WildfireDetection/Swin-Transformer/apex/csrc/megatron/fused_weight_gradient_dense_cuda.cu

#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);
    );
}