elementwise_max_op.h

/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.

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/operators/elementwise_op_function.h"

namespace paddle {
namespace operators {

template <typename T>
struct MaxFunctor {
  inline HOSTDEVICE T operator()(T a, T b) const { return a > b ? a : b; }
};

template <typename DeviceContext, typename T>
class ElementwiseMaxKernel : 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());
    TransformFunctor<MaxFunctor<T>, T, DeviceContext> functor(
        x, y, z, ctx.template device_context<DeviceContext>(), MaxFunctor<T>());

    auto x_dims = x->dims();
    auto y_dims = y->dims();
    PADDLE_ENFORCE_GE(x_dims.size(), y_dims.size(),
                      "Rank of first input must >= rank of second input.");

    if (x_dims == y_dims) {
      functor.Run();
      return;
    }

    int axis = ctx.Attr<int>("axis");
    axis = (axis == -1 ? x_dims.size() - y_dims.size() : axis);
    PADDLE_ENFORCE(axis >= 0 && axis < x_dims.size(),
                   "Axis should be in range [0, x_dims)");

    int pre, n, post;
    get_mid_dims(x_dims, y_dims, axis, pre, n, post);
    if (post == 1) {
      functor.RunRowWise(n, pre);
      return;
    } else {
      functor.RunMidWise(n, pre, post);
      return;
    }
  }
};

template <typename T>
struct ElementwiseSubGradFunctor {
  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 dz_e = framework::EigenVector<T>::Flatten(*dz);
    auto x_e = framework::EigenVector<T>::Flatten(*x);
    auto y_e = framework::EigenVector<T>::Flatten(*y);

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

template <typename T>
struct ElementwiseSubOneGradFunctor {
  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 dz_e = framework::EigenVector<T>::Flatten(*dz);
    auto x_e = framework::EigenVector<T>::Flatten(*x);
    auto y_e = framework::EigenVector<T>::Flatten(*y);
    if (dx) {
      auto dx_e = framework::EigenVector<T>::Flatten(*dx);
      dx_e.device(d) = dz_e;
    }
    if (dy) {
      auto dy_e = framework::EigenVector<T>::Flatten(*dy);
      dy_e.device(d) = (-1.0) * dz_e.sum();
    }
  }
};

}  // namespace operators
}  // namespace paddle