//   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_grad_kernel.h"

#include "paddle/phi/backends/cpu/cpu_context.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/core/tensor_utils.h"
#include "paddle/phi/kernels/cpu/elementwise_grad.h"
#include "paddle/phi/kernels/funcs/elementwise_functor.h"
#include "paddle/phi/kernels/impl/elementwise_grad_kernel_impl.h"

namespace phi {

template <typename T, typename Context>
void MaximumGradKernel(const Context& dev_ctx,
                       const DenseTensor& x,
                       const DenseTensor& y,
                       const DenseTensor& dout,
                       DenseTensor* dx,
                       DenseTensor* dy) {
  funcs::ElementwiseGradPreProcess(dout, dx);
  int axis = -1;
  phi::funcs::ElemwiseGradCompute<Context, T, MaxGradDx<T>, MaxGradDy<T>>(
      dev_ctx, x, y, dout, dout, axis, dx, dy, MaxGradDx<T>(), MaxGradDy<T>());
}

template <typename T, typename Context>
void MinimumGradKernel(const Context& dev_ctx,
                       const DenseTensor& x,
                       const DenseTensor& y,
                       const DenseTensor& dout,
                       DenseTensor* dx,
                       DenseTensor* dy) {
  funcs::ElementwiseGradPreProcess(dout, dx);
  int axis = -1;
  phi::funcs::ElemwiseGradCompute<Context, T, MinGradDx<T>, MinGradDy<T>>(
      dev_ctx, x, y, dout, dout, axis, dx, dy, MinGradDx<T>(), MinGradDy<T>());
}

}  // namespace phi

PD_REGISTER_KERNEL(fmax_grad,
                   CPU,
                   ALL_LAYOUT,
                   phi::ElementwiseFMaxGradKernel,
                   float,
                   double,
                   int,
                   int64_t) {}

PD_REGISTER_KERNEL(fmin_grad,
                   CPU,
                   ALL_LAYOUT,
                   phi::ElementwiseFMinGradKernel,
                   float,
                   double,
                   int,
                   int64_t) {}

PD_REGISTER_KERNEL(maximum_grad,
                   CPU,
                   ALL_LAYOUT,
                   phi::MaximumGradKernel,
                   float,
                   double,
                   int,
                   int64_t,
                   phi::dtype::bfloat16) {}

PD_REGISTER_KERNEL(minimum_grad,
                   CPU,
                   ALL_LAYOUT,
                   phi::MinimumGradKernel,
                   float,
                   double,
                   int,
                   int64_t,
                   phi::dtype::bfloat16) {}

PD_REGISTER_KERNEL(heaviside_grad,
                   CPU,
                   ALL_LAYOUT,
                   phi::HeavisideGradKernel,
                   float,
                   double,
                   int,
                   int64_t) {}

PD_REGISTER_KERNEL(elementwise_pow_grad,
                   CPU,
                   ALL_LAYOUT,
                   phi::ElementwisePowGradKernel,
                   float,
                   double,
                   int,
                   int64_t,
                   phi::dtype::bfloat16) {}
