mirror of
https://github.com/ggerganov/whisper.cpp.git
synced 2025-04-08 03:44:46 +00:00
SYCL: Rename oneMKL to oneMath (llama/12192)
* Rename oneMKL Interface to oneMath * Use oneMath for Intel vendor * Rename occurences to mkl * clang-format * Silence verbose warnings * Set oneMath HIP_TARGETS * Fix silence warnings * Remove step to build oneMath from build instructions * Use fixed oneMath version * Remove INTEL_CPU * Fold CMake oneDNN conditions * Use Intel oneMKL for Intel devices * Improve CMake message * Link against MKL::MKL_SYCL::BLAS only * Move oneMath documentation to Nvidia and AMD sections
This commit is contained in:
Romain Biessy
Georgi Gerganov
parent
5014a789fb
commit
959e437e15
@ -23,6 +23,23 @@ ggml_add_backend_library(ggml-sycl
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../../include/ggml-sycl.h
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)
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file(GLOB GGML_HEADERS_SYCL "*.hpp")
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file(GLOB GGML_SOURCES_SYCL "*.cpp")
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target_sources(ggml-sycl PRIVATE ${GGML_HEADERS_SYCL} ${GGML_SOURCES_SYCL})
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find_package(IntelSYCL)
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if (IntelSYCL_FOUND)
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# Use oneAPI CMake when possible
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target_link_libraries(ggml-sycl PRIVATE IntelSYCL::SYCL_CXX)
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else()
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# Fallback to the simplest way of enabling SYCL when using intel/llvm nightly for instance
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target_compile_options(ggml-sycl PRIVATE "-fsycl")
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target_link_options(ggml-sycl PRIVATE "-fsycl")
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endif()
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target_compile_options(ggml-sycl PRIVATE "-Wno-narrowing")
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# Link against oneDNN
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find_package(DNNL)
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set(GGML_SYCL_DNNL 0)
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if(DNNL_FOUND)
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@ -62,8 +79,6 @@ if (GGML_SYCL_F16)
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add_compile_definitions(GGML_SYCL_F16)
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endif()
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set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-narrowing -fsycl")
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if (GGML_SYCL_TARGET STREQUAL "NVIDIA")
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add_compile_definitions(GGML_SYCL_WARP_SIZE=32)
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elseif (GGML_SYCL_TARGET STREQUAL "AMD")
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@ -76,34 +91,84 @@ else()
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add_compile_definitions(GGML_SYCL_WARP_SIZE=16)
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endif()
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file(GLOB GGML_HEADERS_SYCL "*.hpp")
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file(GLOB GGML_SOURCES_SYCL "*.cpp")
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target_sources(ggml-sycl PRIVATE ${GGML_HEADERS_SYCL} ${GGML_SOURCES_SYCL})
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if (GGML_SYCL_GRAPH)
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target_compile_definitions(ggml-sycl PRIVATE GGML_SYCL_GRAPH)
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endif()
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if (WIN32)
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find_package(IntelSYCL REQUIRED)
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# Link against Intel oneMKL or oneMath
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if (GGML_SYCL_TARGET STREQUAL "INTEL")
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# Intel devices use Intel oneMKL directly instead of oneMath to avoid the limitation of linking Intel oneMKL statically
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# See https://github.com/uxlfoundation/oneMath/issues/654
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find_package(MKL REQUIRED)
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target_link_libraries(ggml-sycl PRIVATE IntelSYCL::SYCL_CXX MKL::MKL MKL::MKL_SYCL)
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target_link_libraries(ggml-sycl PRIVATE MKL::MKL_SYCL::BLAS)
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target_compile_definitions(ggml-sycl PRIVATE GGML_SYCL_USE_INTEL_ONEMKL)
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else()
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if (GGML_SYCL_GRAPH)
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add_compile_definitions(GGML_SYCL_GRAPH)
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find_package(oneMath QUIET)
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if (NOT oneMath_FOUND)
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message(STATUS "oneMath not found: oneMath will be automatically downloaded")
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# Use FetchContent to automatically pull and build oneMath
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include(FetchContent)
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set(BUILD_FUNCTIONAL_TESTS False)
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set(BUILD_EXAMPLES False)
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set(TARGET_DOMAINS blas)
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if (GGML_SYCL_TARGET STREQUAL "NVIDIA")
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set(ENABLE_MKLCPU_BACKEND False)
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set(ENABLE_MKLGPU_BACKEND False)
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set(ENABLE_CUBLAS_BACKEND True)
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elseif (GGML_SYCL_TARGET STREQUAL "AMD")
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set(ENABLE_MKLCPU_BACKEND False)
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set(ENABLE_MKLGPU_BACKEND False)
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set(ENABLE_ROCBLAS_BACKEND True)
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# Ensure setting a string variable here is not overriden by oneMath CACHE variables
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cmake_policy(SET CMP0126 NEW)
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# Setting the device architecture is only needed and useful for AMD devices in oneMath
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set(HIP_TARGETS ${GGML_SYCL_DEVICE_ARCH} CACHE STRING "oneMath HIP target" FORCE)
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endif()
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FetchContent_Declare(
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ONEMATH
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GIT_REPOSITORY https://github.com/uxlfoundation/oneMath.git
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GIT_TAG c255b1b4c41e2ee3059455c1f96a965d6a62568a
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)
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FetchContent_MakeAvailable(ONEMATH)
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# Create alias to match with find_package targets name
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function(onemath_alias target)
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if (TARGET ${target}_obj)
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# Silence verbose warnings from external libraries
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target_compile_options(${target}_obj PRIVATE -w)
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endif()
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if (TARGET ${target})
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add_library(ONEMATH::${target} ALIAS ${target})
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endif()
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endfunction()
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onemath_alias(onemath)
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onemath_alias(onemath_blas_mklcpu)
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onemath_alias(onemath_blas_mklgpu)
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onemath_alias(onemath_blas_cublas)
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onemath_alias(onemath_blas_rocblas)
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endif()
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if (GGML_SYCL_TARGET STREQUAL "INTEL")
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target_link_libraries(ggml-sycl PRIVATE sycl OpenCL mkl_core pthread m dl mkl_sycl_blas mkl_intel_ilp64 mkl_tbb_thread)
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elseif (GGML_SYCL_TARGET STREQUAL "NVIDIA")
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set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fsycl-targets=nvptx64-nvidia-cuda")
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add_compile_definitions(GGML_SYCL_NVIDIA)
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target_link_libraries(ggml-sycl PRIVATE sycl pthread m dl onemkl_blas_cublas)
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# Below oneMath compile-time dispatching is used for better performance
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if (GGML_SYCL_TARGET STREQUAL "NVIDIA")
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target_link_libraries(ggml-sycl PRIVATE ONEMATH::onemath_blas_cublas)
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target_compile_options(ggml-sycl PRIVATE "-fsycl-targets=nvptx64-nvidia-cuda")
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target_link_options(ggml-sycl PRIVATE "-fsycl-targets=nvptx64-nvidia-cuda")
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target_compile_definitions(ggml-sycl PRIVATE GGML_SYCL_NVIDIA)
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elseif (GGML_SYCL_TARGET STREQUAL "AMD")
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if (NOT GGML_SYCL_DEVICE_ARCH)
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message(ERROR "Can't enable SYCL hip backend, GGML_SYCL_DEVICE_ARCH has not been set.")
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endif()
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set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fsycl-targets=amdgcn-amd-amdhsa")
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target_link_libraries(ggml-sycl PRIVATE sycl pthread m dl onemkl)
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target_link_libraries(ggml-sycl PRIVATE ONEMATH::onemath_blas_rocblas)
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target_compile_options(ggml-sycl PRIVATE "-fsycl-targets=amdgcn-amd-amdhsa")
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target_link_options(ggml-sycl PRIVATE "-fsycl-targets=amdgcn-amd-amdhsa")
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target_compile_definitions(ggml-sycl PRIVATE GGML_SYCL_AMD)
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else()
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# Fallback to oneMath runtime dispatcher
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target_link_libraries(ggml-sycl PRIVATE ONEMATH::onemath)
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target_compile_definitions(ggml-sycl PRIVATE GGML_SYCL_GENERIC)
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endif()
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if (GGML_SYCL_DEVICE_ARCH)
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set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Xsycl-target-backend --offload-arch=${GGML_SYCL_DEVICE_ARCH}")
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endif()
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endif()
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if (GGML_SYCL_DEVICE_ARCH)
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target_compile_options(ggml-sycl PRIVATE -Xsycl-target-backend --offload-arch=${GGML_SYCL_DEVICE_ARCH})
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target_link_options(ggml-sycl PRIVATE -Xsycl-target-backend --offload-arch=${GGML_SYCL_DEVICE_ARCH})
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endif()
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@ -16,9 +16,18 @@
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#include <sycl/sycl.hpp>
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#include <sycl/half_type.hpp>
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#include <syclcompat/math.hpp>
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#include <oneapi/mkl.hpp>
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#include <map>
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#ifdef GGML_SYCL_USE_INTEL_ONEMKL
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#include <oneapi/mkl.hpp>
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// Allow to use the same namespace for Intel oneMKL and oneMath
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namespace oneapi {
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namespace math = mkl;
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}
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#else
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#include <oneapi/math.hpp>
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#endif
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#include "ggml.h"
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#if defined(__linux__)
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@ -83,13 +92,32 @@ inline std::string get_device_backend_and_type(const sycl::device &device) {
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}
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template <typename Ts> struct matrix_info_t {
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oneapi::mkl::transpose transpose_info[2];
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oneapi::math::transpose transpose_info[2];
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Ts value_info[2];
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std::int64_t size_info[3];
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std::int64_t ld_info[3];
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std::int64_t groupsize_info;
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};
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inline auto get_onemath_backend(sycl::queue& queue)
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#if defined(GGML_SYCL_GENERIC) || defined(GGML_SYCL_USE_INTEL_ONEMKL)
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-> sycl::queue&
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#endif
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{
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// If the backend is known at compile-time, use oneMath backend_selector to use
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// compile-time dispatching and avoid the need to dlopen libraries. Otherwise
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// fallback to runtime dispatching.
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#if defined(GGML_SYCL_NVIDIA)
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return oneapi::math::backend_selector<oneapi::math::backend::cublas>{ queue };
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#elif defined(GGML_SYCL_AMD)
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return oneapi::math::backend_selector<oneapi::math::backend::rocblas>{ queue };
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#elif defined(GGML_SYCL_GENERIC) || defined(GGML_SYCL_USE_INTEL_ONEMKL)
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return queue;
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#else
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static_assert(false, "Unsupported backend");
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#endif
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}
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namespace dpct
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{
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typedef sycl::queue *queue_ptr;
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@ -1686,26 +1714,18 @@ namespace dpct
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namespace detail
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{
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template <class Ta, class Tb, class Tc, class Ts>
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inline void gemm_impl(sycl::queue &q, oneapi::mkl::transpose a_trans,
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oneapi::mkl::transpose b_trans, int m, int n, int k,
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const void *alpha, const void *a, int lda, const void *b,
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int ldb, const void *beta, void *c, int ldc)
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{
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Ts alpha_value = dpct::get_value(reinterpret_cast<const Ts *>(alpha), q);
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Ts beta_value = dpct::get_value(reinterpret_cast<const Ts *>(beta), q);
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auto data_a = get_memory<const Ta>(a);
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auto data_b = get_memory<const Tb>(b);
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auto data_c = get_memory<Tc>(c);
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#ifdef GGML_SYCL_NVIDIA
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oneapi::mkl::blas::column_major::gemm(oneapi::mkl::backend_selector<oneapi::mkl::backend::cublas>{ q },
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a_trans, b_trans, m, n, k, alpha_value, data_a, lda, data_b, ldb,
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beta_value, data_c, ldc);
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#else
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oneapi::mkl::blas::column_major::gemm(q, a_trans, b_trans, m, n, k, alpha_value, data_a, lda, data_b, ldb,
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beta_value, data_c, ldc);
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#endif
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}
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template <class Ta, class Tb, class Tc, class Ts>
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inline void gemm_impl(sycl::queue & q, oneapi::math::transpose a_trans, oneapi::math::transpose b_trans, int m,
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int n, int k, const void * alpha, const void * a, int lda, const void * b, int ldb,
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const void * beta, void * c, int ldc) {
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Ts alpha_value = dpct::get_value(reinterpret_cast<const Ts *>(alpha), q);
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Ts beta_value = dpct::get_value(reinterpret_cast<const Ts *>(beta), q);
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auto data_a = get_memory<const Ta>(a);
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auto data_b = get_memory<const Tb>(b);
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auto data_c = get_memory<Tc>(c);
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oneapi::math::blas::column_major::gemm(get_onemath_backend(q), a_trans, b_trans, m, n, k, alpha_value, data_a,
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lda, data_b, ldb, beta_value, data_c, ldc);
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}
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template <typename VecT, class BinaryOperation, class = void>
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class vectorized_binary
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@ -1735,7 +1755,7 @@ namespace dpct
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};
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template <class Ta, class Tb, class Tc, class Ts>
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inline void gemm_batch_impl(sycl::queue & q, oneapi::mkl::transpose a_trans, oneapi::mkl::transpose b_trans,
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inline void gemm_batch_impl(sycl::queue & q, oneapi::math::transpose a_trans, oneapi::math::transpose b_trans,
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int m, int n, int k, const void * alpha, const void ** a, int lda, const void ** b,
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int ldb, const void * beta, void ** c, int ldc, int batch_size,
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matrix_info_t<float> * matrix_info) {
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@ -1754,48 +1774,28 @@ namespace dpct
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matrix_info->ld_info[2] = ldc;
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matrix_info->groupsize_info = batch_size;
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#ifdef GGML_SYCL_NVIDIA
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sycl::event e = oneapi::mkl::blas::column_major::gemm_batch(
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oneapi::mkl::backend_selector<oneapi::mkl::backend::cublas>{ q }, matrix_info->transpose_info,
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matrix_info->transpose_info + 1, matrix_info->size_info, matrix_info->size_info + 1,
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matrix_info->size_info + 2, reinterpret_cast<Ts *>(matrix_info->value_info),
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reinterpret_cast<const Ta **>(a), matrix_info->ld_info, reinterpret_cast<const Tb **>(b),
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matrix_info->ld_info + 1, reinterpret_cast<Ts *>(matrix_info->value_info + 1),
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reinterpret_cast<Tc **>(c), matrix_info->ld_info + 2, 1, &(matrix_info->groupsize_info));
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#else
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sycl::event e = oneapi::mkl::blas::column_major::gemm_batch(
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q, matrix_info->transpose_info, matrix_info->transpose_info + 1, matrix_info->size_info,
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matrix_info->size_info + 1, matrix_info->size_info + 2, reinterpret_cast<Ts *>(matrix_info->value_info),
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reinterpret_cast<const Ta **>(a), matrix_info->ld_info, reinterpret_cast<const Tb **>(b),
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matrix_info->ld_info + 1, reinterpret_cast<Ts *>(matrix_info->value_info + 1),
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reinterpret_cast<Tc **>(c), matrix_info->ld_info + 2, 1, &(matrix_info->groupsize_info));
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#endif
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sycl::event e = oneapi::math::blas::column_major::gemm_batch(
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get_onemath_backend(q), matrix_info->transpose_info, matrix_info->transpose_info + 1,
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matrix_info->size_info, matrix_info->size_info + 1, matrix_info->size_info + 2,
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reinterpret_cast<Ts *>(matrix_info->value_info), reinterpret_cast<const Ta **>(a), matrix_info->ld_info,
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reinterpret_cast<const Tb **>(b), matrix_info->ld_info + 1,
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reinterpret_cast<Ts *>(matrix_info->value_info + 1), reinterpret_cast<Tc **>(c),
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matrix_info->ld_info + 2, 1, &(matrix_info->groupsize_info));
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}
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template <class Ta, class Tb, class Tc, class Ts>
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inline void
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gemm_batch_impl(sycl::queue &q, oneapi::mkl::transpose a_trans,
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oneapi::mkl::transpose b_trans, int m, int n,
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int k, const void *alpha, const void *a, int lda,
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long long int stride_a, const void *b, int ldb,
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long long int stride_b, const void *beta, void *c,
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int ldc, long long int stride_c, int batch_size)
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{
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inline void gemm_batch_impl(sycl::queue & q, oneapi::math::transpose a_trans, oneapi::math::transpose b_trans,
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int m, int n, int k, const void * alpha, const void * a, int lda,
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long long int stride_a, const void * b, int ldb, long long int stride_b,
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const void * beta, void * c, int ldc, long long int stride_c, int batch_size) {
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Ts alpha_value = dpct::get_value(reinterpret_cast<const Ts *>(alpha), q);
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Ts beta_value = dpct::get_value(reinterpret_cast<const Ts *>(beta), q);
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auto data_a = get_memory<const Ta>(a);
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auto data_b = get_memory<const Tb>(b);
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auto data_c = get_memory<Tc>(c);
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#ifdef GGML_SYCL_NVIDIA
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oneapi::mkl::blas::column_major::gemm_batch(
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oneapi::mkl::backend_selector<oneapi::mkl::backend::cublas>{ q }, a_trans, b_trans, m, n, k,
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alpha_value, data_a, lda, stride_a, data_b, ldb, stride_b, beta_value, data_c, ldc, stride_c,
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batch_size);
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#else
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oneapi::mkl::blas::column_major::gemm_batch(q, a_trans, b_trans, m, n, k, alpha_value, data_a, lda,
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stride_a, data_b, ldb, stride_b, beta_value, data_c, ldc,
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stride_c, batch_size);
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#endif
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oneapi::math::blas::column_major::gemm_batch(get_onemath_backend(q), a_trans, b_trans, m, n, k, alpha_value,
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data_a, lda, stride_a, data_b, ldb, stride_b, beta_value,
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data_c, ldc, stride_c, batch_size);
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}
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} // namespace detail
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@ -2259,13 +2259,10 @@ namespace dpct
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sycl::range<3>(x, y, 1), direction);
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}
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inline void gemm(sycl::queue &q, oneapi::mkl::transpose a_trans,
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oneapi::mkl::transpose b_trans, int m, int n, int k,
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const void *alpha, const void *a, library_data_t a_type,
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int lda, const void *b, library_data_t b_type, int ldb,
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const void *beta, void *c, library_data_t c_type, int ldc,
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library_data_t scaling_type)
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{
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inline void gemm(sycl::queue & q, oneapi::math::transpose a_trans, oneapi::math::transpose b_trans, int m, int n,
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int k, const void * alpha, const void * a, library_data_t a_type, int lda, const void * b,
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library_data_t b_type, int ldb, const void * beta, void * c, library_data_t c_type, int ldc,
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library_data_t scaling_type) {
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if (scaling_type == library_data_t::real_float &&
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c_type == library_data_t::complex_float)
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{
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@ -2329,9 +2326,8 @@ namespace dpct
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library_data_t::real_bfloat16, library_data_t::real_bfloat16,
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library_data_t::real_float, library_data_t::real_float):
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{
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detail::gemm_impl<oneapi::mkl::bfloat16, oneapi::mkl::bfloat16, float,
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float>(q, a_trans, b_trans, m, n, k, alpha, a, lda, b,
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ldb, beta, c, ldc);
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detail::gemm_impl<oneapi::math::bfloat16, oneapi::math::bfloat16, float, float>(
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q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc);
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break;
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}
|
||||
case detail::get_type_combination_id(
|
||||
@ -2369,8 +2365,7 @@ namespace dpct
|
||||
library_data_t::real_bfloat16, library_data_t::real_bfloat16,
|
||||
library_data_t::real_bfloat16, library_data_t::real_float):
|
||||
{
|
||||
detail::gemm_impl<oneapi::mkl::bfloat16, oneapi::mkl::bfloat16,
|
||||
oneapi::mkl::bfloat16, float>(
|
||||
detail::gemm_impl<oneapi::math::bfloat16, oneapi::math::bfloat16, oneapi::math::bfloat16, float>(
|
||||
q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc);
|
||||
break;
|
||||
}
|
||||
@ -2390,7 +2385,7 @@ namespace dpct
|
||||
default:
|
||||
throw std::runtime_error("the combination of data type is unsupported");
|
||||
}
|
||||
} // gemm()
|
||||
} // gemm()
|
||||
|
||||
/// Computes a batch of matrix-matrix product with general matrices.
|
||||
/// \param [in] q The queue where the routine should be executed.
|
||||
@ -2412,7 +2407,7 @@ namespace dpct
|
||||
/// \param [in] ldc Leading dimension of C.
|
||||
/// \param [in] batch_size Specifies the number of matrix multiply operations to perform.
|
||||
/// \param [in] scaling_type Data type of the scaling factors.
|
||||
inline void gemm_batch(sycl::queue & q, oneapi::mkl::transpose a_trans, oneapi::mkl::transpose b_trans, int m,
|
||||
inline void gemm_batch(sycl::queue & q, oneapi::math::transpose a_trans, oneapi::math::transpose b_trans, int m,
|
||||
int n, int k, const void * alpha, const void * a[], library_data_t a_type, int lda,
|
||||
const void * b[], library_data_t b_type, int ldb, const void * beta, void * c[],
|
||||
library_data_t c_type, int ldc, int batch_size, library_data_t scaling_type,
|
||||
@ -2450,7 +2445,7 @@ namespace dpct
|
||||
library_data_t::real_bfloat16, library_data_t::real_bfloat16,
|
||||
library_data_t::real_bfloat16, library_data_t::real_float):
|
||||
{
|
||||
detail::gemm_batch_impl<oneapi::mkl::bfloat16, oneapi::mkl::bfloat16, oneapi::mkl::bfloat16, float>(
|
||||
detail::gemm_batch_impl<oneapi::math::bfloat16, oneapi::math::bfloat16, oneapi::math::bfloat16, float>(
|
||||
q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc, batch_size, matrix_info);
|
||||
break;
|
||||
}
|
||||
@ -2458,7 +2453,7 @@ namespace dpct
|
||||
library_data_t::real_bfloat16, library_data_t::real_bfloat16,
|
||||
library_data_t::real_float, library_data_t::real_float):
|
||||
{
|
||||
detail::gemm_batch_impl<oneapi::mkl::bfloat16, oneapi::mkl::bfloat16, float, float>(
|
||||
detail::gemm_batch_impl<oneapi::math::bfloat16, oneapi::math::bfloat16, float, float>(
|
||||
q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc, batch_size, matrix_info);
|
||||
break;
|
||||
}
|
||||
@ -2534,15 +2529,11 @@ namespace dpct
|
||||
/// \param [in] stride_c Stride between the different C matrices.
|
||||
/// \param [in] batch_size Specifies the number of matrix multiply operations to perform.
|
||||
/// \param [in] scaling_type Data type of the scaling factors.
|
||||
inline void gemm_batch(sycl::queue &q, oneapi::mkl::transpose a_trans,
|
||||
oneapi::mkl::transpose b_trans, int m, int n, int k,
|
||||
const void *alpha, const void *a, library_data_t a_type,
|
||||
int lda, long long int stride_a, const void *b,
|
||||
library_data_t b_type, int ldb, long long int stride_b,
|
||||
const void *beta, void *c, library_data_t c_type,
|
||||
int ldc, long long int stride_c, int batch_size,
|
||||
library_data_t scaling_type)
|
||||
{
|
||||
inline void gemm_batch(sycl::queue & q, oneapi::math::transpose a_trans, oneapi::math::transpose b_trans, int m,
|
||||
int n, int k, const void * alpha, const void * a, library_data_t a_type, int lda,
|
||||
long long int stride_a, const void * b, library_data_t b_type, int ldb,
|
||||
long long int stride_b, const void * beta, void * c, library_data_t c_type, int ldc,
|
||||
long long int stride_c, int batch_size, library_data_t scaling_type) {
|
||||
if (scaling_type == library_data_t::real_float &&
|
||||
c_type == library_data_t::complex_float)
|
||||
{
|
||||
@ -2611,20 +2602,18 @@ namespace dpct
|
||||
library_data_t::real_bfloat16, library_data_t::real_bfloat16,
|
||||
library_data_t::real_bfloat16, library_data_t::real_float):
|
||||
{
|
||||
detail::gemm_batch_impl<oneapi::mkl::bfloat16, oneapi::mkl::bfloat16,
|
||||
oneapi::mkl::bfloat16, float>(
|
||||
q, a_trans, b_trans, m, n, k, alpha, a, lda, stride_a, b, ldb, stride_b,
|
||||
beta, c, ldc, stride_c, batch_size);
|
||||
detail::gemm_batch_impl<oneapi::math::bfloat16, oneapi::math::bfloat16, oneapi::math::bfloat16, float>(
|
||||
q, a_trans, b_trans, m, n, k, alpha, a, lda, stride_a, b, ldb, stride_b, beta, c, ldc, stride_c,
|
||||
batch_size);
|
||||
break;
|
||||
}
|
||||
case detail::get_type_combination_id(
|
||||
library_data_t::real_bfloat16, library_data_t::real_bfloat16,
|
||||
library_data_t::real_float, library_data_t::real_float):
|
||||
{
|
||||
detail::gemm_batch_impl<oneapi::mkl::bfloat16, oneapi::mkl::bfloat16, float,
|
||||
float>(q, a_trans, b_trans, m, n, k, alpha, a, lda,
|
||||
stride_a, b, ldb, stride_b, beta, c, ldc,
|
||||
stride_c, batch_size);
|
||||
detail::gemm_batch_impl<oneapi::math::bfloat16, oneapi::math::bfloat16, float, float>(
|
||||
q, a_trans, b_trans, m, n, k, alpha, a, lda, stride_a, b, ldb, stride_b, beta, c, ldc, stride_c,
|
||||
batch_size);
|
||||
break;
|
||||
}
|
||||
#endif
|
||||
|
||||
@ -2059,8 +2059,8 @@ inline void ggml_sycl_op_mul_mat_sycl(
|
||||
const sycl::half alpha_f16 = 1.0f;
|
||||
const sycl::half beta_f16 = 0.0f;
|
||||
SYCL_CHECK(CHECK_TRY_ERROR(dpct::gemm(
|
||||
*stream, oneapi::mkl::transpose::trans,
|
||||
oneapi::mkl::transpose::nontrans, row_diff, src1_ncols, ne10,
|
||||
*stream, oneapi::math::transpose::trans,
|
||||
oneapi::math::transpose::nontrans, row_diff, src1_ncols, ne10,
|
||||
&alpha_f16, src0_ptr, dpct::library_data_t::real_half, ne00,
|
||||
src1_ptr, dpct::library_data_t::real_half, ne10, &beta_f16,
|
||||
dst_f16.get(), dpct::library_data_t::real_half, ldc,
|
||||
@ -2097,17 +2097,10 @@ inline void ggml_sycl_op_mul_mat_sycl(
|
||||
#if !GGML_SYCL_DNNL
|
||||
const float alpha = 1.0f;
|
||||
const float beta = 0.0f;
|
||||
# ifdef GGML_SYCL_NVIDIA
|
||||
SYCL_CHECK(CHECK_TRY_ERROR(oneapi::mkl::blas::column_major::gemm(
|
||||
oneapi::mkl::backend_selector<oneapi::mkl::backend::cublas>{ *stream }, oneapi::mkl::transpose::trans,
|
||||
oneapi::mkl::transpose::nontrans, row_diff, src1_ncols, ne10, dpct::get_value(&alpha, *stream), src0_ddf_i,
|
||||
ne00, src1_ddf1_i, ne10, dpct::get_value(&beta, *stream), dst_dd_i, ldc)));
|
||||
# else
|
||||
SYCL_CHECK(CHECK_TRY_ERROR(oneapi::mkl::blas::column_major::gemm(
|
||||
*stream, oneapi::mkl::transpose::trans, oneapi::mkl::transpose::nontrans, row_diff, src1_ncols, ne10,
|
||||
dpct::get_value(&alpha, *stream), src0_ddf_i, ne00, src1_ddf1_i, ne10, dpct::get_value(&beta, *stream),
|
||||
dst_dd_i, ldc)));
|
||||
# endif
|
||||
SYCL_CHECK(CHECK_TRY_ERROR(oneapi::math::blas::column_major::gemm(
|
||||
get_onemath_backend(*stream), oneapi::math::transpose::trans, oneapi::math::transpose::nontrans, row_diff,
|
||||
src1_ncols, ne10, dpct::get_value(&alpha, *stream), src0_ddf_i, ne00, src1_ddf1_i, ne10,
|
||||
dpct::get_value(&beta, *stream), dst_dd_i, ldc)));
|
||||
#else
|
||||
DnnlGemmWrapper::row_gemm(ctx, false, true, src1_ncols, row_diff, ne10, src1_ddf1_i,
|
||||
DnnlGemmWrapper::to_dt<float>(), src0_ddf_i, DnnlGemmWrapper::to_dt<float>(),
|
||||
@ -2836,14 +2829,10 @@ static void ggml_sycl_mul_mat_batched_sycl(ggml_backend_sycl_context & ctx,
|
||||
if (r2 == 1 && r3 == 1 && ggml_is_contiguous_2(src0) && ggml_is_contiguous_2(src1)) {
|
||||
// there is no broadcast and src0, src1 are contiguous across dims 2, 3
|
||||
SYCL_CHECK(CHECK_TRY_ERROR(dpct::gemm_batch(
|
||||
*main_stream, oneapi::mkl::transpose::trans,
|
||||
oneapi::mkl::transpose::nontrans, ne01, ne11, ne10, alpha,
|
||||
(const char *)src0_as_f16, dpct::library_data_t::real_half,
|
||||
nb01 / nb00, nb02 / nb00,
|
||||
(const char *)src1_f16, dpct::library_data_t::real_half,
|
||||
nb11 / nb10, nb12 / nb10, beta,
|
||||
(char *)dst_t, cu_data_type, ne01, nb2 / nb0,
|
||||
ne12 * ne13, cu_compute_type)));
|
||||
*main_stream, oneapi::math::transpose::trans, oneapi::math::transpose::nontrans, ne01, ne11, ne10, alpha,
|
||||
(const char *) src0_as_f16, dpct::library_data_t::real_half, nb01 / nb00, nb02 / nb00,
|
||||
(const char *) src1_f16, dpct::library_data_t::real_half, nb11 / nb10, nb12 / nb10, beta, (char *) dst_t,
|
||||
cu_data_type, ne01, nb2 / nb0, ne12 * ne13, cu_compute_type)));
|
||||
} else {
|
||||
const int ne23 = ne12*ne13;
|
||||
|
||||
@ -2878,7 +2867,7 @@ static void ggml_sycl_mul_mat_batched_sycl(ggml_backend_sycl_context & ctx,
|
||||
});
|
||||
}
|
||||
SYCL_CHECK(CHECK_TRY_ERROR(dpct::gemm_batch(
|
||||
*main_stream, oneapi::mkl::transpose::trans, oneapi::mkl::transpose::nontrans, ne01, ne11, ne10, alpha,
|
||||
*main_stream, oneapi::math::transpose::trans, oneapi::math::transpose::nontrans, ne01, ne11, ne10, alpha,
|
||||
(const void **) (ptrs_src.get() + 0 * ne23), dpct::library_data_t::real_half, nb01 / nb00,
|
||||
(const void **) (ptrs_src.get() + 1 * ne23), dpct::library_data_t::real_half, nb11 / nb10, beta,
|
||||
(void **) (ptrs_dst.get() + 0 * ne23), cu_data_type, ne01, ne23, cu_compute_type, matrix_info.get())));
|
||||
|
||||
@ -1,8 +1,5 @@
|
||||
#include <sycl/sycl.hpp>
|
||||
#include <oneapi/mkl.hpp>
|
||||
#include "outprod.hpp"
|
||||
|
||||
|
||||
void ggml_sycl_op_out_prod(ggml_backend_sycl_context& ctx, ggml_tensor* dst) {
|
||||
const ggml_tensor *src0 = dst->src[0];
|
||||
const ggml_tensor *src1 = dst->src[1];
|
||||
@ -34,20 +31,13 @@ void ggml_sycl_op_out_prod(ggml_backend_sycl_context& ctx, ggml_tensor* dst) {
|
||||
|
||||
// Handle transposition of src1
|
||||
const bool src1_T = ggml_is_transposed(src1);
|
||||
const oneapi::mkl::transpose src1_op =
|
||||
src1_T ? oneapi::mkl::transpose::nontrans : oneapi::mkl::transpose::trans;
|
||||
const oneapi::math::transpose src1_op = src1_T ? oneapi::math::transpose::nontrans : oneapi::math::transpose::trans;
|
||||
const int64_t ldb = (src1_T ? nb10 : nb11) / sizeof(float);
|
||||
|
||||
try {
|
||||
// Perform matrix multiplication using oneMKL GEMM
|
||||
#ifdef GGML_SYCL_NVIDIA
|
||||
oneapi::mkl::blas::column_major::gemm(oneapi::mkl::backend_selector<oneapi::mkl::backend::cublas>{ *stream },
|
||||
oneapi::mkl::transpose::nontrans, src1_op, ne0, ne1, ne01, alpha, src0_d,
|
||||
ne00, src1_d, ldb, beta, dst_d, ne0);
|
||||
#else
|
||||
oneapi::mkl::blas::column_major::gemm(*stream, oneapi::mkl::transpose::nontrans, src1_op, ne0, ne1, ne01, alpha,
|
||||
src0_d, ne00, src1_d, ldb, beta, dst_d, ne0);
|
||||
#endif
|
||||
// Perform matrix multiplication using oneMath GEMM
|
||||
oneapi::math::blas::column_major::gemm(get_onemath_backend(*stream), oneapi::math::transpose::nontrans, src1_op,
|
||||
ne0, ne1, ne01, alpha, src0_d, ne00, src1_d, ldb, beta, dst_d, ne0);
|
||||
}
|
||||
catch (sycl::exception const& exc) {
|
||||
std::cerr << exc.what() << std::endl;
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user