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123 lines
3.3 KiB
123 lines
3.3 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 "paddle/utils/Logging.h"
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#include "Layer.h"
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#include "paddle/math/Matrix.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 sum-to-one normalization,
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* which is used in NEURAL TURING MACHINE.
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* \f[
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* out[i] = \frac {in[i]} {\sum_{k=1}^N in[k]}
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* \f]
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* where \f$in\f$ is a (batchSize x dataDim) input vector,
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* and \f$out\f$ is a (batchSize x dataDim) output vector.
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*
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* The config file api is sum_to_one_norm_layer.
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*/
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class SumToOneNormLayer : public Layer {
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protected:
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/// reciprocalRowSum_ = \f$1 / \sum_{k=1}^N in[k]\f$
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MatrixPtr reciprocalRowSum_;
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/// dotSum = output_.grad \f$.*\f$ output_.value
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MatrixPtr dotSum_;
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public:
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explicit SumToOneNormLayer(const LayerConfig& config) : Layer(config) {}
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~SumToOneNormLayer() {}
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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(sum_to_one_norm, SumToOneNormLayer);
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bool SumToOneNormLayer::init(const LayerMap& layerMap,
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const ParameterMap& parameterMap) {
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Layer::init(layerMap, parameterMap);
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CHECK_EQ(inputLayers_.size(), 1U);
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return true;
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}
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void SumToOneNormLayer::forward(PassType passType) {
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Layer::forward(passType);
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MatrixPtr inV = getInputValue(0);
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/* malloc memory for the output_ if necessary */
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size_t batchSize = inV->getHeight();
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size_t dataDim = getSize();
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CHECK_EQ(dataDim, inV->getWidth());
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{
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REGISTER_TIMER_INFO("FwResetTimer", getName().c_str());
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resetOutput(batchSize, dataDim);
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}
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MatrixPtr outV = getOutputValue();
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{
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REGISTER_TIMER_INFO("FwSumToOneNormTimer", getName().c_str());
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Matrix::resizeOrCreate(reciprocalRowSum_, batchSize, 1, false, useGpu_);
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inV->rowSum(*reciprocalRowSum_);
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// todo: matrix checks
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CHECK_GT(reciprocalRowSum_->getMin(), 0.0);
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reciprocalRowSum_->scalarDiv(*reciprocalRowSum_, 1.0);
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// outV = inV * reciprocalRowSum
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outV->rowScale(0, *inV, *reciprocalRowSum_);
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}
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}
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void SumToOneNormLayer::backward(const UpdateCallback& callback) {
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MatrixPtr inV = getInputValue(0);
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MatrixPtr inG = getInputGrad(0);
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MatrixPtr outV = getOutputValue();
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MatrixPtr outG = getOutputGrad();
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size_t batchSize = inV->getHeight();
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if (inG) {
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REGISTER_TIMER_INFO("BwSumToOneTimer", getName().c_str());
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Matrix::resizeOrCreate(dotSum_, batchSize, 1, false, useGpu_);
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// dotSum = outG .* outV
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dotSum_->zeroMem();
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dotSum_->rowDotMul(0, *outG, *outV);
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// inG += -1 * (dotSum / rowSum)
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dotSum_->dotMul(*dotSum_, *reciprocalRowSum_);
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inG->rowAdd(0, *inG, *dotSum_, -1.0);
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// inG += outG * (1/rowSum)
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inG->addRowScale(0, *outG, *reciprocalRowSum_);
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}
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}
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} // namespace paddle
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