// 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/stack_grad_kernel.h"

#include "paddle/phi/backends/xpu/enforce_xpu.h"
#include "paddle/phi/core/kernel_registry.h"

namespace phi {

template <typename T, typename Context>
void StackGradKernel(const Context& dev_ctx,
                     const DenseTensor& out,
                     int axis,
                     std::vector<DenseTensor*> x_grad) {
  using XPUType = typename XPUTypeTrait<T>::Type;
  auto outs = x_grad;
  auto dy_dims = out.dims();

  if (axis < 0) axis += dy_dims.size();
  auto dy_shape = phi::vectorize<int>(dy_dims);

  std::vector<int> dx_dims_list(x_grad.size(), 1);
  std::vector<XPUType*> dx_lists;
  for (size_t j = 0; j < outs.size(); ++j) {
    dev_ctx.template Alloc<T>(outs[j]);
    dx_lists.push_back(reinterpret_cast<XPUType*>(outs[j]->data<T>()));
  }

  int r = xpu::split<XPUType>(dev_ctx.x_context(),
                              reinterpret_cast<const XPUType*>(out.data<T>()),
                              dx_lists,
                              dy_shape,
                              dx_dims_list,
                              axis);

  PADDLE_ENFORCE_XDNN_SUCCESS(r, "split in stack_grad op");
}
}  // namespace phi

PD_REGISTER_KERNEL(
    stack_grad, XPU, ALL_LAYOUT, phi::StackGradKernel, float, int) {}
