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104 lines
3.2 KiB
104 lines
3.2 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 "AverageLayer.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(average, AverageLayer);
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bool AverageLayer::init(const LayerMap& layerMap,
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const ParameterMap& parameterMap) {
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SequencePoolLayer::init(layerMap, parameterMap);
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dataMtx_ = Matrix::create(nullptr, 1, 1, false, useGpu_);
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outMtx_ = Matrix::create(nullptr, 1, getSize(), false, useGpu_);
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// average strategy
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if (config_.average_strategy() == "average") {
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mode_ = kAverage;
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} else if (config_.average_strategy() == "sum") {
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mode_ = kSum;
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} else if (config_.average_strategy() == "squarerootn") {
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mode_ = kAverageSquareRootN;
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} else {
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LOG(FATAL) << "Unknown average strategy: " << config_.average_strategy();
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}
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return true;
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}
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void AverageLayer::forward(PassType passType) {
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SequencePoolLayer::forward(passType);
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MatrixPtr inputValue = getInputValue(0);
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getOutputValue()->sequenceAvgForward(
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*inputValue, *startPositions_->getVector(useGpu_), mode_);
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/* add the bias-vector AFTER average operation */
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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 */ { forwardActivation(); }
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}
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void AverageLayer::backward(const UpdateCallback& callback) {
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SequencePoolLayer::backward(callback);
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const int* starts = startPositions_->getData(false);
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MatrixPtr grad = getInputGrad(0);
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if (grad) {
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size_t dim = getSize();
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real* gradientData = getInputGrad(0)->getData();
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real* gradient = getOutputGrad()->getData();
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size_t numSequences = startPositions_->getSize() - 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 (0 == sequenceLength) {
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// empty sequence
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continue;
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}
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dataMtx_->setData(gradientData + starts[sequenceId] * dim, sequenceLength,
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dim);
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outMtx_->setData(gradient + sequenceId * dim);
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switch (mode_) {
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case kAverage: {
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// plain average
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dataMtx_->addBias(*outMtx_, 1.0f / sequenceLength);
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break;
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}
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case kSum: {
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// sum instead of average
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dataMtx_->addBias(*outMtx_, 1.0f);
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break;
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}
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case kAverageSquareRootN: {
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// divide by square root of sequenceLength
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dataMtx_->addBias(*outMtx_, 1.0f / sqrt(sequenceLength));
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break;
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}
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default: { LOG(FATAL) << "should not reach here"; }
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}
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}
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}
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}
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} // namespace paddle
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