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134 lines
4.6 KiB
134 lines
4.6 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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#include "ExpandLayer.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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REGISTER_LAYER(expand, ExpandLayer);
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bool ExpandLayer::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(inputLayers_.size(), 2UL);
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/* initialize biases_ */
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if (biasParameter_.get() != NULL) {
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biases_ = std::unique_ptr<Weight>(new Weight(1, getSize(), biasParameter_));
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}
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// which sequence type of input[0]
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if (config_.trans_type() == "non-seq") {
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type_ = kNonSeq;
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} else if (config_.trans_type() == "seq") {
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type_ = kSeq;
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} else {
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LOG(FATAL) << "Unknown trans_type: " << config_.trans_type();
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}
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setNeedSequenceInfo(false);
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return true;
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}
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void ExpandLayer::forward(PassType passType) {
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Layer::forward(passType);
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// Expand layer should have exactly 2 input, one for data, one for size
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CHECK_EQ(2U, inputLayers_.size());
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// using two input:
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// * first one for data;
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// * second one only for sequence info
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const Argument& shapeInput = getInput(1);
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const Argument& dataInput = getInput(0);
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size_t outputBatchSize = shapeInput.getBatchSize();
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auto startPositions = type_ ? shapeInput.subSequenceStartPositions
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: shapeInput.sequenceStartPositions;
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size_t numSequences = startPositions->getSize() - 1;
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const int* starts = startPositions->getData(false);
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CHECK_EQ(starts[numSequences], shapeInput.getBatchSize());
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if (type_) {
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// when trans_type = seq, input[1] must hasSubseq
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CHECK_EQ(shapeInput.hasSubseq(), 1UL);
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CHECK_EQ(dataInput.getNumSequences(), shapeInput.getNumSequences());
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} else {
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CHECK_EQ(dataInput.getBatchSize(), shapeInput.getNumSequences());
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}
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// set output sequence info as shape sequence
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output_.sequenceStartPositions = shapeInput.sequenceStartPositions;
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if (shapeInput.hasSubseq()) {
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output_.subSequenceStartPositions = shapeInput.subSequenceStartPositions;
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}
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// reserve output: Expand output to batchsize of sequence data.
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reserveOutput(outputBatchSize, dataInput.value->getWidth());
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MatrixPtr inputValue = getInputValue(0);
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MatrixPtr outputValue = getOutputValue();
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ICpuGpuVector::resizeOrCreate(expandStartsPos_, outputBatchSize, false);
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int* expandStarts = expandStartsPos_->getMutableData(false);
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for (size_t sequenceId = 0; sequenceId < numSequences; ++sequenceId) {
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int sequenceLength = starts[sequenceId + 1] - starts[sequenceId];
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for (int j = 0; j < sequenceLength; j++) {
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expandStarts[starts[sequenceId] + j] = sequenceId;
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}
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}
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outputValue->copyByRowIndex(*inputValue,
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*expandStartsPos_->getVector(useGpu_));
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if (biases_.get() != NULL) {
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outputValue->addBias(*(biases_->getW()), 1);
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}
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}
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void ExpandLayer::backward(const UpdateCallback& callback) {
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if (biases_ && biases_->getWGrad()) {
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biases_->getWGrad()->collectBias(*getOutputGrad(), 1);
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/* Increasing the number of gradient */
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biases_->getParameterPtr()->incUpdate(callback);
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}
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if (!getInputGrad(0)) return;
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MatrixPtr inputGrad = getInputGrad(0);
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MatrixPtr outputGrad = getOutputGrad();
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auto cpuSeqStartPos = type_ ? getInput(1).subSequenceStartPositions
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: getInput(1).sequenceStartPositions;
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size_t numSequences = cpuSeqStartPos->getSize() - 1;
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const int* starts = cpuSeqStartPos->getData(false);
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CHECK_EQ(inputGrad->getWidth(), outputGrad->getWidth());
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CHECK_EQ(outputGrad->getHeight(), (size_t)starts[numSequences]);
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AsyncGpuBlock asyncGpuBlock;
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// sum to get the grad
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real scale = 1;
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for (size_t sequenceId = 0; sequenceId < numSequences; sequenceId++) {
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// TODO(Dangqingqing) optimization for GPU
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int sequenceLength = starts[sequenceId + 1] - starts[sequenceId];
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if (sequenceLength == 0) {
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// empty sequence
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continue;
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}
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MatrixPtr copyData = inputGrad->subMatrix(sequenceId, 1);
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copyData->collectBias(
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*outputGrad->subMatrix(starts[sequenceId], sequenceLength), scale);
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}
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}
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} // namespace paddle
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