caoying03
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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#include "Layer.h"
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#include "paddle/math/Matrix.h"
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#include "paddle/math/Vector.h"
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#include "paddle/utils/Logging.h"
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#include "paddle/utils/Stat.h"
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namespace paddle {
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class SubNestedSequenceLayer : public Layer {
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public:
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explicit SubNestedSequenceLayer(const LayerConfig& config) : Layer(config) {}
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bool init(const LayerMap& layerMap,
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const ParameterMap& parameterMap) override;
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void forward(PassType passType) override;
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void backward(const UpdateCallback& callback = nullptr) override;
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private:
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void checkInputs(const Argument& inputSeq, const Argument& seqScores);
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void calSelectedCols(const Argument& scores,
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const int* subSeqStartPos,
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size_t topK);
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void partialSortIndex(const std::vector<real>& values,
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int k,
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std::vector<size_t>& indices);
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void buildOutputSeqInfo();
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std::vector<int> outSeqStartInfo_;
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std::vector<int> outSubSeqStartInfo_;
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MatrixPtr scoreOverInputSeq_;
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// rowIdx_ and selectedRows_ actually share a same memory.
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IVectorPtr rowIndice_;
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std::vector<int> selectedRows_;
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};
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REGISTER_LAYER(sub_nested_seq, SubNestedSequenceLayer);
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bool SubNestedSequenceLayer::init(const LayerMap& layerMap,
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const ParameterMap& parameterMap) {
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/* Initialize the basic parent class */
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Layer::init(layerMap, parameterMap);
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CHECK_EQ(2U, inputLayers_.size());
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setNeedSequenceInfo(false);
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return true;
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}
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void SubNestedSequenceLayer::checkInputs(const Argument& inputSeq,
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const Argument& seqScores) {
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CHECK(inputSeq.hasSubseq()) << "The first input of SubNestSequence layer "
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<< "must be a nested sequence.";
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CHECK(seqScores.hasSeq())
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<< "The second input of SubNestSequence layer must be a sequence.";
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CHECK_EQ(seqScores.value->getWidth(), 1U)
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<< "The second input of SubNestedSequenceLayer is scores "
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<< "over each sequence in a nested sequence, "
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<< "so its size should be 1.";
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CHECK_EQ(inputSeq.getNumSubSequences(), seqScores.value->getHeight())
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<< "The second input of SubNestedSequenceLayer is scores "
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<< "over each sequence in a nested sequence, so its height should be "
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<< "equal to number of sequence in the first input.";
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}
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void SubNestedSequenceLayer::partialSortIndex(const std::vector<real>& values,
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int k,
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std::vector<size_t>& indices) {
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CHECK_GE(values.size(), k);
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indices.resize(values.size(), 0);
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std::iota(begin(indices), end(indices), 0U);
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std::partial_sort(begin(indices),
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begin(indices) + k,
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end(indices),
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[&](size_t a, size_t b) { return values[a] > values[b]; });
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}
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void SubNestedSequenceLayer::calSelectedCols(const Argument& scores,
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const int* subSeqStartPos,
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size_t topK) {
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selectedRows_.clear();
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outSubSeqStartInfo_.resize(1, 0);
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outSeqStartInfo_.resize(1, 0);
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real* seqScores = nullptr;
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if (useGpu_) {
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Matrix::resizeOrCreate(scoreOverInputSeq_,
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scores.value->getHeight(),
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scores.value->getWidth(),
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false /* trans */,
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false /* useGpu */);
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scoreOverInputSeq_->copyFrom(*scores.value);
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seqScores = scoreOverInputSeq_->getData();
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} else {
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seqScores = scores.value->getData();
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}
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int* scoreSeqStartPos = scores.sequenceStartPositions->getMutableData(false);
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for (int i = 0; i < scores.getNumSequences(); ++i) {
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int seqLen = scoreSeqStartPos[i + 1] - scoreSeqStartPos[i];
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int selectedSeqNum = std::min(static_cast<int>(config_.top_k()), seqLen);
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std::vector<size_t> sortedIdx;
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partialSortIndex(std::vector<real>(seqScores + scoreSeqStartPos[i],
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seqScores + scoreSeqStartPos[i + 1]),
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selectedSeqNum,
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sortedIdx);
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for (int j = 0; j < selectedSeqNum; ++j) {
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int begPos = subSeqStartPos[scoreSeqStartPos[i] + sortedIdx[j]];
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int endPos = subSeqStartPos[scoreSeqStartPos[i] + sortedIdx[j] + 1];
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for (int m = begPos; m < endPos; ++m) selectedRows_.push_back(m);
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outSubSeqStartInfo_.push_back(outSubSeqStartInfo_.back() + endPos -
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begPos);
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}
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outSeqStartInfo_.push_back(outSubSeqStartInfo_.back());
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}
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}
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void SubNestedSequenceLayer::buildOutputSeqInfo() {
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Argument& output = getOutput();
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ICpuGpuVector::resizeOrCreate(
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output.sequenceStartPositions, outSeqStartInfo_.size(), false);
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output.sequenceStartPositions->copyFrom(
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outSeqStartInfo_.data(), outSeqStartInfo_.size(), false);
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ICpuGpuVector::resizeOrCreate(
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output.subSequenceStartPositions, outSubSeqStartInfo_.size(), false);
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output.subSequenceStartPositions->copyFrom(
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outSubSeqStartInfo_.data(), outSubSeqStartInfo_.size(), false);
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}
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void SubNestedSequenceLayer::forward(PassType passType) {
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Layer::forward(passType);
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const Argument& inputSeq = getInput(0);
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const Argument& seqScores = getInput(1);
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checkInputs(inputSeq, seqScores);
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calSelectedCols(seqScores,
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inputSeq.subSequenceStartPositions->getMutableData(false),
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config_.top_k());
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resetOutput(selectedRows_.size(), getSize());
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buildOutputSeqInfo();
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if (useGpu_) {
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rowIndice_ = IVector::create(selectedRows_.size(), useGpu_);
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rowIndice_->copyFrom(selectedRows_.data(), selectedRows_.size());
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} else {
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rowIndice_ =
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IVector::create(selectedRows_.data(), selectedRows_.size(), useGpu_);
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}
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getOutputValue()->selectRows(*getInputValue(0), *rowIndice_);
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}
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void SubNestedSequenceLayer::backward(const UpdateCallback& callback) {
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MatrixPtr inputGrad1 = getInputGrad(0);
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MatrixPtr outputGrad = getOutputGrad();
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if (inputGrad1) outputGrad->addToRows(*inputGrad1, *rowIndice_);
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}
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} // namespace paddle
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