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146 lines
4.5 KiB
146 lines
4.5 KiB
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
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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 "TensorLayer.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(tensor, TensorLayer);
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bool TensorLayer::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 the weightList */
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CHECK_EQ(inputLayers_.size(), 2LU);
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CHECK(parameters_[0]);
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CHECK(!parameters_[1]);
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// Option the parameters
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size_t height = inputLayers_[0]->getSize();
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size_t width = inputLayers_[1]->getSize();
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CHECK_EQ(width * height * getSize(), parameters_[0]->getSize());
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for (size_t i = 0; i < getSize(); ++i) {
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// create a new weight
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Weight* w = new Weight(height, width, parameters_[0], i * width * height);
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// append the new weight to the list
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weights_.emplace_back(w);
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}
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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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return true;
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}
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void TensorLayer::forward(PassType passType) {
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Layer::forward(passType);
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/* malloc memory for the output_ if necessary */
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int batchSize = getInputValue(0)->getHeight();
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int size = getSize();
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{ resetOutput(batchSize, size); }
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MatrixPtr outV = getOutputValue();
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/* add the bias-vector */
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if (biases_.get() != NULL) {
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outV->addBias(*(biases_->getW()), 1);
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}
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/* e1 * W * trans(e2) */ {
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MatrixPtr input1 = getInputValue(0);
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MatrixPtr input2 = getInputValue(1);
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MatrixPtr tmpMat = Matrix::create(input2->getHeight(),
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input2->getWidth(),
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/* trans= */ false,
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input2->useGpu());
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REGISTER_TIMER_INFO("TensorFwMulTimer", getName().c_str());
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for (size_t i = 0; i < getSize(); ++i) {
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MatrixPtr weights = weights_[i]->getW();
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tmpMat->mul(*input1, *weights, 1, 0);
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outV->rowDotMul(i, *tmpMat, *input2);
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}
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}
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/* activation */ { forwardActivation(); }
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}
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void TensorLayer::backward(const UpdateCallback& callback) {
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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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bool syncFlag = hl_get_sync_flag();
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/* Calculate the W-gradient for the current layer */
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MatrixPtr input1 = getInputValue(0);
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MatrixPtr input2 = getInputValue(1);
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MatrixPtr oGrad = getOutputGrad();
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MatrixPtr tmpMat = Matrix::create(input1->getHeight(),
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input1->getWidth(),
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/* trans= */ false,
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input1->useGpu());
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/* trans(grad * e1) * e2 */ {
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REGISTER_TIMER_INFO("TensorGradMulTimer", getName().c_str());
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for (size_t i = 0; i < getSize(); ++i) {
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if (weights_[i]->getWGrad()) {
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tmpMat->rowScale(i, *input1, *oGrad);
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MatrixPtr input1_T = tmpMat->getTranspose();
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weights_[i]->getWGrad()->mul(*input1_T, *input2, 1, 1);
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}
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}
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}
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hl_set_sync_flag(false);
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/* Calculate the input layers error */ {
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MatrixPtr preGrad1 = getInputGrad(0);
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MatrixPtr preGrad2 = getInputGrad(1);
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REGISTER_TIMER_INFO("TensorBpMulTimer", getName().c_str());
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for (size_t i = 0; i < getSize(); ++i) {
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MatrixPtr weights = weights_[i]->getW();
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if (NULL != preGrad1) { /* (grad * e2) * trans(W) */
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tmpMat->rowScale(i, *input2, *oGrad);
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MatrixPtr weights_T = weights->getTranspose();
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preGrad1->mul(*tmpMat, *weights_T, 1, 1);
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}
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if (NULL != preGrad2) { /* (grad * e1) * W */
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tmpMat->rowScale(i, *input1, *oGrad);
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preGrad2->mul(*tmpMat, *weights, 1, 1);
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}
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}
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}
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hl_set_sync_flag(syncFlag);
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parameters_[0]->incUpdate(callback);
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}
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} // namespace paddle
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