index_add_grad_kernel.cu

// 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/index_add_grad_kernel.h"

#include "paddle/fluid/platform/device/gpu/gpu_launch_config.h"
#include "paddle/fluid/platform/device/gpu/gpu_primitives.h"
#include "paddle/phi/backends/gpu/gpu_info.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/core/utils/data_type.h"
#include "paddle/phi/kernels/funcs/math_function.h"
#include "paddle/phi/kernels/gpu/index_select_impl.h"

namespace phi {

using paddle::platform::PADDLE_CUDA_NUM_THREADS;

template <typename T, typename Context>
void IndexAddGradKernel(const Context& ctx,
                        const DenseTensor& index,
                        const DenseTensor& add_value,
                        const DenseTensor& out_grad,
                        int dim,
                        DenseTensor* x_grad,
                        DenseTensor* add_value_grad) {
  auto* output_grad_data = out_grad.data<T>();
  auto* in_grad_data = ctx.template Alloc<T>(x_grad);
  auto* add_value_grad_data = ctx.template Alloc<T>(add_value_grad);

  auto input_dim = x_grad->dims();
  auto output_dim = out_grad.dims();
  auto add_value_dim = add_value_grad->dims();
  dim = dim >= 0 ? dim : dim + input_dim.size();
  auto stride_dim = phi::stride(input_dim);
  int64_t stride = stride_dim[dim];
  int64_t size = add_value_dim[dim];
  int64_t delta = input_dim[dim] - size;
  const auto& index_type = index.dtype();

  bool index_type_match =
      index_type == phi::DataType::INT64 || index_type == phi::DataType::INT32;
  PADDLE_ENFORCE_EQ(index_type_match,
                    true,
                    phi::errors::InvalidArgument(
                        "Input(Index) holds the wrong type, it holds %s, but "
                        "desires to be %s or %s",
                        index_type,
                        phi::DataType::INT32,
                        phi::DataType::INT64));

  int64_t numel = add_value_grad->numel();
  if (numel == 0) {
    return;
  }
  auto stream = ctx.stream();

  // get x_grad: copy out_grad to x_grad.
  phi::Copy(ctx, out_grad, ctx.GetPlace(), false, x_grad);

  // get add_value_grad: index_select(out_grad, index, axis)
  unsigned int block_dim = PADDLE_CUDA_NUM_THREADS;
  dim3 grid_dim = dim3((numel + block_dim - 1) / block_dim);
  paddle::platform::LimitGridDim(ctx, &grid_dim);

  if (index_type == phi::DataType::INT64) {
    const int64_t* index_data = index.data<int64_t>();
    index_select_cuda_kernel<T, int64_t>
        <<<grid_dim, block_dim, 0, stream>>>(output_grad_data,
                                             add_value_grad_data,
                                             index_data,
                                             numel,
                                             stride,
                                             size,
                                             delta);
  } else {
    const int* index_data = index.data<int>();
    index_select_cuda_kernel<T, int>
        <<<grid_dim, block_dim, 0, stream>>>(output_grad_data,
                                             add_value_grad_data,
                                             index_data,
                                             numel,
                                             stride,
                                             size,
                                             delta);
  }
}

}  // namespace phi

PD_REGISTER_KERNEL(index_add_grad,
                   GPU,
                   ALL_LAYOUT,
                   phi::IndexAddGradKernel,
                   float,
                   double,
                   int,
                   int64_t) {}