// 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/reduce_grad_kernel.h"

#include "paddle/phi/backends/gpu/gpu_context.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/kernels/funcs/reduce_function.h"
#include "paddle/phi/kernels/gpu/reduce_grad.h"
#include "paddle/phi/kernels/impl/reduce_max_grad_kernel_impl.h"
#include "paddle/phi/kernels/impl/reduce_min_grad_kernel_impl.h"
#include "paddle/phi/kernels/impl/reduce_prod_grad_kernel_impl.h"

namespace phi {

template <typename T, typename Context>
void ReduceSumGradKernel(const Context& dev_ctx,
                         const DenseTensor& x,
                         const DenseTensor& out_grad,
                         const std::vector<int64_t>& dims,
                         bool keep_dim,
                         bool reduce_all,
                         DataType in_dtype,
                         DataType out_dtype,
                         DenseTensor* x_grad) {
  ReduceGradKernel<T, Context, kps::IdentityFunctor>(dev_ctx,
                                                     x,
                                                     out_grad,
                                                     dims,
                                                     keep_dim,
                                                     reduce_all,
                                                     in_dtype,
                                                     out_dtype,
                                                     x_grad);
}

template <typename T, typename Context>
void ReduceMeanGradKernel(const Context& dev_ctx,
                          const DenseTensor& x,
                          const DenseTensor& out_grad,
                          const std::vector<int64_t>& dims,
                          bool keep_dim,
                          bool reduce_all,
                          DataType in_dtype,
                          DataType out_dtype,
                          DenseTensor* x_grad) {
  ReduceGradKernel<T, Context, kps::DivideFunctor>(dev_ctx,
                                                   x,
                                                   out_grad,
                                                   dims,
                                                   keep_dim,
                                                   reduce_all,
                                                   in_dtype,
                                                   out_dtype,
                                                   x_grad);
}

}  // namespace phi

PD_REGISTER_KERNEL(sum_grad,
                   GPU,
                   ALL_LAYOUT,
                   phi::ReduceSumGradKernel,
                   bool,
                   float,
                   double,
                   phi::dtype::float16,
                   phi::dtype::bfloat16,
                   int,
                   int64_t,
                   phi::dtype::complex<float>,
                   phi::dtype::complex<double>) {}

PD_REGISTER_KERNEL(mean_grad,
                   GPU,
                   ALL_LAYOUT,
                   phi::ReduceMeanGradKernel,
                   bool,
                   float,
                   double,
                   phi::dtype::float16) {}

PD_REGISTER_KERNEL(prod_grad,
                   GPU,
                   ALL_LAYOUT,
                   phi::ReduceProdGradKernel,
                   float,
                   double,
                   int,
                   int64_t) {}

PD_REGISTER_KERNEL(max_grad,
                   GPU,
                   ALL_LAYOUT,
                   phi::ReduceMaxGradKernel,
                   float,
                   double,
                   int,
                   int64_t) {}

PD_REGISTER_KERNEL(min_grad,
                   GPU,
                   ALL_LAYOUT,
                   phi::ReduceMinGradKernel,
                   float,
                   double,
                   int,
                   int64_t) {}