87 lines
3.5 KiB
C++
Vendored
87 lines
3.5 KiB
C++
Vendored
#include <cublas_v2.h>
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#include <cuda.h>
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#include <cuda_fp16.h>
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#include <cuda_runtime.h>
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#include <torch/extension.h>
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#define CUBLAS_CHECK(condition) \
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for (cublasStatus_t _cublas_check_status = (condition); \
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_cublas_check_status != CUBLAS_STATUS_SUCCESS;) \
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throw std::runtime_error("cuBLAS error " + \
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std::to_string(_cublas_check_status) + " at " + \
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std::to_string(__LINE__));
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#define CUDA_CHECK(condition) \
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for (cudaError_t _cuda_check_status = (condition); \
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_cuda_check_status != cudaSuccess;) \
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throw std::runtime_error( \
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"CUDA error " + std::string(cudaGetErrorString(_cuda_check_status)) + \
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" at " + std::to_string(__LINE__));
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cublasHandle_t get_cublas_handle() {
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static cublasHandle_t cublas_handle = []() {
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cublasHandle_t handle = nullptr;
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CUBLAS_CHECK(cublasCreate(&handle));
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#if CUDA_VERSION < 11000
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CUBLAS_CHECK(cublasSetMathMode(handle, CUBLAS_TENSOR_OP_MATH));
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#else
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CUBLAS_CHECK(cublasSetMathMode(handle, CUBLAS_DEFAULT_MATH));
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#endif // CUDA_VERSION < 11000
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return handle;
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}();
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return cublas_handle;
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}
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/*
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NOTE: blas gemm is column-major by default, but we need row-major output.
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The data of row-major, transposed matrix is exactly the same as the
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column-major, non-transposed matrix, and C = A * B ---> C^T = B^T * A^T
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*/
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void gemm_fp16_cublas(torch::Tensor a, torch::Tensor b, torch::Tensor c) {
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const auto cuda_data_type = CUDA_R_16F;
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const auto cuda_c_data_type =
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c.dtype() == torch::kFloat32 ? CUDA_R_32F : CUDA_R_16F;
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const auto compute_type = CUDA_R_32F;
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const float sp_alpha = 1.f;
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// swap a and b, and use CUBLAS_OP_N. see the notes above
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std::swap(a, b);
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const cublasOperation_t cublas_trans_a = CUBLAS_OP_N;
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const cublasOperation_t cublas_trans_b = CUBLAS_OP_N;
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// m = (B^T).size(0) = B.size(1), and = A.size(1) after swap,
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// negative axis is used because of the existence of batch matmul.
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const int m = a.size(-1);
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const int k = a.size(-2);
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const int n = b.size(-2);
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const int cublas_lda = m;
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const int cublas_ldb = k;
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const int cublas_ldc = m;
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cublasHandle_t cublas_handle = get_cublas_handle();
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#if CUDA_VERSION >= 11000
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cublasGemmAlgo_t algo = CUBLAS_GEMM_DEFAULT;
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#else
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cublasGemmAlgo_t algo = CUBLAS_GEMM_DFALT_TENSOR_OP;
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#endif
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const float sp_beta = 0.f;
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if (a.sizes().size() == 2 && b.sizes().size() == 2) {
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CUBLAS_CHECK(cublasGemmEx(
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cublas_handle, cublas_trans_a, cublas_trans_b, m, n, k, &sp_alpha,
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a.data_ptr(), cuda_data_type, cublas_lda, b.data_ptr(), cuda_data_type,
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cublas_ldb, &sp_beta, c.data_ptr(), cuda_c_data_type, cublas_ldc,
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compute_type, algo));
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} else {
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// batch matmul
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assert(a.sizes().size() == 3 && b.sizes().size() == 3);
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const long long int cublas_stride_a = m * k;
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const long long int cublas_stride_b = k * n;
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const long long int cublas_stride_c = m * n;
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CUBLAS_CHECK(cublasGemmStridedBatchedEx(
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cublas_handle, cublas_trans_a, cublas_trans_b, m,
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n, k, &sp_alpha, a.data_ptr(), cuda_data_type, cublas_lda,
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cublas_stride_a, b.data_ptr(), cuda_data_type, cublas_ldb, cublas_stride_b,
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&sp_beta, c.data_ptr(), cuda_c_data_type, cublas_ldc, cublas_stride_c,
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a.size(0), compute_type, algo));
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}
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}
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