/* Copyright (c) 2016 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 "paddle/fluid/operators/elementwise_op_function.h"

namespace paddle {
namespace operators {

template <typename T>
struct DivFunctor {
  inline HOSTDEVICE T operator()(T a, T b) const { return a / b; }
};

template <typename DeviceContext, typename T>
class ElementwiseDivKernel : public framework::OpKernel<T> {
 public:
  void Compute(const framework::ExecutionContext& ctx) const override {
    using Tensor = framework::Tensor;

    auto* x = ctx.Input<Tensor>("X");
    auto* y = ctx.Input<Tensor>("Y");
    auto* z = ctx.Output<Tensor>("Out");
    z->mutable_data<T>(ctx.GetPlace());
    int axis = ctx.Attr<int>("axis");
    ElementwiseComputeEx<DivFunctor<T>, DeviceContext, T>(ctx, x, y, axis,
                                                          DivFunctor<T>(), z);
  }
};

template <typename T>
struct ElementwiseDivGradFunctor {
  template <typename Device, typename X, typename Y, typename Z, typename dX,
            typename dY, typename dZ>
  void operator()(Device d, X x, Y y, Z z, dX dx, dY dy, dZ dz) {
    auto y_e = framework::EigenVector<T>::Flatten(*y);
    auto z_e = framework::EigenVector<T>::Flatten(*z);
    auto dz_e = framework::EigenVector<T>::Flatten(*dz);

    if (dx) {
      auto dx_e = framework::EigenVector<T>::Flatten(*dx);
      dx_e.device(d) = dz_e / y_e;
    }

    if (dy) {
      auto dy_e = framework::EigenVector<T>::Flatten(*dy);
      dy_e.device(d) = -1.0 * dz_e * z_e / y_e;
    }
  }
};

template <typename T>
struct ElementwiseDivBroadCastGradFunctor {
  template <typename Device, typename X, typename Y, typename Z, typename dX,
            typename dY, typename dZ, typename Pre, typename N>
  void operator()(Device d, X x, Y y, Z z, dX dx, dY dy, dZ dz, Pre pre, N n) {
    auto x_e = framework::EigenVector<T>::Flatten(*x);
    auto y_e = framework::EigenVector<T>::Flatten(*y);
    auto dz_e = framework::EigenVector<T>::Flatten(*dz);

    auto y_e_bcast = y_e.reshape(Eigen::DSizes<int, 2>(1, n))
                         .broadcast(Eigen::DSizes<int, 2>(pre, 1))
                         .reshape(Eigen::DSizes<int, 1>(x_e.size()));

    if (dx) {
      auto dx_e = framework::EigenVector<T>::Flatten(*dx);
      dx_e.device(d) = dz_e / y_e_bcast;
    }

    if (dy) {
      auto dy_e = framework::EigenVector<T>::Flatten(*dy);
      dy_e.device(d) = (-1.0 * (x_e * dz_e) / (y_e_bcast * y_e_bcast))
                           .reshape(Eigen::DSizes<int, 2>(pre, n))
                           .sum(Eigen::array<int, 1>{{0}});
    }
  }
};

template <typename T>
struct ElementwiseDivBroadCast2GradFunctor {
  template <typename Device, typename X, typename Y, typename Z, typename dX,
            typename dY, typename dZ, typename Pre, typename N, typename Post>
  void operator()(Device d, X x, Y y, Z z, dX dx, dY dy, dZ dz, Pre pre, N n,
                  Post post) {
    auto x_e = framework::EigenVector<T>::Flatten(*x);
    auto y_e = framework::EigenVector<T>::Flatten(*y);
    auto dz_e = framework::EigenVector<T>::Flatten(*dz);

    auto y_e_bcast = y_e.reshape(Eigen::DSizes<int, 3>(1, n, 1))
                         .broadcast(Eigen::DSizes<int, 3>(pre, 1, post))
                         .reshape(Eigen::DSizes<int, 1>(x_e.size()));
    if (dx) {
      auto dx_e = framework::EigenVector<T>::Flatten(*dx);
      dx_e.device(d) = dz_e / y_e_bcast;
    }

    if (dy) {
      auto dy_e = framework::EigenVector<T>::Flatten(*dy);
      dy_e.device(d) = (-1.0 * (x_e * dz_e) / (y_e_bcast * y_e_bcast))
                           .reshape(Eigen::DSizes<int, 3>(pre, n, post))
                           .sum(Eigen::array<int, 2>{{0, 2}});
    }
  }
};

template <typename DeviceContext, typename T>
class ElementwiseDivGradKernel : public framework::OpKernel<T> {
 public:
  void Compute(const framework::ExecutionContext& ctx) const override {
    using Tensor = framework::Tensor;

    auto* x = ctx.Input<Tensor>("X");
    auto* y = ctx.Input<Tensor>("Y");
    auto* out = ctx.Input<Tensor>("Out");
    auto* dout = ctx.Input<Tensor>(framework::GradVarName("Out"));
    auto* dx = ctx.Output<Tensor>(framework::GradVarName("X"));
    auto* dy = ctx.Output<Tensor>(framework::GradVarName("Y"));
    int axis = ctx.Attr<int>("axis");
    ElementwiseGradCompute<DeviceContext, T, ElementwiseDivGradFunctor<T>,
                           ElementwiseDivBroadCastGradFunctor<T>,
                           ElementwiseDivBroadCast2GradFunctor<T>>(
        ctx, x, y, out, dout, axis, dx, dy);
  }
};

}  // namespace operators
}  // namespace paddle