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119 lines
4.2 KiB
119 lines
4.2 KiB
/* Copyright (c) 2018 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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using Tensor = framework::Tensor;
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template <typename T>
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class TeacherStudentSigmoidLossOpKernel : 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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Tensor* y = context.Output<Tensor>("Y");
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const Tensor* x = context.Input<Tensor>("X");
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const Tensor* labels = context.Input<Tensor>("Label");
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T* y_data = y->mutable_data<T>(context.GetPlace());
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const T* x_data = x->data<T>();
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const T* label_data = labels->data<T>();
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int64_t batch_size = x->dims()[0];
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// loss = max(x, 0) - x * z + log(1 + exp(-abs(x))) + max(x, 0) - x * z' +
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// log(1 + exp(-abs(x)))
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// z is click or not
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// z' is value q of feed_fine
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// label = {-2, -1, [0, 2]}
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// when z' is not exist, clk = 0 : label = -2;
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// when z' is not exist, clk = 1 : label = -1;
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// when z' is exist , clk = 0 : label = 0 + z';
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// when z' is exist , clk = 1 : label = 1 + z';
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for (int i = 0; i < batch_size; ++i) {
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if (label_data[i] < -1.0) {
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y_data[i] = (x_data[i] > 0 ? x_data[i] : 0.0) +
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log(1.0 + exp(-fabs(x_data[i])));
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} else if (label_data[i] < 0.0) {
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y_data[i] = (x_data[i] > 0 ? x_data[i] : 0.0) - x_data[i] +
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log(1.0 + exp(-fabs(x_data[i])));
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} else if (label_data[i] < 1.0) {
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y_data[i] = (x_data[i] > 0 ? x_data[i] : 0.0) +
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log(1.0 + exp(-fabs(x_data[i]))) +
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(x_data[i] > 0 ? x_data[i] : 0.0) -
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x_data[i] * label_data[i] +
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log(1.0 + exp(-fabs(x_data[i])));
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} else {
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y_data[i] = (x_data[i] > 0 ? x_data[i] : 0.0) - x_data[i] +
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log(1.0 + exp(-fabs(x_data[i]))) +
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(x_data[i] > 0 ? x_data[i] : 0.0) -
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x_data[i] * (label_data[i] - 1.0) +
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log(1.0 + exp(-fabs(x_data[i])));
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}
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}
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}
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};
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template <typename T>
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class TeacherStudentSigmoidLossGradOpKernel : 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 Tensor* x = context.Input<Tensor>("X");
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const T* x_data = x->data<T>();
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Tensor* dx = context.Output<Tensor>(framework::GradVarName("X"));
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T* dx_data = dx->mutable_data<T>(context.GetPlace());
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const Tensor* labels = context.Input<Tensor>("Label");
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const T* label_data = labels->data<T>();
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T soft_max_up_bound =
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static_cast<T>(context.Attr<float>("soft_max_up_bound"));
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T soft_max_lower_bound =
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static_cast<T>(context.Attr<float>("soft_max_lower_bound"));
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int64_t batch_size = x->dims()[0];
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const framework::Tensor* dOut =
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context.Input<framework::Tensor>(framework::GradVarName("Y"));
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const T* dout_data = dOut->data<T>();
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for (int i = 0; i < batch_size; ++i) {
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T sum_val = x_data[i];
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if (sum_val > soft_max_up_bound) {
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sum_val = soft_max_up_bound;
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} else {
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if (sum_val < soft_max_lower_bound) {
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sum_val = soft_max_lower_bound;
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}
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}
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T pred = 1.0 / (1.0 + exp(-sum_val));
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if (label_data[i] < -1.0) {
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dx_data[i] = 0.0 - pred;
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} else if (label_data[i] < 0.0) {
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dx_data[i] = 1.0 - pred;
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} else {
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dx_data[i] = label_data[i] - 2.0 * pred;
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}
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if (sum_val >= soft_max_up_bound || sum_val <= soft_max_lower_bound) {
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dx_data[i] = 0;
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}
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dx_data[i] *= dout_data[i] * -1;
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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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