You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
103 lines
3.4 KiB
103 lines
3.4 KiB
/* 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 AddFunctor {
|
|
inline HOSTDEVICE T operator()(T a, T b) const { return a + b; }
|
|
};
|
|
|
|
template <typename DeviceContext, typename T>
|
|
class ElementwiseAddKernel : public framework::OpKernel<T> {
|
|
public:
|
|
void Compute(const framework::ExecutionContext& ctx) const override {
|
|
ElementwiseComputeEx<AddFunctor<T>, DeviceContext, T>(ctx);
|
|
}
|
|
};
|
|
|
|
template <typename T>
|
|
struct ElementwiseAddGradFunctor {
|
|
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);
|
|
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) = dz_e;
|
|
}
|
|
}
|
|
};
|
|
|
|
template <typename T>
|
|
struct ElementwiseAddBroadCastGradFunctor {
|
|
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 dz_e = framework::EigenVector<T>::Flatten(*dz);
|
|
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) = dz_e.reshape(Eigen::DSizes<int, 2>(pre, n))
|
|
.sum(Eigen::array<int, 1>{{0}});
|
|
}
|
|
}
|
|
};
|
|
|
|
template <typename T>
|
|
struct ElementwiseAddBroadCast2GradFunctor {
|
|
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 dz_e = framework::EigenVector<T>::Flatten(*dz);
|
|
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) = dz_e.reshape(Eigen::DSizes<int, 3>(pre, n, post))
|
|
.sum(Eigen::array<int, 2>{{0, 2}});
|
|
}
|
|
}
|
|
};
|
|
|
|
template <typename DeviceContext, typename T>
|
|
class ElementwiseAddGradKernel : public framework::OpKernel<T> {
|
|
public:
|
|
void Compute(const framework::ExecutionContext& ctx) const override {
|
|
ElementwiseGradCompute<DeviceContext, T, ElementwiseAddGradFunctor<T>,
|
|
ElementwiseAddBroadCastGradFunctor<T>,
|
|
ElementwiseAddBroadCast2GradFunctor<T>>(ctx);
|
|
}
|
|
};
|
|
|
|
} // namespace operators
|
|
} // namespace paddle
|