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129 lines
4.0 KiB
129 lines
4.0 KiB
/* Copyright (c) 2016 Baidu, Inc. 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 "MaxLayer.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(max, MaxLayer);
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bool MaxLayer::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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/* 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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// transform to which sequence type
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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 MaxLayer::forward(PassType passType) {
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Layer::forward(passType);
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// max layer should have exactly 1 input
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CHECK_EQ(1U, inputLayers_.size());
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size_t dim = getSize();
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const Argument& input = getInput(0);
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int64_t newBatchSize =
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type_ ? input.getNumSubSequences() : input.getNumSequences();
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ICpuGpuVectorPtr startPositions =
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type_ ? input.subSequenceStartPositions
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: input.sequenceStartPositions;
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auto starts = startPositions->getVector(useGpu_);
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size_t numSequences = startPositions->getSize() - 1;
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CHECK_EQ(dim, input.value->getWidth());
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CHECK_EQ(numSequences, (size_t)newBatchSize);
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CHECK_EQ(startPositions->getData(false)[numSequences], input.getBatchSize());
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if (type_) {
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// when trans_type = seq, input must hasSubseq
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CHECK_EQ(input.hasSubseq(), 1UL);
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}
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// reset output: resize to "num of sequences", not "batch size".
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resetOutput(newBatchSize, dim);
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IVector::resizeOrCreate(maxIndex_, newBatchSize * dim, useGpu(deviceId_));
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maxIndex_->zeroMem();
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MatrixPtr inputValue = getInputValue(0);
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MatrixPtr outputValue = getOutputValue();
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{
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REGISTER_TIMER_INFO("MaxLayerForward", getName().c_str());
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outputValue->maxSequenceForward(*inputValue, *starts, *maxIndex_);
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}
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/* If type_ = kNonSeq, both seq has or not has sub-seq degrade to a non-seq,
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* thus, in this case, output_ has no cpuSequenceStartPositions.
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* If type_ = kSeq, seq has sub-seq degrades to a seq, thus, only in this
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* case, we should compute the new cpuSequenceStartPositions.
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*/
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if (type_) {
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output_.degradeSequence(input, useGpu_);
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}
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if (config_.output_max_index()) {
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// copy maxIndex_ to output
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outputValue->copyFrom(*maxIndex_);
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} else {
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/* add the bias-vector AFTER max operation */
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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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/* activation */ { forwardActivation(); }
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}
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}
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void MaxLayer::backward(const UpdateCallback& callback) {
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CHECK(!config_.output_max_index())
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<< "backward is not available when output_max_index is set";
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/* Do derivation */ { 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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if (inputGrad) {
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ICpuGpuVectorPtr starts =
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type_ ? getInput(0).subSequenceStartPositions
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: getInput(0).sequenceStartPositions;
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REGISTER_TIMER_INFO("MaxLayerBackward", getName().c_str());
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inputGrad->maxSequenceBackward(*outputGrad,
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*(starts->getVector(useGpu_)), *maxIndex_);
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}
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}
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} // namespace paddle
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