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103 lines
2.9 KiB
103 lines
2.9 KiB
/* Copyright (c) 2017 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 "MkldnnFcLayer.h"
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#include "paddle/utils/Stat.h"
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namespace paddle {
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REGISTER_LAYER(mkldnn_fc, MkldnnFcLayer);
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bool MkldnnFcLayer::init(const LayerMap& layerMap,
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const ParameterMap& parameterMap) {
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if (!MkldnnLayer::init(layerMap, parameterMap)) {
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return false;
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}
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CHECK_EQ(inputLayers_.size(), 1) << "Only support one input layer yet!";
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CHECK_EQ(inputLayers_.size(), parameters_.size());
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CHECK(!parameters_[0]->isSparse()) << "Do not support sparse yet";
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// output size, cat not be changed
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oc_ = getSize();
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oh_ = 1;
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ow_ = 1;
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// input size can not change in FC
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iLayerSize_ = inputLayers_[0]->getSize();
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CHECK_EQ(parameters_[0]->getSize(), iLayerSize_ * oc_);
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// create weight
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weight_ =
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std::unique_ptr<Weight>(new Weight(oc_, iLayerSize_, parameters_[0], 0));
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// create biases
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if (biasParameter_.get() != NULL) {
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biases_ = std::unique_ptr<Weight>(new Weight(1, oc_, biasParameter_));
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}
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return true;
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}
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void MkldnnFcLayer::reshape() {
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const Argument& input = getInput(0);
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int batchSize = input.getBatchSize();
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if (bs_ == batchSize) {
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return;
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}
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bs_ = batchSize;
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ih_ = input.getFrameHeight();
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iw_ = input.getFrameWidth();
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if (ih_ == 0) {
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ih_ = 1;
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}
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if (iw_ == 0) {
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iw_ = 1;
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}
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CHECK_EQ(iLayerSize_, inputLayers_[0]->getSize());
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ic_ = iLayerSize_ / (ih_ * iw_);
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CHECK_EQ(size_t(ic_ * ih_ * iw_), iLayerSize_) << "not divisible";
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CHECK_EQ(size_t(oc_), getSize());
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// reset output
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output_.setFrameHeight(oh_);
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output_.setFrameWidth(ow_);
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resetOutput(bs_, oc_);
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}
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void MkldnnFcLayer::forward(PassType passType) {
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Layer::forward(passType);
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reshape();
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{
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REGISTER_TIMER_INFO("mkldnn_FwdTimer", getName().c_str());
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real* input = getInputValue(0)->getData();
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real* output = getOutputValue()->getData();
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real* wgt = weight_->getW()->getData();
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bool hasBias = biases_ && biases_->getW();
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real* bias = hasBias ? biases_->getW()->getData() : NULL;
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mkldnnForwardFC(bs_, ic_, ih_, iw_, input, oc_, output, wgt, bias);
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}
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/* activation */ {
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REGISTER_TIMER_INFO("FwActTimer", getName().c_str());
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forwardActivation();
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}
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}
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void MkldnnFcLayer::backward(const UpdateCallback& callback) {
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; // bool hasBias = biases_ && biases_->getWGrad();
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}
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} // namespace paddle
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