matmul_grad_kernel.cc

// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
//     http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.

#include "paddle/phi/kernels/matmul_grad_kernel.h"

#include "paddle/phi/backends/onednn/onednn_reuse.h"
#include "paddle/phi/core/kernel_registry.h"

namespace phi {

std::vector<int64_t> ExtendDimsWithOnes(const std::vector<int64_t> &dims,
                                        int new_size) {
  std::vector<int64_t> new_dims(new_size, 1);
  for (size_t i = 0; i < dims.size(); ++i) {
    new_dims[new_size - dims.size() + i] = dims[i];
  }

  return new_dims;
}

template <typename T>
void CalculateGradMatrixDims(const OneDNNContext &dev_ctx,
                             DenseTensor *dx_tmp,
                             DenseTensor *dy_tmp,
                             const std::vector<int64_t> &dx_dims,
                             const std::vector<int64_t> &dy_dims,
                             std::vector<int64_t> *dx_bd_dims,
                             std::vector<int64_t> *dy_bd_dims) {
  for (size_t i = 0; i < dx_dims.size() - 2; ++i) {
    if (dx_dims[i] != dy_dims[i]) {
      if (dx_dims[i] == 1) {
        (*dx_bd_dims)[i] = dy_dims[i];
      } else {
        (*dy_bd_dims)[i] = dx_dims[i];
      }
    }
  }

  dx_tmp->Resize(make_ddim((*dx_bd_dims)));
  dev_ctx.template Alloc<T>(dx_tmp);
  dy_tmp->Resize(make_ddim((*dy_bd_dims)));
  dev_ctx.template Alloc<T>(dy_tmp);
}

template <typename T>
void ReduceSumForMatmulGradOutput(const OneDNNContext &dev_ctx,
                                  const DenseTensor *dx_tmp,
                                  DenseTensor *dx,
                                  const std::vector<int64_t> &dx_dims,
                                  const std::vector<int64_t> &squeezed_dims) {
  funcs::ReductionOneDNNHandler<T> handler(dnnl::algorithm::reduction_sum,
                                           0.0f,
                                           0.0f,
                                           dev_ctx.GetEngine(),
                                           dev_ctx.GetPlace(),
                                           dx_tmp,
                                           dx,
                                           dx_dims);

  auto src_memory_p = handler.AcquireSrcMemory(dx_tmp);
  auto dst_memory_p = handler.AcquireDstMemory(dx);

  std::unordered_map<int, dnnl::memory> reduction_args = {
      {DNNL_ARG_SRC, *src_memory_p}, {DNNL_ARG_DST, *dst_memory_p}};

  auto &astream = OneDNNContext::tls().get_stream();
  auto reduction_p = handler.AcquireForwardPrimitive();

  reduction_p->execute(astream, reduction_args);
  astream.wait();

  dx->set_mem_desc(dst_memory_p->get_desc().reshape(squeezed_dims));
}

template <typename T, typename Context>
void MatmulGradKernel(const Context &dev_ctx,
                      const DenseTensor &x,
                      const DenseTensor &y,
                      const DenseTensor &dout,
                      bool transpose_x,
                      bool transpose_y,
                      DenseTensor *dx,
                      DenseTensor *dy) {
  auto x_dims = vectorize(x.dims());
  auto y_dims = vectorize(y.dims());
  auto dout_dims = vectorize(dout.dims());

  size_t ndims = std::max(x_dims.size(), y_dims.size());
  ndims = std::max<size_t>(ndims, 3);

  if (x_dims.size() != ndims) {
    x_dims = ExtendDimsWithOnes(x_dims, ndims);
  } else if (y_dims.size() != ndims) {
    y_dims = ExtendDimsWithOnes(y_dims, ndims);
  }

  // in broadcasting scenario new memory is required because
  // reduce sum must be calculated upon broadcasted dims
  DenseTensor dx_tmp, dy_tmp;
  std::vector<int64_t> dx_bd_dims(x_dims);
  std::vector<int64_t> dy_bd_dims(y_dims);

  CalculateGradMatrixDims<T>(
      dev_ctx, &dx_tmp, &dy_tmp, x_dims, y_dims, &dx_bd_dims, &dy_bd_dims);

  if (transpose_x && transpose_y) {
    funcs::ExecuteMatmul<T, T>(
        dev_ctx, y, dout, y_dims, dout_dims, true, true, &dx_tmp);
    funcs::ExecuteMatmul<T, T>(
        dev_ctx, dout, x, dout_dims, x_dims, true, true, &dy_tmp);
  } else if (transpose_x) {
    funcs::ExecuteMatmul<T, T>(
        dev_ctx, y, dout, y_dims, dout_dims, false, true, &dx_tmp);
    funcs::ExecuteMatmul<T, T>(
        dev_ctx, x, dout, x_dims, dout_dims, false, false, &dy_tmp);
  } else if (transpose_y) {
    funcs::ExecuteMatmul<T, T>(
        dev_ctx, dout, y, dout_dims, y_dims, false, false, &dx_tmp);
    funcs::ExecuteMatmul<T, T>(
        dev_ctx, dout, x, dout_dims, x_dims, true, false, &dy_tmp);
  } else {
    funcs::ExecuteMatmul<T, T>(
        dev_ctx, dout, y, dout_dims, y_dims, false, true, &dx_tmp);
    funcs::ExecuteMatmul<T, T>(
        dev_ctx, x, dout, x_dims, dout_dims, true, false, &dy_tmp);
  }

  if (x_dims != dx_bd_dims) {
    ReduceSumForMatmulGradOutput<T>(
        dev_ctx, &dx_tmp, dx, x_dims, vectorize(x.dims()));
  } else {
    *dx = std::move(dx_tmp);
  }
  if (y_dims != dy_bd_dims) {
    ReduceSumForMatmulGradOutput<T>(
        dev_ctx, &dy_tmp, dy, y_dims, vectorize(y.dims()));
  } else {
    *dy = std::move(dy_tmp);
  }

  dx->Resize(x.dims());
  dy->Resize(y.dims());
}

}  // namespace phi

PD_REGISTER_KERNEL(matmul_grad,
                   OneDNN,
                   ONEDNN,
                   phi::MatmulGradKernel,
                   float,
                   phi::dtype::bfloat16) {}