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107 lines
3.1 KiB
107 lines
3.1 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 "RowConvLayer.h"
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#include "paddle/utils/Stat.h"
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namespace paddle {
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REGISTER_LAYER(row_conv, RowConvLayer);
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bool RowConvLayer::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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contexLength_ = config_.inputs(0).row_conv_conf().context_length();
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CHECK_EQ(inputLayers_.size(), 1UL);
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weight_.reset(new Weight(contexLength_, getSize(), parameters_[0]));
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createFunction(forward_, "RowConv", FuncConfig());
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createFunction(backward_, "RowConvGrad", FuncConfig());
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return true;
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}
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void RowConvLayer::forward(PassType passType) {
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Layer::forward(passType);
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MatrixPtr input = getInputValue(0);
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size_t height = input->getHeight();
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size_t width = input->getWidth();
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CHECK_EQ(width, getSize());
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resetOutput(height, width);
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const auto startPos = getInput(0).sequenceStartPositions->getVector(useGpu_);
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MatrixPtr w = weight_->getW();
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wDims_ = TensorShape({w->getHeight(), w->getWidth()});
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MatrixPtr outV = getOutputValue();
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BufferArgs inputs;
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BufferArgs outputs;
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inputs.addArg(*getInputValue(0), *startPos);
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inputs.addArg(*w, wDims_);
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outputs.addArg(*getOutputValue(), *startPos, ADD_TO);
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{
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REGISTER_TIMER_INFO("RowConvForward", getName().c_str());
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forward_[0]->calc(inputs, outputs);
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}
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/* activation */ {
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REGISTER_TIMER_INFO("FwAtvTimer", getName().c_str());
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forwardActivation();
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}
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}
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void RowConvLayer::backward(const UpdateCallback& callback) {
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/* Do derivation */ {
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REGISTER_TIMER_INFO("BpAvtTimer", getName().c_str());
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backwardActivation();
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}
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const auto startPos = getInput(0).sequenceStartPositions->getVector(useGpu_);
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BufferArgs inputs;
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BufferArgs outputs;
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inputs.addArg(*getOutputGrad(), *startPos);
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inputs.addArg(*getInputValue(0), *startPos);
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inputs.addArg(*weight_->getW(), wDims_);
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MatrixPtr inGrad = getInputGrad(0);
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MatrixPtr wGrad = weight_->getWGrad();
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size_t h = getInputValue(0)->getHeight();
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size_t w = getInputValue(0)->getWidth();
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outputs.addArg(
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inGrad ? (*inGrad) : *(Matrix::create(nullptr, h, w, false, useGpu_)),
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*startPos,
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ADD_TO);
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outputs.addArg(
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wGrad ? (*wGrad)
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: *(Matrix::create(nullptr, contexLength_, w, false, useGpu_)),
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wDims_,
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ADD_TO);
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{
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REGISTER_TIMER_INFO("RowConvBackward", getName().c_str());
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backward_[0]->calc(inputs, outputs);
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}
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{
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REGISTER_TIMER_INFO("WeightUpdate", getName().c_str());
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weight_->getParameterPtr()->incUpdate(callback);
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}
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}
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} // namespace paddle
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