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354 lines
12 KiB
354 lines
12 KiB
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#pragma once
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#include "paddle/framework/eigen.h"
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#include "paddle/framework/op_registry.h"
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namespace paddle {
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namespace operators {
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template <typename Place, typename T, typename Functor>
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class ActivationKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& context) const override {
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auto* X = context.Input<framework::Tensor>("X");
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auto* Y = context.Output<framework::Tensor>("Y");
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Y->mutable_data<T>(context.GetPlace());
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auto x = framework::EigenVector<T>::Flatten(*X);
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auto y = framework::EigenVector<T>::Flatten(*Y);
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auto place = context.GetEigenDevice<Place>();
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Functor functor;
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functor(place, x, y);
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}
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};
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template <typename Place, typename T, typename Functor>
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class ActivationGradKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& context) const override {
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auto* X = context.Input<framework::Tensor>("X");
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auto* Y = context.Input<framework::Tensor>("Y");
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auto* dY = context.Input<framework::Tensor>(framework::GradVarName("Y"));
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auto* dX = context.Output<framework::Tensor>(framework::GradVarName("X"));
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dX->mutable_data<T>(context.GetPlace());
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auto dy = framework::EigenVector<T>::Flatten(*dY);
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auto x = framework::EigenVector<T>::Flatten(*X);
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auto y = framework::EigenVector<T>::Flatten(*Y);
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auto dx = framework::EigenVector<T>::Flatten(*dX);
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auto place = context.GetEigenDevice<Place>();
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Functor functor;
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functor(place, x, y, dy, dx);
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}
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};
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// sigmoid(x) = 1 / (1 + exp(-x))
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template <typename T>
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struct SigmoidFunctor {
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template <typename Device, typename X, typename Y>
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void operator()(Device d, X x, Y y) {
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y.device(d) = static_cast<T>(1) / (static_cast<T>(1) + (-x).exp());
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}
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};
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template <typename T>
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struct SigmoidGradFunctor {
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template <typename Device, typename X, typename Y, typename dY, typename dX>
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void operator()(Device d, X x, Y y, dY dy, dX dx) {
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dx.device(d) = dy * y * (static_cast<T>(1) - y);
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}
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};
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// exp(x) = e^x
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struct ExpFunctor {
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template <typename Device, typename X, typename Y>
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void operator()(Device d, X x, Y y) {
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y.device(d) = x.exp();
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}
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};
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struct ExpGradFunctor {
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template <typename Device, typename X, typename Y, typename dY, typename dX>
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void operator()(Device d, X x, Y y, dY dy, dX dx) {
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dx.device(d) = dy * y;
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}
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};
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// relu(x) = max(x, 0)
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template <typename T>
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struct ReluFunctor {
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template <typename Device, typename X, typename Y>
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void operator()(Device d, X x, Y y) {
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y.device(d) = x.cwiseMax(static_cast<T>(0));
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}
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};
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template <typename T>
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struct ReluGradFunctor {
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template <typename Device, typename X, typename Y, typename dY, typename dX>
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void operator()(Device d, X x, Y y, dY dy, dX dx) {
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dx.device(d) = dy * (x > static_cast<T>(0)).template cast<T>();
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}
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};
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// tanh(x) = (exp(x) - exp(-x)) / (exp(x) + exp(-x))
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struct TanhFunctor {
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template <typename Device, typename X, typename Y>
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void operator()(Device d, X x, Y y) {
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y.device(d) = x.tanh();
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}
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};
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template <typename T>
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struct TanhGradFunctor {
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template <typename Device, typename X, typename Y, typename dY, typename dX>
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void operator()(Device d, X x, Y y, dY dy, dX dx) {
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dx.device(d) = dy * (static_cast<T>(1) - y * y);
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}
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};
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// sqrt(x) = x^(1/2)
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struct SqrtFunctor {
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template <typename Device, typename X, typename Y>
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void operator()(Device d, X x, Y y) {
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y.device(d) = x.sqrt();
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}
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};
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template <typename T>
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struct SqrtGradFunctor {
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template <typename Device, typename X, typename Y, typename dY, typename dX>
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void operator()(Device d, X x, Y y, dY dy, dX dx) {
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const Y y_conj = Eigen::numext::conj(y);
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dx.device(d) = static_cast<T>(0.5) * dy / y_conj;
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}
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};
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// abs(x) = |x|
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struct AbsFunctor {
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template <typename Device, typename X, typename Y>
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void operator()(Device d, X x, Y y) {
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y.device(d) = x.abs();
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}
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};
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struct AbsGradFunctor {
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template <typename Device, typename X, typename Y, typename dY, typename dX>
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void operator()(Device d, X x, Y y, dY dy, dX dx) {
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dx.device(d) = dy * x.sign();
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}
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};
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// reciprocal(x) = 1 / x
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template <typename T>
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struct ReciprocalFunctor {
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template <typename Device, typename X, typename Y>
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void operator()(Device d, X x, Y y) {
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y.device(d) = static_cast<T>(1) / x;
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}
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};
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template <typename T>
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struct ReciprocalGradFunctor {
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template <typename Device, typename X, typename Y, typename dY, typename dX>
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void operator()(Device d, X x, Y y, dY dy, dX dx) {
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dx.device(d) = dy * static_cast<T>(-1) * y * y;
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}
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};
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// log(x) = natural logarithm of x
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struct LogFunctor {
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template <typename Device, typename X, typename Y>
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void operator()(Device d, X x, Y y) {
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y.device(d) = x.log();
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}
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};
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template <typename T>
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struct LogGradFunctor {
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template <typename Device, typename X, typename Y, typename dY, typename dX>
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void operator()(Device d, X x, Y y, dY dy, dX dx) {
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dx.device(d) = dy * (static_cast<T>(1) / x);
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}
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};
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// square(x) = x^2
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struct SquareFunctor {
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template <typename Device, typename X, typename Y>
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void operator()(Device d, X x, Y y) {
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y.device(d) = x.square();
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}
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};
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template <typename T>
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struct SquareGradFunctor {
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template <typename Device, typename X, typename Y, typename dY, typename dX>
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void operator()(Device d, X x, Y y, dY dy, dX dx) {
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dx.device(d) = dy * static_cast<T>(2) * x;
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}
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};
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template <typename Place, typename T, typename AttrType = T>
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class BReluKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& context) const override {
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auto* X = context.Input<framework::Tensor>("X");
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auto* Y = context.Output<framework::Tensor>("Y");
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auto t_min = static_cast<T>(context.Attr<AttrType>("t_min"));
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auto t_max = static_cast<T>(context.Attr<AttrType>("t_max"));
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Y->mutable_data<T>(context.GetPlace());
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auto x = framework::EigenVector<T>::Flatten(*X);
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auto y = framework::EigenVector<T>::Flatten(*Y);
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auto place = context.GetEigenDevice<Place>();
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y.device(place) = x.cwiseMax(t_min).cwiseMin(t_max);
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}
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};
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template <typename Place, typename T, typename AttrType = T>
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class BReluGradKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& context) const override {
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auto* X = context.Input<framework::Tensor>("X");
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auto* dY = context.Input<framework::Tensor>(framework::GradVarName("Y"));
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auto* dX = context.Output<framework::Tensor>(framework::GradVarName("X"));
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auto t_min = static_cast<T>(context.Attr<AttrType>("t_min"));
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auto t_max = static_cast<T>(context.Attr<AttrType>("t_max"));
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dX->mutable_data<T>(context.GetPlace());
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auto dy = framework::EigenVector<T>::Flatten(*dY);
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auto x = framework::EigenVector<T>::Flatten(*X);
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auto dx = framework::EigenVector<T>::Flatten(*dX);
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auto place = context.GetEigenDevice<Place>();
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dx.device(place) = dy * ((x > t_min) * (x < t_max)).template cast<T>();
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}
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};
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template <typename Place, typename T, typename AttrType = T>
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class SoftReluKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& context) const override {
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auto* X = context.Input<framework::Tensor>("X");
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auto* Y = context.Output<framework::Tensor>("Y");
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auto threshold = static_cast<T>(context.Attr<AttrType>("threshold"));
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Y->mutable_data<T>(context.GetPlace());
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auto x = framework::EigenVector<T>::Flatten(*X);
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auto y = framework::EigenVector<T>::Flatten(*Y);
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auto place = context.GetEigenDevice<Place>();
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auto temp = x.cwiseMax(-threshold).cwiseMin(threshold).eval();
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y.device(place) = (static_cast<T>(1) + temp.exp()).log();
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}
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};
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template <typename Place, typename T, typename AttrType = T>
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class SoftReluGradKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& context) const override {
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auto* X = context.Input<framework::Tensor>("X");
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auto* Y = context.Input<framework::Tensor>("Y");
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auto* dY = context.Input<framework::Tensor>(framework::GradVarName("Y"));
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auto* dX = context.Output<framework::Tensor>(framework::GradVarName("X"));
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auto threshold = static_cast<T>(context.Attr<AttrType>("threshold"));
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dX->mutable_data<T>(context.GetPlace());
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auto x = framework::EigenVector<T>::Flatten(*X);
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auto y = framework::EigenVector<T>::Flatten(*Y);
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auto dy = framework::EigenVector<T>::Flatten(*dY);
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auto dx = framework::EigenVector<T>::Flatten(*dX);
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auto place = context.GetEigenDevice<Place>();
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auto temp = ((x > -threshold) * (x < threshold)).template cast<T>().eval();
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dx.device(place) = dy * (static_cast<T>(1) - (-y).exp()) * temp;
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}
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};
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template <typename Place, typename T, typename AttrType = T>
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class PowKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& context) const override {
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auto* X = context.Input<framework::Tensor>("X");
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auto* Y = context.Output<framework::Tensor>("Y");
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auto factor = static_cast<T>(context.Attr<AttrType>("factor"));
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Y->mutable_data<T>(context.GetPlace());
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auto x = framework::EigenVector<T>::Flatten(*X);
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auto y = framework::EigenVector<T>::Flatten(*Y);
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auto place = context.GetEigenDevice<Place>();
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y.device(place) = x.pow(factor);
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}
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};
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template <typename Place, typename T, typename AttrType = T>
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class PowGradKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& context) const override {
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auto* X = context.Input<framework::Tensor>("X");
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auto* dY = context.Input<framework::Tensor>(framework::GradVarName("Y"));
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auto* dX = context.Output<framework::Tensor>(framework::GradVarName("X"));
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auto factor = static_cast<T>(context.Attr<AttrType>("factor"));
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dX->mutable_data<T>(context.GetPlace());
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auto dy = framework::EigenVector<T>::Flatten(*dY);
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auto x = framework::EigenVector<T>::Flatten(*X);
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auto dx = framework::EigenVector<T>::Flatten(*dX);
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auto place = context.GetEigenDevice<Place>();
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dx.device(place) = dy * factor * x.pow(factor - static_cast<T>(1));
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}
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};
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template <typename Place, typename T, typename AttrType = T>
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class STanhKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& context) const override {
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auto* X = context.Input<framework::Tensor>("X");
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auto* Y = context.Output<framework::Tensor>("Y");
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auto scale_a = static_cast<T>(context.Attr<AttrType>("scale_a"));
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auto scale_b = static_cast<T>(context.Attr<AttrType>("scale_b"));
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Y->mutable_data<T>(context.GetPlace());
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auto x = framework::EigenVector<T>::Flatten(*X);
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auto y = framework::EigenVector<T>::Flatten(*Y);
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auto place = context.GetEigenDevice<Place>();
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y.device(place) = scale_b * (scale_a * x).tanh();
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}
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};
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template <typename Place, typename T, typename AttrType = T>
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class STanhGradKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& context) const override {
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auto* X = context.Input<framework::Tensor>("X");
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auto* dY = context.Input<framework::Tensor>(framework::GradVarName("Y"));
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auto* dX = context.Output<framework::Tensor>(framework::GradVarName("X"));
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auto scale_a = static_cast<T>(context.Attr<AttrType>("scale_a"));
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auto scale_b = static_cast<T>(context.Attr<AttrType>("scale_b"));
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dX->mutable_data<T>(context.GetPlace());
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auto dy = framework::EigenVector<T>::Flatten(*dY);
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auto x = framework::EigenVector<T>::Flatten(*X);
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auto dx = framework::EigenVector<T>::Flatten(*dX);
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auto place = context.GetEigenDevice<Place>();
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auto temp = (scale_a * x).tanh() * (scale_a * x).tanh();
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dx.device(place) = dy * scale_a * scale_b * (static_cast<T>(1) - temp);
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}
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};
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} // namespace operators
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} // namespace paddle
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