// 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/elementwise_divide_grad_kernel.h"

#include <memory>
#include <string>

#include "paddle/phi/backends/xpu/xpu_context.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/kernels/funcs/elementwise_base.h"
#include "paddle/phi/kernels/xpu/elementwise.h"

namespace phi {

template <typename T, typename Context>
void DivideGradKernel(const Context& dev_ctx,
                      const DenseTensor& x,
                      const DenseTensor& y,
                      const DenseTensor& out,
                      const DenseTensor& dout,
                      int axis,
                      DenseTensor* dx,
                      DenseTensor* dy) {
  using XPUType = typename XPUTypeTrait<T>::Type;
  funcs::ElementwiseGradPreProcess(dout, dx);
  XPUElementwiseGrad<T, XPUType>(dev_ctx,
                                 x,
                                 y,
                                 dout,
                                 axis,
                                 dx,
                                 dy,
                                 xpu::broadcast_div_grad<XPUType>,
                                 true);
}

}  // namespace phi

PD_REGISTER_KERNEL(divide_grad,
                   XPU,
                   ALL_LAYOUT,
                   phi::DivideGradKernel,
                   phi::dtype::float16,
                   float) {}
