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85 lines
3.2 KiB
85 lines
3.2 KiB
8 years ago
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/* 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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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 EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
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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 Place, typename T>
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class SquaredL2DistanceKernel : public framework::OpKernel {
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public:
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void Compute(const framework::ExecutionContext& context) const override {
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auto* input0 = context.Input<Tensor>("X");
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auto* input1 = context.Input<Tensor>("Y");
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auto* output0 = context.Output<Tensor>("sub_result");
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auto* output1 = context.Output<Tensor>("Out");
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output0->mutable_data<T>(context.GetPlace());
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output1->mutable_data<T>(context.GetPlace());
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auto X = EigenMatrix<T>::From(*input0);
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auto Y = EigenMatrix<T>::From(*input1);
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auto subResult = EigenMatrix<T>::From(*output0);
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auto Z = EigenMatrix<T>::From(*output1);
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auto place = context.GetEigenDevice<Place>();
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// buffer the substraction result
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subResult.device(place) = X - Y;
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const auto& inDims = X.dimensions();
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const auto& subResMat = subResult.reshape(Eigen::array<int, 2>(
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{static_cast<int>(inDims[0]), static_cast<int>(X.size() / inDims[0])}));
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Z.device(place) = subResMat.pow(2).sum(Eigen::array<int, 1>({1}));
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}
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};
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template <typename Place, typename T>
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class SquaredL2DistanceGradKernel : public framework::OpKernel {
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public:
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void Compute(const framework::ExecutionContext& context) const override {
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auto* input0 = context.Input<Tensor>("sub_result");
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auto* OG = context.Input<Tensor>(framework::GradVarName("Out"));
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auto* IG = context.Output<Tensor>(framework::GradVarName("X"));
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IG->mutable_data<T>(context.GetPlace());
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auto subResult = EigenMatrix<T>::From(*input0);
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auto outGrad = EigenMatrix<T>::From(*OG);
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auto inGrad = EigenMatrix<T>::From(*IG);
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const auto& subResDims = subResult.dimensions();
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int firstDim = static_cast<int>(subResDims[0]);
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int cols = subResult.size() / firstDim;
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const auto subResMat =
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subResult.reshape(Eigen::array<int, 2>({firstDim, cols}));
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// create a matrix view for input gradient tensor
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auto inGradMat = inGrad.reshape(Eigen::array<int, 2>({firstDim, cols}));
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inGradMat.device(context.GetEigenDevice<Place>()) =
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2 * (outGrad.broadcast(Eigen::array<int, 2>({1, cols}))) * subResMat;
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}
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};
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} // namespace operators
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} // namespace paddle
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