// Copyright (c) 2018 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.

#pragma once

#include <vector>

#include "paddle/fluid/operators/reduce_op.h"

namespace paddle {
namespace operators {

// use for loop to speed up Eigen broadcast. 4 timer faster then broadcast
template <typename DeviceContext, typename T, typename Functor>
class ReduceSumGradKernel : public framework::OpKernel<T> {
 public:
  void Compute(const framework::ExecutionContext& context) const override {
    auto dims = context.Attr<std::vector<int>>("dim");
    if (context.GetPlace().type() == typeid(platform::CPUPlace) &&
        dims.size() == 1) {
      auto* input0 = context.Input<Tensor>("X");
      auto* input2 = context.Input<Tensor>(framework::GradVarName("Out"));
      auto* output = context.Output<Tensor>(framework::GradVarName("X"));
      output->mutable_data<T>(context.GetPlace());
      const auto* input2_d = input2->data<T>();
      auto* output_d = output->data<T>();

      // handle reduce_all
      if (input2->dims().size() == 1 && input2->dims()[0] == 1) {
        for (int64_t i = 0; i < framework::product(input0->dims()); ++i) {
          output_d[i] = input2_d[0];
        }
        return;
      }

      // handle reduce by one dimension
      int reduce_dim_index = dims[0];
      if (reduce_dim_index < 0) {
        reduce_dim_index += input0->dims().size();
      }

      auto& input_dim = input0->dims();
      int64_t before_dim = 1;
      for (int i = 0; i < reduce_dim_index; ++i) {
        before_dim *= input_dim[i];
      }
      int64_t reduce_dim = input_dim[reduce_dim_index];
      int64_t after_dim = 1;
      for (int i = reduce_dim_index + 1; i < input_dim.size(); ++i) {
        after_dim *= input_dim[i];
      }
      for (int64_t i = 0; i < before_dim; ++i) {
        for (int64_t j = 0; j < reduce_dim; ++j) {
          for (int64_t k = 0; k < after_dim; ++k) {
            output_d[i * reduce_dim * after_dim + j * after_dim + k] =
                input2_d[i * after_dim + k];
          }
        }
      }
      return;
    }

    // default use Eigen broadcast
    ReduceGradKernel<DeviceContext, T, Functor> kernel;
    kernel.Compute(context);
  }
};

struct SumFunctor {
  template <typename DeviceContext, typename X, typename Y, typename Dim>
  void operator()(const DeviceContext& place, X* x, Y* y, const Dim& dim) {
    y->device(place) = x->sum(dim);
  }
};

struct SumGradFunctor {
  template <typename DeviceContext, typename X, typename Y, typename DX,
            typename DY, typename Dim>
  void operator()(const DeviceContext& place, X* x, Y* y, DX* dx, DY* dy,
                  const Dim& dim, int size) {
    dx->device(place) = dy->eval().broadcast(dim);
  }
};

}  // namespace operators
}  // namespace paddle
