Open-Sora / apex /csrc /megatron /fused_weight_gradient_dense_cuda.cu
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#include <cassert>
#include <cstdio>
#include <cstdlib>
#include <cstring>
#include <torch/extension.h>
#include <ATen/ATen.h>
#include <ATen/cuda/CUDAContext.h>
/* Includes, cuda */
#include <cublas_v2.h>
#include <cuda_runtime.h>
#include "type_shim.h"
// BF16 Tensor core wrapper around cublas GEMMEx
void gemmex_wrapper(
cublasHandle_t handle,
cublasOperation_t transa,
cublasOperation_t transb,
int m,
int n,
int k,
const float* alpha,
at::BFloat16* A,
int lda,
at::BFloat16* B,
int ldb,
const float* beta,
float* C,
int ldc) {
TORCH_CUDABLAS_CHECK(cublasGemmEx(
handle,
transa,
transb,
m,
n,
k,
alpha,
A,
CUDA_R_16BF,
lda,
B,
CUDA_R_16BF,
ldb,
beta,
C,
CUDA_R_32F,
ldc,
CUDA_R_32F,
CUBLAS_GEMM_DEFAULT_TENSOR_OP));
}
// FP16 Tensor core wrapper around cublas GEMMEx
void gemmex_wrapper(
cublasHandle_t handle,
cublasOperation_t transa,
cublasOperation_t transb,
int m,
int n,
int k,
const float* alpha,
at::Half* A,
int lda,
at::Half* B,
int ldb,
const float* beta,
float* C,
int ldc) {
TORCH_CUDABLAS_CHECK(cublasGemmEx(
handle,
transa,
transb,
m,
n,
k,
alpha,
A,
CUDA_R_16F,
lda,
B,
CUDA_R_16F,
ldb,
beta,
C,
CUDA_R_32F,
ldc,
CUDA_R_32F,
CUBLAS_GEMM_DEFAULT_TENSOR_OP));
}
// FP32 wrapper around cublas GEMMEx
void gemmex_wrapper(
cublasHandle_t handle,
cublasOperation_t transa,
cublasOperation_t transb,
int m,
int n,
int k,
const float *alpha,
float *A,
int lda,
float *B,
int ldb,
const float *beta,
float *C,
int ldc) {
TORCH_CUDABLAS_CHECK(cublasGemmEx(
handle,
transa,
transb,
m,
n,
k,
alpha,
A,
CUDA_R_32F,
lda,
B,
CUDA_R_32F,
ldb,
beta,
C,
CUDA_R_32F,
ldc,
CUDA_R_32F,
CUBLAS_GEMM_DEFAULT_TENSOR_OP));
}
template <typename T>
void wgrad_gemm_accum_fp32_cuda(T *input, T *d_output, float *d_weight, int in_dim, int hidden_dim, int out_dim) {
cublasHandle_t handle = at::cuda::getCurrentCUDABlasHandle();
cudaStream_t stream;
cublasGetStream(handle, &stream);
const float alpha = 1.0;
const float beta = 1.0;
gemmex_wrapper(
handle,
CUBLAS_OP_N,
CUBLAS_OP_T,
in_dim,
out_dim,
hidden_dim,
&alpha,
input,
in_dim,
d_output,
out_dim,
&beta,
d_weight,
in_dim);
}
template void wgrad_gemm_accum_fp32_cuda<at::Half>(at::Half *input, at::Half *d_output, float *d_weight, int in_dim, int hidden_dim, int out_dim);
template void wgrad_gemm_accum_fp32_cuda<at::BFloat16>(at::BFloat16 *input, at::BFloat16 *d_output, float *d_weight, int in_dim, int hidden_dim, int out_dim);
template void wgrad_gemm_accum_fp32_cuda<float>(float *input, float *d_output, float *d_weight, int in_dim, int hidden_dim, int out_dim);
void wgrad_gemm_accum_fp32_cuda_stub(
at::Tensor &input,
at::Tensor &d_output,
at::Tensor &d_weight
) {
at::Tensor input_2d, d_output_2d;
// input tensor: collapse to the first dim
auto in_sizes = input.sizes();
if (input.dim() > 2) {
input_2d = input.view({-1, in_sizes[in_sizes.size() - 1]});
} else {
input_2d = input;
}
// d_output tensor: collapse to the first dim
auto d_out_sizes = d_output.sizes();
if (d_output.dim() > 2) {
d_output_2d = d_output.view({-1, d_out_sizes[d_out_sizes.size() - 1]});
} else {
d_output_2d = d_output;
}
const int hidden_dim = input_2d.size(0);
const int in_dim = input_2d.size(1);
const int out_dim = d_weight.size(0);
DISPATCH_FLOAT_HALF_AND_BFLOAT(input_2d.scalar_type(), 0, "wgrad_gemm_accum_fp32",
wgrad_gemm_accum_fp32_cuda<scalar_t_0>(
input_2d.data_ptr<scalar_t_0>(),
d_output_2d.data_ptr<scalar_t_0>(),
d_weight.data_ptr<float>(),
in_dim,
hidden_dim,
out_dim);
);
}