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144 lines
4.0 KiB
144 lines
4.0 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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#include "Layer.h"
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#include "paddle/math/Matrix.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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/**
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* A layer for reshaping the sequence
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* Input: a sequence
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* Output: a sequence
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*/
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class SequenceReshapeLayer : public Layer {
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protected:
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std::unique_ptr<Weight> biases_;
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MatrixPtr reshapedOutputGrad;
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public:
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explicit SequenceReshapeLayer(const LayerConfig& config) : Layer(config) {}
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~SequenceReshapeLayer() {}
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bool init(const LayerMap& layerMap, const ParameterMap& parameterMap);
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void forward(PassType passType);
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void backward(const UpdateCallback& callback = nullptr);
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};
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REGISTER_LAYER(seqreshape, SequenceReshapeLayer);
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bool SequenceReshapeLayer::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(1U, inputLayers_.size());
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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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setNeedSequenceInfo(false);
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return true;
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}
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void SequenceReshapeLayer::forward(PassType passType) {
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Layer::forward(passType);
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const Argument& input = getInput(0);
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size_t inDim = input.value->getWidth();
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size_t outDim = getSize();
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size_t numSequences = input.getNumSequences();
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auto startPositions = input.sequenceStartPositions->getVector(false);
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const int* starts = startPositions->getData();
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CHECK_EQ(starts[numSequences], input.getBatchSize());
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CHECK_EQ(numSequences, startPositions->getSize() - 1);
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for (size_t seqID = 0; seqID < numSequences; seqID++) {
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size_t inNumIns = starts[seqID + 1] - starts[seqID];
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size_t outNumIns = inNumIns * inDim / outDim;
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CHECK_EQ(outNumIns * outDim, inNumIns * inDim);
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}
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MatrixPtr inputValue = getInputValue(0);
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// reset output
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reserveOutput(inputValue->getHeight() * inDim / outDim, outDim);
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MatrixPtr outputValue = getOutputValue();
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{
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AsyncGpuBlock asyncGpuBlock;
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REGISTER_TIMER_INFO("SequenceReshapeLayerForward", getName().c_str());
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outputValue->copyFrom(*inputValue);
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// modify the sequenceStartPositions
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ICpuGpuVector::resizeOrCreate(
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output_.sequenceStartPositions, numSequences + 1, false);
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int* tgtBuf = output_.sequenceStartPositions->getMutableData(false);
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for (size_t seqId = 0; seqId < numSequences + 1; ++seqId) {
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tgtBuf[seqId] = starts[seqId] * inDim / outDim;
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}
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}
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if (biases_.get() != NULL) {
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MatrixPtr outV = getOutputValue();
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outV->addBias(*(biases_->getW()), 1);
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}
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/* activation */
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forwardActivation();
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}
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void SequenceReshapeLayer::backward(const UpdateCallback& callback) {
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/* activation */
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backwardActivation();
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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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MatrixPtr inputGrad = getInputGrad(0);
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MatrixPtr outputGrad = getOutputGrad();
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AsyncGpuBlock asyncGpuBlock;
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REGISTER_TIMER_INFO("SequenceReshapeLayerBackward", getName().c_str());
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if (inputGrad) {
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Matrix::resizeOrCreate(reshapedOutputGrad,
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inputGrad->getHeight(),
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inputGrad->getWidth(),
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false,
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useGpu_);
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reshapedOutputGrad->copyFrom(*outputGrad);
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inputGrad->add(*reshapedOutputGrad);
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}
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}
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} // namespace paddle
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