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122 lines
3.9 KiB
122 lines
3.9 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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#include "paddle/platform/hostdevice.h"
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namespace paddle {
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namespace operators {
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using Tensor = framework::Tensor;
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template <typename T, int MajorType = Eigen::RowMajor,
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typename IndexType = Eigen::DenseIndex>
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using EigenVector = framework::EigenVector<T, MajorType, IndexType>;
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template <typename T>
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struct HuberLossForward {
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HOSTDEVICE HuberLossForward(const T& delta) : delta(delta) {}
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HOSTDEVICE T operator()(const T& val) const {
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T abs_val = std::abs(val);
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if (abs_val <= delta) {
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return static_cast<T>(0.5) * val * val;
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} else {
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return delta * (abs_val - static_cast<T>(0.5) * delta);
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}
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}
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T delta;
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};
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template <typename DeviceContext, typename T, typename AttrType = T>
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class HuberLossKernel : 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* in0 = context.Input<Tensor>("X");
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auto* in1 = context.Input<Tensor>("Y");
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auto* out0 = context.Output<Tensor>("Residual");
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auto* out1 = context.Output<Tensor>("Out");
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auto delta = static_cast<T>(context.Attr<AttrType>("delta"));
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auto& place =
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*context.template device_context<DeviceContext>().eigen_device();
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auto x = EigenVector<T>::Flatten(*in0);
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auto y = EigenVector<T>::Flatten(*in1);
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out0->mutable_data<T>(context.GetPlace());
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auto residual = EigenVector<T>::Flatten(*out0);
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residual.device(place) = y - x;
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out1->mutable_data<T>(context.GetPlace());
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auto loss = EigenVector<T>::Flatten(*out1);
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loss.device(place) = residual.unaryExpr(HuberLossForward<T>(delta));
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}
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};
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template <typename T>
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struct HuberLossBackward {
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HOSTDEVICE HuberLossBackward(const T& delta, T sign)
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: sign(sign), delta(delta) {}
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HOSTDEVICE T operator()(const T& val) const {
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T abs_val = std::abs(val);
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if (abs_val <= delta) {
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return sign * val;
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} else {
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if (val > 0) {
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return sign * delta;
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} else {
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return -1 * sign * delta;
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}
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}
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}
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T sign;
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T delta;
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};
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template <typename DeviceContext, typename T, typename AttrType = T>
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class HuberLossGradKernel : 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* in0 = context.Input<Tensor>("Residual");
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auto* in1 = context.Input<Tensor>(framework::GradVarName("Out"));
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auto* out0 = context.Output<Tensor>(framework::GradVarName("X"));
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auto* out1 = context.Output<Tensor>(framework::GradVarName("Y"));
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auto delta = static_cast<T>(context.op().Attr<AttrType>("delta"));
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auto& place =
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*context.template device_context<DeviceContext>().eigen_device();
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auto residual = EigenVector<T>::Flatten(*in0);
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auto out_grad = EigenVector<T>::Flatten(*in1);
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if (out0) {
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out0->mutable_data<T>(context.GetPlace());
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auto x_grad = EigenVector<T>::Flatten(*out0);
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x_grad.device(place) =
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out_grad * residual.unaryExpr(HuberLossBackward<T>(delta, -1.0));
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}
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if (out1) {
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out1->mutable_data<T>(context.GetPlace());
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auto y_grad = EigenVector<T>::Flatten(*out1);
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y_grad.device(place) =
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out_grad * residual.unaryExpr(HuberLossBackward<T>(delta, 1.0));
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}
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}
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};
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} // namespace operators
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} // namespace paddle
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