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121 lines
3.0 KiB
121 lines
3.0 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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* This layer applys a power function to a vector element-wise,
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* which is used in NEURAL TURING MACHINE.
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* \f[
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* y = x^w
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* \f]
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* where \f$x\f$ is a input vector, \f$w\f$ is scalar weight,
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* and output \f$y\f$ is a vector.
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*
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* The config file api is power_layer.
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*/
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class PowerLayer : public Layer {
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protected:
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MatrixPtr tmpMtx;
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public:
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explicit PowerLayer(const LayerConfig& config) : Layer(config) {}
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~PowerLayer() {}
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bool init(const LayerMap& layerMap,
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const ParameterMap& parameterMap) override;
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void forward(PassType passType) override;
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void backward(const UpdateCallback& callback = nullptr) override;
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};
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REGISTER_LAYER(power, PowerLayer);
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bool PowerLayer::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(), 2U);
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return true;
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}
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void PowerLayer::forward(PassType passType) {
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Layer::forward(passType);
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MatrixPtr inV0 = getInputValue(0);
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MatrixPtr inV1 = getInputValue(1);
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size_t batchSize = inV1->getHeight();
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size_t dataDim = inV1->getWidth();
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CHECK_EQ(getSize(), dataDim);
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CHECK_EQ(1U, inV0->getWidth());
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CHECK_EQ(batchSize, inV0->getHeight());
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{
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REGISTER_TIMER_INFO("FwResetTimer", getName().c_str());
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reserveOutput(batchSize, dataDim);
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}
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MatrixPtr outV = getOutputValue();
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{
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REGISTER_TIMER_INFO("FwPowerTimer", getName().c_str());
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outV->rowPow(0, *inV1, *inV0);
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}
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}
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void PowerLayer::backward(const UpdateCallback& callback) {
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MatrixPtr inV0 = getInputValue(0);
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MatrixPtr inV1 = getInputValue(1);
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MatrixPtr inG0 = getInputGrad(0);
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MatrixPtr inG1 = getInputGrad(1);
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MatrixPtr outV = getOutputValue();
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MatrixPtr outG = getOutputGrad();
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size_t batchSize = inV1->getHeight();
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size_t dataDim = inV1->getWidth();
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{
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REGISTER_TIMER_INFO("BwPowerTimer", getName().c_str());
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Matrix::resizeOrCreate(tmpMtx, batchSize, dataDim, false, useGpu_);
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if (inG0) {
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tmpMtx->log2(*inV1);
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tmpMtx->dotMul(*tmpMtx, *outV);
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// inG0 += outG .* (log(inV1) * outV)
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inG0->rowDotMul(0, *outG, *tmpMtx);
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}
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if (inG1) {
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// tmp = (outV / inV1) * inV0
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tmpMtx->dotDiv(*outV, *inV1);
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tmpMtx->rowScale(0, *tmpMtx, *inV0);
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inG1->addDotMul(*outG, *tmpMtx, 1, 1);
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}
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}
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}
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} // namespace paddle
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