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107 lines
2.8 KiB
107 lines
2.8 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 each row of a matrix, multiplying with a element of a vector,
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* which is used in NEURAL TURING MACHINE.
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* \f[
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* y.row[i] = w[i] * x.row[i]
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* \f]
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* where \f$x\f$ is (batchSize x dataDim) input, \f$w\f$ is
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* (batchSize x 1) weight vector, and \f$y\f$ is (batchSize x dataDim) output.
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*
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* The config file api is scaling_layer.
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*/
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class ScalingLayer : public Layer {
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public:
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explicit ScalingLayer(const LayerConfig& config) : Layer(config) {}
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~ScalingLayer() {}
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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(scaling, ScalingLayer);
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bool ScalingLayer::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 ScalingLayer::forward(PassType passType) {
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Layer::forward(passType);
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MatrixPtr weightV = 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(dataDim, getSize());
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CHECK_EQ(weightV->getWidth(), 1U);
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CHECK_EQ(weightV->getHeight(), batchSize);
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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("FwScalingTimer", getName().c_str());
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// outV += inV1 * weight
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outV->addRowScale(0, *inV1, *weightV);
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}
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}
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void ScalingLayer::backward(const UpdateCallback& callback) {
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MatrixPtr weightV = 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 outG = getOutputGrad();
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{
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REGISTER_TIMER_INFO("BwScalingTimer", getName().c_str());
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if (inG0) {
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// inG0 += outG .* inV1
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inG0->rowDotMul(0, *outG, *inV1);
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}
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if (inG1) {
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// inG1 += outG * weight;
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inG1->addRowScale(0, *outG, *weightV);
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}
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}
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}
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} // namespace paddle
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