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81 lines
3.0 KiB
81 lines
3.0 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 DeviceContext, typename T>
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class RankLossKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& ctx) const {
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auto* out_t = ctx.Output<framework::Tensor>("Out");
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auto* label_t = ctx.Input<framework::Tensor>("Label");
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auto* left_t = ctx.Input<framework::Tensor>("Left");
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auto* right_t = ctx.Input<framework::Tensor>("Right");
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out_t->mutable_data<T>(ctx.GetPlace());
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auto out = framework::EigenVector<T>::Flatten(*out_t);
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auto label = framework::EigenVector<T>::Flatten(*label_t);
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auto left = framework::EigenVector<T>::Flatten(*left_t);
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auto right = framework::EigenVector<T>::Flatten(*right_t);
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auto& dev = *ctx.template device_context<DeviceContext>().eigen_device();
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out.device(dev) =
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(1. + (left - right).exp()).log() - label * (left - right);
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}
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};
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template <typename DeviceContext, typename T>
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class RankLossGradKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& ctx) const {
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auto* d_left_t =
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ctx.Output<framework::Tensor>(framework::GradVarName("Left"));
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auto* d_right_t =
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ctx.Output<framework::Tensor>(framework::GradVarName("Right"));
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auto* d_out_t = ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
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auto* label_t = ctx.Input<framework::Tensor>("Label");
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auto* left_t = ctx.Input<framework::Tensor>("Left");
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auto* right_t = ctx.Input<framework::Tensor>("Right");
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auto& dev = *ctx.template device_context<DeviceContext>().eigen_device();
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auto d_out = framework::EigenVector<T>::Flatten(*d_out_t);
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auto label = framework::EigenVector<T>::Flatten(*label_t);
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auto left = framework::EigenVector<T>::Flatten(*left_t);
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auto right = framework::EigenVector<T>::Flatten(*right_t);
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// compute d_left
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if (d_left_t) {
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d_left_t->mutable_data<T>(ctx.GetPlace());
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auto d_left = framework::EigenVector<T>::Flatten(*d_left_t);
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d_left.device(dev) = d_out * (1. / (1. + (right - left).exp()) - label);
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}
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// compute d_right
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if (d_right_t) {
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d_right_t->mutable_data<T>(ctx.GetPlace());
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auto d_right = framework::EigenVector<T>::Flatten(*d_right_t);
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d_right.device(dev) =
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-d_out * (1.0 / (1. + (right - left).exp()) - label);
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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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