SYCL: Rename oneMKL to oneMath (#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
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5 changed files with 189 additions and 181 deletions
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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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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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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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}
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case detail::get_type_combination_id(
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library_data_t::real_bfloat16, library_data_t::real_bfloat16,
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library_data_t::real_bfloat16, library_data_t::real_float):
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{
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detail::gemm_impl<oneapi::mkl::bfloat16, oneapi::mkl::bfloat16,
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oneapi::mkl::bfloat16, float>(
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detail::gemm_impl<oneapi::math::bfloat16, oneapi::math::bfloat16, oneapi::math::bfloat16, 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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}
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default:
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throw std::runtime_error("the combination of data type is unsupported");
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}
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} // gemm()
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} // gemm()
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/// Computes a batch of matrix-matrix product with general matrices.
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/// \param [in] q The queue where the routine should be executed.
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/// \param [in] ldc Leading dimension of C.
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/// \param [in] batch_size Specifies the number of matrix multiply operations to perform.
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/// \param [in] scaling_type Data type of the scaling factors.
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inline void gemm_batch(sycl::queue & q, oneapi::mkl::transpose a_trans, oneapi::mkl::transpose b_trans, int m,
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inline void gemm_batch(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[], library_data_t a_type, int lda,
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const void * b[], library_data_t b_type, int ldb, const void * beta, void * c[],
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library_data_t c_type, int ldc, int batch_size, library_data_t scaling_type,
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library_data_t::real_bfloat16, library_data_t::real_bfloat16,
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library_data_t::real_bfloat16, library_data_t::real_float):
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{
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detail::gemm_batch_impl<oneapi::mkl::bfloat16, oneapi::mkl::bfloat16, oneapi::mkl::bfloat16, float>(
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detail::gemm_batch_impl<oneapi::math::bfloat16, oneapi::math::bfloat16, oneapi::math::bfloat16, float>(
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q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc, batch_size, matrix_info);
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break;
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}
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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_batch_impl<oneapi::mkl::bfloat16, oneapi::mkl::bfloat16, float, float>(
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detail::gemm_batch_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, batch_size, matrix_info);
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break;
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}
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/// \param [in] stride_c Stride between the different C matrices.
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/// \param [in] batch_size Specifies the number of matrix multiply operations to perform.
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/// \param [in] scaling_type Data type of the scaling factors.
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inline void gemm_batch(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, long long int stride_a, const void *b,
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library_data_t b_type, int ldb, long long int stride_b,
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const void *beta, void *c, library_data_t c_type,
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int ldc, long long int stride_c, int batch_size,
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library_data_t scaling_type)
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{
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inline void gemm_batch(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, library_data_t a_type, int lda,
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long long int stride_a, const void * b, library_data_t b_type, int ldb,
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long long int stride_b, const void * beta, void * c, library_data_t c_type, int ldc,
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long long int stride_c, int batch_size, 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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library_data_t::real_bfloat16, library_data_t::real_bfloat16,
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library_data_t::real_bfloat16, library_data_t::real_float):
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{
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detail::gemm_batch_impl<oneapi::mkl::bfloat16, oneapi::mkl::bfloat16,
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oneapi::mkl::bfloat16, float>(
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q, a_trans, b_trans, m, n, k, alpha, a, lda, stride_a, b, ldb, stride_b,
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beta, c, ldc, stride_c, batch_size);
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detail::gemm_batch_impl<oneapi::math::bfloat16, oneapi::math::bfloat16, oneapi::math::bfloat16, float>(
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q, a_trans, b_trans, m, n, k, alpha, a, lda, stride_a, b, ldb, stride_b, beta, c, ldc, stride_c,
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batch_size);
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break;
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}
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case detail::get_type_combination_id(
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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_batch_impl<oneapi::mkl::bfloat16, oneapi::mkl::bfloat16, float,
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float>(q, a_trans, b_trans, m, n, k, alpha, a, lda,
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stride_a, b, ldb, stride_b, beta, c, ldc,
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stride_c, batch_size);
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detail::gemm_batch_impl<oneapi::math::bfloat16, oneapi::math::bfloat16, float, float>(
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q, a_trans, b_trans, m, n, k, alpha, a, lda, stride_a, b, ldb, stride_b, beta, c, ldc, stride_c,
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batch_size);
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break;
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}
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#endif
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