You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
325 lines
10 KiB
325 lines
10 KiB
/* Copyright (c) 2017 PaddlePaddle Authors. All Rights Reserve.
|
|
|
|
Licensed under the Apache License, Version 2.0 (the "License");
|
|
you may not use this file except in compliance with the License.
|
|
You may obtain a copy of the License at
|
|
|
|
http://www.apache.org/licenses/LICENSE-2.0
|
|
|
|
Unless required by applicable law or agreed to in writing, software
|
|
distributed under the License is distributed on an "AS IS" BASIS,
|
|
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
See the License for the specific language governing permissions and
|
|
limitations under the License. */
|
|
|
|
#include "MKLDNNFcLayer.h"
|
|
#include "paddle/utils/Logging.h"
|
|
|
|
using namespace mkldnn; // NOLINT
|
|
typedef memory::format format;
|
|
|
|
namespace paddle {
|
|
|
|
REGISTER_LAYER(mkldnn_fc, MKLDNNFcLayer);
|
|
|
|
bool MKLDNNFcLayer::init(const LayerMap& layerMap,
|
|
const ParameterMap& parameterMap) {
|
|
if (!MKLDNNLayer::init(layerMap, parameterMap)) {
|
|
return false;
|
|
}
|
|
|
|
CHECK_EQ(inputLayers_.size(), 1UL) << "Only support one input layer yet";
|
|
CHECK_EQ(inputLayers_.size(), parameters_.size());
|
|
CHECK(!parameters_[0]->isSparse()) << "Do not support sparse yet";
|
|
|
|
// output size, cat not be changed
|
|
oc_ = getSize();
|
|
oh_ = 1;
|
|
ow_ = 1;
|
|
ih_ = 1;
|
|
iw_ = 1;
|
|
|
|
// input size can not change in FC
|
|
iLayerSize_ = inputLayers_[0]->getSize();
|
|
CHECK_EQ(parameters_[0]->getSize(), iLayerSize_ * oc_);
|
|
|
|
// create weight
|
|
weight_ =
|
|
std::unique_ptr<Weight>(new Weight(oc_, iLayerSize_, parameters_[0], 0));
|
|
|
|
// create biases
|
|
if (biasParameter_.get() != NULL) {
|
|
biases_ = std::unique_ptr<Weight>(new Weight(1, oc_, biasParameter_, 0));
|
|
}
|
|
return true;
|
|
}
|
|
|
|
void MKLDNNFcLayer::convertWeightsFromPaddle() {
|
|
if (hasInitedWgt_) {
|
|
return;
|
|
}
|
|
|
|
CHECK(wgtVal_) << "should have been initialized";
|
|
bool hasNoSpatial_ = ih_ == 1 && iw_ == 1;
|
|
auto targetDim = wgtVal_->getDims();
|
|
auto srcFmt = hasNoSpatial_ ? memory::format::io : memory::format::ihwo;
|
|
wgtVal_->reorderDataFrom(wgtVal_, srcFmt, targetDim);
|
|
hasInitedWgt_ = true;
|
|
}
|
|
|
|
void MKLDNNFcLayer::convertWeightsToPaddle() {
|
|
CHECK(wgtVal_) << "should have been initialized";
|
|
bool hasNoSpatial_ = ih_ == 1 && iw_ == 1;
|
|
auto targetDim = wgtVal_->getDims();
|
|
auto dstFmt = hasNoSpatial_ ? memory::format::io : memory::format::ihwo;
|
|
wgtVal_->reorderDataTo(wgtVal_, dstFmt, targetDim);
|
|
}
|
|
|
|
void MKLDNNFcLayer::reshape(
|
|
int& bs, int& ic, int& ih, int& iw, int oc, int& oh, int& ow) {
|
|
reshapeInput(bs, ih, iw);
|
|
|
|
CHECK_EQ(iLayerSize_, inputLayers_[0]->getSize());
|
|
ic = iLayerSize_ / (ih * iw);
|
|
CHECK_EQ(size_t(ic * ih * iw), iLayerSize_) << "not divisible";
|
|
CHECK_EQ(size_t(oc), getSize());
|
|
|
|
reshapeOutput(oh, ow);
|
|
resizeOutput(bs, oc);
|
|
|
|
printSizeInfo();
|
|
}
|
|
|
|
void MKLDNNFcLayer::resetFwd(std::vector<primitive>& pipeline,
|
|
MKLDNNMatrixPtr& in,
|
|
MKLDNNMatrixPtr& wgt,
|
|
MKLDNNMatrixPtr& bias,
|
|
MKLDNNMatrixPtr& out) {
|
|
resetFwdBuffers(in, wgt, bias, out);
|
|
|
|
resetFwdPD(fwdPD_, in, wgt, bias, out);
|
|
|
|
resetFwdPipeline(pipeline, fwdPD_, in, wgt, bias, out);
|
|
|
|
printValueFormatFlow();
|
|
}
|
|
|
|
void MKLDNNFcLayer::resetBwd(std::vector<primitive>& pipeline,
|
|
MKLDNNMatrixPtr& in,
|
|
MKLDNNMatrixPtr& wgt,
|
|
MKLDNNMatrixPtr& bias,
|
|
MKLDNNMatrixPtr& out) {
|
|
std::shared_ptr<fc_bwdWgt::primitive_desc> bwdWgtPD;
|
|
std::shared_ptr<fc_bwdData::primitive_desc> bwdDataPD;
|
|
|
|
resetBwdBuffers(in, wgt, bias, out);
|
|
|
|
resetBwdWgtPD(bwdWgtPD, wgt, bias, out);
|
|
|
|
resetBwdDataPD(bwdDataPD, in, out);
|
|
|
|
resetBwdPipeline(pipeline, bwdWgtPD, bwdDataPD, in, wgt, bias, out);
|
|
|
|
printGradFormatFlow();
|
|
}
|
|
|
|
void MKLDNNFcLayer::updateInputData() {
|
|
inVal_->setData(getInputValue(0, CPU_DEVICE)->getData());
|
|
}
|
|
|
|
void MKLDNNFcLayer::updateWeights(const UpdateCallback& callback) {
|
|
weight_->getParameterPtr()->incUpdate(callback);
|
|
if (biases_ && biases_->getWGrad()) {
|
|
biases_->getParameterPtr()->incUpdate(callback);
|
|
}
|
|
}
|
|
|
|
void MKLDNNFcLayer::resetFwdBuffers(MKLDNNMatrixPtr& in,
|
|
MKLDNNMatrixPtr& wgt,
|
|
MKLDNNMatrixPtr& bias,
|
|
MKLDNNMatrixPtr& out) {
|
|
resetInValue(in);
|
|
|
|
resetWgtBiasValue(wgt, bias);
|
|
|
|
resetOutValue(out);
|
|
}
|
|
|
|
void MKLDNNFcLayer::resetInValue(MKLDNNMatrixPtr& in) {
|
|
if (inputIsOnlyMKLDNN()) {
|
|
const MatrixPtr& dnnIn = getInputValue(0);
|
|
in = std::dynamic_pointer_cast<MKLDNNMatrix>(dnnIn);
|
|
CHECK(in) << "Input should be MKLDNNMatrix";
|
|
} else {
|
|
CHECK_EQ(getPrev(0)->getDeviceId(), CPU_DEVICE) << "Only support CPU yet";
|
|
const MatrixPtr& cpuIn = getInputValue(0, CPU_DEVICE);
|
|
in = MKLDNNMatrix::create(
|
|
cpuIn, {bs_, ic_, ih_, iw_}, format::nchw, engine_);
|
|
}
|
|
in->downSpatial();
|
|
}
|
|
|
|
void MKLDNNFcLayer::resetWgtBiasValue(MKLDNNMatrixPtr& wgt,
|
|
MKLDNNMatrixPtr& bias) {
|
|
format wgtFmt = format::oihw;
|
|
if (inVal_->getFormat() == format::nChw8c) {
|
|
wgtFmt = format::oIhw8i;
|
|
} else if (inVal_->getFormat() == format::nChw16c) {
|
|
wgtFmt = format::oIhw16i;
|
|
}
|
|
wgt = MKLDNNMatrix::create(
|
|
weight_->getW(), {oc_, ic_, ih_, iw_}, wgtFmt, engine_);
|
|
wgt->downSpatial();
|
|
VLOG(MKLDNN_FMTS) << "Weight value format: " << wgt->getFormat();
|
|
|
|
bias = (biases_ && biases_->getW())
|
|
? MKLDNNMatrix::create(biases_->getW(), {oc_}, format::x, engine_)
|
|
: nullptr;
|
|
}
|
|
|
|
void MKLDNNFcLayer::resetOutValue(MKLDNNMatrixPtr& out) {
|
|
out = MKLDNNMatrix::create(output_.value, {bs_, oc_}, format::nc, engine_);
|
|
if (!outputIsOnlyMKLDNN()) {
|
|
// fc cpu output value do not need create convert, just share data
|
|
getOutput(CPU_DEVICE).value->setData(out->getData());
|
|
}
|
|
output_.value = std::dynamic_pointer_cast<Matrix>(out);
|
|
}
|
|
|
|
void MKLDNNFcLayer::resetFwdPD(std::shared_ptr<fc_fwd::primitive_desc>& pd,
|
|
MKLDNNMatrixPtr in,
|
|
MKLDNNMatrixPtr wgt,
|
|
MKLDNNMatrixPtr bias,
|
|
MKLDNNMatrixPtr out) {
|
|
CHECK(in);
|
|
CHECK(wgt);
|
|
CHECK(out);
|
|
prop_kind pk = prop_kind::forward;
|
|
fc_fwd::desc fwdDesc = bias != nullptr ? fc_fwd::desc(pk,
|
|
in->getMemoryDesc(),
|
|
wgt->getMemoryDesc(),
|
|
bias->getMemoryDesc(),
|
|
out->getMemoryDesc())
|
|
: fc_fwd::desc(pk,
|
|
in->getMemoryDesc(),
|
|
wgt->getMemoryDesc(),
|
|
out->getMemoryDesc());
|
|
pd.reset(new fc_fwd::primitive_desc(fwdDesc, engine_));
|
|
}
|
|
|
|
void MKLDNNFcLayer::resetFwdPipeline(
|
|
std::vector<primitive>& pipeline,
|
|
std::shared_ptr<fc_fwd::primitive_desc>& pd,
|
|
MKLDNNMatrixPtr& in,
|
|
MKLDNNMatrixPtr& wgt,
|
|
MKLDNNMatrixPtr& bias,
|
|
MKLDNNMatrixPtr& out) {
|
|
if (bias) {
|
|
fwd_.reset(new fc_fwd(*pd, *in, *wgt, *bias, *out));
|
|
} else {
|
|
fwd_.reset(new fc_fwd(*pd, *in, *wgt, *out));
|
|
}
|
|
|
|
pipeline.push_back(*fwd_);
|
|
}
|
|
|
|
void MKLDNNFcLayer::resetBwdBuffers(MKLDNNMatrixPtr& in,
|
|
MKLDNNMatrixPtr& wgt,
|
|
MKLDNNMatrixPtr& bias,
|
|
MKLDNNMatrixPtr& out) {
|
|
resetOutGrad(out);
|
|
|
|
resetWgtBiasGrad(wgt, bias);
|
|
|
|
resetInGrad(in);
|
|
}
|
|
|
|
void MKLDNNFcLayer::resetOutGrad(MKLDNNMatrixPtr& out) {
|
|
CHECK(outVal_);
|
|
if (outputIsOnlyMKLDNN()) {
|
|
MKLDNNLayer::resetOutGrad(out, outVal_->getPrimitiveDesc());
|
|
} else {
|
|
const MatrixPtr& cpuOut = getOutput(CPU_DEVICE).grad;
|
|
output_.grad->setData(cpuOut->getData());
|
|
out = MKLDNNMatrix::create(cpuOut, outVal_->getPrimitiveDesc());
|
|
}
|
|
}
|
|
|
|
void MKLDNNFcLayer::resetWgtBiasGrad(MKLDNNMatrixPtr& wgt,
|
|
MKLDNNMatrixPtr& bias) {
|
|
CHECK(wgtVal_);
|
|
wgt = MKLDNNMatrix::create(weight_->getWGrad(), wgtVal_->getPrimitiveDesc());
|
|
|
|
bias = nullptr;
|
|
if (biasVal_ == nullptr) {
|
|
return;
|
|
}
|
|
bias =
|
|
MKLDNNMatrix::create(biases_->getWGrad(), biasVal_->getPrimitiveDesc());
|
|
}
|
|
|
|
void MKLDNNFcLayer::resetInGrad(MKLDNNMatrixPtr& in) {
|
|
in = nullptr;
|
|
if (inputLayers_[0]->getOutput().grad == nullptr) {
|
|
return;
|
|
}
|
|
CHECK(inVal_);
|
|
MKLDNNLayer::resetInGrad(in, inVal_->getPrimitiveDesc());
|
|
}
|
|
|
|
void MKLDNNFcLayer::resetBwdWgtPD(
|
|
std::shared_ptr<fc_bwdWgt::primitive_desc>& pd,
|
|
MKLDNNMatrixPtr& wgt,
|
|
MKLDNNMatrixPtr& bias,
|
|
MKLDNNMatrixPtr& out) {
|
|
CHECK(inVal_);
|
|
fc_bwdWgt::desc bwdWgtDesc = bias ? fc_bwdWgt::desc(inVal_->getMemoryDesc(),
|
|
wgt->getMemoryDesc(),
|
|
bias->getMemoryDesc(),
|
|
out->getMemoryDesc())
|
|
: fc_bwdWgt::desc(inVal_->getMemoryDesc(),
|
|
wgt->getMemoryDesc(),
|
|
out->getMemoryDesc());
|
|
pd.reset(new fc_bwdWgt::primitive_desc(bwdWgtDesc, engine_, *fwdPD_));
|
|
}
|
|
|
|
void MKLDNNFcLayer::resetBwdDataPD(
|
|
std::shared_ptr<fc_bwdData::primitive_desc>& pd,
|
|
MKLDNNMatrixPtr& in,
|
|
MKLDNNMatrixPtr& out) {
|
|
pd = nullptr;
|
|
if (in == nullptr) {
|
|
return;
|
|
}
|
|
CHECK(wgtVal_);
|
|
fc_bwdData::desc bwdDataDesc = fc_bwdData::desc(
|
|
in->getMemoryDesc(), wgtVal_->getMemoryDesc(), out->getMemoryDesc());
|
|
pd.reset(new fc_bwdData::primitive_desc(bwdDataDesc, engine_, *fwdPD_));
|
|
}
|
|
|
|
void MKLDNNFcLayer::resetBwdPipeline(
|
|
std::vector<primitive>& pipeline,
|
|
std::shared_ptr<fc_bwdWgt::primitive_desc>& bwdWgtPD,
|
|
std::shared_ptr<fc_bwdData::primitive_desc>& bwdDataPD,
|
|
MKLDNNMatrixPtr& in,
|
|
MKLDNNMatrixPtr& wgt,
|
|
MKLDNNMatrixPtr& bias,
|
|
MKLDNNMatrixPtr& out) {
|
|
CHECK(inVal_);
|
|
if (bias) {
|
|
bwdWgt_.reset(new fc_bwdWgt(*bwdWgtPD, *inVal_, *out, *wgt, *bias));
|
|
} else {
|
|
bwdWgt_.reset(new fc_bwdWgt(*bwdWgtPD, *inVal_, *out, *wgt));
|
|
}
|
|
pipeline.push_back(*bwdWgt_);
|
|
|
|
if (bwdDataPD == nullptr) {
|
|
return;
|
|
}
|
|
CHECK(wgtVal_) << "Should have weight memory";
|
|
bwdData_.reset(new fc_bwdData(*bwdDataPD, *out, *wgtVal_, *in));
|
|
pipeline.push_back(*bwdData_);
|
|
}
|
|
|
|
} // namespace paddle
|