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66 lines
2.4 KiB
66 lines
2.4 KiB
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
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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/fluid/framework/eigen.h"
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#include "paddle/fluid/framework/op_registry.h"
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namespace paddle {
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namespace operators {
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// Out = sum(abs(X))
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template <typename DeviceContext, typename T>
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class L1NormKernel : 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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const framework::Tensor *X = context.Input<framework::Tensor>("X");
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framework::Tensor *Out = context.Output<framework::Tensor>("Out");
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Out->mutable_data<T>(context.GetPlace());
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auto x = framework::EigenVector<T>::Flatten(*X);
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auto out = framework::EigenScalar<T>::From(*Out);
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auto &place =
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*context.template device_context<DeviceContext>().eigen_device();
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out.device(place) = x.abs().sum();
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}
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};
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// dX = dout * sign(X)
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template <typename DeviceContext, typename T>
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class L1NormGradKernel : 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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const framework::Tensor *x = context.Input<framework::Tensor>("X");
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const framework::Tensor *d_out =
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context.Input<framework::Tensor>(framework::GradVarName("Out"));
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PADDLE_ENFORCE(d_out->numel() == 1, "L1 Norm Gradient should be scalar");
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framework::Tensor *dx =
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context.Output<framework::Tensor>(framework::GradVarName("X"));
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dx->mutable_data<T>(context.GetPlace());
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auto x_eigen = framework::EigenVector<T>::Flatten(*x);
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auto d_out_eigen = framework::EigenVector<T>::Flatten(*d_out);
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auto dx_eigen = framework::EigenVector<T>::Flatten(*dx);
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auto &place =
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*context.template device_context<DeviceContext>().eigen_device();
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Eigen::DSizes<int, 1> x_dsize(x->numel());
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dx_eigen.device(place) = d_out_eigen.broadcast(x_dsize) * x_eigen.sign();
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}
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};
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} // namespace operators
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} // namespace paddle
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