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117 lines
3.9 KiB
117 lines
3.9 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 "CudnnConvLayer.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(cudnn_conv, CudnnConvLayer);
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bool CudnnConvLayer::init(const LayerMap &layerMap,
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const ParameterMap ¶meterMap) {
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if (!ConvBaseLayer::init(layerMap, parameterMap)) return false;
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CHECK(useGpu_) << "CudnnConvLayer only support gpu";
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CHECK_EQ(inputLayers_.size(), parameters_.size());
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projections_.reserve(inputLayers_.size());
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projConf_.reserve(inputLayers_.size());
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numFilters_ = config_.num_filters();
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CHECK(config_.shared_biases());
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for (size_t i = 0; i < inputLayers_.size(); i++) {
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ProjectionConfig *conf = new ProjectionConfig();
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conf->set_type("conv");
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conf->set_num_filters(numFilters_);
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ConvConfig *convConf = conf->mutable_conv_conf();
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*convConf = *(config_.mutable_inputs(i)->mutable_conv_conf());
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conf->set_input_size(getPrev(i)->getSize());
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conf->set_output_size(getSize());
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projConf_.emplace_back(conf);
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projections_.emplace_back(
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Projection::create(*projConf_[i], parameters_[i], useGpu_));
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}
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if (biases_.get() && sharedBiases_) {
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hl_create_tensor_descriptor(&biasDesc_);
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hl_create_tensor_descriptor(&outputDesc_);
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hl_tensor_reshape(biasDesc_, 1, numFilters_ / groups_[0], 1, 1);
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biasOffset_ = numFilters_ / groups_[0];
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}
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return true;
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}
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void CudnnConvLayer::forward(PassType passType) {
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Layer::forward(passType);
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int batchSize = getInput(0).getBatchSize();
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resetOutput(batchSize, calOutputSize());
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for (size_t i = 0; i != inputLayers_.size(); ++i) {
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projections_[i]->forward(&getInput(i), &getOutput(), passType);
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}
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if (biases_) {
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REGISTER_TIMER_INFO("CudnnConvBiasTimer", getName().c_str());
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int batchSize = inputLayers_[0]->getOutputValue()->getHeight();
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hl_tensor_reshape(outputDesc_,
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batchSize,
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numFilters_ / groups_[0],
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outputH_[0],
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outputW_[0],
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numFilters_ * outputH_[0] * outputW_[0],
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outputH_[0] * outputW_[0],
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outputW_[0],
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1);
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outputOffset_ = getOutputValue()->getWidth() / groups_[0];
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for (int g = 0; g < groups_[0]; ++g) {
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real *biasData = biases_->getW()->getData() + biasOffset_ * g;
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real *outData = getOutputValue()->getData() + outputOffset_ * g;
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hl_convolution_forward_add_bias(
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biasDesc_, biasData, outputDesc_, outData);
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}
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}
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forwardActivation();
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}
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void CudnnConvLayer::backward(const UpdateCallback &callback) {
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backwardActivation();
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if (biases_ && biases_->getWGrad()) {
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REGISTER_TIMER_INFO("CudnnConvBpBiasTimer", getName().c_str());
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for (int g = 0; g < groups_[0]; ++g) {
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real *biasGrad = biases_->getWGrad()->getData() + biasOffset_ * g;
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real *outGrad = getOutputGrad()->getData() + outputOffset_ * g;
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hl_convolution_backward_bias(biasDesc_, biasGrad, outputDesc_, outGrad);
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}
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biases_->getParameterPtr()->incUpdate(callback);
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}
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for (size_t i = 0; i != inputLayers_.size(); ++i) {
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projections_[i]->backward(callback);
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}
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}
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CudnnConvLayer::~CudnnConvLayer() {
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if (biases_) {
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hl_destroy_tensor_descriptor(biasDesc_);
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hl_destroy_tensor_descriptor(outputDesc_);
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}
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}
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} // namespace paddle
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