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107 lines
3.6 KiB
107 lines
3.6 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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#include "paddle/fluid/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 CheckLabelValue {
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HOSTDEVICE T operator()(const T& val) const {
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PADDLE_ASSERT(val == static_cast<T>(0) || val == static_cast<T>(1));
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}
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};
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template <typename T>
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struct ModifiedHuberLossForward {
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HOSTDEVICE T operator()(const T& val) const {
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if (val < -1) {
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return -4 * val;
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} else if (val < 1) {
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return (1 - val) * (1 - val);
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} else {
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return static_cast<T>(0);
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}
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}
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};
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template <typename DeviceContext, typename T>
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class ModifiedHuberLossKernel : 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<framework::Tensor>("IntermediateVal");
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auto* out1 = context.Output<framework::Tensor>("Out");
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out0->mutable_data<T>(context.GetPlace());
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out1->mutable_data<T>(context.GetPlace());
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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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// make sure value's of Y in {0, 1}
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y.unaryExpr(CheckLabelValue<T>());
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auto inter_val = EigenVector<T>::Flatten(*out0);
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// scale y to {-1, +1} and compute x * y
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inter_val.device(place) = x * (2 * y - static_cast<T>(1));
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auto loss = EigenVector<T>::Flatten(*out1);
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loss.device(place) = inter_val.unaryExpr(ModifiedHuberLossForward<T>());
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}
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};
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// CPU backward kernel
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template <typename T>
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class ModifiedHuberLossGradCPUKernel : 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>("Y");
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auto* in1 = context.Input<framework::Tensor>("IntermediateVal");
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auto* in2 = context.Input<framework::Tensor>(framework::GradVarName("Out"));
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auto* out0 = context.Output<framework::Tensor>(framework::GradVarName("X"));
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if (out0) {
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const T* y_ptr = in0->data<T>();
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const T* inter_val_ptr = in1->data<T>();
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const T* out_grad_ptr = in2->data<T>();
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size_t counts = static_cast<size_t>(framework::product(in1->dims()));
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T* x_grad_ptr = out0->mutable_data<T>(context.GetPlace());
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for (size_t i = 0; i < counts; ++i) {
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if (inter_val_ptr[i] < -1) {
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x_grad_ptr[i] = -4 * (2 * y_ptr[i] - 1) * out_grad_ptr[i];
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} else if (inter_val_ptr[i] < 1) {
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x_grad_ptr[i] = -2 * (1 - inter_val_ptr[i]) * (2 * y_ptr[i] - 1) *
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out_grad_ptr[i];
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} else {
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x_grad_ptr[i] = 0;
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}
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}
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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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