text: /* 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.h"
  #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 {
      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());
      int axis = ctx.Attr<int>("axis");
      ElementwiseComputeEx<DivFunctor<T>, DeviceContext, T>(ctx, x, y, axis,
                                                            DivFunctor<T>(), z);
    }
  };
  
  template <typename T>
  struct DivGradDX {
    HOSTDEVICE T operator()(T x, T y, T out, T dout) const { return dout / y; }
  };
  
  template <typename T>
  struct DivGradDY {
    HOSTDEVICE T operator()(T x, T y, T out, T dout) const {
      return -dout * x / (y * y);
    }
  };
  
  template <typename DeviceContext, typename T>
  class ElementwiseDivGradKernel : 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* 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");
      ElemwiseGradCompute<DeviceContext, T, DivGradDX<T>, DivGradDY<T>>(
          ctx, *x, *y, *out, *dout, axis, dx, dy, DivGradDX<T>(), DivGradDY<T>());
    }
  };
  
  }  // namespace operators
  }  // namespace paddle
  