Spaces:

BlinkDL
/

RWKV-Gradio-1

Running on T4

App Files Files Community

RWKV-Gradio-1 / cuda /gemm_fp16_cublas.cpp

BlinkDL

Upload 16 files

cade2a3 5 months ago

raw history blame contribute delete

No virus

3.37 kB

	#include <cublas_v2.h>
	#include <cuda.h>
	#include <cuda_fp16.h>
	#include <cuda_runtime.h>
	#include <torch/extension.h>
	#include <c10/cuda/CUDAGuard.h>
	#include <ATen/cuda/CUDAContext.h>

	#define CUBLAS_CHECK(condition) \
	for (cublasStatus_t _cublas_check_status = (condition); \
	_cublas_check_status != CUBLAS_STATUS_SUCCESS;) \
	throw std::runtime_error("cuBLAS error " + \
	std::to_string(_cublas_check_status) + " at " + \
	std::to_string(__LINE__));

	#define CUDA_CHECK(condition) \
	for (cudaError_t _cuda_check_status = (condition); \
	_cuda_check_status != cudaSuccess;) \
	throw std::runtime_error( \
	"CUDA error " + std::string(cudaGetErrorString(_cuda_check_status)) + \
	" at " + std::to_string(__LINE__));

	/*
	NOTE: blas gemm is column-major by default, but we need row-major output.
	The data of row-major, transposed matrix is exactly the same as the
	column-major, non-transposed matrix, and C = A * B ---> C^T = B^T * A^T
	*/
	void gemm_fp16_cublas(torch::Tensor a, torch::Tensor b, torch::Tensor c) {
	const at::cuda::OptionalCUDAGuard device_guard(device_of(a));
	const auto cuda_data_type = CUDA_R_16F;
	const auto cuda_c_data_type =
	c.dtype() == torch::kFloat32 ? CUDA_R_32F : CUDA_R_16F;
	const auto compute_type = CUDA_R_32F;
	const float sp_alpha = 1.f;
	// swap a and b, and use CUBLAS_OP_N. see the notes above
	std::swap(a, b);
	const cublasOperation_t cublas_trans_a = CUBLAS_OP_N;
	const cublasOperation_t cublas_trans_b = CUBLAS_OP_N;
	// m = (B^T).size(0) = B.size(1), and = A.size(1) after swap,
	// negative axis is used because of the existence of batch matmul.
	const int m = a.size(-1);
	const int k = a.size(-2);
	const int n = b.size(-2);
	const int cublas_lda = m;
	const int cublas_ldb = k;
	const int cublas_ldc = m;
	cublasHandle_t cublas_handle = at::cuda::getCurrentCUDABlasHandle();

	#if CUDA_VERSION >= 11000
	cublasGemmAlgo_t algo = CUBLAS_GEMM_DEFAULT;
	#else
	cublasGemmAlgo_t algo = CUBLAS_GEMM_DFALT_TENSOR_OP;
	#endif
	const float sp_beta = 0.f;
	if (a.sizes().size() == 2 && b.sizes().size() == 2) {
	CUBLAS_CHECK(cublasGemmEx(
	cublas_handle, cublas_trans_a, cublas_trans_b, m, n, k, &sp_alpha,
	a.data_ptr(), cuda_data_type, cublas_lda, b.data_ptr(), cuda_data_type,
	cublas_ldb, &sp_beta, c.data_ptr(), cuda_c_data_type, cublas_ldc,
	compute_type, algo));
	} else {
	// batch matmul
	assert(a.sizes().size() == 3 && b.sizes().size() == 3);

	const long long int cublas_stride_a = m * k;
	const long long int cublas_stride_b = k * n;
	const long long int cublas_stride_c = m * n;
	CUBLAS_CHECK(cublasGemmStridedBatchedEx(
	cublas_handle, cublas_trans_a, cublas_trans_b, m,
	n, k, &sp_alpha, a.data_ptr(), cuda_data_type, cublas_lda,
	cublas_stride_a, b.data_ptr(), cuda_data_type, cublas_ldb, cublas_stride_b,
	&sp_beta, c.data_ptr(), cuda_c_data_type, cublas_ldc, cublas_stride_c,
	a.size(0), compute_type, algo));
	}
	}