/* 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/elementwise_op.h"
#include "paddle/fluid/operators/elementwise/elementwise_op_function.cu.h"
#include "paddle/fluid/operators/elementwise/elementwise_op_function.h"
#include "paddle/fluid/operators/math/blas.h"

namespace paddle {
namespace operators {

template <typename DeviceContext, typename T>
void DefaultElementwiseAddGrad(const framework::ExecutionContext &ctx,
                               const framework::Tensor *x,
                               const framework::Tensor *y,
                               framework::Tensor *z) {
  int axis = ctx.Attr<int>("axis");
  auto x_dims = x->dims();
  auto y_dims = y->dims();
  if (x_dims.size() >= y_dims.size()) {
    ElementwiseComputeEx<AddFunctor<T>, DeviceContext, T>(ctx, x, y, axis,
                                                          AddFunctor<T>(), z);
  } else {
    ElementwiseComputeEx<InverseAddFunctor<T>, DeviceContext, T>(
        ctx, x, y, axis, InverseAddFunctor<T>(), z);
  }
}

template <typename DeviceContext, typename T, class Enable = void>
struct SameDimsElemwiseAdd {
  void operator()(const framework::ExecutionContext &ctx,
                  const framework::Tensor *x, const framework::Tensor *y,
                  framework::Tensor *z);
};

template <typename DeviceContext, typename T>
class ElementwiseAddKernel : public framework::OpKernel<T> {
 public:
  void Compute(const framework::ExecutionContext &ctx) const override {
    auto *x = ctx.Input<framework::LoDTensor>("X");
    auto *y = ctx.Input<framework::LoDTensor>("Y");
    auto *z = ctx.Output<framework::LoDTensor>("Out");
    z->mutable_data<T>(ctx.GetPlace());
    auto dims_equal = x->dims() == y->dims();
    if (dims_equal) {
      SameDimsElemwiseAdd<DeviceContext, T> same_dims_add;
      same_dims_add(ctx, x, y, z);
    } else {
      DefaultElementwiseAddGrad<DeviceContext, T>(ctx, x, y, z);
    }
  }
};

template <typename T>
struct IdentityGrad {
  HOSTDEVICE T operator()(T x, T y, T out, T dout) const { return dout; }
};

template <typename DeviceContext, typename T>
void DefaultElementwiseAddGrad(const framework::ExecutionContext &ctx,
                               const framework::Tensor *x,
                               const framework::Tensor *y,
                               const framework::Tensor *out,
                               const framework::Tensor *dout,
                               framework::Tensor *dx, framework::Tensor *dy) {
  int axis = ctx.Attr<int>("axis");

  ElemwiseExplicitGradCompute<DeviceContext, T, IdentityGrad<T>,
                              IdentityGrad<T>>(ctx, *x, *y, *out, *dout, axis,
                                               dx, dy, IdentityGrad<T>(),
                                               IdentityGrad<T>());
}

template <typename DeviceContext, typename T>
typename std::enable_if<
    std::is_floating_point<T>::value &&
    std::is_same<DeviceContext, platform::CPUDeviceContext>::value>::type
ElementwiseAddGrad(const framework::ExecutionContext &ctx,
                   const framework::Tensor *x, const framework::Tensor *y,
                   const framework::Tensor *out, const framework::Tensor *dout,
                   framework::Tensor *dx, framework::Tensor *dy) {
  auto blas = math::GetBlas<DeviceContext, T>(ctx);
  if (dx) {
    blas.VCOPY(dout->numel(), dout->data<T>(),
               dx->mutable_data<T>(ctx.GetPlace()));
  }

  if (dy) {
    blas.VCOPY(dout->numel(), dout->data<T>(),
               dy->mutable_data<T>(ctx.GetPlace()));
  }
}

template <typename DeviceContext, typename T>
typename std::enable_if<
    !std::is_floating_point<T>::value &&
    std::is_same<DeviceContext, platform::CPUDeviceContext>::value>::type
ElementwiseAddGrad(const framework::ExecutionContext &ctx,
                   const framework::Tensor *x, const framework::Tensor *y,
                   const framework::Tensor *out, const framework::Tensor *dout,
                   framework::Tensor *dx, framework::Tensor *dy) {
  DefaultElementwiseAddGrad<DeviceContext, T>(ctx, x, y, out, dout, dx, dy);
}

#ifdef PADDLE_WITH_CUDA
// cuda definition
template <typename DeviceContext, typename T>
typename std::enable_if<
    std::is_same<DeviceContext, platform::CUDADeviceContext>::value>::type
ElementwiseAddGrad(const framework::ExecutionContext &ctx,
                   const framework::Tensor *x, const framework::Tensor *y,
                   const framework::Tensor *out, const framework::Tensor *dout,
                   framework::Tensor *dx, framework::Tensor *dy);
#endif

template <typename DeviceContext, typename T>
class ElementwiseAddGradKernel : public ElemwiseGradKernel<T> {
 public:
  void Compute(const framework::ExecutionContext &ctx) const override {
    ElemwiseGradKernel<T>::Compute(ctx);

    using Tensor = framework::Tensor;

    auto *x = ctx.Input<Tensor>("X");
    auto *y = ctx.Input<Tensor>("Y");
    auto *dout = ctx.Input<Tensor>(framework::GradVarName("Out"));
    auto *dx = ctx.Output<Tensor>(framework::GradVarName("X"));
    auto *dy = ctx.Output<Tensor>(framework::GradVarName("Y"));
    // skip out
    auto *out = dout;

    // Special case when dy is not needed and dx doesn't reduce
    if (dx != nullptr && dy == nullptr && dx->dims() == dout->dims()) {
      VLOG(4) << "Special case when dy is not needed and dx doesn't "
                 "reduce";
      framework::TensorCopy(
          *dout, ctx.GetPlace(),
          ctx.template device_context<platform::DeviceContext>(), dx);
    } else if (dx == nullptr && dy != nullptr && dy->dims() == dout->dims()) {
      VLOG(4) << "Special case when dx is not needed and dy doesn't "
                 "reduce";
      framework::TensorCopy(
          *dout, ctx.GetPlace(),
          ctx.template device_context<platform::DeviceContext>(), dy);
    } else if (dx != nullptr && dy != nullptr && (dx->dims() == dy->dims())) {
      ElementwiseAddGrad<DeviceContext, T>(ctx, x, y, out, dout, dx, dy);
    } else {
      DefaultElementwiseAddGrad<DeviceContext, T>(ctx, x, y, out, dout, dx, dy);
    }
  }
};

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

    auto *y = ctx.Input<Tensor>("Y");
    auto *dout = ctx.Input<Tensor>("DOut");
    auto *ddx = ctx.Input<Tensor>("DDX");
    auto *ddy = ctx.Input<Tensor>("DDY");

    auto *ddout = ctx.Output<Tensor>("DDOut");

    // ddOut = ddx + ddy
    if (ddout) {
      Tensor ddx_safe, ddy_safe;
      GetDoubleGradSafeTensor<DeviceContext, T>(ctx, dout, ddx, &ddx_safe);
      GetDoubleGradSafeTensor<DeviceContext, T>(ctx, y, ddy, &ddy_safe);

      ddout->mutable_data<T>(ctx.GetPlace());
      DefaultElementwiseAddGrad<DeviceContext, T>(ctx, &ddx_safe, &ddy_safe,
                                                  ddout);
    }
  }
};

}  // namespace operators
}  // namespace paddle