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99 lines
3.3 KiB
99 lines
3.3 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 T>
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struct ReLU {
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HOSTDEVICE T operator()(const T& val) const {
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return val > 0 ? val : static_cast<T>(0);
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}
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};
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template <typename T>
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struct Heaviside {
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HOSTDEVICE T operator()(const T& val) const {
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return static_cast<T>(val > 0 ? 1 : 0);
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}
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};
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template <typename DeviceContext, typename T>
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class MarginRankLossKernel : 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* act_t = ctx.Output<framework::Tensor>("Activated");
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auto* label_t = ctx.Input<framework::Tensor>("Label");
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auto* x1_t = ctx.Input<framework::Tensor>("X1");
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auto* x2_t = ctx.Input<framework::Tensor>("X2");
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out_t->mutable_data<T>(ctx.GetPlace());
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act_t->mutable_data<T>(ctx.GetPlace());
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auto margin = static_cast<T>(ctx.Attr<T>("margin"));
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auto out = framework::EigenVector<T>::Flatten(*out_t);
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auto act = framework::EigenVector<T>::Flatten(*act_t);
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auto label = framework::EigenVector<T>::Flatten(*label_t);
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auto x1 = framework::EigenVector<T>::Flatten(*x1_t);
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auto x2 = framework::EigenVector<T>::Flatten(*x2_t);
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auto& dev = *ctx.template device_context<DeviceContext>().eigen_device();
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out.device(dev) = (-label * (x1 - x2) + margin).unaryExpr(ReLU<T>());
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act.device(dev) = out.unaryExpr(Heaviside<T>());
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}
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};
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template <typename DeviceContext, typename T>
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class MarginRankLossGradKernel : 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_x1_t =
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ctx.Output<framework::LoDTensor>(framework::GradVarName("X1"));
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auto* d_x2_t =
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ctx.Output<framework::LoDTensor>(framework::GradVarName("X2"));
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auto* act_t = ctx.Input<framework::Tensor>("Activated");
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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 d_out = framework::EigenVector<T>::Flatten(*d_out_t);
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auto act = framework::EigenVector<T>::Flatten(*act_t);
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auto label = framework::EigenVector<T>::Flatten(*label_t);
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auto& dev = *ctx.template device_context<DeviceContext>().eigen_device();
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// compute d_x1
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if (d_x1_t) {
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d_x1_t->mutable_data<T>(ctx.GetPlace());
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auto d_x1 = framework::EigenVector<T>::Flatten(*d_x1_t);
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d_x1.device(dev) = -d_out * act * label;
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}
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// compute d_x2
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if (d_x2_t) {
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d_x2_t->mutable_data<T>(ctx.GetPlace());
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auto d_x2 = framework::EigenVector<T>::Flatten(*d_x2_t);
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d_x2.device(dev) = d_out * act * 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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