yes","SYCL" "SYCL0","CPY","type_src=f16,type_dst=f16,ne_src=[128,2,3,1],ne_dst=[128,2,3,1],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0,dst_alloc=[128,4,3,1]","support","1","yes","SYCL" +"SYCL0","CPY","type_src=f32,type_dst=f32,ne_src=[64,16,1,1],ne_dst=[1,64,16,1],permute_src=[1,2,0,3],permute_dst=[0,0,0,0],_src_transpose=0,dst_alloc=[32,64,16,1]","support","1","yes","SYCL" +"SYCL0","CPY","type_src=f16,type_dst=f16,ne_src=[64,16,1,1],ne_dst=[1,64,16,1],permute_src=[1,2,0,3],permute_dst=[0,0,0,0],_src_transpose=0,dst_alloc=[32,64,16,1]","support","1","yes","SYCL" "SYCL0","CPY","type_src=f32,type_dst=f32,ne_src=[3,5,7,32],ne_dst=[32,7,5,3],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","1","yes","SYCL" "SYCL0","CPY","type_src=f32,type_dst=f32,ne_src=[3,5,32,7],ne_dst=[3,5,7,32],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","1","yes","SYCL" "SYCL0","CPY","type_src=f32,type_dst=f32,ne_src=[3,5,32,7],ne_dst=[32,7,5,3],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","1","yes","SYCL" @@ -8333,6 +8525,10 @@ "SYCL0","SUB","type=f16,ne=[64,262144,1,1],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL" "SYCL0","MUL","type=f16,ne=[64,262144,1,1],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL" "SYCL0","DIV","type=f16,ne=[64,262144,1,1],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL" +"SYCL0","ADD","type=f16,ne=[128,1,8,1],nr=[1,4,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL" +"SYCL0","SUB","type=f16,ne=[128,1,8,1],nr=[1,4,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL" +"SYCL0","MUL","type=f16,ne=[128,1,8,1],nr=[1,4,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL" +"SYCL0","DIV","type=f16,ne=[128,1,8,1],nr=[1,4,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL" "SYCL0","ADD","type=f32,ne=[1,1,8,1],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL" "SYCL0","SUB","type=f32,ne=[1,1,8,1],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL" "SYCL0","MUL","type=f32,ne=[1,1,8,1],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL" @@ -8513,10 +8709,213 @@ "SYCL0","SUB","type=f32,ne=[64,262144,1,1],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL" "SYCL0","MUL","type=f32,ne=[64,262144,1,1],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL" "SYCL0","DIV","type=f32,ne=[64,262144,1,1],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL" +"SYCL0","ADD","type=f32,ne=[128,1,8,1],nr=[1,4,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL" +"SYCL0","SUB","type=f32,ne=[128,1,8,1],nr=[1,4,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL" +"SYCL0","MUL","type=f32,ne=[128,1,8,1],nr=[1,4,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL" +"SYCL0","DIV","type=f32,ne=[128,1,8,1],nr=[1,4,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL" +"SYCL0","ADD","type=bf16,ne=[1,1,8,1],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL" +"SYCL0","SUB","type=bf16,ne=[1,1,8,1],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL" +"SYCL0","MUL","type=bf16,ne=[1,1,8,1],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL" +"SYCL0","DIV","type=bf16,ne=[1,1,8,1],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL" +"SYCL0","ADD","type=bf16,ne=[1,1,1,1],nr=[32,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL" +"SYCL0","SUB","type=bf16,ne=[1,1,1,1],nr=[32,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL" +"SYCL0","MUL","type=bf16,ne=[1,1,1,1],nr=[32,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL" +"SYCL0","DIV","type=bf16,ne=[1,1,1,1],nr=[32,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL" +"SYCL0","ADD","type=bf16,ne=[1,1,320,320],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL" +"SYCL0","SUB","type=bf16,ne=[1,1,320,320],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL" +"SYCL0","MUL","type=bf16,ne=[1,1,320,320],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL" +"SYCL0","DIV","type=bf16,ne=[1,1,320,320],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL" +"SYCL0","ADD","type=bf16,ne=[10,5,1,1],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL" +"SYCL0","SUB","type=bf16,ne=[10,5,1,1],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL" +"SYCL0","MUL","type=bf16,ne=[10,5,1,1],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL" +"SYCL0","DIV","type=bf16,ne=[10,5,1,1],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL" +"SYCL0","ADD","type=bf16,ne=[10,5,4,1],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL" +"SYCL0","SUB","type=bf16,ne=[10,5,4,1],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL" +"SYCL0","MUL","type=bf16,ne=[10,5,4,1],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL" +"SYCL0","DIV","type=bf16,ne=[10,5,4,1],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL" +"SYCL0","ADD","type=bf16,ne=[10,5,4,3],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL" +"SYCL0","SUB","type=bf16,ne=[10,5,4,3],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL" +"SYCL0","MUL","type=bf16,ne=[10,5,4,3],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL" +"SYCL0","DIV","type=bf16,ne=[10,5,4,3],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL" +"SYCL0","ADD","type=bf16,ne=[10,5,4,3],nr=[2,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL" +"SYCL0","SUB","type=bf16,ne=[10,5,4,3],nr=[2,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL" +"SYCL0","MUL","type=bf16,ne=[10,5,4,3],nr=[2,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL" +"SYCL0","DIV","type=bf16,ne=[10,5,4,3],nr=[2,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL" +"SYCL0","ADD","type=bf16,ne=[10,5,4,3],nr=[1,2,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL" +"SYCL0","SUB","type=bf16,ne=[10,5,4,3],nr=[1,2,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL" +"SYCL0","MUL","type=bf16,ne=[10,5,4,3],nr=[1,2,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL" +"SYCL0","DIV","type=bf16,ne=[10,5,4,3],nr=[1,2,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL" +"SYCL0","ADD","type=bf16,ne=[10,5,4,3],nr=[1,1,2,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL" +"SYCL0","SUB","type=bf16,ne=[10,5,4,3],nr=[1,1,2,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL" +"SYCL0","MUL","type=bf16,ne=[10,5,4,3],nr=[1,1,2,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL" +"SYCL0","DIV","type=bf16,ne=[10,5,4,3],nr=[1,1,2,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL" +"SYCL0","ADD","type=bf16,ne=[10,5,4,3],nr=[1,1,1,2],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL" +"SYCL0","SUB","type=bf16,ne=[10,5,4,3],nr=[1,1,1,2],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL" +"SYCL0","MUL","type=bf16,ne=[10,5,4,3],nr=[1,1,1,2],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL" +"SYCL0","DIV","type=bf16,ne=[10,5,4,3],nr=[1,1,1,2],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL" +"SYCL0","ADD","type=bf16,ne=[10,5,4,3],nr=[1,1,2,2],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL" 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+"SYCL0","DIV","type=bf16,ne=[64,262144,1,1],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL" +"SYCL0","ADD","type=bf16,ne=[128,1,8,1],nr=[1,4,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL" +"SYCL0","SUB","type=bf16,ne=[128,1,8,1],nr=[1,4,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL" +"SYCL0","MUL","type=bf16,ne=[128,1,8,1],nr=[1,4,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL" +"SYCL0","DIV","type=bf16,ne=[128,1,8,1],nr=[1,4,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL" +"SYCL0","ADD","type=bf16,ne=[10,5,4,3],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0,type_b=f32","support","1","yes","SYCL" +"SYCL0","ADD","type=bf16,ne=[10,5,4,3],nr=[2,2,2,2],nf=1,perm1=0,src_overlap=0,type_b=f32","support","1","yes","SYCL" +"SYCL0","ADD","type=bf16,ne=[5120,1,1,1],nr=[1,32,1,1],nf=1,perm1=0,src_overlap=0,type_b=f32","support","1","yes","SYCL" +"SYCL0","SUB","type=bf16,ne=[10,5,4,3],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0,type_b=f32","support","1","yes","SYCL" +"SYCL0","SUB","type=bf16,ne=[10,5,4,3],nr=[2,2,2,2],nf=1,perm1=0,src_overlap=0,type_b=f32","support","1","yes","SYCL" +"SYCL0","SUB","type=bf16,ne=[5120,1,1,1],nr=[1,32,1,1],nf=1,perm1=0,src_overlap=0,type_b=f32","support","1","yes","SYCL" +"SYCL0","MUL","type=bf16,ne=[10,5,4,3],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0,type_b=f32","support","1","yes","SYCL" +"SYCL0","MUL","type=bf16,ne=[10,5,4,3],nr=[2,2,2,2],nf=1,perm1=0,src_overlap=0,type_b=f32","support","1","yes","SYCL" +"SYCL0","MUL","type=bf16,ne=[5120,1,1,1],nr=[1,32,1,1],nf=1,perm1=0,src_overlap=0,type_b=f32","support","1","yes","SYCL" +"SYCL0","DIV","type=bf16,ne=[10,5,4,3],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0,type_b=f32","support","1","yes","SYCL" +"SYCL0","DIV","type=bf16,ne=[10,5,4,3],nr=[2,2,2,2],nf=1,perm1=0,src_overlap=0,type_b=f32","support","1","yes","SYCL" +"SYCL0","DIV","type=bf16,ne=[5120,1,1,1],nr=[1,32,1,1],nf=1,perm1=0,src_overlap=0,type_b=f32","support","1","yes","SYCL" "SYCL0","SCALE","type=f32,ne=[10,10,10,10],scale=2.000000,bias=0.000000,inplace=0","support","1","yes","SYCL" "SYCL0","SCALE","type=f32,ne=[10,10,10,10],scale=2.000000,bias=1.000000,inplace=0","support","1","yes","SYCL" "SYCL0","SCALE","type=f32,ne=[10,10,10,10],scale=2.000000,bias=1.000000,inplace=1","support","1","yes","SYCL" "SYCL0","SCALE","type=f32,ne=[100,10,10,10],scale=2.000000,bias=1.000000,inplace=0","support","1","yes","SYCL" +"SYCL0","SCALE","type=bf16,ne=[10,10,10,10],scale=2.000000,bias=1.000000,inplace=0","support","1","yes","SYCL" +"SYCL0","SCALE","type=bf16,ne=[10,10,10,10],scale=2.000000,bias=1.000000,inplace=1","support","1","yes","SYCL" +"SYCL0","SCALE","type=bf16,ne=[100,10,10,10],scale=0.500000,bias=0.000000,inplace=0","support","1","yes","SYCL" "SYCL0","SILU_BACK","type=f32,ne=[64,5,4,3],eps=0.000001","support","0","no","SYCL" "SYCL0","NORM","type=f32,ne=[64,5,4,3],v=0,eps=0.000000,noncontig_rows=0","support","1","yes","SYCL" "SYCL0","RMS_NORM","type=f32,ne=[64,5,4,3],v=0,eps=0.000000,inplace=0","support","1","yes","SYCL" @@ -8669,6 +9068,10 @@ "SYCL0","NORM","type=f32,ne=[260,5,4,3],v=1,eps=10.000000,noncontig_rows=0","support","1","yes","SYCL" "SYCL0","RMS_NORM","type=f32,ne=[260,5,4,3],v=1,eps=10.000000,inplace=0","support","1","yes","SYCL" "SYCL0","RMS_NORM","type=f32,ne=[64,5,4,3],v=0,eps=0.000001,inplace=1","support","1","yes","SYCL" +"SYCL0","NORM","type=f32,ne=[4,1,65536,1],v=0,eps=0.000001,noncontig_rows=0","support","1","yes","SYCL" +"SYCL0","RMS_NORM","type=f32,ne=[4,1,65536,1],v=0,eps=0.000001,inplace=0","support","1","yes","SYCL" +"SYCL0","RMS_NORM","type=f32,ne=[4,1,1,65536],v=0,eps=0.000001,inplace=0","support","1","yes","SYCL" +"SYCL0","L2_NORM","type=f32,ne=[4,1,65536,1],eps=0.000000,v=0,noncontig_rows=0","support","1","yes","SYCL" "SYCL0","SSM_CONV","type=f32,ne_a=[3,1024,1,1],ne_b=[3,1024,1,1]","support","1","yes","SYCL" "SYCL0","SSM_CONV","type=f32,ne_a=[6,1024,1,1],ne_b=[3,1024,1,1]","support","1","yes","SYCL" "SYCL0","SSM_CONV","type=f32,ne_a=[3,1024,4,1],ne_b=[3,1024,1,1]","support","1","yes","SYCL" @@ -8715,6 +9118,8 @@ "SYCL0","SSM_CONV","type=f32,ne_a=[72,2048,1,1],ne_b=[9,2048,1,1]","support","1","yes","SYCL" "SYCL0","SSM_CONV","type=f32,ne_a=[72,2048,4,1],ne_b=[9,2048,1,1]","support","1","yes","SYCL" "SYCL0","SSM_SCAN","type=f32,d_state=16,head_dim=1,n_head=1024,n_group=1,n_seq_tokens=32,n_seqs=4,xbc_overlap=0,K=1,weak_decay=0","support","0","no","SYCL" +"SYCL0","SSM_SCAN","type=f32,d_state=96,head_dim=64,n_head=128,n_group=8,n_seq_tokens=1,n_seqs=1,xbc_overlap=0,K=1,weak_decay=0","support","0","no","SYCL" +"SYCL0","SSM_SCAN","type=f32,d_state=96,head_dim=64,n_head=16,n_group=8,n_seq_tokens=300,n_seqs=2,xbc_overlap=0,K=1,weak_decay=1","support","0","no","SYCL" "SYCL0","SSM_SCAN","type=f32,d_state=128,head_dim=64,n_head=16,n_group=2,n_seq_tokens=32,n_seqs=4,xbc_overlap=0,K=1,weak_decay=0","support","1","yes","SYCL" "SYCL0","SSM_SCAN","type=f32,d_state=256,head_dim=64,n_head=8,n_group=2,n_seq_tokens=32,n_seqs=4,xbc_overlap=0,K=1,weak_decay=0","support","1","yes","SYCL" "SYCL0","SSM_SCAN","type=f32,d_state=128,head_dim=128,n_head=4,n_group=4,n_seq_tokens=16,n_seqs=2,xbc_overlap=1,K=1,weak_decay=0","support","1","yes","SYCL" @@ -8740,2640 +9145,3876 @@ "SYCL0","GATED_LINEAR_ATTN","type=f32,head_count=32,head_size=64,n_seq_tokens=32,n_seqs=1","support","1","yes","SYCL" "SYCL0","GATED_LINEAR_ATTN","type=f32,head_count=32,head_size=64,n_seq_tokens=32,n_seqs=4","support","1","yes","SYCL" "SYCL0","GATED_LINEAR_ATTN","type=f32,head_count=32,head_size=64,n_seq_tokens=128,n_seqs=4","support","1","yes","SYCL" -"SYCL0","MUL_MAT_HADAMARD","type_a=f32,type_b=f32,m=128,n=1,k=128,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT_HADAMARD","type_a=f32,type_b=f32,m=64,n=1,k=64,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT_HADAMARD","type_a=f32,type_b=f32,m=256,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT_HADAMARD","type_a=f32,type_b=f32,m=512,n=1,k=512,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT_HADAMARD","type_a=f32,type_b=f32,m=128,n=32,k=128,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT_HADAMARD","type_a=f32,type_b=f32,m=128,n=4,k=128,bs=[2,3],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT_HADAMARD","type_a=f32,type_b=f32,m=256,n=512,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT_HADAMARD","type_a=f32,type_b=f32,m=32,n=1,k=32,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT_HADAMARD","type_a=f32,type_b=f32,m=1024,n=1,k=1024,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT_HADAMARD","type_a=f32,type_b=f16,m=64,n=1,k=64,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT_HADAMARD","type_a=f32,type_b=f16,m=128,n=1,k=128,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT_HADAMARD","type_a=f32,type_b=f16,m=256,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT_HADAMARD","type_a=f32,type_b=f16,m=512,n=1,k=512,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT_HADAMARD","type_a=f32,type_b=f16,m=128,n=32,k=128,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT_HADAMARD","type_a=f32,type_b=f16,m=128,n=4,k=128,bs=[2,3],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT_HADAMARD","type_a=f32,type_b=f16,m=256,n=512,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=4096,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=8,k=4096,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=4096,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=16,n=8,k=4096,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" 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-"SYCL0","MUL_MAT","type_a=q2_0,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=q2_0,type_b=f32,m=16,n=1,k=4096,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=q2_0,type_b=f32,m=16,n=8,k=4096,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" 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-"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=8,k=4096,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" 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-"SYCL0","MUL_MAT","type_a=q2_K,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=q2_K,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=q2_K,type_b=f32,m=16,n=1,k=4096,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=q2_K,type_b=f32,m=16,n=8,k=4096,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=q3_K,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=q3_K,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=q3_K,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=q3_K,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=q3_K,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=q3_K,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=q3_K,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=q3_K,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=q3_K,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=q3_K,type_b=f32,m=16,n=1,k=4096,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" 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-"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=4,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=16,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=16,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=4,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=1,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=8,k=256,bs=[1536,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=1,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=1,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=1,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=1,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=4,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=16,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=16,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=4,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=1,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=8,k=256,bs=[1536,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=1,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=1,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=4,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=16,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=16,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=4,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=1,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=8,k=256,bs=[1536,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=1,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=1,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=1,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=1,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=4,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=16,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=16,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=4,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=1,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=8,k=256,bs=[1536,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=4,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=16,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=16,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=4,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0","support","1","yes","SYCL" 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+"SYCL0","MUL_MAT_ID","type_a=iq4_xs,type_b=f32,n_mats=4,n_used=2,b=0,m=512,n=1,k=256,amax=1.000000,m_v=0,ids_offset=0","support","1","yes","SYCL" +"SYCL0","MUL_MAT_ID","type_a=iq4_xs,type_b=f32,n_mats=4,n_used=2,b=0,m=512,n=32,k=256,amax=1.000000,m_v=0,ids_offset=0","support","1","yes","SYCL" +"SYCL0","MUL_MAT_ID","type_a=bf16,type_b=f32,n_mats=4,n_used=2,b=0,m=512,n=1,k=256,amax=1.000000,m_v=0,ids_offset=0","support","1","yes","SYCL" +"SYCL0","MUL_MAT_ID","type_a=bf16,type_b=f32,n_mats=4,n_used=2,b=0,m=512,n=32,k=256,amax=1.000000,m_v=0,ids_offset=0","support","1","yes","SYCL" "SYCL0","OUT_PROD","type_a=f32,type_b=f32,m=256,n=1,k=1,bs=[1,1],nr=[1,1],trans_b=0","support","1","yes","SYCL" "SYCL0","OUT_PROD","type_a=f32,type_b=f32,m=256,n=1,k=1,bs=[1,1],nr=[1,2],trans_b=0","support","1","yes","SYCL" "SYCL0","OUT_PROD","type_a=f32,type_b=f32,m=256,n=1,k=1,bs=[1,1],nr=[2,1],trans_b=0","support","1","yes","SYCL" @@ -12830,7 +14471,8 @@ "SYCL0","LOG","type=f16,ne=[10,5,4,3]","support","1","yes","SYCL" "SYCL0","SIN","type=f16,ne=[10,2,2,2]","support","1","yes","SYCL" "SYCL0","COS","type=f16,ne=[10,2,2,2]","support","1","yes","SYCL" -"SYCL0","CLAMP","type=f16,ne=[10,5,4,3],min=-0.500000,max=0.500000","support","1","yes","SYCL" +"SYCL0","CLAMP","type=f16,ne=[10,5,4,3],min=-0.500000,max=0.500000,v=0","support","1","yes","SYCL" +"SYCL0","CLAMP","type=f16,ne=[10,5,4,3],min=-0.500000,max=0.500000,v=1","support","1","yes","SYCL" "SYCL0","LEAKY_RELU","type=f16,ne_a=[10,5,4,3],negative_slope=0.100000","support","1","yes","SYCL" "SYCL0","FLOOR","type=f16,ne=[10,2,2,2]","support","1","yes","SYCL" "SYCL0","CEIL","type=f16,ne=[10,2,2,2]","support","1","yes","SYCL" @@ -12846,8 +14488,8 @@ "SYCL0","SIN","type=f16,ne=[1024,1024,1,1]","support","1","yes","SYCL" "SYCL0","COS","type=f16,ne=[7,1,5,3]","support","1","yes","SYCL" "SYCL0","COS","type=f16,ne=[1024,1024,1,1]","support","1","yes","SYCL" -"SYCL0","CLAMP","type=f16,ne=[7,1,5,3],min=-0.500000,max=0.500000","support","1","yes","SYCL" -"SYCL0","CLAMP","type=f16,ne=[1024,1024,1,1],min=-0.500000,max=0.500000","support","1","yes","SYCL" +"SYCL0","CLAMP","type=f16,ne=[7,1,5,3],min=-0.500000,max=0.500000,v=0","support","1","yes","SYCL" +"SYCL0","CLAMP","type=f16,ne=[1024,1024,1,1],min=-0.500000,max=0.500000,v=0","support","1","yes","SYCL" "SYCL0","LEAKY_RELU","type=f16,ne_a=[7,1,5,3],negative_slope=0.100000","support","1","yes","SYCL" "SYCL0","LEAKY_RELU","type=f16,ne_a=[1024,1024,1,1],negative_slope=0.100000","support","1","yes","SYCL" "SYCL0","FLOOR","type=f16,ne=[7,1,5,3]","support","1","yes","SYCL" @@ -12863,7 +14505,8 @@ "SYCL0","LOG","type=f32,ne=[10,5,4,3]","support","1","yes","SYCL" "SYCL0","SIN","type=f32,ne=[10,2,2,2]","support","1","yes","SYCL" "SYCL0","COS","type=f32,ne=[10,2,2,2]","support","1","yes","SYCL" -"SYCL0","CLAMP","type=f32,ne=[10,5,4,3],min=-0.500000,max=0.500000","support","1","yes","SYCL" +"SYCL0","CLAMP","type=f32,ne=[10,5,4,3],min=-0.500000,max=0.500000,v=0","support","1","yes","SYCL" +"SYCL0","CLAMP","type=f32,ne=[10,5,4,3],min=-0.500000,max=0.500000,v=1","support","1","yes","SYCL" "SYCL0","LEAKY_RELU","type=f32,ne_a=[10,5,4,3],negative_slope=0.100000","support","1","yes","SYCL" "SYCL0","FLOOR","type=f32,ne=[10,2,2,2]","support","1","yes","SYCL" "SYCL0","CEIL","type=f32,ne=[10,2,2,2]","support","1","yes","SYCL" @@ -12879,8 +14522,8 @@ "SYCL0","SIN","type=f32,ne=[1024,1024,1,1]","support","1","yes","SYCL" "SYCL0","COS","type=f32,ne=[7,1,5,3]","support","1","yes","SYCL" "SYCL0","COS","type=f32,ne=[1024,1024,1,1]","support","1","yes","SYCL" -"SYCL0","CLAMP","type=f32,ne=[7,1,5,3],min=-0.500000,max=0.500000","support","1","yes","SYCL" -"SYCL0","CLAMP","type=f32,ne=[1024,1024,1,1],min=-0.500000,max=0.500000","support","1","yes","SYCL" +"SYCL0","CLAMP","type=f32,ne=[7,1,5,3],min=-0.500000,max=0.500000,v=0","support","1","yes","SYCL" +"SYCL0","CLAMP","type=f32,ne=[1024,1024,1,1],min=-0.500000,max=0.500000,v=0","support","1","yes","SYCL" "SYCL0","LEAKY_RELU","type=f32,ne_a=[7,1,5,3],negative_slope=0.100000","support","1","yes","SYCL" "SYCL0","LEAKY_RELU","type=f32,ne_a=[1024,1024,1,1],negative_slope=0.100000","support","1","yes","SYCL" "SYCL0","FLOOR","type=f32,ne=[7,1,5,3]","support","1","yes","SYCL" @@ -14222,6 +15865,12 @@ "SYCL0","CONCAT","type=q8_0,ne_a=[128,12,13,14],ne_b_d=7,dim=1,v=12","support","1","yes","SYCL" "SYCL0","CONCAT","type=q8_0,ne_a=[128,12,13,14],ne_b_d=7,dim=2,v=12","support","1","yes","SYCL" "SYCL0","CONCAT","type=q8_0,ne_a=[128,12,13,14],ne_b_d=7,dim=3,v=12","support","1","yes","SYCL" +"SYCL0","CONCAT","type=f32,ne_a=[98304,8,1,1],ne_b_d=288,dim=0,v=0","support","1","yes","SYCL" +"SYCL0","CONCAT","type=f32,ne_a=[20000,3,2,1],ne_b_d=333,dim=0,v=1","support","1","yes","SYCL" +"SYCL0","CONCAT","type=q8_0,ne_a=[8192,2,1,1],ne_b_d=4096,dim=0,v=0","support","1","yes","SYCL" +"SYCL0","CONCAT","type=f32,ne_a=[98304,8,1,1],ne_b_d=2,dim=1,v=0","support","1","yes","SYCL" +"SYCL0","CONCAT","type=f32,ne_a=[20000,3,2,1],ne_b_d=1,dim=1,v=1","support","1","yes","SYCL" +"SYCL0","CONCAT","type=q8_0,ne_a=[8192,2,1,1],ne_b_d=1,dim=1,v=0","support","1","yes","SYCL" "SYCL0","ARGSORT","type=f32,ne=[3,1,1,1],order=0","support","1","yes","SYCL" "SYCL0","ARGSORT","type=f32,ne=[4,1,1,1],order=0","support","1","yes","SYCL" "SYCL0","ARGSORT","type=f32,ne=[7,1,1,1],order=0","support","1","yes","SYCL" @@ -14679,6 +16328,8 @@ "SYCL0","TOP_K","type=f32,ne=[8192,2,1,1],k=2051,ties=0","support","1","yes","SYCL" "SYCL0","TOP_K","type=f32,ne=[33024,1,1,1],k=2051,ties=0","support","1","yes","SYCL" "SYCL0","TOP_K","type=f32,ne=[33024,4,1,1],k=2051,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[500,40000,1,1],k=16,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[1100,33000,1,1],k=16,ties=0","support","1","yes","SYCL" "SYCL0","UPSCALE","type=f32,ne=[512,512,3,2],scale_factor=2,mode=nearest,transpose=0","support","1","yes","SYCL" "SYCL0","UPSCALE","type=f32,ne=[512,512,3,2],scale_factor=2,mode=nearest,transpose=1","support","1","yes","SYCL" "SYCL0","UPSCALE","type=f32,ne=[2,5,7,11],ne_tgt=[5,7,11,13],mode=nearest","support","1","yes","SYCL" @@ -14753,6 +16404,9 @@ "SYCL0","PAD","type=f32,ne_a=[100,1,1,1],pad_0=100,pad_1=0,circular=0","support","1","yes","SYCL" "SYCL0","PAD","type=f32,ne_a=[100,1,1,1],pad_0=0,pad_1=100,circular=0","support","1","yes","SYCL" "SYCL0","PAD","type=f32,ne_a=[100,100,1,1],pad_0=50,pad_1=50,circular=0","support","1","yes","SYCL" +"SYCL0","PAD","type=f32,ne_a=[4,70000,1,1],pad_0=1,pad_1=1,circular=0","support","1","yes","SYCL" +"SYCL0","PAD","type=f32,ne_a=[4,70000,1,1],pad_0=1,pad_1=1,circular=1","support","0","no","SYCL" +"SYCL0","PAD","type=f32,ne_a=[4,2,300,300],lp0=1,rp0=1,lp1=0,rp1=0,lp2=0,rp2=0,lp3=0,rp3=0,tfrm=0,circular=0","support","1","yes","SYCL" "SYCL0","PAD_REFLECT_1D","type=f32,ne_a=[512,34,2,1],pad_0=10,pad_1=9","support","1","yes","SYCL" "SYCL0","PAD_REFLECT_1D","type=f32,ne_a=[3000,384,4,1],pad_0=10,pad_1=9","support","1","yes","SYCL" "SYCL0","ROLL","shift0=3,shift1=-2,shift3=1,shift4=-1,permute=0","support","1","yes","SYCL" @@ -14778,8 +16432,10 @@ "SYCL0","CUMSUM","type=f32,ne=[20481,4,1,1]","support","1","yes","SYCL" "SYCL0","XIELU","type=f32,ne=[10,5,4,3]","support","1","yes","SYCL" "SYCL0","XIELU","type=f16,ne=[10,5,4,3]","support","1","yes","SYCL" +"SYCL0","XIELU","type=bf16,ne=[10,5,4,3]","support","1","yes","SYCL" "SYCL0","XIELU","type=f32,ne=[512,16,1,1]","support","1","yes","SYCL" "SYCL0","XIELU","type=f16,ne=[512,16,1,1]","support","1","yes","SYCL" +"SYCL0","XIELU","type=bf16,ne=[512,16,1,1]","support","1","yes","SYCL" "SYCL0","TRI","type=f32,ne=[10,10,4,3],tri_type=3","support","1","yes","SYCL" "SYCL0","TRI","type=f32,ne=[10,10,4,3],tri_type=2","support","1","yes","SYCL" "SYCL0","TRI","type=f32,ne=[10,10,4,3],tri_type=1","support","1","yes","SYCL" @@ -14788,6 +16444,9 @@ "SYCL0","FILL","type=f32,ne=[303,207,11,3],c=2.000000","support","1","yes","SYCL" "SYCL0","FILL","type=f32,ne=[800,600,4,4],c=-152.000000","support","1","yes","SYCL" "SYCL0","FILL","type=f32,ne=[2048,512,2,2],c=3.500000","support","1","yes","SYCL" +"SYCL0","FILL","type=f16,ne=[303,207,11,3],c=2.000000","support","1","yes","SYCL" +"SYCL0","FILL","type=f16,ne=[800,600,4,4],c=-152.000000","support","1","yes","SYCL" +"SYCL0","FILL","type=f16,ne=[2048,512,2,2],c=3.500000","support","1","yes","SYCL" "SYCL0","DIAG","type=f32,ne=[10,1,4,3]","support","1","yes","SYCL" "SYCL0","DIAG","type=f32,ne=[79,1,19,13]","support","1","yes","SYCL" "SYCL0","DIAG","type=f32,ne=[256,1,8,16]","support","1","yes","SYCL" @@ -14817,16 +16476,22 @@ "SYCL0","SOLVE_TRI","type=f32,ne_lhs=[64,64,4,4],ne_rhs=[384,64,4,4]","support","0","no","SYCL" "SYCL0","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,tfrm=0,circular=0","support","1","yes","SYCL" "SYCL0","PAD","type=f32,ne_a=[11,22,33,44],lp0=1,rp0=2,lp1=3,rp1=4,lp2=5,rp2=6,lp3=7,rp3=8,tfrm=0,circular=0","support","1","yes","SYCL" +"SYCL0","PAD","type=f32,ne_a=[11,22,33,44],lp0=0,rp0=2,lp1=0,rp1=4,lp2=0,rp2=6,lp3=0,rp3=8,tfrm=0,circular=0","support","1","yes","SYCL" "SYCL0","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,tfrm=0,circular=1","support","0","no","SYCL" "SYCL0","PAD","type=f32,ne_a=[11,22,33,44],lp0=1,rp0=2,lp1=3,rp1=4,lp2=5,rp2=6,lp3=7,rp3=8,tfrm=0,circular=1","support","0","no","SYCL" +"SYCL0","PAD","type=f32,ne_a=[11,22,33,44],lp0=0,rp0=2,lp1=0,rp1=4,lp2=0,rp2=6,lp3=0,rp3=8,tfrm=0,circular=1","support","0","no","SYCL" "SYCL0","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,tfrm=1,circular=0","support","1","yes","SYCL" "SYCL0","PAD","type=f32,ne_a=[11,22,33,44],lp0=1,rp0=2,lp1=3,rp1=4,lp2=5,rp2=6,lp3=7,rp3=8,tfrm=1,circular=0","support","1","yes","SYCL" +"SYCL0","PAD","type=f32,ne_a=[11,22,33,44],lp0=0,rp0=2,lp1=0,rp1=4,lp2=0,rp2=6,lp3=0,rp3=8,tfrm=1,circular=0","support","1","yes","SYCL" "SYCL0","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,tfrm=1,circular=1","support","0","no","SYCL" "SYCL0","PAD","type=f32,ne_a=[11,22,33,44],lp0=1,rp0=2,lp1=3,rp1=4,lp2=5,rp2=6,lp3=7,rp3=8,tfrm=1,circular=1","support","0","no","SYCL" +"SYCL0","PAD","type=f32,ne_a=[11,22,33,44],lp0=0,rp0=2,lp1=0,rp1=4,lp2=0,rp2=6,lp3=0,rp3=8,tfrm=1,circular=1","support","0","no","SYCL" "SYCL0","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,tfrm=2,circular=0","support","1","yes","SYCL" "SYCL0","PAD","type=f32,ne_a=[11,22,33,44],lp0=1,rp0=2,lp1=3,rp1=4,lp2=5,rp2=6,lp3=7,rp3=8,tfrm=2,circular=0","support","1","yes","SYCL" +"SYCL0","PAD","type=f32,ne_a=[11,22,33,44],lp0=0,rp0=2,lp1=0,rp1=4,lp2=0,rp2=6,lp3=0,rp3=8,tfrm=2,circular=0","support","1","yes","SYCL" "SYCL0","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,tfrm=2,circular=1","support","0","no","SYCL" "SYCL0","PAD","type=f32,ne_a=[11,22,33,44],lp0=1,rp0=2,lp1=3,rp1=4,lp2=5,rp2=6,lp3=7,rp3=8,tfrm=2,circular=1","support","0","no","SYCL" +"SYCL0","PAD","type=f32,ne_a=[11,22,33,44],lp0=0,rp0=2,lp1=0,rp1=4,lp2=0,rp2=6,lp3=0,rp3=8,tfrm=2,circular=1","support","0","no","SYCL" "SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=8,nr23=[4,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3],kv_view=1,v_is_view_of_k=0,n_kv_max=0","support","1","yes","SYCL" "SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=8,nr23=[4,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q8_0,type_V=q8_0,permute=[0,1,2,3],kv_view=1,v_is_view_of_k=0,n_kv_max=0","support","1","yes","SYCL" "SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=8,nr23=[4,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3],kv_view=1,v_is_view_of_k=0,n_kv_max=0","support","1","yes","SYCL" @@ -20065,6 +21730,9 @@ "SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3],kv_view=1,v_is_view_of_k=1,n_kv_max=0","support","0","no","SYCL" "SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=64,nh=4,nr23=[1,1],kv=256,nb=2,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3],kv_view=1,v_is_view_of_k=0,n_kv_max=0","support","0","no","SYCL" "SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=72,nh=4,nr23=[1,1],kv=256,nb=2,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3],kv_view=1,v_is_view_of_k=0,n_kv_max=0","support","0","no","SYCL" +"SYCL0","FLASH_ATTN_EXT","hsk=65,hsv=67,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3],kv_view=1,v_is_view_of_k=0,n_kv_max=0","support","0","no","SYCL" +"SYCL0","FLASH_ATTN_EXT","hsk=65,hsv=67,nh=4,nr23=[1,1],kv=17,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=1.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3],kv_view=1,v_is_view_of_k=0,n_kv_max=0","support","0","no","SYCL" +"SYCL0","FLASH_ATTN_EXT","hsk=65,hsv=67,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=1.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3],kv_view=1,v_is_view_of_k=0,n_kv_max=0","support","0","no","SYCL" "SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=128,nb=2,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q8_0,type_V=q4_0,permute=[0,1,2,3],kv_view=1,v_is_view_of_k=0,n_kv_max=0","support","1","yes","SYCL" "SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=128,nb=2,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=f16,permute=[0,1,2,3],kv_view=1,v_is_view_of_k=0,n_kv_max=0","support","1","yes","SYCL" "SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=96,nb=2,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q8_0,permute=[0,1,2,3],kv_view=1,v_is_view_of_k=0,n_kv_max=0","support","1","yes","SYCL" @@ -20087,6 +21755,8 @@ "SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[8,1],kv=113,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q8_0,type_V=q8_0,permute=[0,1,2,3],kv_view=1,v_is_view_of_k=1,n_kv_max=0","support","0","no","SYCL" "SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[8,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q8_0,type_V=q8_0,permute=[0,1,2,3],kv_view=1,v_is_view_of_k=1,n_kv_max=0","support","1","yes","SYCL" "SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[8,1],kv=1024,nb=64,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q8_0,type_V=q8_0,permute=[0,1,2,3],kv_view=1,v_is_view_of_k=1,n_kv_max=0","support","1","yes","SYCL" +"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[8,1],kv=4096,nb=24,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q8_0,type_V=q8_0,permute=[0,1,2,3],kv_view=1,v_is_view_of_k=1,n_kv_max=0","support","1","yes","SYCL" +"SYCL0","FLASH_ATTN_EXT","hsk=512,hsv=512,nh=1,nr23=[8,1],kv=4096,nb=24,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q8_0,type_V=q8_0,permute=[0,1,2,3],kv_view=1,v_is_view_of_k=0,n_kv_max=0","support","1","yes","SYCL" "SYCL0","FLASH_ATTN_EXT","hsk=512,hsv=512,nh=1,nr23=[8,1],kv=4096,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3],kv_view=1,v_is_view_of_k=0,n_kv_max=512","support","1","yes","SYCL" "SYCL0","FLASH_ATTN_EXT","hsk=512,hsv=512,nh=1,nr23=[8,2],kv=4096,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3],kv_view=1,v_is_view_of_k=0,n_kv_max=768","support","1","yes","SYCL" "SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[16,1],kv=4096,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3],kv_view=1,v_is_view_of_k=1,n_kv_max=512","support","1","yes","SYCL" @@ -20108,6 +21778,24 @@ "SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=1,nr23=[8,1],kv=4096,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q8_0,type_V=q8_0,permute=[0,1,2,3],kv_view=1,v_is_view_of_k=0,n_kv_max=512","support","1","yes","SYCL" "SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=1,nr23=[8,1],kv=4096,nb=64,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q8_0,type_V=q8_0,permute=[0,1,2,3],kv_view=1,v_is_view_of_k=0,n_kv_max=512","support","1","yes","SYCL" "SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=2,nr23=[12,1],kv=8192,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3],kv_view=1,v_is_view_of_k=0,n_kv_max=2048","support","1","yes","SYCL" +"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=2,nr23=[12,1],kv=32768,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q8_0,type_V=q8_0,permute=[0,1,2,3],kv_view=1,v_is_view_of_k=0,n_kv_max=2048","support","1","yes","SYCL" +"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=2,nr23=[12,1],kv=32768,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3],kv_view=1,v_is_view_of_k=0,n_kv_max=2048","support","1","yes","SYCL" +"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=1,nr23=[1,1],kv=8192,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q8_0,type_V=q8_0,permute=[0,1,2,3],kv_view=1,v_is_view_of_k=0,n_kv_max=512","support","1","yes","SYCL" +"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=1,nr23=[1,1],kv=8192,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3],kv_view=1,v_is_view_of_k=0,n_kv_max=512","support","1","yes","SYCL" +"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=1,nr23=[8,1],kv=5003,nb=4,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3],kv_view=1,v_is_view_of_k=0,n_kv_max=512","support","1","yes","SYCL" +"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=96,nh=2,nr23=[8,2],kv=5003,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3],kv_view=1,v_is_view_of_k=0,n_kv_max=257","support","0","no","SYCL" +"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=96,nh=2,nr23=[8,2],kv=5003,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3],kv_view=1,v_is_view_of_k=0,n_kv_max=257","support","0","no","SYCL" +"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=96,nh=2,nr23=[8,2],kv=5003,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3],kv_view=1,v_is_view_of_k=0,n_kv_max=257","support","0","no","SYCL" +"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=96,nh=2,nr23=[8,2],kv=5003,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3],kv_view=1,v_is_view_of_k=0,n_kv_max=257","support","0","no","SYCL" +"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=96,nh=2,nr23=[8,2],kv=5003,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3],kv_view=1,v_is_view_of_k=0,n_kv_max=257","support","0","no","SYCL" +"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=96,nh=2,nr23=[8,2],kv=5003,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3],kv_view=1,v_is_view_of_k=0,n_kv_max=257","support","0","no","SYCL" +"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=96,nh=2,nr23=[8,2],kv=5003,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_1,type_V=q5_1,permute=[0,1,2,3],kv_view=1,v_is_view_of_k=0,n_kv_max=257","support","0","no","SYCL" +"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=96,nh=2,nr23=[8,2],kv=5003,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q8_0,type_V=q8_0,permute=[0,1,2,3],kv_view=1,v_is_view_of_k=0,n_kv_max=257","support","0","no","SYCL" +"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=96,nh=2,nr23=[8,2],kv=5003,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3],kv_view=1,v_is_view_of_k=0,n_kv_max=257","support","0","no","SYCL" +"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=96,nh=2,nr23=[8,2],kv=5003,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q8_0,type_V=f16,permute=[0,2,1,3],kv_view=1,v_is_view_of_k=0,n_kv_max=257","support","0","no","SYCL" +"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=96,nh=2,nr23=[8,2],kv=5003,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=q4_0,permute=[0,2,1,3],kv_view=1,v_is_view_of_k=0,n_kv_max=257","support","0","no","SYCL" +"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=96,nh=2,nr23=[8,2],kv=5003,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q8_0,type_V=f32,permute=[0,1,2,3],kv_view=1,v_is_view_of_k=0,n_kv_max=257","support","0","no","SYCL" +"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=96,nh=2,nr23=[8,2],kv=5003,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=q8_0,permute=[0,1,2,3],kv_view=1,v_is_view_of_k=0,n_kv_max=257","support","0","no","SYCL" "SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[32,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3],kv_view=1,v_is_view_of_k=1,n_kv_max=0","support","0","no","SYCL" "SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[8,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3],kv_view=1,v_is_view_of_k=1,n_kv_max=0","support","0","no","SYCL" "SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=8,nr23=[4,1],kv=512,nb=8,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3],kv_view=1,v_is_view_of_k=1,n_kv_max=0","support","1","yes","SYCL" @@ -20132,6 +21820,10 @@ "SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=8,nr23=[4,1],kv=4096,nb=8,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3],kv_view=1,v_is_view_of_k=0,n_kv_max=0","support","1","yes","SYCL" "SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[2,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3],kv_view=1,v_is_view_of_k=0,n_kv_max=0","support","1","yes","SYCL" "SYCL0","FLASH_ATTN_EXT","hsk=512,hsv=512,nh=4,nr23=[2,1],kv=1024,nb=4,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3],kv_view=1,v_is_view_of_k=0,n_kv_max=0","support","1","yes","SYCL" +"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=16,nr23=[4,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3],kv_view=1,v_is_view_of_k=0,n_kv_max=0,q_range=20","support","1","yes","SYCL" +"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=8,nr23=[4,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3],kv_view=1,v_is_view_of_k=0,n_kv_max=0,q_range=20","support","1","yes","SYCL" +"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3],kv_view=1,v_is_view_of_k=0,n_kv_max=0,q_range=20","support","1","yes","SYCL" +"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3],kv_view=1,v_is_view_of_k=0,n_kv_max=0,q_range=20","support","1","yes","SYCL" "SYCL0","CROSS_ENTROPY_LOSS","type=f32,ne=[10,5,4,3]","support","1","yes","SYCL" "SYCL0","CROSS_ENTROPY_LOSS","type=f32,ne=[30000,1,1,1]","support","1","yes","SYCL" "SYCL0","CROSS_ENTROPY_LOSS_BACK","type=f32,ne=[10,5,4,3]","support","1","yes","SYCL" @@ -20174,6 +21866,33 @@ "SYCL0","GATED_DELTA_NET","type=f32,head_count=8,head_size=32,n_seq_tokens=4,n_seqs=2,v_repeat=2,permuted=0,kda=1,K=4","support","1","yes","SYCL" "SYCL0","GATED_DELTA_NET","type=f32,head_count=4,head_size=32,n_seq_tokens=8,n_seqs=1,v_repeat=1,permuted=0,kda=0,K=3","support","1","yes","SYCL" "SYCL0","GATED_DELTA_NET","type=f32,head_count=4,head_size=64,n_seq_tokens=16,n_seqs=2,v_repeat=1,permuted=0,kda=0,K=4","support","1","yes","SYCL" +"SYCL0","LIGHTNING_INDEXER","hsk=128,nh=4,kv=256,nb=1,ns=1,nm=1,type_K=f32","support","1","yes","SYCL" +"SYCL0","LIGHTNING_INDEXER","hsk=128,nh=4,kv=256,nb=1,ns=1,nm=1,type_K=f16","support","1","yes","SYCL" +"SYCL0","LIGHTNING_INDEXER","hsk=128,nh=4,kv=256,nb=1,ns=1,nm=1,type_K=bf16","support","1","yes","SYCL" +"SYCL0","LIGHTNING_INDEXER","hsk=128,nh=4,kv=256,nb=1,ns=1,nm=1,type_K=q8_0","support","1","yes","SYCL" +"SYCL0","LIGHTNING_INDEXER","hsk=128,nh=4,kv=256,nb=1,ns=1,nm=1,type_K=q5_1","support","1","yes","SYCL" +"SYCL0","LIGHTNING_INDEXER","hsk=128,nh=4,kv=256,nb=1,ns=1,nm=1,type_K=q5_0","support","1","yes","SYCL" +"SYCL0","LIGHTNING_INDEXER","hsk=128,nh=4,kv=256,nb=1,ns=1,nm=1,type_K=q4_1","support","1","yes","SYCL" +"SYCL0","LIGHTNING_INDEXER","hsk=128,nh=4,kv=256,nb=1,ns=1,nm=1,type_K=q4_0","support","1","yes","SYCL" +"SYCL0","LIGHTNING_INDEXER","hsk=128,nh=4,kv=256,nb=1,ns=1,nm=1,type_K=iq4_nl","support","1","yes","SYCL" +"SYCL0","LIGHTNING_INDEXER","hsk=128,nh=4,kv=256,nb=1,ns=4,nm=4,type_K=f32","support","1","yes","SYCL" +"SYCL0","LIGHTNING_INDEXER","hsk=128,nh=4,kv=256,nb=1,ns=4,nm=4,type_K=f16","support","1","yes","SYCL" +"SYCL0","LIGHTNING_INDEXER","hsk=128,nh=4,kv=256,nb=1,ns=4,nm=4,type_K=bf16","support","1","yes","SYCL" +"SYCL0","LIGHTNING_INDEXER","hsk=128,nh=4,kv=256,nb=1,ns=4,nm=4,type_K=q8_0","support","1","yes","SYCL" +"SYCL0","LIGHTNING_INDEXER","hsk=128,nh=4,kv=256,nb=1,ns=4,nm=4,type_K=q5_1","support","1","yes","SYCL" +"SYCL0","LIGHTNING_INDEXER","hsk=128,nh=4,kv=256,nb=1,ns=4,nm=4,type_K=q5_0","support","1","yes","SYCL" +"SYCL0","LIGHTNING_INDEXER","hsk=128,nh=4,kv=256,nb=1,ns=4,nm=4,type_K=q4_1","support","1","yes","SYCL" +"SYCL0","LIGHTNING_INDEXER","hsk=128,nh=4,kv=256,nb=1,ns=4,nm=4,type_K=q4_0","support","1","yes","SYCL" +"SYCL0","LIGHTNING_INDEXER","hsk=128,nh=4,kv=256,nb=1,ns=4,nm=4,type_K=iq4_nl","support","1","yes","SYCL" +"SYCL0","LIGHTNING_INDEXER","hsk=128,nh=4,kv=256,nb=1,ns=4,nm=1,type_K=f32","support","1","yes","SYCL" +"SYCL0","LIGHTNING_INDEXER","hsk=128,nh=4,kv=256,nb=1,ns=4,nm=1,type_K=f16","support","1","yes","SYCL" +"SYCL0","LIGHTNING_INDEXER","hsk=128,nh=4,kv=256,nb=1,ns=4,nm=1,type_K=bf16","support","1","yes","SYCL" +"SYCL0","LIGHTNING_INDEXER","hsk=128,nh=4,kv=256,nb=1,ns=4,nm=1,type_K=q8_0","support","1","yes","SYCL" +"SYCL0","LIGHTNING_INDEXER","hsk=128,nh=4,kv=256,nb=1,ns=4,nm=1,type_K=q5_1","support","1","yes","SYCL" +"SYCL0","LIGHTNING_INDEXER","hsk=128,nh=4,kv=256,nb=1,ns=4,nm=1,type_K=q5_0","support","1","yes","SYCL" +"SYCL0","LIGHTNING_INDEXER","hsk=128,nh=4,kv=256,nb=1,ns=4,nm=1,type_K=q4_1","support","1","yes","SYCL" +"SYCL0","LIGHTNING_INDEXER","hsk=128,nh=4,kv=256,nb=1,ns=4,nm=1,type_K=q4_0","support","1","yes","SYCL" +"SYCL0","LIGHTNING_INDEXER","hsk=128,nh=4,kv=256,nb=1,ns=4,nm=1,type_K=iq4_nl","support","1","yes","SYCL" "SYCL0","LIGHTNING_INDEXER","hsk=128,nh=32,kv=256,nb=1,ns=1,nm=1,type_K=f32","support","1","yes","SYCL" "SYCL0","LIGHTNING_INDEXER","hsk=128,nh=32,kv=256,nb=1,ns=1,nm=1,type_K=f16","support","1","yes","SYCL" "SYCL0","LIGHTNING_INDEXER","hsk=128,nh=32,kv=256,nb=1,ns=1,nm=1,type_K=bf16","support","1","yes","SYCL" @@ -20228,6 +21947,33 @@ "SYCL0","LIGHTNING_INDEXER","hsk=128,nh=64,kv=256,nb=1,ns=4,nm=1,type_K=q4_1","support","1","yes","SYCL" "SYCL0","LIGHTNING_INDEXER","hsk=128,nh=64,kv=256,nb=1,ns=4,nm=1,type_K=q4_0","support","1","yes","SYCL" "SYCL0","LIGHTNING_INDEXER","hsk=128,nh=64,kv=256,nb=1,ns=4,nm=1,type_K=iq4_nl","support","1","yes","SYCL" +"SYCL0","LIGHTNING_INDEXER","hsk=128,nh=4,kv=256,nb=512,ns=1,nm=1,type_K=f32","support","1","yes","SYCL" +"SYCL0","LIGHTNING_INDEXER","hsk=128,nh=4,kv=256,nb=512,ns=1,nm=1,type_K=f16","support","1","yes","SYCL" +"SYCL0","LIGHTNING_INDEXER","hsk=128,nh=4,kv=256,nb=512,ns=1,nm=1,type_K=bf16","support","1","yes","SYCL" +"SYCL0","LIGHTNING_INDEXER","hsk=128,nh=4,kv=256,nb=512,ns=1,nm=1,type_K=q8_0","support","1","yes","SYCL" +"SYCL0","LIGHTNING_INDEXER","hsk=128,nh=4,kv=256,nb=512,ns=1,nm=1,type_K=q5_1","support","1","yes","SYCL" +"SYCL0","LIGHTNING_INDEXER","hsk=128,nh=4,kv=256,nb=512,ns=1,nm=1,type_K=q5_0","support","1","yes","SYCL" +"SYCL0","LIGHTNING_INDEXER","hsk=128,nh=4,kv=256,nb=512,ns=1,nm=1,type_K=q4_1","support","1","yes","SYCL" +"SYCL0","LIGHTNING_INDEXER","hsk=128,nh=4,kv=256,nb=512,ns=1,nm=1,type_K=q4_0","support","1","yes","SYCL" +"SYCL0","LIGHTNING_INDEXER","hsk=128,nh=4,kv=256,nb=512,ns=1,nm=1,type_K=iq4_nl","support","1","yes","SYCL" +"SYCL0","LIGHTNING_INDEXER","hsk=128,nh=4,kv=256,nb=512,ns=4,nm=4,type_K=f32","support","1","yes","SYCL" +"SYCL0","LIGHTNING_INDEXER","hsk=128,nh=4,kv=256,nb=512,ns=4,nm=4,type_K=f16","support","1","yes","SYCL" +"SYCL0","LIGHTNING_INDEXER","hsk=128,nh=4,kv=256,nb=512,ns=4,nm=4,type_K=bf16","support","1","yes","SYCL" +"SYCL0","LIGHTNING_INDEXER","hsk=128,nh=4,kv=256,nb=512,ns=4,nm=4,type_K=q8_0","support","1","yes","SYCL" +"SYCL0","LIGHTNING_INDEXER","hsk=128,nh=4,kv=256,nb=512,ns=4,nm=4,type_K=q5_1","support","1","yes","SYCL" +"SYCL0","LIGHTNING_INDEXER","hsk=128,nh=4,kv=256,nb=512,ns=4,nm=4,type_K=q5_0","support","1","yes","SYCL" +"SYCL0","LIGHTNING_INDEXER","hsk=128,nh=4,kv=256,nb=512,ns=4,nm=4,type_K=q4_1","support","1","yes","SYCL" +"SYCL0","LIGHTNING_INDEXER","hsk=128,nh=4,kv=256,nb=512,ns=4,nm=4,type_K=q4_0","support","1","yes","SYCL" +"SYCL0","LIGHTNING_INDEXER","hsk=128,nh=4,kv=256,nb=512,ns=4,nm=4,type_K=iq4_nl","support","1","yes","SYCL" +"SYCL0","LIGHTNING_INDEXER","hsk=128,nh=4,kv=256,nb=512,ns=4,nm=1,type_K=f32","support","1","yes","SYCL" +"SYCL0","LIGHTNING_INDEXER","hsk=128,nh=4,kv=256,nb=512,ns=4,nm=1,type_K=f16","support","1","yes","SYCL" +"SYCL0","LIGHTNING_INDEXER","hsk=128,nh=4,kv=256,nb=512,ns=4,nm=1,type_K=bf16","support","1","yes","SYCL" +"SYCL0","LIGHTNING_INDEXER","hsk=128,nh=4,kv=256,nb=512,ns=4,nm=1,type_K=q8_0","support","1","yes","SYCL" +"SYCL0","LIGHTNING_INDEXER","hsk=128,nh=4,kv=256,nb=512,ns=4,nm=1,type_K=q5_1","support","1","yes","SYCL" +"SYCL0","LIGHTNING_INDEXER","hsk=128,nh=4,kv=256,nb=512,ns=4,nm=1,type_K=q5_0","support","1","yes","SYCL" +"SYCL0","LIGHTNING_INDEXER","hsk=128,nh=4,kv=256,nb=512,ns=4,nm=1,type_K=q4_1","support","1","yes","SYCL" +"SYCL0","LIGHTNING_INDEXER","hsk=128,nh=4,kv=256,nb=512,ns=4,nm=1,type_K=q4_0","support","1","yes","SYCL" +"SYCL0","LIGHTNING_INDEXER","hsk=128,nh=4,kv=256,nb=512,ns=4,nm=1,type_K=iq4_nl","support","1","yes","SYCL" "SYCL0","LIGHTNING_INDEXER","hsk=128,nh=32,kv=256,nb=512,ns=1,nm=1,type_K=f32","support","1","yes","SYCL" "SYCL0","LIGHTNING_INDEXER","hsk=128,nh=32,kv=256,nb=512,ns=1,nm=1,type_K=f16","support","1","yes","SYCL" "SYCL0","LIGHTNING_INDEXER","hsk=128,nh=32,kv=256,nb=512,ns=1,nm=1,type_K=bf16","support","1","yes","SYCL" diff --git a/ggml/src/ggml-sycl/conv2d.cpp b/ggml/src/ggml-sycl/conv2d.cpp index 3b3b49d05d3c..d13ba97234ef 100644 --- a/ggml/src/ggml-sycl/conv2d.cpp +++ b/ggml/src/ggml-sycl/conv2d.cpp @@ -53,7 +53,16 @@ static inline int64_t whcn_output_index(int64_t n, int64_t c, int64_t y, int64_t return n * (P.OC * P.OW * P.OH) + c * P.OW * P.OH + y * P.OW + x; } -template +// cwhn layout helpers: buffers are [N][H][W][C] input and [KH][KW][IC][OC] kernel +static inline int64_t cwhn_input_index(int64_t n, int64_t c, int64_t y, int64_t x, const conv2d_params & P) { + return ((n * P.IH + y) * P.IW + x) * P.IC + c; +} + +static inline int64_t cwhn_kernel_index(int64_t c_out, int64_t c_in, int64_t ky, int64_t kx, const conv2d_params & P) { + return ((ky * P.KW + kx) * P.IC + c_in) * P.OC + c_out; +} + +template static void conv2d_kernel(const float * input, const T * kernel, float * output, const conv2d_params P, const sycl::nd_item<3> & item_ct1) { const int64_t global_idx = item_ct1.get_local_id(2) + @@ -77,8 +86,8 @@ static void conv2d_kernel(const float * input, const T * kernel, float * output, const int64_t in_y = calculate_input_coord(out_y, ky, P.ST_Y, P.DL_Y, P.PD_Y); for (int64_t kx = bounds.x_min; kx < bounds.x_max; ++kx) { const int64_t in_x = calculate_input_coord(out_x, kx, P.ST_X, P.DL_X, P.PD_X); - const float input_val = input[whcn_input_index(n, c_in, in_y, in_x, P)]; - const T kernel_val = kernel[whcn_kernel_index(c_out, c_in, ky, kx, P)]; + const float input_val = input[CWHN ? cwhn_input_index(n, c_in, in_y, in_x, P) : whcn_input_index(n, c_in, in_y, in_x, P)]; + const T kernel_val = kernel[CWHN ? cwhn_kernel_index(c_out, c_in, ky, kx, P) : whcn_kernel_index(c_out, c_in, ky, kx, P)]; acc += input_val * ggml_sycl_cast(kernel_val); } } @@ -87,7 +96,7 @@ static void conv2d_kernel(const float * input, const T * kernel, float * output, output[whcn_output_index(n, c_out, out_y, out_x, P)] = acc; } -template +template static void conv2d_sycl(const float * X_D, const T * K_D, float * Y_D, const conv2d_params P, const queue_ptr & stream) { const int num_blocks = (P.TOTAL + SYCL_CONV2D_BLOCK_SIZE - 1) / SYCL_CONV2D_BLOCK_SIZE; @@ -95,7 +104,7 @@ static void conv2d_sycl(const float * X_D, const T * K_D, float * Y_D, const sycl::range<3> block_nums(1, 1, num_blocks); stream->parallel_for(sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) { - conv2d_kernel(X_D, K_D, Y_D, P, item_ct1); + conv2d_kernel(X_D, K_D, Y_D, P, item_ct1); }); } @@ -108,7 +117,14 @@ void ggml_sycl_op_conv2d(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { const float * X_D = (const float *) input->data; float * Y_D = (float *) dst->data; - GGML_ASSERT(ggml_is_contiguous(kernel)); + // WHCN: contiguous [KW,KH,IC,OC] kernel and [W,H,C,N] input + // CWHN: channel-fastest buffer [N][H][W][C] input and [KH][KW][IC][OC] kernel (OC fastest) + const bool whcn = ggml_is_contiguous(kernel) && ggml_is_contiguous(input); + const bool cwhn = input->nb[2] == ggml_type_size(input->type) && + kernel->nb[3] == ggml_type_size(kernel->type); + GGML_ASSERT(whcn || cwhn); + const bool cwhn_layout = !whcn && cwhn; // prefer whcn when both match (degenerate size-1 dims) + GGML_ASSERT(kernel->type == GGML_TYPE_F16 || kernel->type == GGML_TYPE_F32); GGML_ASSERT(input->type == GGML_TYPE_F32); GGML_ASSERT(dst->type == GGML_TYPE_F32); @@ -126,9 +142,6 @@ void ggml_sycl_op_conv2d(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { const int DL_X = p[4]; const int DL_Y = p[5]; - // no cwhn layout support - GGML_ASSERT(p[6] == 0); - const int IW = input->ne[0]; const int IH = input->ne[1]; const int OW = dst->ne[0]; @@ -142,9 +155,17 @@ void ggml_sycl_op_conv2d(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { const int64_t total = (int64_t) B * OC * OH * OW; const conv2d_params params = { IW, IH, OW, OH, KW, KH, ST_X, ST_Y, PD_X, PD_Y, DL_X, DL_Y, IC, OC, B, total }; - if (kernel->type == GGML_TYPE_F16) { - conv2d_sycl(X_D, (const sycl::half *) K_D, Y_D, params, stream); + if (cwhn_layout) { + if (kernel->type == GGML_TYPE_F16) { + conv2d_sycl(X_D, (const sycl::half *) K_D, Y_D, params, stream); + } else { + conv2d_sycl(X_D, K_D, Y_D, params, stream); + } } else { - conv2d_sycl(X_D, K_D, Y_D, params, stream); + if (kernel->type == GGML_TYPE_F16) { + conv2d_sycl(X_D, (const sycl::half *) K_D, Y_D, params, stream); + } else { + conv2d_sycl(X_D, K_D, Y_D, params, stream); + } } } diff --git a/ggml/src/ggml-sycl/ggml-sycl.cpp b/ggml/src/ggml-sycl/ggml-sycl.cpp index 2a62f91962f8..bfe53c5d7f88 100644 --- a/ggml/src/ggml-sycl/ggml-sycl.cpp +++ b/ggml/src/ggml-sycl/ggml-sycl.cpp @@ -6912,6 +6912,11 @@ static bool do_ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, cons return false; } case GGML_OP_CONV_2D: + // WHCN: contiguous [KW,KH,IC,OC] kernel and [W,H,C,N] input + // CWHN: channel-fastest input (C fastest) and kernel with OC fastest + return (ggml_is_contiguous(op->src[0]) && ggml_is_contiguous(op->src[1])) || + (op->src[1]->nb[2] == ggml_type_size(op->src[1]->type) && + op->src[0]->nb[3] == ggml_type_size(op->src[0]->type)); case GGML_OP_CONV_2D_DW: case GGML_OP_CONV_TRANSPOSE_2D: return true; From ff98cfbe8d7d2876bcccc986dac5562c6a9e9e96 Mon Sep 17 00:00:00 2001 From: Raman Shinde Date: Thu, 22 Oct 2026 13:46:52 +0530 Subject: [PATCH 68/69] qcom_tilek_change 1 --- ggml/src/ggml-vulkan/CMakeLists.txt | 11 +- ggml/src/ggml-vulkan/ggml-vulkan-types.h | 21 ++ ggml/src/ggml-vulkan/ggml-vulkan.cpp | 157 +++++++++++++- .../ggml-vulkan/vulkan-shaders/CMakeLists.txt | 4 + .../coopmat_tilek_conversion.comp | 14 ++ .../vulkan-shaders/flash_attn.comp | 26 ++- .../vulkan-shaders/flash_attn_base.glsl | 1 + .../vulkan-shaders/mul_mm_tilek.comp | 204 ++++++++++++++++++ .../ggml-vulkan/vulkan-shaders/rms_norm.comp | 16 +- .../vulkan-shaders/swiglu_tilek.comp | 52 +++++ .../vulkan-shaders/tilek_layout.glsl | 27 +++ .../vulkan-shaders/vulkan-shaders-gen.cpp | 7 + 12 files changed, 533 insertions(+), 7 deletions(-) create mode 100644 ggml/src/ggml-vulkan/vulkan-shaders/feature-tests/coopmat_tilek_conversion.comp create mode 100644 ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_tilek.comp create mode 100644 ggml/src/ggml-vulkan/vulkan-shaders/swiglu_tilek.comp create mode 100644 ggml/src/ggml-vulkan/vulkan-shaders/tilek_layout.glsl diff --git a/ggml/src/ggml-vulkan/CMakeLists.txt b/ggml/src/ggml-vulkan/CMakeLists.txt index d7dec92a63c3..c3d1f7e223f9 100644 --- a/ggml/src/ggml-vulkan/CMakeLists.txt +++ b/ggml/src/ggml-vulkan/CMakeLists.txt @@ -38,11 +38,12 @@ endfunction() function(test_shader_extension_support EXTENSION_NAME TEST_SHADER_FILE RESULT_VARIABLE) execute_process( COMMAND ${Vulkan_GLSLC_EXECUTABLE} -o - -fshader-stage=compute --target-env=vulkan1.3 "${TEST_SHADER_FILE}" + RESULT_VARIABLE glslc_result OUTPUT_VARIABLE glslc_output ERROR_VARIABLE glslc_error ) - if (${glslc_error} MATCHES ".*extension not supported: ${EXTENSION_NAME}.*") + if (NOT glslc_result EQUAL 0) message(STATUS "${EXTENSION_NAME} not supported by glslc") set(${RESULT_VARIABLE} OFF PARENT_SCOPE) else() @@ -89,6 +90,14 @@ if (Vulkan_FOUND) "GGML_VULKAN_COOPMAT2_GLSLC_SUPPORT" ) + if (GGML_VULKAN_COOPMAT_GLSLC_SUPPORT) + test_shader_extension_support( + "GL_QCOM_cooperative_matrix_conversion" + "${CMAKE_CURRENT_SOURCE_DIR}/vulkan-shaders/feature-tests/coopmat_tilek_conversion.comp" + "GGML_VULKAN_TILEK_GLSLC_SUPPORT" + ) + endif() + test_shader_extension_support( "GL_NV_cooperative_matrix_decode_vector" "${CMAKE_CURRENT_SOURCE_DIR}/vulkan-shaders/feature-tests/coopmat2_decode_vector.comp" diff --git a/ggml/src/ggml-vulkan/ggml-vulkan-types.h b/ggml/src/ggml-vulkan/ggml-vulkan-types.h index 00d20e843b78..9aedfd13262d 100644 --- a/ggml/src/ggml-vulkan/ggml-vulkan-types.h +++ b/ggml/src/ggml-vulkan/ggml-vulkan-types.h @@ -61,6 +61,7 @@ typedef struct VkPhysicalDeviceCooperativeMatrixDecodeVectorFeaturesNV { #include #include #include +#include #include #include #include @@ -211,6 +212,10 @@ static bool is_pow2(uint32_t x) { return x > 1 && (x & (x-1)) == 0; } } \ } while (0) +// std::vector qcom_layer_tilekfirst = { "attn_norm-", "ffn_norm-", "ffn_gate_par-", "kqv_merged_cont-","ffn_swiglu-", "kqv_out-" }; +// K dimension of the TileK-first activation layout. Must match TILEK in vulkan-shaders/tile_layout.glsl, and the device's cooperative-matrix K size. +static constexpr uint32_t GGML_VK_TILEK = 16; + #ifdef GGML_VULKAN_DEBUG #define VK_LOG_DEBUG(msg) std::cerr << msg << std::endl #else @@ -733,6 +738,7 @@ struct vk_device_struct { bool coopmat_bf16_support {}; bool coopmat_support_16x16x16_f16acc {}; bool coopmat_support_16x16x16_f32acc {}; + bool tilek_support {}; bool coopmat1_fa_support {}; uint32_t coopmat_m; uint32_t coopmat_n; @@ -782,6 +788,7 @@ struct vk_device_struct { vk::DescriptorSetLayout dsl; std::map> pipeline_matmul; + vk_pipeline pipeline_matmul_tilek_q4_0; matmul_tile_selector_t matmul_tile_selector; matmul_tile_selector_t matmul_id_tile_selector; @@ -909,6 +916,10 @@ struct vk_device_struct { vk_pipeline pipeline_geglu_erf[2]; vk_pipeline pipeline_geglu_quick[2]; + // Tilek specific: RMS_norm and SwiGLU writing TileK-first + vk_pipeline pipeline_swiglu_tilek_f32; + vk_pipeline pipeline_rms_norm_mul_tilek_f32; + vk_pipeline pipeline_leaky_relu[2]; vk_pipeline pipeline_silu_back_f32; vk_pipeline pipeline_diag_mask_inf_f32; @@ -1243,6 +1254,16 @@ struct ggml_backend_vk_context { // True when the K dimension in prealloc_y is padded. bool prealloc_y_last_k_padded {}; + // [Qcom specific] + // Tensors that may be produced in the TileK-first layout: every consumer in + // the graph is a matmul that can read that layout. Filled by a pre-pass in + // ggml_backend_vk_graph_compute. + std::unordered_set tilek_candidates; + // Subset of the above that a producer actually emitted TileK-first. The + // producer inserts into this when it picks a TileK pipeline, and consumers + // read it later in the same graph, so the two can never disagree. + std::unordered_set tilek_tensors; + // Track which nodes have been used since the last sync, and whether they were written to std::vector unsynced_nodes_written; std::vector unsynced_nodes_read; diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp index e603cbcb11a2..5fc7c21b4b0c 100644 --- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp +++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp @@ -2287,6 +2287,7 @@ void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) { create_mm_pipelines({GGML_TYPE_BF16, GGML_TYPE_BF16, false, false}, tc_mm, "matmul_bf16", matmul_bf16_cm2_len, matmul_bf16_cm2_data, sizeof(vk_mat_mat_push_constants), 3, cm2_spec, true); } #endif + for (const auto type : non_lut_quant_types) { // regression in unified shader on Ampere if (type == GGML_TYPE_Q4_K || type == GGML_TYPE_Q5_K) { @@ -2375,6 +2376,27 @@ void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) { if (device->coopmat_support) { spec_fn_t cm1_spec = [&](const std::vector& wt, bool a) { return ggml_vk_mul_mm_spec(wt, a); }; +#if defined(GGML_VULKAN_TILEK_GLSLC_SUPPORT) + if (device->tilek_support) { + const uint32_t tilek_bk = 32; + const std::vector tilek_warptile = { + device->subgroup_size, + device->coopmat_m, device->coopmat_n, + tilek_bk, + device->coopmat_m, device->coopmat_n, + 1, + device->coopmat_m, device->coopmat_n, + device->coopmat_k, + device->subgroup_size, + }; + const std::array tilek_wg_denoms = { device->coopmat_m, device->coopmat_n, 1 }; + + ggml_vk_create_pipeline(device, device->pipeline_matmul_tilek_q4_0, "matmul_q4_0_f32_tilek_f16acc", + matmul_q4_0_f32_tilek_f16acc_cm1_len, matmul_q4_0_f32_tilek_f16acc_cm1_data, "main", 3, sizeof(vk_mat_mat_push_constants), + tilek_wg_denoms, cm1_spec(tilek_warptile, true), tilek_bk, false, true, device->subgroup_size); + } +#endif + // Intel coopmat1 pins each pipeline's required subgroup size to its warptile WARP element. const bool cm1_pin = device->vendor_id == VK_VENDOR_ID_INTEL; @@ -3270,6 +3292,10 @@ void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) { ggml_vk_create_pipeline(device, device->pipeline_rms_norm_mul_rope_f32_f16, "rms_norm_mul_rope_f32_f16", rms_norm_mul_rope_f32_f16_len, rms_norm_mul_rope_f32_f16_data, "main", 7, sizeof(vk_op_rms_norm_mul_rope_push_constants), {1, 1, 1}, {0, 1}, 1, true); } + if (device->tilek_support) { + ggml_vk_create_pipeline(device, device->pipeline_rms_norm_mul_tilek_f32, "rms_norm_mul_tilek_f32", rms_norm_f32_len, rms_norm_f32_data, "main", 4, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {0, 1, 0, 1}, 1, true); + } + ggml_vk_create_pipeline(device, device->pipeline_rms_norm_back_f32, "rms_norm_back_f32", rms_norm_back_f32_len, rms_norm_back_f32_data, "main", 3, sizeof(vk_op_push_constants), {1, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_l2_norm_f32, "l2_norm_f32", l2_norm_f32_len, l2_norm_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {1, 1, 1}, {}, 1); @@ -3481,6 +3507,13 @@ void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) { CREATE_GLU(geglu_quick) #undef CREATE_GLU +// Todo: only for adreno with coopmat support +#if defined(GGML_VULKAN_TILEK_GLSLC_SUPPORT) + if (device->tilek_support) { + ggml_vk_create_pipeline(device, device->pipeline_swiglu_tilek_f32, "swiglu_tilek_f32", swiglu_tilek_f32_len, swiglu_tilek_f32_data, "main", 3, sizeof(vk_op_glu_push_constants), {512, 1, 1}, {}, 1, true); + } +#endif + ggml_vk_create_pipeline(device, device->pipeline_silu_back_f32, "silu_back_f32", silu_back_f32_len, silu_back_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_diag_mask_inf_f32, "diag_mask_inf_f32", diag_mask_inf_f32_len, diag_mask_inf_f32_data, "main", 2, sizeof(vk_op_diag_mask_push_constants), {1, 512, 1}, {}, 1, true); @@ -4760,11 +4793,19 @@ vk_device ggml_vk_get_device(size_t idx) { GGML_LOG_DEBUG("ggml_vulkan: WARNING: No suitable matrix core mode found. Disabling matrix cores.\n"); device->coopmat_support = false; } + + device->tilek_support = device->architecture == QUALCOMM_ADRENO && + device->coopmat_support && + device->coopmat_support_16x16x16_f16acc && + device->coopmat_support_16x16x16_f32acc; } if (device->coopmat_support) { device_extensions.push_back("VK_KHR_cooperative_matrix"); } + if (device->tilek_support) { + device_extensions.push_back("VK_QCOM_cooperative_matrix_conversion"); + } #endif device->name = GGML_VK_NAME + std::to_string(idx); @@ -8685,6 +8726,29 @@ static vk_conv_shapes ggml_vk_conv_select_shape(ggml_backend_vk_context * ctx, u } } +// Whether the fused rms_norm+mul writing dst should emit TileK-first. Used by +// both the pipeline lookup and the dispatch, so the two cannot disagree about +// what layout the tensor ended up in. +static bool ggml_vk_rms_norm_use_tilek(const ggml_backend_vk_context * ctx, const ggml_tensor * dst) { +// VK_LOG_DEBUG("ggml_vk_rms_norm_use_tilek( Tilek layout =" << ctx->device->tilek_support << ", TileK candidates =" << ctx->tilek_candidates.count(dst)); + + return ctx->device->tilek_support && + !ctx->do_add_rms_partials && + ctx->num_additional_fused_ops == 1 && // rms_norm + mul, no rope + ctx->tilek_candidates.count(dst) > 0; +} + +// Same contract for GLU: consulted by the pipeline lookup and by the dispatch +// that records the layout, so the two cannot disagree. Only SwiGLU has a TileK +// shader; the other GLU ops keep the shared row-major path. +static bool ggml_vk_glu_use_tilek(const ggml_backend_vk_context * ctx, const ggml_tensor * dst) { + return ctx->device->tilek_support && + dst->type == GGML_TYPE_F32 && + ggml_get_glu_op(dst) == GGML_GLU_OP_SWIGLU && + ctx->device->pipeline_swiglu_tilek_f32 != nullptr && + ctx->tilek_candidates.count(dst) > 0; +} + static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, const ggml_tensor * dst, ggml_op op) { switch (op) { case GGML_OP_GET_ROWS: @@ -8924,8 +8988,11 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { if (ctx->do_add_rms_partials) { return ctx->fused_rms_norm_mode == RMS_NORM_MUL ? ctx->device->pipeline_rms_norm_mul_partials_f32 : ctx->device->pipeline_rms_norm_partials_f32; - } + } else if (ggml_vk_rms_norm_use_tilek(ctx, dst)) { + return ctx->device->pipeline_rms_norm_mul_tilek_f32; + } else { return ctx->fused_rms_norm_mode == RMS_NORM_MUL ? ctx->device->pipeline_rms_norm_mul_f32 : ctx->device->pipeline_rms_norm_f32; + } } return nullptr; case GGML_OP_RMS_NORM_BACK: @@ -9001,6 +9068,10 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const return nullptr; } + if (ggml_vk_glu_use_tilek(ctx, dst)) { + return ctx->device->pipeline_swiglu_tilek_f32; + } + switch (ggml_get_glu_op(dst)) { case GGML_GLU_OP_GEGLU: return ctx->device->pipeline_geglu[dst->type == GGML_TYPE_F16]; @@ -11015,6 +11086,11 @@ void ggml_vk_rms_norm(ggml_backend_vk_context * ctx, vk_context& subctx, const s ggml_vk_subbuffer(ctx, buf[6], offset[6]), }, pc, elements); } else { + // Record the layout before dispatching, so the consuming matmuls later + // in this graph pick the matching B loader. + if (ggml_vk_rms_norm_use_tilek(ctx, dst)) { + ctx->tilek_tensors.insert(dst); + } GGML_ASSERT(ctx->fused_rms_norm_mode == RMS_NORM_MUL || ctx->fused_rms_norm_mode == RMS_NORM_COUNT); ggml_vk_op_f32(ctx, subctx, src0, src1, nullptr, nullptr, dst, GGML_OP_RMS_NORM, std::move(bin)); } @@ -11064,6 +11140,12 @@ void ggml_vk_glu(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_t GGML_ASSERT(src0->type == src1->type); } + // Record the layout before dispatching, so the consuming matmuls later in + // this graph pick the matching B loader. + if (ggml_vk_glu_use_tilek(ctx, dst)) { + ctx->tilek_tensors.insert(dst); + } + const uint32_t mode = split ? 2 : (swapped ? 1 : 0); const uint32_t src0_type_size = ggml_type_size(src0->type); const uint32_t src1_type_size = split ? ggml_type_size(src1->type) : src0_type_size; @@ -14279,6 +14361,75 @@ static int32_t find_first_set(uint32_t x) { return ret; } +// A tensor may only be written in the TileK-first layout if every consumer +// reads it in that layout, so the decision belongs to the tensor rather than to +// the producing op. This checks the one consumer shape we support: a mul_mat +// reading the tensor as B, dispatched through the mat-mat path, with a TileK +// matmul available for the weight type. +static bool ggml_vk_tilek_consumer_ok(const ggml_backend_vk_context * ctx, const ggml_tensor * use, const ggml_tensor * t) { + if (use->op != GGML_OP_MUL_MAT || use->src[1] != t) { + return false; + } + const ggml_tensor * a = use->src[0]; + if (a->type != GGML_TYPE_Q4_0 || ctx->device->pipeline_matmul_tilek_q4_0 == nullptr) { + return false; + } + // mul_mat_vec would read B row-major. Only take over where + // ggml_vk_mul_mat() would have picked the mat-mat path anyway. + if (use->ne[1] <= mul_mat_vec_max_cols) { + return false; + } + // The TileK matmul walks A one quantization block at a time and indexes B + // by whole K-tiles, so K has to divide evenly by both. + if (a->ne[0] % ggml_blck_size(a->type) != 0 || a->ne[0] % GGML_VK_TILEK != 0) { + return false; + } + return true; +} + +// Collect the tensors whose consumers all read TileK-first. Producers consult +// this, and record what they actually emitted in ctx->tilek_tensors. + static void ggml_vk_find_tilek_candidates(ggml_backend_vk_context * ctx, const ggml_cgraph * cgraph) { + ctx->tilek_candidates.clear(); + ctx->tilek_tensors.clear(); + + if (!ctx->device->tilek_support) { + return; + } + + std::unordered_map ok; // candidate -> all consumers OK so far + std::unordered_set used; // candidate -> has >=1 consumer + + for (int i = 0; i < cgraph->n_nodes; i++) { + const ggml_tensor * node = cgraph->nodes[i]; + + // Resolve this node's srcs against anything registered as a candidate so far. + for (int s = 0; s < GGML_MAX_SRC; s++) { + const ggml_tensor * src = node->src[s]; + auto it = ok.find(src); + if (it != ok.end()) { + it->second = it->second && ggml_vk_tilek_consumer_ok(ctx, node, src); + used.insert(src); + } + } + + // Register this node as a candidate for everything still downstream. + if (node->type == GGML_TYPE_F32 && ggml_is_contiguous(node) && + node->ne[0] % GGML_VK_TILEK == 0 && node->ne[1] > 1 && + node->ne[2] == 1 && node->ne[3] == 1 && + !(node->flags & GGML_TENSOR_FLAG_OUTPUT)) { + ok[node] = true; + } + } + + for (auto & [t, all_ok] : ok) { + if (all_ok && used.count(t)) { + ctx->tilek_candidates.insert(t); + } + } + } + + static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) { VK_LOG_DEBUG("ggml_backend_vk_graph_compute(" << cgraph->n_nodes << " nodes)"); ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context; @@ -14299,6 +14450,9 @@ static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cg ctx->do_add_rms_partials = false; ctx->do_add_rms_partials_offset_calculation = false; + // Todo: Run for adreno with coopmat + ggml_vk_find_tilek_candidates(ctx, cgraph); + int last_node = cgraph->n_nodes - 1; // If the last op in the cgraph isn't backend GPU, the command buffer doesn't get closed properly @@ -16696,4 +16850,3 @@ void ggml_vk_debug_label::begin(vk_context & ctx, const std::string & name) { subctx->debug_labels.push_back(name); ggml_vk_cmd_label_begin(subctx->s->buffer->buf, subctx->debug_labels.back().c_str()); } - diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/CMakeLists.txt b/ggml/src/ggml-vulkan/vulkan-shaders/CMakeLists.txt index cbe7a68bf372..58eff6a0d8e4 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/CMakeLists.txt +++ b/ggml/src/ggml-vulkan/vulkan-shaders/CMakeLists.txt @@ -11,6 +11,10 @@ if (GGML_VULKAN_COOPMAT2_GLSLC_SUPPORT) add_compile_definitions(GGML_VULKAN_COOPMAT2_GLSLC_SUPPORT) message(STATUS "Enabling coopmat2 glslc support") endif() +if (GGML_VULKAN_TILEK_GLSLC_SUPPORT) + add_compile_definitions(GGML_VULKAN_TILEK_GLSLC_SUPPORT) + message(STATUS "Enabling coopmat tilek glslc support") +endif() if (GGML_VULKAN_COOPMAT2_DECODE_VECTOR_GLSLC_SUPPORT) add_compile_definitions(GGML_VULKAN_COOPMAT2_DECODE_VECTOR_GLSLC_SUPPORT) message(STATUS "Enabling coopmat2 decode_vector glslc support") diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/feature-tests/coopmat_tilek_conversion.comp b/ggml/src/ggml-vulkan/vulkan-shaders/feature-tests/coopmat_tilek_conversion.comp new file mode 100644 index 000000000000..f74567e67134 --- /dev/null +++ b/ggml/src/ggml-vulkan/vulkan-shaders/feature-tests/coopmat_tilek_conversion.comp @@ -0,0 +1,14 @@ +#version 460 + +#extension GL_EXT_shader_explicit_arithmetic_types_float16 : require +#extension GL_KHR_cooperative_matrix : require +#extension GL_KHR_memory_scope_semantics : require +#extension GL_QCOM_cooperative_matrix_conversion : require + +layout(local_size_x = 1, local_size_y = 1, local_size_z = 1) in; + +void main() { + coopmat mat; + float16_t values[16]; + vectorToCoopmatQCOM(values, mat); +} diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn.comp b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn.comp index 2c5e85908576..b96d453018cd 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn.comp @@ -21,6 +21,7 @@ #extension GL_KHR_shader_subgroup_vote : enable #include "types.glsl" +#include "tilek_layout.glsl" #include "dot_product_funcs.glsl" #include "flash_attn_base.glsl" #include "flash_attn_dequant.glsl" @@ -754,7 +755,30 @@ void main() { } } } - } else { + } else if (DstTileK != 0) { + // Same values as the row-major branch below, permuted into the + // TileK-first layout for mul_mm_tilek.comp. Viewed as a 2D tensor, dst + // is nrows x K with nrows = N (tokens, dst->ne[2]) and K = p.ne1 * HSV + // (all heads of one token, contiguous), which is exactly the reshape + // the graph applies before feeding this into the wo matmul. + // + // o_offset is not added: the host only sets DstTileK when gqa_ratio == 1 + // (so gqa_iq1 == 0) and dst has a single batch (so iq3 == 0), which + // makes it zero. tilek_first_idx() addresses the whole tensor anyway. + [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) { + const uint row = tile_row(r); + if (i * Br + row < N) { + [[unroll]] for (uint32_t d = 0; d < HSV_per_thread / 4; ++d) { + // Scalar K index of the first element of this vec4. It is a + // multiple of 4 and TILEK is 16, so all four elements land in + // the same tile at consecutive offsets and the vec4 store is + // still contiguous. + const uint kidx = iq2 * HSV + (d * D_split + d_tid) * 4; + data_ov4[tilek_first_idx(i * Br + row, kidx, N) / 4] = D_TYPEV4(Of[r][d]); + } + } + } + } else { [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) { const uint row = tile_row(r); if (i * Br + row < N) { diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_base.glsl b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_base.glsl index 2e0e23bc11c1..5817922cda8e 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_base.glsl +++ b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_base.glsl @@ -19,6 +19,7 @@ layout (constant_id = 13) const uint32_t FaTypeV = 0; // sizeof(decode buffer): quants -> ggml block size; F32 -> 16 (decodeBufF32 vec4). layout (constant_id = 14) const uint32_t FaBlockBytesK = 2; layout (constant_id = 15) const uint32_t FaBlockBytesV = 2; +layout (constant_id = 16) const uint32_t DstTileK = 0; const bool USE_MASK_OPT = (Flags & 1) != 0; const bool MASK_ENABLE = (Flags & 2) != 0; diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_tilek.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_tilek.comp new file mode 100644 index 000000000000..e67ced488850 --- /dev/null +++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_tilek.comp @@ -0,0 +1,204 @@ +#version 450 + +// Cooperative-matrix matmul for a quantized A and a TileK-first f32 B. +// +// Differences from mul_mm.comp: +// - B is read in TileK-first order (see tile_layout.glsl), so a subgroup's +// whole B fragment is one contiguous run and no shared-memory staging or +// transpose is needed. +// - A is dequantized straight into registers and handed to the matrix units +// through the vector <-> cooperative-matrix conversion builtins. +// - One subgroup per workgroup, producing exactly one TM x TN output tile. +// The host builds the specialization so that BM == TM and BN == TN, both +// equal to the subgroup size; see ggml_vk_tilek_warptile(). + +#extension GL_EXT_control_flow_attributes : enable +#extension GL_EXT_shader_16bit_storage : require + +#extension GL_EXT_shader_explicit_arithmetic_types_float16 : require +#extension GL_EXT_shader_explicit_arithmetic_types_int32 : require +#extension GL_EXT_shader_explicit_arithmetic_types_int16 : require +#extension GL_EXT_shader_explicit_arithmetic_types_int8 : require + +#extension GL_KHR_cooperative_matrix : enable +#extension GL_KHR_memory_scope_semantics : enable +#extension GL_KHR_shader_subgroup_basic : enable + +// vectorToCoopmatQCOM / coopmatToVectorQCOM / extractSubArrayQCOM +#extension GL_QCOM_cooperative_matrix_conversion : require + +#include "tilek_layout.glsl" + +// Number of quantized values in one A block, and how many cooperative-matrix +// K-tiles that covers. Only q4_0 is wired up so far. +#define QUANT_K 32 +#define TILES_PER_BLOCK (QUANT_K / TILEK) + +struct block_q4_0_packed16 { + float16_t d; + uint16_t qs[QUANT_K / 4]; +}; + +layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in; + +layout(binding = 0) readonly buffer A_PACKED16 { block_q4_0_packed16 data_a_packed16[]; }; +layout(binding = 1) readonly buffer B { float data_b[]; }; +layout(binding = 2) writeonly buffer D { float data_d[]; }; + +layout(push_constant) uniform parameter +{ + uint M; + uint N; + uint K; + uint stride_a; + uint stride_b; + uint stride_d; + + uint batch_stride_a; + uint batch_stride_b; + uint batch_stride_d; + + uint base_work_group_z; + uint num_batches; + uint k_split; + uint ne02; + uint ne12; + uint broadcast2; + uint broadcast3; + uint padded_N; +} p; + +// Specialization constants match mul_mm.comp so the host can reuse the warptile +// plumbing. BK/WM/WN/WMITER are unused here: this shader has no shared-memory +// tile and no per-subgroup sub-tiling. +layout(constant_id = 0) const uint BLOCK_SIZE = 64; +layout(constant_id = 1) const uint BM = 64; +layout(constant_id = 2) const uint BN = 64; +layout(constant_id = 3) const uint BK = 32; +layout(constant_id = 4) const uint WM = 64; +layout(constant_id = 5) const uint WN = 64; +layout(constant_id = 6) const uint WMITER = 1; +layout(constant_id = 7) const uint TM = 64; +layout(constant_id = 8) const uint TN = 64; +layout(constant_id = 9) const uint TK = 16; +layout(constant_id = 10) const uint WARP = 64; +layout(constant_id = 11) const bool ALIGNED = true; + +void main() { + // One subgroup per workgroup covering the whole BM x BN tile. Any other + // geometry would have subgroups writing into neighbouring tiles, so refuse + // to run; ggml_vk_tilek_warptile() asserts the same relationship host-side. + if (BM != TM || BN != TN || TK != TILEK || gl_NumSubgroups != 1) { + return; + } + + const uint batch_idx = gl_WorkGroupID.z + p.base_work_group_z; + + const uint i13 = batch_idx / p.ne12; + const uint i12 = batch_idx % p.ne12; + + const uint i03 = i13 / p.broadcast3; + const uint i02 = i12 / p.broadcast2; + + const uint batch_idx_a = i03 * p.ne02 + i02; + + const uint blocks_m = (p.M + BM - 1) / BM; + const uint ir = gl_WorkGroupID.x % blocks_m; // row tile + const uint ik = gl_WorkGroupID.x / blocks_m; // split_k slice + const uint ic = gl_WorkGroupID.y; // column tile + + const uint lane = gl_SubgroupInvocationID; + + const uint start_k = ik * p.k_split; + const uint end_k = min(p.K, (ik + 1) * p.k_split); + + // A is row-major and lane m holds row (ir*BM + m). stride_a, batch_stride_a + // and start_k are all multiples of QUANT_K, so the block index divides out + // exactly. + uint ib = (batch_idx_a * p.batch_stride_a + (ir * BM + lane) * p.stride_a + start_k) / QUANT_K; + + // B is TileK-first: the K-tile containing kidx for column n starts at + // (kidx / TILEK) * TILEK * N + n * TILEK, and start_k is a multiple of + // TILEK. Lane n holds column ic*BN + n, i.e. one contiguous TILEK run. + uint pos_b = batch_idx * p.batch_stride_b + start_k * p.N + (ic * BN + lane) * TILEK; + + coopmat matA; + coopmat matB; + coopmat matC = + coopmat(0.0f); + + float16_t vecA[QUANT_K]; + // The conversion builtins require the array length to be the coopmat's + // column count, spelled with the same specialization constant. + float16_t vecA_tile[TK]; + float16_t vecB[TK]; + + for (uint k = start_k; k < end_k; k += QUANT_K) { + // Dequantize one q4_0 block per lane into registers. Low nibbles hold + // values 0..15 of the block, high nibbles values 16..31. + const float16_t d = data_a_packed16[ib].d; + [[unroll]] for (uint i = 0; i < QUANT_K / 8; i++) { + const uint vui = uint(data_a_packed16[ib].qs[2 * i]) | (uint(data_a_packed16[ib].qs[2 * i + 1]) << 16); + const f16vec4 v0 = (f16vec4(unpack8((vui >> 0) & 0x0F0F0F0F)) - float16_t(8.0f)) * d; + const f16vec4 v1 = (f16vec4(unpack8((vui >> 4) & 0x0F0F0F0F)) - float16_t(8.0f)) * d; + vecA[4 * i + 0] = v0.x; + vecA[4 * i + 1] = v0.y; + vecA[4 * i + 2] = v0.z; + vecA[4 * i + 3] = v0.w; + vecA[4 * i + 16] = v1.x; + vecA[4 * i + 17] = v1.y; + vecA[4 * i + 18] = v1.z; + vecA[4 * i + 19] = v1.w; + } + ib += 1; + + // TILES_PER_BLOCK and TILEK are literals, so the offset handed to + // extractSubArrayQCOM stays a compile-time constant after unrolling. + [[unroll]] for (uint t = 0; t < TILES_PER_BLOCK; t++) { + extractSubArrayQCOM(vecA, t * TILEK, vecA_tile); + + [[unroll]] for (uint kk = 0; kk < TK; kk++) { + vecB[kk] = float16_t(data_b[pos_b + kk]); + } + pos_b += TILEK * p.N; + + vectorToCoopmatQCOM(vecA_tile, matA); + vectorToCoopmatQCOM(vecB, matB); + + matC = coopMatMulAdd(matA, matB, matC); + } + } + + // Lanes past M/N accumulate whatever robustBufferAccess handed them; their + // results are dropped by the bounds checks below. + const uint dr = ir * BM; + const uint dc = ic * BN; + + const uint offsets = batch_idx * p.batch_stride_d + ik * p.batch_stride_d * p.num_batches; + + // D holds dst->ne[0] == M contiguously, i.e. column-major for an M x N result. + const bool is_aligned = p.stride_d % 4 == 0; + const bool is_in_bounds = dr + TM <= p.M && dc + TN <= p.N; + + if (is_aligned && is_in_bounds) { + coopmat cm_dtype = + coopmat(matC); + coopMatStore(cm_dtype, data_d, offsets + dc * p.stride_d + dr, p.stride_d, gl_CooperativeMatrixLayoutColumnMajor); + return; + } + + if (dr + lane >= p.M) { + return; + } + + // Scalar fallback: coopmatToVectorQCOM gives lane m row (dr + m) of the + // tile, one element per output column. + float16_t vecC[TN]; + coopmatToVectorQCOM(matC, vecC); + + [[unroll]] for (uint col = 0; col < TN; col++) { + if (dc + col < p.N) { + data_d[offsets + (dc + col) * p.stride_d + dr + lane] = float(vecC[col]); + } + } +} diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/rms_norm.comp b/ggml/src/ggml-vulkan/vulkan-shaders/rms_norm.comp index 314c8e565e68..2e69f42c50e0 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/rms_norm.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/rms_norm.comp @@ -2,6 +2,7 @@ #include "generic_binary_head.glsl" #include "types.glsl" +#include "tilek_layout.glsl" #if RMS_NORM_ROPE_FUSION @@ -45,11 +46,20 @@ layout (constant_id = 1) const bool do_multiply = false; #if RMS_NORM_ADD_FUSION layout (constant_id = 2) const bool do_post_multiply = false; #endif +layout (constant_id = 3) const bool dst_tilek = false; layout(local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in; shared FLOAT_TYPE sumsh[BLOCK_SIZE]; +uint rms_norm_dst_idx(const uint row, const uint col, const uint d_offset, const uint nrows) { +#if RMS_NORM_ROPE_FUSION + return d_offset + col; +#else + return dst_tilek ? get_doffset() + tilek_first_idx(row, col, nrows) : d_offset + col; +#endif +} + void rms_norm(uint num_iters) { const uint ncols = p.ne00; const uint nrows = gl_NumWorkGroups.x; @@ -116,7 +126,7 @@ void rms_norm(uint num_iters) { value *= FLOAT_TYPE(data_e[0]); } #endif - data_d[d_offset + col] = D_TYPE(value); + data_d[rms_norm_dst_idx(row, col, d_offset, nrows)] = D_TYPE(value); } } else { [[unroll]] for (uint col = tid, idx = 0; idx < num_iters; col += BLOCK_SIZE, ++idx) { @@ -130,7 +140,7 @@ void rms_norm(uint num_iters) { value *= FLOAT_TYPE(data_e[0]); } #endif - data_d[d_offset + col] = D_TYPE(value); + data_d[rms_norm_dst_idx(row, col, d_offset, nrows)] = D_TYPE(value); } } } else { @@ -138,7 +148,7 @@ void rms_norm(uint num_iters) { if (col >= ncols) { continue; } - data_d[d_offset + col] = D_TYPE(scale * FLOAT_TYPE(data_a[a_offset + col])); + data_d[rms_norm_dst_idx(row, col, d_offset, nrows)] = D_TYPE(scale * FLOAT_TYPE(data_a[a_offset + col])); } } #if RMS_NORM_ROPE_FUSION diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/swiglu_tilek.comp b/ggml/src/ggml-vulkan/vulkan-shaders/swiglu_tilek.comp new file mode 100644 index 000000000000..752994216815 --- /dev/null +++ b/ggml/src/ggml-vulkan/vulkan-shaders/swiglu_tilek.comp @@ -0,0 +1,52 @@ +#version 450 + +// SwiGLU writing its result in TileK-first order (see tile_layout.glsl), for +// activations that feed the TileK cooperative-matrix matmul. + +#include "glu_head.glsl" +#include "tilek_layout.glsl" + +float op(float a, float b) { + return a / (1.0f + exp(-a)) * b; +} + +void main() { + const uint i = gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x; + + if (i >= p.N) { + return; + } + + const uint i23 = fastdiv(i, p.ne2_012mp, p.ne2_012L); + const uint i23_offset = i23 * p.ne22*p.ne21*p.ne20; + const uint i22 = fastdiv(i - i23_offset, p.ne2_01mp, p.ne2_01L); + const uint i22_offset = i22*p.ne21*p.ne20; + const uint i21 = fastdiv(i - i23_offset - i22_offset, p.ne2_0mp, p.ne2_0L); + const uint i20 = i - i23_offset - i22_offset - i21*p.ne20; + + const uint src_idx_a = get_aoffset() + i23 * p.nb03 + i22 * p.nb02 + i21 * p.nb01 + i20 * p.nb00; + const uint src_idx_b = get_boffset() + i23 * p.nb13 + i22 * p.nb12 + i21 * p.nb11 + i20 * p.nb10; + + // The only line that differs from glu_main.glsl. i20 indexes dst->ne[0] + // (the K dimension) and i21 indexes dst->ne[1] (the row), which is exactly + // what tilek_first_idx wants. The host only enables this for single-batch + // tensors, so i22 and i23 are zero here. + const uint dst_idx = get_doffset() + tilek_first_idx(i21, i20, p.ne21); + + if (p.mode == 0) { + // Default + const uint offset = (p.ne00 / 2) * p.nb00; + const uint idx = src_idx_a; + + data_d[dst_idx] = D_TYPE(op(float(data_a[idx]), float(data_a[idx + offset]))); + } else if (p.mode == 1) { + // Swapped + const uint offset = (p.ne00 / 2) * p.nb00; + const uint idx = src_idx_a; + + data_d[dst_idx] = D_TYPE(op(float(data_a[idx + offset]), float(data_a[idx]))); + } else { + // Split + data_d[dst_idx] = D_TYPE(op(float(data_a[src_idx_a]), float(data_b[src_idx_b]))); + } +} \ No newline at end of file diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/tilek_layout.glsl b/ggml/src/ggml-vulkan/vulkan-shaders/tilek_layout.glsl new file mode 100644 index 000000000000..4ea641e73d68 --- /dev/null +++ b/ggml/src/ggml-vulkan/vulkan-shaders/tilek_layout.glsl @@ -0,0 +1,27 @@ +#ifndef TILEK_LAYOUT_GLSL +#define TILEK_LAYOUT_GLSL + +// TileK-first layout: an nrows x K tensor stored as K/TILEK strips, +// each holding TILEK contiguous K values for every row. +// Requires K % TILEK == 0 and TILEK == device coopmat K (checked on host). + +#ifndef TILEK +#define TILEK 16 +#endif + +#if (TILEK == 0) || ((TILEK & (TILEK - 1)) != 0) +#error "TILEK must be a power of two" +#endif + +const uint TILEK_U = uint(TILEK); + +uint tilek_first_idx(const uint row, const uint kidx, const uint nrows) { + return TILEK_U * ((kidx / TILEK_U) * nrows + row) + (kidx % TILEK_U); +} + +// coopMatLoad element offset for the tile at (row0, k0); use stride = TILEK. +uint tilek_first_tile_start(const uint row0, const uint k0, const uint nrows) { + return k0 * nrows + row0 * TILEK_U; +} + +#endif // TILEK_LAYOUT_GLSL diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp index f545015464f1..5d2989f94336 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp @@ -709,6 +709,10 @@ void process_shaders() { } } +#if defined(GGML_VULKAN_TILEK_GLSLC_SUPPORT) + string_to_spv("matmul_q4_0_f32_tilek", "mul_mm_tilek.comp", {}, true, true, false, true); +#endif + for (const bool& fp16 : {false, true}) { std::map base_dict; if (fp16) { @@ -1056,6 +1060,9 @@ void process_shaders() { string_to_spv("reglu_f32", "reglu.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); string_to_spv("swiglu_f16", "swiglu.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}}); string_to_spv("swiglu_f32", "swiglu.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); +#if defined(GGML_VULKAN_TILEK_GLSLC_SUPPORT) + string_to_spv("swiglu_tilek_f32", "swiglu_tilek.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); +#endif string_to_spv("swiglu_oai_f16", "swiglu_oai.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}}); string_to_spv("swiglu_oai_f32", "swiglu_oai.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); string_to_spv("swiglu_clamp_f16", "swiglu_clamp.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}}); From 02ee9ceac3b6cd21975c2553655c00448988d25a Mon Sep 17 00:00:00 2001 From: Raman Shinde Date: Thu, 22 Oct 2026 15:55:28 +0530 Subject: [PATCH 69/69] Tilek version 1 --- ggml/src/ggml-vulkan/ggml-vulkan.cpp | 83 +++++++++++++++++----------- 1 file changed, 51 insertions(+), 32 deletions(-) diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp index 5fc7c21b4b0c..201db6c1d043 100644 --- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp +++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp @@ -6046,7 +6046,7 @@ void ggml_vk_matmul( ctx->prealloc_split_k_need_sync = true; } -static bool ggml_vk_get_mul_mat_mat_f16acc(ggml_backend_vk_context * ctx, ggml_type src0_type, ggml_type src1_type, ggml_prec prec) { +static bool ggml_vk_get_mul_mat_mat_f16acc(const ggml_backend_vk_context * ctx, ggml_type src0_type, ggml_type src1_type, ggml_prec prec) { if (src0_type == GGML_TYPE_F32 || src0_type == GGML_TYPE_BF16) return false; if (src1_type == GGML_TYPE_Q8_1) return false; if (src0_type == GGML_TYPE_F16) { @@ -6425,23 +6425,28 @@ static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context& sub // If src0 is BF16, try to use a BF16 x BF16 multiply ggml_type f16_type = src0->type == GGML_TYPE_BF16 ? GGML_TYPE_BF16 : GGML_TYPE_F16; + const bool tilek_b = ctx->tilek_tensors.count(src1) > 0; + const bool is_coopmat = ctx->device->coopmat_support || ctx->device->coopmat2; - const bool can_quantize_y = (ctx->device->integer_dot_product || ctx->device->coopmat_int_support) && + const bool can_quantize_y = !tilek_b && + (ctx->device->integer_dot_product || ctx->device->coopmat_int_support) && src1->type == GGML_TYPE_F32 && ggml_is_contiguous(src1) && (ne11 * ne10) % 4 == 0; // coopmat shaders are fast enough that memory bandwidth reduction from f16 activations helps const bool prefer_f16_b = is_coopmat && ggml_is_quantized(src0->type) && src1->type == GGML_TYPE_F32; - const ggml_type y_kernel_type = ggml_vk_mul_mat_b_type(ctx, src0->type, src1, f16_type, - (ggml_prec)dst->op_params[0], false, can_quantize_y, prefer_f16_b); + const ggml_type y_kernel_type = tilek_b ? GGML_TYPE_F32 : + ggml_vk_mul_mat_b_type(ctx, src0->type, src1, f16_type, + (ggml_prec)dst->op_params[0], false, can_quantize_y, prefer_f16_b); const bool quantize_y = y_kernel_type == GGML_TYPE_Q8_1; const bool y_f32_kernel = y_kernel_type == GGML_TYPE_F32; - const bool y_needs_reformat = !quantize_y && (!ggml_vk_dim01_contiguous(src1) || y_kernel_type != src1->type); + const bool y_needs_reformat = !tilek_b && !quantize_y && + (!ggml_vk_dim01_contiguous(src1) || y_kernel_type != src1->type); - const std::vector* mmp_map = + const std::vector* mmp_map = tilek_b ? nullptr : ggml_vk_get_mul_mat_mat_pipeline_map(ctx, src0->type, y_kernel_type, (ggml_prec)dst->op_params[0]); - const bool qx_needs_dequant = mmp_map == nullptr || x_non_contig; + const bool qx_needs_dequant = !tilek_b && (mmp_map == nullptr || x_non_contig); const bool qy_needs_dequant = y_needs_reformat; if (qx_needs_dequant) { @@ -6449,12 +6454,17 @@ static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context& sub mmp_map = ggml_vk_get_mul_mat_mat_pipeline_map(ctx, f16_type, y_kernel_type, (ggml_prec)dst->op_params[0]); } - GGML_ASSERT(mmp_map != nullptr); - - const uint32_t kpad = quantize_y ? 0 : ggml_vk_align_size(ne10, ggml_vk_guess_matmul_pipeline_align_map(ctx, *mmp_map, ne01, ne11, false)); - const bool aligned = !quantize_y && ne10 == kpad && ne01 > 8 && ne11 > 8; - - vk_pipeline pipeline = ggml_vk_guess_matmul_pipeline_map(ctx, *mmp_map, ne01, ne11, aligned, false); + vk_pipeline pipeline; + if (tilek_b) { + pipeline = ctx->device->pipeline_matmul_tilek_q4_0; + GGML_ASSERT(pipeline != nullptr); + GGML_ASSERT(ne10 % pipeline->align == 0); + } else { + GGML_ASSERT(mmp_map != nullptr); + const uint32_t kpad = quantize_y ? 0 : ggml_vk_align_size(ne10, ggml_vk_guess_matmul_pipeline_align_map(ctx, *mmp_map, ne01, ne11, false)); + const bool aligned = !quantize_y && ne10 == kpad && ne01 > 8 && ne11 > 8; + pipeline = ggml_vk_guess_matmul_pipeline_map(ctx, *mmp_map, ne01, ne11, aligned, false); + } if (ggml_nbytes(src0) > ctx->device->properties.limits.maxStorageBufferRange) { pipeline = ggml_vk_get_64b_indexing_pipeline(ctx, pipeline); @@ -14367,24 +14377,33 @@ static int32_t find_first_set(uint32_t x) { // reading the tensor as B, dispatched through the mat-mat path, with a TileK // matmul available for the weight type. static bool ggml_vk_tilek_consumer_ok(const ggml_backend_vk_context * ctx, const ggml_tensor * use, const ggml_tensor * t) { - if (use->op != GGML_OP_MUL_MAT || use->src[1] != t) { - return false; - } - const ggml_tensor * a = use->src[0]; - if (a->type != GGML_TYPE_Q4_0 || ctx->device->pipeline_matmul_tilek_q4_0 == nullptr) { - return false; - } - // mul_mat_vec would read B row-major. Only take over where - // ggml_vk_mul_mat() would have picked the mat-mat path anyway. - if (use->ne[1] <= mul_mat_vec_max_cols) { - return false; - } - // The TileK matmul walks A one quantization block at a time and indexes B - // by whole K-tiles, so K has to divide evenly by both. - if (a->ne[0] % ggml_blck_size(a->type) != 0 || a->ne[0] % GGML_VK_TILEK != 0) { - return false; - } - return true; + if (use->op != GGML_OP_MUL_MAT || use->src[1] != t) { + return false; + } + const ggml_tensor * a = use->src[0]; + if (a->type != GGML_TYPE_Q4_0 || ctx->device->pipeline_matmul_tilek_q4_0 == nullptr) { + return false; + } + if (!ggml_vk_dim01_contiguous(a)) { + return false; + } + if (!ggml_vk_get_mul_mat_mat_f16acc(ctx, a->type, t->type, (ggml_prec) use->op_params[0])) { + return false; + } + // mul_mat_vec would read B row-major. Only take over where + // ggml_vk_mul_mat() would have picked the mat-mat path anyway. + if (use->ne[1] <= mul_mat_vec_max_cols) { + return false; + } + if (a->ne[1] % 64 != 0 || use->ne[1] % 64 != 0) { + return false; + } + // The TileK matmul walks A one quantization block at a time and indexes B + // by whole K-tiles, so K has to divide evenly by both. + if (a->ne[0] % ggml_blck_size(a->type) != 0 || a->ne[0] % GGML_VK_TILEK != 0) { + return false; + } + return true; } // Collect the tensors whose consumers all read TileK-first. Producers consult @@ -14415,7 +14434,7 @@ static bool ggml_vk_tilek_consumer_ok(const ggml_backend_vk_context * ctx, const // Register this node as a candidate for everything still downstream. if (node->type == GGML_TYPE_F32 && ggml_is_contiguous(node) && - node->ne[0] % GGML_VK_TILEK == 0 && node->ne[1] > 1 && + node->ne[0] % GGML_VK_TILEK == 0 && node->ne[1] > 1 && node->ne[1] % 64 == 0 && node->ne[2] == 1 && node->ne[3] == 1 && !(node->flags & GGML_TENSOR_FLAG_OUTPUT)) { ok[node] = true;