// Copyright (c) 2023 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_select_grad_kernel.h"

#include "paddle/phi/backends/xpu/enforce_xpu.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/core/utils/data_type.h"
#include "paddle/phi/kernels/full_kernel.h"
namespace phi {

template <typename T, typename Context>
void IndexSelectGradKernel(const Context& dev_ctx,
                           const DenseTensor& x,
                           const DenseTensor& index,
                           const DenseTensor& out_grad,
                           int dim,
                           DenseTensor* x_grad) {
  using XPUType = typename XPUTypeTrait<T>::Type;
  if (out_grad.numel() == 0) {
    Full<T, Context>(dev_ctx, x.dims(), 0, x_grad);
    return;
  }
  if (dim < 0) {
    dim += out_grad.dims().size();
  }
  const auto& index_type = index.dtype();
  bool index_type_match =
      index_type == DataType::INT32 || index_type == DataType::INT64;
  PADDLE_ENFORCE_EQ(index_type_match,
                    true,
                    common::errors::InvalidArgument(
                        "Input(Index) holds the wrong type, it holds %s, but "
                        "desires to be %s or %s",
                        index_type,
                        DataType::INT32,
                        DataType::INT64));

  XPUType* x_grad_data =
      reinterpret_cast<XPUType*>((dev_ctx.template Alloc<T>(x_grad)));
  const XPUType* out_grad_data =
      reinterpret_cast<const XPUType*>(out_grad.data<T>());

  auto out_grad_shape = vectorize<int64_t>(out_grad.dims());
  auto x_grad_shape = vectorize<int64_t>(x_grad->dims());

  int r = 0;
  if (index_type == DataType::INT32) {
    const int* index_data = index.data<int>();
    r = xpu::index_select_grad<XPUType, int>(dev_ctx.x_context(),
                                             nullptr,
                                             index_data,
                                             out_grad_data,
                                             dim,
                                             x_grad_data,
                                             out_grad_shape,
                                             x_grad_shape);
  } else if (index_type == DataType::INT64) {
    const int64_t* index_data = index.data<int64_t>();
    r = xpu::index_select_grad<XPUType, int64_t>(dev_ctx.x_context(),
                                                 nullptr,
                                                 index_data,
                                                 out_grad_data,
                                                 dim,
                                                 x_grad_data,
                                                 out_grad_shape,
                                                 x_grad_shape);
  }
  PADDLE_ENFORCE_XDNN_SUCCESS(r, "index_select_grad");
}

}  // namespace phi

PD_REGISTER_KERNEL(index_select_grad,
                   XPU,
                   ALL_LAYOUT,
                   phi::IndexSelectGradKernel,
                   float,
                   phi::float16,
                   phi::bfloat16) {}