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提交 abce9eb7 编写于 作者: T Tao Luo 提交者： GitHub
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Merge pull request #4953 from tensor-tang/merge_grad_gtest 

refine the mkldnn logic
上级 c91de280 5c892db6
隐藏空白更改
内联 并排
Showing with 612 addition and 711 deletion
paddle/gserver/activations/MKLDNNActivation.cpp
浏览文件 @ abce9eb7
......@@ -126,7 +126,7 @@ void MKLDNNEltwiseActivation::resetFwd(Argument& act) {
copyInVal_ = nullptr;
if (act.grad && algo == algorithm::eltwise_tanh) {
// tanh need save src input for backward
inVal_ = MKLDNNMatrix::create(nullptr, val_->getPrimitiveDesc());
inVal_ = MKLDNNMatrix::create(val_->getPrimitiveDesc());
copyInVal_ = std::make_shared<mkldnn::reorder>(*val_, *inVal_);
CHECK(copyInVal_) << "should not be emptry";
pipelineFwd_.push_back(*copyInVal_);
......@@ -145,7 +145,7 @@ void MKLDNNEltwiseActivation::resetBwd(Argument& act) {
algorithm algo = getAlgo(this->getName());
float alpha = getBwdAlpha();
float beta = getBeta();
grad_ = MKLDNNMatrix::create(act.grad, val_->getPrimitiveDesc());
grad_ = MKLDNNMatrix::create(val_->getPrimitiveDesc(), act.grad);
auto eng = CPUEngine::Instance().getEngine();
auto bwdDesc = eltwise_bwd::desc(
algo, grad_->getMemoryDesc(), val_->getMemoryDesc(), alpha, beta);
......@@ -230,7 +230,7 @@ void MKLDNNActivation::resetFwd(Argument& act) {
int ic = cnt_ / bs / ih / iw;
CHECK_EQ(cnt_, (size_t)bs * ic * ih * iw);
val_ = MKLDNNMatrix::create(
act.value, {bs, ic, ih, iw}, mkldnn::memory::format::nchw, *engine_);
{bs, ic, ih, iw}, mkldnn::memory::format::nchw, *engine_, act.value);
CHECK(val_);
val_->downSpatial();
}
......
paddle/gserver/layers/MKLDNNBase.h
浏览文件 @ abce9eb7
......@@ -21,8 +21,8 @@ namespace paddle {
typedef enum {
MKLDNN_BASE = 1, // basical info of MKLDNN
MKLDNN_TESTS = 1, // gtest info of MKLDNN
MKLDNN_SIZES = 2, // size info of MKLDNN
MKLDNN_FMTS = 3, // format info of MKLDNN
MKLDNN_FMTS = 2, // format info of MKLDNN
MKLDNN_SIZES = 3, // size info of MKLDNN
MKLDNN_ALL = 4, // show all info of MKLDNN
} MKLDNN_LOG_LEVEL;
......
paddle/gserver/layers/MKLDNNConvLayer.cpp
浏览文件 @ abce9eb7
......@@ -116,8 +116,6 @@ void MKLDNNConvLayer::resetFwd(std::vector<primitive>& pipeline,
resetFwdBuffers(fwdPD_, in, wgt, bias, out);
resetFwdPipeline(pipeline, fwdPD_, in, wgt, bias, out);
printValueFormatFlow();
}
void MKLDNNConvLayer::resetBwd(std::vector<primitive>& pipeline,
......@@ -135,12 +133,6 @@ void MKLDNNConvLayer::resetBwd(std::vector<primitive>& pipeline,
resetBwdBuffers(bwdWgtPD, bwdDataPD, in, wgt, bias, out);
resetBwdPipeline(pipeline, bwdWgtPD, bwdDataPD, in, wgt, bias, out);
printGradFormatFlow();
}
void MKLDNNConvLayer::updateInputData() {
cpuInVal_->setData(getInputValue(0, CPU_DEVICE)->getData());
}
void MKLDNNConvLayer::updateWeights(const UpdateCallback& callback) {
......@@ -211,11 +203,18 @@ void MKLDNNConvLayer::resetFwdBuffers(
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out) {
CHECK(pd);
resetInValue(pd, in);
resetInValue(
in, std::make_shared<memory::primitive_desc>(pd->src_primitive_desc()));
resetOutValue(out, pd->dst_primitive_desc());
resetWgtBiasValue(pd, wgt, bias);
resetWithMatrix(wgt, weight_->getW(), pd->weights_primitive_desc());
resetOutValue(pd, out);
if (biases_ && biases_->getW()) {
resetWithMatrix(bias, biases_->getW(), pd->bias_primitive_desc());
} else {
bias = nullptr;
}
}
void MKLDNNConvLayer::resetFwdPipeline(
......@@ -225,104 +224,12 @@ void MKLDNNConvLayer::resetFwdPipeline(
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out) {
if (cvtInVal_) {
pipeline.push_back(*cvtInVal_);
}
if (bias) {
fwd_.reset(new conv_fwd(*pd, *in, *wgt, *bias, *out));
} else {
fwd_.reset(new conv_fwd(*pd, *in, *wgt, *out));
}
pipeline.push_back(*fwd_);
if (cvtOutVal_) {
pipeline.push_back(*cvtOutVal_);
}
}
void MKLDNNConvLayer::resetInValue(
std::shared_ptr<conv_fwd::primitive_desc>& pd, MKLDNNMatrixPtr& in) {
const MatrixPtr& inMat = inputLayers_[0]->getOutputValue();
in = MKLDNNMatrix::create(inMat, pd->src_primitive_desc());
// create buffer and reorder if input value do not match
cpuInVal_ = nullptr;
cvtInVal_ = nullptr;
MKLDNNMatrixPtr dnnIn = std::dynamic_pointer_cast<MKLDNNMatrix>(inMat);
CHECK_EQ(inputIsOnlyMKLDNN(), dnnIn != nullptr);
if (dnnIn != nullptr && dnnIn->getPrimitiveDesc() == in->getPrimitiveDesc()) {
in = dnnIn;
return;
}
if (dnnIn) {
if (dnnIn->getFormat() == format::nc) {
CHECK(ih_ == 1 && iw_ == 1) << "when input is nc format";
// create a new one with nchw format and same data
memory::dims inDims = memory::dims{bs_, ic_, 1, 1};
dnnIn = MKLDNNMatrix::create(inMat, inDims, format::nchw, engine_);
}
if (dnnIn->getPrimitiveDesc() == in->getPrimitiveDesc()) {
in = dnnIn;
return;
}
cpuInVal_ = dnnIn;
in = MKLDNNMatrix::create(nullptr, pd->src_primitive_desc());
cvtInVal_ = MKLDNNMatrix::createReorder(cpuInVal_, in);
CHECK(cvtInVal_) << "should not be emptry";
} else {
memory::dims inDims = memory::dims{bs_, ic_, ih_, iw_};
cpuInVal_ = MKLDNNMatrix::create(inMat, inDims, format::nchw, engine_);
if (cpuInVal_->getPrimitiveDesc() != in->getPrimitiveDesc()) {
// create new mkldnn matrix
in = MKLDNNMatrix::create(nullptr, pd->src_primitive_desc());
cvtInVal_ = MKLDNNMatrix::createReorder(cpuInVal_, in);
CHECK(cvtInVal_) << "should not be emptry";
} else {
in = cpuInVal_;
}
}
}
void MKLDNNConvLayer::resetWgtBiasValue(
std::shared_ptr<conv_fwd::primitive_desc>& pd,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias) {
wgt = MKLDNNMatrix::create(weight_->getW(), pd->weights_primitive_desc());
VLOG(MKLDNN_FMTS) << "Weight value format: " << wgt->getFormat();
bias = (biases_ && biases_->getW())
? MKLDNNMatrix::create(biases_->getW(), pd->bias_primitive_desc())
: nullptr;
}
void MKLDNNConvLayer::resetOutValue(
std::shared_ptr<conv_fwd::primitive_desc>& pd, MKLDNNMatrixPtr& out) {
out = MKLDNNMatrix::create(output_.value, pd->dst_primitive_desc());
// create reorder if output value has cpu device and pd do not match
cpuOutVal_ = nullptr;
cvtOutVal_ = nullptr;
if (!outputIsOnlyMKLDNN()) {
const MatrixPtr& cpuOut = getOutput(CPU_DEVICE).value;
memory::dims outDims = memory::dims{bs_, oc_, oh_, ow_};
cpuOutVal_ = MKLDNNMatrix::create(cpuOut, outDims, format::nchw, engine_);
if (cpuOutVal_->getPrimitiveDesc() != pd->dst_primitive_desc()) {
out = MKLDNNMatrix::create(nullptr, pd->dst_primitive_desc());
cvtOutVal_ = MKLDNNMatrix::createReorder(out, cpuOutVal_);
CHECK(cvtOutVal_) << "should not be empty";
} else {
cpuOut->setData(output_.value->getData());
cpuOutVal_ = out;
}
// when output is cpu device, change the mkldnn output value and make them
// share the same data. Then if next layer use inputlayer->getOuputValue()
// to achieve the input value, it will get the right data.
output_.value = std::dynamic_pointer_cast<Matrix>(cpuOutVal_);
return;
}
output_.value = std::dynamic_pointer_cast<Matrix>(out);
}
void MKLDNNConvLayer::resetBwdWgtPD(
......@@ -331,8 +238,8 @@ void MKLDNNConvLayer::resetBwdWgtPD(
loadConvSettings(wgtDims, biasDims, strides, dilations, padL, padR);
// create backward weight using input, output and weight value memory desc
CHECK(inVal_) << "Should have input value";
CHECK(outVal_) << "Should have output value";
CHECK(inVal_) << "Should have internal input value";
CHECK(outVal_) << "Should have internal output value";
CHECK(wgtVal_) << "Should have weight value";
algorithm algo = algorithm::convolution_direct;
padding_kind padKind = padding_kind::zero;
......@@ -372,8 +279,8 @@ void MKLDNNConvLayer::resetBwdDataPD(
memory::dims wgtDims, biasDims, strides, dilations, padL, padR;
loadConvSettings(wgtDims, biasDims, strides, dilations, padL, padR);
CHECK(inVal_) << "Should have input value";
CHECK(outVal_) << "Should have output value";
CHECK(inVal_) << "Should have internal input value";
CHECK(outVal_) << "Should have internal output value";
// create backward data using input and output value memory desc
// but using weight memory desc with any format
auto bwdDataDesc = conv_bwdData::desc(algorithm::convolution_direct,
......@@ -399,12 +306,27 @@ void MKLDNNConvLayer::resetBwdBuffers(
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out) {
CHECK(wgtPD);
resetOutGrad(wgtPD, out);
resetOutGrad(out, wgtPD->diff_dst_primitive_desc());
resetWgtBiasGrad(wgtPD, wgt, bias);
resetWithMatrix(
wgt, weight_->getWGrad(), wgtPD->diff_weights_primitive_desc());
CHECK(wgtVal_ != nullptr &&
wgt->getPrimitiveDesc() == wgtVal_->getPrimitiveDesc())
<< "primitive desc of weight grad and value should be equal";
resetInGrad(dataPD, in);
bias = nullptr;
if (biases_ && biases_->getWGrad()) {
resetWithMatrix(
bias, biases_->getWGrad(), wgtPD->diff_bias_primitive_desc());
CHECK(bias && biasVal_ &&
bias->getPrimitiveDesc() == biasVal_->getPrimitiveDesc())
<< "primitive desc of bias grad should equal the bias value";
}
if (dataPD == nullptr) {
return;
}
resetInGrad(in, dataPD->diff_src_primitive_desc());
resetWgtValBwdData(dataPD, wgtValBwdData_);
}
......@@ -416,10 +338,7 @@ void MKLDNNConvLayer::resetBwdPipeline(
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out) {
if (cvtOutGrad_) {
pipeline.push_back(*cvtOutGrad_);
}
CHECK(inVal_);
// add bwdWgt handle
if (bias) {
bwdWgt_.reset(new conv_bwdWgt(*wgtPD, *inVal_, *out, *wgt, *bias));
......@@ -431,99 +350,13 @@ void MKLDNNConvLayer::resetBwdPipeline(
if (dataPD == nullptr) {
return;
}
if (cvtWgtVal_) {
pipeline.push_back(*cvtWgtVal_);
}
// add bwdData handle
CHECK(wgtValBwdData_) << "Should have weight memory";
bwdData_.reset(new conv_bwdData(*dataPD, *out, *wgtValBwdData_, *in));
pipeline.push_back(*bwdData_);
if (cvtInGrad_) {
pipeline.push_back(*cvtInGrad_);
}
}
void MKLDNNConvLayer::resetOutGrad(
std::shared_ptr<conv_bwdWgt::primitive_desc>& wgtPD, MKLDNNMatrixPtr& out) {
cpuOutGrad_ = nullptr;
cvtOutGrad_ = nullptr;
CHECK(outVal_ != nullptr &&
outVal_->getPrimitiveDesc() == wgtPD->diff_dst_primitive_desc())
<< "primitive desc of out grad and value should be equal";
if (outputIsOnlyMKLDNN()) {
MKLDNNLayer::resetOutGrad(out, outVal_->getPrimitiveDesc());
} else {
const MatrixPtr& cpuOut = getOutput(CPU_DEVICE).grad;
// always share the same grad data of CPU output
// then the activation can get the right grad from output_.grad
output_.grad->setData(cpuOut->getData());
// same PrimitiveDesc with cpuInVal_
CHECK(cpuOutVal_);
cpuOutGrad_ = MKLDNNMatrix::create(cpuOut, cpuOutVal_->getPrimitiveDesc());
// create reorder if primitive desc does not match
if (cpuOutGrad_->getPrimitiveDesc() != outVal_->getPrimitiveDesc()) {
out = MKLDNNMatrix::create(nullptr, outVal_->getPrimitiveDesc());
cvtOutGrad_ = MKLDNNMatrix::createReorder(cpuOutGrad_, out);
CHECK(cvtOutGrad_);
} else {
out = cpuOutGrad_;
}
}
}
void MKLDNNConvLayer::resetWgtBiasGrad(
std::shared_ptr<conv_bwdWgt::primitive_desc>& wgtPD,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias) {
wgt = MKLDNNMatrix::create(weight_->getWGrad(),
wgtPD->diff_weights_primitive_desc());
CHECK(nullptr != wgtVal_ &&
wgt->getPrimitiveDesc() == wgtVal_->getPrimitiveDesc())
<< "primitive desc of weight grad and value should be equal";
VLOG(MKLDNN_FMTS) << "weight grad format: " << wgt->getFormat();
bias = nullptr;
if (biasVal_ == nullptr) {
return;
}
bias = MKLDNNMatrix::create(biases_->getWGrad(),
wgtPD->diff_bias_primitive_desc());
CHECK(bias->getPrimitiveDesc() == biasVal_->getPrimitiveDesc())
<< "primitive desc of bias grad should equal the bias value";
}
void MKLDNNConvLayer::resetInGrad(
std::shared_ptr<conv_bwdData::primitive_desc>& dataPD,
MKLDNNMatrixPtr& in) {
in = nullptr;
cpuInGrad_ = nullptr;
cvtInGrad_ = nullptr;
if (dataPD == nullptr) {
return;
}
if (inputIsOnlyMKLDNN()) {
MKLDNNLayer::resetInGrad(in, dataPD->diff_src_primitive_desc());
CHECK(nullptr != inVal_ &&
in->getPrimitiveDesc() == inVal_->getPrimitiveDesc())
<< "primitive desc of input grad and value should be equal";
} else {
const MatrixPtr& cpuIn = getInputGrad(0, CPU_DEVICE);
// same PrimitiveDesc with cpuInVal_
CHECK(cpuInVal_);
cpuInGrad_ = MKLDNNMatrix::create(cpuIn, cpuInVal_->getPrimitiveDesc());
in = cpuInGrad_;
// create reorder if PrimitiveDesc does not match
if (cpuInGrad_->getPrimitiveDesc() != dataPD->diff_src_primitive_desc()) {
in = MKLDNNMatrix::create(getInputGrad(0, MKLDNN_DEVICE),
dataPD->diff_src_primitive_desc());
cvtInGrad_ = MKLDNNMatrix::createReorder(in, cpuInGrad_);
CHECK(cvtInGrad_);
}
}
}
void MKLDNNConvLayer::resetWgtValBwdData(
......@@ -537,8 +370,7 @@ void MKLDNNConvLayer::resetWgtValBwdData(
// since the primitive_desc would be different with wgtVal_
CHECK(wgtVal_) << "should have weight value";
if (dataPD->weights_primitive_desc() != wgtVal_->getPrimitiveDesc()) {
wgtValBwdData_ =
MKLDNNMatrix::create(nullptr, dataPD->weights_primitive_desc());
wgtValBwdData_ = MKLDNNMatrix::create(dataPD->weights_primitive_desc());
cvtWgtVal_ = MKLDNNMatrix::createReorder(wgtVal_, wgtValBwdData_);
CHECK(cvtWgtVal_);
} else {
......
paddle/gserver/layers/MKLDNNConvLayer.h
浏览文件 @ abce9eb7
......@@ -48,17 +48,6 @@ protected:
// save forward primitive_desc, which can be used backward
std::shared_ptr<conv_fwd::primitive_desc> fwdPD_;
// MKLDNNMatrixPtr which should be created from CPU Device
MKLDNNMatrixPtr cpuInVal_;
MKLDNNMatrixPtr cpuInGrad_;
MKLDNNMatrixPtr cpuOutVal_;
MKLDNNMatrixPtr cpuOutGrad_;
// convert handle between CPU device and MKLDNN device
std::shared_ptr<mkldnn::reorder> cvtInVal_;
std::shared_ptr<mkldnn::reorder> cvtInGrad_;
std::shared_ptr<mkldnn::reorder> cvtOutVal_;
std::shared_ptr<mkldnn::reorder> cvtOutGrad_;
// whether the weight has been init
bool hasInitedWgt_;
......@@ -94,8 +83,6 @@ public:
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out) override;
void updateInputData() override;
void updateWeights(const UpdateCallback& callback) override;
void convertWeightsFromPaddle() override;
......@@ -109,26 +96,6 @@ public:
<< ", sw: " << sw_ << ", dh: " << dh_ << ", dw: " << dw_;
}
void printValueFormatFlow() override {
if (cpuInVal_) {
VLOG(MKLDNN_FMTS) << cpuInVal_->getFormat() << " >>>";
}
MKLDNNLayer::printValueFormatFlow();
if (cpuOutVal_) {
VLOG(MKLDNN_FMTS) << " >>> " << cpuOutVal_->getFormat();
}
}
void printGradFormatFlow() override {
if (cpuInGrad_) {
VLOG(MKLDNN_FMTS) << cpuInGrad_->getFormat() << " <<<";
}
MKLDNNLayer::printGradFormatFlow();
if (cpuOutGrad_) {
VLOG(MKLDNN_FMTS) << " <<< " << cpuOutGrad_->getFormat();
}
}
protected:
/**
* load the dims settings of this conv
......@@ -162,23 +129,6 @@ protected:
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out);
/**
* reset MKLDNNMatrix of input value
*/
void resetInValue(std::shared_ptr<conv_fwd::primitive_desc>& pd,
MKLDNNMatrixPtr& in);
/**
* reset MKLDNNMatrix of weight and bias value
*/
void resetWgtBiasValue(std::shared_ptr<conv_fwd::primitive_desc>& pd,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias);
/**
* reset MKLDNNMatrix of output value
*/
void resetOutValue(std::shared_ptr<conv_fwd::primitive_desc>& pd,
MKLDNNMatrixPtr& out);
/**
* reset the backward weight primitive descriptor.
*/
......@@ -207,22 +157,6 @@ protected:
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out);
/**
* reset MKLDNNMatrix of output grad
*/
void resetOutGrad(std::shared_ptr<conv_bwdWgt::primitive_desc>& wgtPD,
MKLDNNMatrixPtr& out);
/**
* reset MKLDNNMatrix of weight and bias grad
*/
void resetWgtBiasGrad(std::shared_ptr<conv_bwdWgt::primitive_desc>& wgtPD,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias);
/**
* reset MKLDNNMatrix of input grad
*/
void resetInGrad(std::shared_ptr<conv_bwdData::primitive_desc>& dataPD,
MKLDNNMatrixPtr& in);
/**
* reset MKLDNNMatrix of weight value for backward data
* since the primitive_desc would be different with wgtVal_
......
paddle/gserver/layers/MKLDNNFcLayer.cpp
浏览文件 @ abce9eb7
......@@ -62,7 +62,7 @@ void MKLDNNFcLayer::convertWeightsFromPaddle() {
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;
auto srcFmt = hasNoSpatial_ ? format::io : format::ihwo;
wgtVal_->reorderDataFrom(wgtVal_, srcFmt, targetDim);
hasInitedWgt_ = true;
}
......@@ -71,7 +71,7 @@ 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;
auto dstFmt = hasNoSpatial_ ? format::io : format::ihwo;
wgtVal_->reorderDataTo(wgtVal_, dstFmt, targetDim);
}
......@@ -100,8 +100,6 @@ void MKLDNNFcLayer::resetFwd(std::vector<primitive>& pipeline,
resetFwdPD(fwdPD_, in, wgt, bias, out);
resetFwdPipeline(pipeline, fwdPD_, in, wgt, bias, out);
printValueFormatFlow();
}
void MKLDNNFcLayer::resetBwd(std::vector<primitive>& pipeline,
......@@ -119,12 +117,6 @@ void MKLDNNFcLayer::resetBwd(std::vector<primitive>& pipeline,
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) {
......@@ -139,51 +131,30 @@ void MKLDNNFcLayer::resetFwdBuffers(MKLDNNMatrixPtr& in,
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_);
}
CHECK(in);
in->downSpatial();
}
void MKLDNNFcLayer::resetWgtBiasValue(MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias) {
auto outPD =
MKLDNNMatrix::createPrimitiveDesc({bs_, oc_}, format::nc, engine_);
resetOutValue(out, outPD);
format wgtFmt = format::oihw;
if (inVal_->getFormat() == format::nChw8c) {
if (in->getFormat() == format::nChw8c) {
wgtFmt = format::oIhw8i;
} else if (inVal_->getFormat() == format::nChw16c) {
} else if (in->getFormat() == format::nChw16c) {
wgtFmt = format::oIhw16i;
}
wgt = MKLDNNMatrix::create(
weight_->getW(), {oc_, ic_, ih_, iw_}, wgtFmt, engine_);
auto wgtPD =
MKLDNNMatrix::createPrimitiveDesc({oc_, ic_, ih_, iw_}, wgtFmt, engine_);
resetWithMatrix(wgt, weight_->getW(), wgtPD);
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());
if (biases_ && biases_->getW()) {
auto biasPD = MKLDNNMatrix::createPrimitiveDesc({oc_}, format::x, engine_);
resetWithMatrix(bias, biases_->getW(), biasPD);
} else {
bias = nullptr;
}
output_.value = std::dynamic_pointer_cast<Matrix>(out);
}
void MKLDNNFcLayer::resetFwdPD(std::shared_ptr<fc_fwd::primitive_desc>& pd,
......@@ -219,7 +190,6 @@ void MKLDNNFcLayer::resetFwdPipeline(
} else {
fwd_.reset(new fc_fwd(*pd, *in, *wgt, *out));
}
pipeline.push_back(*fwd_);
}
......@@ -227,44 +197,18 @@ 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());
}
}
CHECK(inVal_ && outVal_);
resetOutGrad(out, outVal_->getPrimitiveDesc());
resetInGrad(in, inVal_->getPrimitiveDesc());
void MKLDNNFcLayer::resetWgtBiasGrad(MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias) {
CHECK(wgtVal_);
wgt = MKLDNNMatrix::create(weight_->getWGrad(), wgtVal_->getPrimitiveDesc());
resetWithMatrix(wgt, 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;
if (biasVal_) {
resetWithMatrix(bias, biases_->getWGrad(), biasVal_->getPrimitiveDesc());
} else {
bias = nullptr;
}
CHECK(inVal_);
MKLDNNLayer::resetInGrad(in, inVal_->getPrimitiveDesc());
}
void MKLDNNFcLayer::resetBwdWgtPD(
......
paddle/gserver/layers/MKLDNNFcLayer.h
浏览文件 @ abce9eb7
......@@ -66,8 +66,6 @@ public:
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out) override;
void updateInputData() override;
void updateWeights(const UpdateCallback& callback) override;
void convertWeightsFromPaddle() override;
......@@ -84,9 +82,6 @@ protected:
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out);
void resetInValue(MKLDNNMatrixPtr& in);
void resetWgtBiasValue(MKLDNNMatrixPtr& wgt, MKLDNNMatrixPtr& bias);
void resetOutValue(MKLDNNMatrixPtr& out);
void resetFwdPD(std::shared_ptr<fc_fwd::primitive_desc>& pd,
MKLDNNMatrixPtr in,
MKLDNNMatrixPtr wgt,
......@@ -109,9 +104,6 @@ protected:
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out);
void resetOutGrad(MKLDNNMatrixPtr& out);
void resetWgtBiasGrad(MKLDNNMatrixPtr& wgt, MKLDNNMatrixPtr& bias);
void resetInGrad(MKLDNNMatrixPtr& in);
void resetBwdWgtPD(std::shared_ptr<fc_bwdWgt::primitive_desc>& pd,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
......
paddle/gserver/layers/MKLDNNLayer.cpp 0 → 100644
浏览文件 @ abce9eb7
/* 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 "MKLDNNLayer.h"
using namespace mkldnn; // NOLINT
typedef memory::format format;
namespace paddle {
bool MKLDNNLayer::init(const LayerMap& layerMap,
const ParameterMap& parameterMap) {
CHECK(FLAGS_use_mkldnn) << "MkldnnLayers only support use_mkldnn."
<< "Please set WITH_MKLDNN=ON "
<< "and set use_mkldnn=True";
CHECK(!useGpu_) << "Do not support GPU yet";
// set device id before Layer::init
setDevice(MKLDNN_DEVICE);
// change param device to MKLDNN device
setParamsDevice(MKLDNN_DEVICE, parameterMap);
if (!Layer::init(layerMap, parameterMap)) {
return false;
}
setOutputMap();
checkCPUOutputsNumber();
stream_.reset(new MKLDNNStream());
engine_ = CPUEngine::Instance().getEngine();
return true;
}
void MKLDNNLayer::forward(PassType passType) {
passType_ = passType;
{
REGISTER_TIMER_INFO("mkldnn_FwdTimer", getName().c_str());
CHECK(!inputLayers_.empty());
copySeqInfoToOutputs();
size_t elemenCnt = inputLayers_[0]->getOutputValue()->getElementCnt();
if (inputElemenCnt_ != elemenCnt) {
VLOG(MKLDNN_BASE) << getName() << " reset mkldnn forward";
// reset when input total sizes changed, not only the batchsize
inputElemenCnt_ = elemenCnt;
pipelineFwd_.clear();
reshape(bs_, ic_, ih_, iw_, oc_, oh_, ow_);
// all cpu device output grad or value share output's
shareCPUDevice();
resetFwd(pipelineFwd_, inVal_, wgtVal_, biasVal_, outVal_);
// MKLDNNLayer output value should be MKLDNNMatrix
// so external output value is necessary.
// Then external input value is not necessary,
// since input may be mkldnn internal buffer.
CHECK(extOutVal_) << "external output value is necessary";
output_.value = std::dynamic_pointer_cast<Matrix>(extOutVal_);
CHECK(inVal_ && outVal_) << "internal memories are necessary";
if (cvtInVal_) {
pipelineFwd_.insert(pipelineFwd_.begin(), *cvtInVal_);
}
if (cvtOutVal_) {
pipelineFwd_.push_back(*cvtOutVal_);
}
convertWeightsFromPaddle();
printSizeInfo();
printValueFormat();
needResetBwd_ = true;
}
if (inputLayers_[0]->getType() == "data") {
// Update input value data when input layer is "data" type,
// since the input value data address might be changed.
CHECK(extInVal_);
extInVal_->setData(getInputValue(0, CPU_DEVICE)->getData());
}
if (!outputOnlyMKLDNN_) {
clearGrads();
}
stream_->submit(pipelineFwd_);
}
{
REGISTER_TIMER_INFO("FwActTimer", getName().c_str());
forwardActivation();
}
}
void MKLDNNLayer::backward(const UpdateCallback& callback) {
if (needResetBwd_) {
VLOG(MKLDNN_BASE) << getName() << " reset mkldnn backward";
pipelineBwd_.clear();
pipelineMergeGrad_.clear();
mergeGrad_ = nullptr;
resetBwd(pipelineBwd_, inGrad_, wgtGrad_, biasGrad_, outGrad_);
// external output grad is not necessary
// since output may be mkldnn internal buffer or merge them directly.
CHECK(outGrad_) << "internal output grad is necessary";
if (extOutGrad_) {
CHECK_EQ(extOutGrad_->getData(), output_.grad->getData())
<< "the external buffer should share the same data with output_.grad";
}
if (cvtOutGrad_) {
pipelineBwd_.insert(pipelineBwd_.begin(), *cvtOutGrad_);
}
if (cvtInGrad_) {
pipelineBwd_.push_back(*cvtInGrad_);
}
printGradFormat();
needResetBwd_ = false;
}
// merge grad must before backward activation
if (mergeGrad_) {
REGISTER_TIMER_INFO("MergeBpGrad", getName().c_str());
stream_->submit(pipelineMergeGrad_);
}
{
REGISTER_TIMER_INFO("BpActTimer", getName().c_str());
backwardActivation();
}
{
REGISTER_TIMER_INFO("mkldnn_bwdTimer", getName().c_str());
stream_->submit(pipelineBwd_);
}
{
REGISTER_TIMER_INFO("WeightUpdate", getName().c_str());
updateWeights(callback);
}
}
void MKLDNNLayer::reshapeInput(int& batchsize, int& height, int& width) {
const Argument& input = inputLayers_[0]->getOutput();
batchsize = input.getBatchSize();
int h = input.getFrameHeight();
int w = input.getFrameWidth();
if (h != 0) {
height = h;
}
if (w != 0) {
width = w;
}
}
void MKLDNNLayer::reshapeOutput(size_t height, size_t width) {
output_.setFrameHeight(height);
output_.setFrameWidth(width);
for (size_t i = 0; i < outputOtherDevice_.size(); i++) {
outputOtherDevice_[i].setFrameHeight(height);
outputOtherDevice_[i].setFrameWidth(width);
}
}
void MKLDNNLayer::resetWithMatrix(MKLDNNMatrixPtr& dnn,
const MatrixPtr& mat,
memory::primitive_desc pd) {
dnn = nullptr;
if (mat == nullptr) {
return;
}
dnn = MKLDNNMatrix::create(pd, mat);
}
void MKLDNNLayer::resetInValue(
MKLDNNMatrixPtr& in, const std::shared_ptr<memory::primitive_desc>& intPD) {
cvtInVal_ = nullptr;
extInVal_ = nullptr;
in = nullptr;
CHECK_GT(bs_ * ic_ * ih_ * iw_, 0);
auto extPD = MKLDNNMatrix::createPrimitiveDesc(
{bs_, ic_, ih_, iw_}, format::nchw, engine_);
const MatrixPtr& inMat = inputLayers_[0]->getOutputValue();
in = std::dynamic_pointer_cast<MKLDNNMatrix>(inMat);
CHECK_EQ(inputIsOnlyMKLDNN(), in != nullptr);
if (in == nullptr || in->getFormat() == format::nc) {
in = MKLDNNMatrix::create(extPD, inMat);
}
extInVal_ = isPaddleFormat(in->getFormat()) ? in : nullptr;
if (in->getFormat() == format::nc) {
CHECK(ih_ == 1 && iw_ == 1);
}
if (nullptr == intPD || in->getPrimitiveDesc() == *intPD) {
return;
}
// need create reorder
in = MKLDNNMatrix::create(*intPD);
extInVal_ = extInVal_ ? extInVal_ : MKLDNNMatrix::create(extPD, inMat);
cvtInVal_ = MKLDNNMatrix::createReorder(extInVal_, in);
CHECK(cvtInVal_) << "should not be emptry";
}
void MKLDNNLayer::resetOutValue(MKLDNNMatrixPtr& out,
memory::primitive_desc intPD) {
cvtOutVal_ = nullptr;
out = MKLDNNMatrix::create(intPD, output_.value);
extOutVal_ = out;
if (outputIsOnlyMKLDNN() || isPaddleFormat(extOutVal_->getFormat())) {
return;
}
// need create reorder
CHECK_GT(bs_ * oc_ * oh_ * ow_, 0);
extOutVal_ = MKLDNNMatrix::create(
memory::dims{bs_, oc_, oh_, ow_}, format::nchw, engine_, output_.value);
out = MKLDNNMatrix::create(intPD);
cvtOutVal_ = MKLDNNMatrix::createReorder(out, extOutVal_);
CHECK(cvtOutVal_) << "should not be empty";
}
void MKLDNNLayer::resetInGrad(MKLDNNMatrixPtr& in,
memory::primitive_desc intPD) {
cvtInGrad_ = nullptr;
extInGrad_ = nullptr;
in = nullptr;
LayerPtr& input = inputLayers_[0];
if (input->getOutputGrad() == nullptr) {
// no need input grad
return;
}
CHECK(inputIsOnlyMKLDNN() || input->getOutputMapSize() <= 1)
<< "only support input is MKLDNN layer or only have one output layer";
// when input is a mkldnn branch node,
// this layer will save input grad to a internal buffer,
// and the mkldnn input layer will merge them to actual prev->output_.grad
const MatrixPtr& inMat =
input->getOutputMapSize() <= 1 ? input->getOutputGrad() : nullptr;
in = MKLDNNMatrix::create(intPD, inMat);
Argument& arg = input->getOutput(this->getName());
arg.grad = std::dynamic_pointer_cast<Matrix>(in);
CHECK(inVal_);
CHECK(inVal_->getPrimitiveDesc() == intPD) << "the primitive desc must equal";
if (inputIsOnlyMKLDNN()) {
return;
}
extInGrad_ = in;
if (isPaddleFormat(extInGrad_->getFormat())) {
return;
}
// need create reorder
// TODO(TJ): add macro definition to simplify it
CHECK(extInVal_ != nullptr && isPaddleFormat(extInVal_->getFormat()))
<< "should have external input value and the format must be nchw(nc)";
extInGrad_ = MKLDNNMatrix::create(extInVal_->getPrimitiveDesc(), inMat);
CHECK(inVal_ != nullptr && inVal_->getPrimitiveDesc() == intPD)
<< "should have internal input value and primitive desc must equal";
in = MKLDNNMatrix::create(intPD);
cvtInGrad_ = MKLDNNMatrix::createReorder(in, extInGrad_);
CHECK(cvtInGrad_);
}
void MKLDNNLayer::resetOutGrad(MKLDNNMatrixPtr& out,
memory::primitive_desc intPD) {
cvtOutGrad_ = nullptr;
extOutGrad_ = nullptr;
out = nullptr;
MatrixPtr& outMat = output_.grad;
out = MKLDNNMatrix::create(intPD, outMat);
resetMergeGrad(out);
if (outputIsOnlyMKLDNN()) {
return;
}
CHECK_LE(outputMap_.size(), 1U) << "do not support mixed with cpu device";
extOutGrad_ = out;
if (isPaddleFormat(extOutGrad_->getFormat())) {
return;
}
// need create reorder
CHECK(extOutVal_ != nullptr && isPaddleFormat(extOutVal_->getFormat()))
<< "should have external output value and the format must be nchw(nc)";
extOutGrad_ = MKLDNNMatrix::create(extOutVal_->getPrimitiveDesc(), outMat);
CHECK(outVal_ != nullptr && outVal_->getPrimitiveDesc() == intPD)
<< "should have internal output value and primitive desc must equal";
out = MKLDNNMatrix::create(intPD);
cvtOutGrad_ = MKLDNNMatrix::createReorder(extOutGrad_, out);
CHECK(cvtOutGrad_);
}
void MKLDNNLayer::resetMergeGrad(MKLDNNMatrixPtr& out) {
mergeGrad_ = nullptr;
pipelineMergeGrad_.clear();
if (outputMap_.size() <= 1 || !outputIsOnlyMKLDNN()) {
// do not merge when output is not all MKLDNN or only one output
return;
}
CHECK(out) << "should have reset internal ouput grad";
std::vector<double> scales(outputMap_.size(), 1.0);
std::vector<memory::primitive_desc> srcPDs;
std::vector<primitive::at> srcs;
for (auto it = outputMap_.begin(); it != outputMap_.end(); ++it) {
MKLDNNMatrixPtr src =
std::dynamic_pointer_cast<MKLDNNMatrix>(it->second->grad);
CHECK(src) << "should be MKLDNNMatrix";
auto srcDims = src->getDims();
auto dstDims = out->getDims();
CHECK_EQ(srcDims.size(), dstDims.size());
for (size_t i = 0; i < srcDims.size(); ++i) {
CHECK_EQ(srcDims[i], dstDims[i]);
}
VLOG(MKLDNN_BASE) << getName() << " has output grad " << it->first
<< ", format " << src->getFormat();
srcPDs.push_back(src->getPrimitiveDesc());
srcs.push_back(*src);
}
// TODO(TJ): remove me when mkldnn sum support different formats
for (size_t i = 1; i < srcPDs.size(); ++i) {
CHECK(srcPDs[0] == srcPDs[i]);
}
tmpOutGrad_ = out;
tmpCvt_ = nullptr;
if (out->getPrimitiveDesc() != srcPDs[0]) {
tmpOutGrad_ = MKLDNNMatrix::create(srcPDs[0]);
tmpCvt_ = MKLDNNMatrix::createReorder(tmpOutGrad_, out);
CHECK(tmpCvt_);
pipelineMergeGrad_.push_back(*tmpCvt_);
}
auto sumPD =
sum::primitive_desc(tmpOutGrad_->getMemoryDesc(), scales, srcPDs);
mergeGrad_.reset(new sum(sumPD, srcs, *tmpOutGrad_));
pipelineMergeGrad_.insert(pipelineMergeGrad_.begin(), *mergeGrad_);
}
} // namespace paddle
paddle/gserver/layers/MKLDNNLayer.h
浏览文件 @ abce9eb7
......@@ -58,11 +58,31 @@ protected:
std::vector<mkldnn::primitive> pipelineFwd_;
std::vector<mkldnn::primitive> pipelineBwd_;
// MKLDNNMatrixPtr with internal format
/* Value and grad are seperated as internal and external buffers.
* Each MKLDNNLayer must init or reset internal buffer at least,
* and the external buffer format is always nchw of nc(when h==w==1),
* which is the same format as paddle.
* The output_.value and output_.grad always save the external data,
* when mixed with cpu device.
* When all layers are mkldnn layers, they could save internal data.
*/
// below MKLDNNMatrix buffers are all internal buffers
MKLDNNMatrixPtr inVal_;
MKLDNNMatrixPtr inGrad_;
MKLDNNMatrixPtr outVal_;
MKLDNNMatrixPtr outGrad_;
// below are external value and grad
MKLDNNMatrixPtr extInVal_;
MKLDNNMatrixPtr extInGrad_;
MKLDNNMatrixPtr extOutVal_;
MKLDNNMatrixPtr extOutGrad_;
// convert handle between external and internal buffers
std::shared_ptr<mkldnn::reorder> cvtInVal_;
std::shared_ptr<mkldnn::reorder> cvtInGrad_;
std::shared_ptr<mkldnn::reorder> cvtOutVal_;
std::shared_ptr<mkldnn::reorder> cvtOutGrad_;
// weight and bias are always internal buffers
MKLDNNMatrixPtr wgtVal_;
MKLDNNMatrixPtr wgtGrad_;
MKLDNNMatrixPtr biasVal_;
......@@ -91,6 +111,7 @@ public:
oh_(0),
ow_(0),
needResetBwd_(true),
outputOnlyMKLDNN_(false),
engine_(mkldnn::engine::cpu, 0),
stream_(nullptr),
fwd_(nullptr),
......@@ -99,92 +120,9 @@ public:
~MKLDNNLayer() {}
virtual bool init(const LayerMap& layerMap,
const ParameterMap& parameterMap) {
CHECK(FLAGS_use_mkldnn) << "MkldnnLayers only support use_mkldnn."
<< "Please set WITH_MKLDNN=ON "
<< "and set use_mkldnn=True";
CHECK(!useGpu_) << "Do not support GPU yet";
// set device id before Layer::init
setDevice(MKLDNN_DEVICE);
// change param device to MKLDNN device
setParamsDevice(MKLDNN_DEVICE, parameterMap);
if (!Layer::init(layerMap, parameterMap)) {
return false;
}
setOutputMap();
checkCPUOutputsNumber();
stream_.reset(new MKLDNNStream());
engine_ = CPUEngine::Instance().getEngine();
return true;
}
void forward(PassType passType) override {
passType_ = passType;
{
REGISTER_TIMER_INFO("mkldnn_FwdTimer", getName().c_str());
CHECK(!inputLayers_.empty());
copySeqInfoToOutputs();
size_t elemenCnt = inputLayers_[0]->getOutput().value->getElementCnt();
if (inputElemenCnt_ != elemenCnt) {
VLOG(MKLDNN_BASE) << getName() << " reset mkldnn forward";
// reset when input total sizes changed, not only the batchsize
inputElemenCnt_ = elemenCnt;
pipelineFwd_.clear();
reshape(bs_, ic_, ih_, iw_, oc_, oh_, ow_);
resetFwd(pipelineFwd_, inVal_, wgtVal_, biasVal_, outVal_);
convertWeightsFromPaddle();
needResetBwd_ = true;
}
if (inputLayers_[0]->getType() == "data") {
updateInputData();
}
if (!outputOnlyMKLDNN_) {
clearGrads();
}
stream_->submit(pipelineFwd_);
}
/* activation */ {
REGISTER_TIMER_INFO("FwActTimer", getName().c_str());
forwardActivation();
}
}
void backward(const UpdateCallback& callback) override {
if (needResetBwd_) {
VLOG(MKLDNN_BASE) << getName() << " reset mkldnn backward";
pipelineBwd_.clear();
pipelineMergeGrad_.clear();
mergeGrad_ = nullptr;
resetBwd(pipelineBwd_, inGrad_, wgtGrad_, biasGrad_, outGrad_);
needResetBwd_ = false;
}
// merge grad must before backward activation
if (mergeGrad_) {
REGISTER_TIMER_INFO("MergeBpGrad", getName().c_str());
stream_->submit(pipelineMergeGrad_);
}
{
REGISTER_TIMER_INFO("BpActTimer", getName().c_str());
backwardActivation();
}
{
REGISTER_TIMER_INFO("mkldnn_bwdTimer", getName().c_str());
stream_->submit(pipelineBwd_);
}
{
REGISTER_TIMER_INFO("WeightUpdate", getName().c_str());
updateWeights(callback);
}
}
virtual bool init(const LayerMap& layerMap, const ParameterMap& parameterMap);
virtual void forward(PassType passType);
virtual void backward(const UpdateCallback& callback);
/**
* reshape the input image sizes
......@@ -195,7 +133,7 @@ public:
int& bs, int& ic, int& ih, int& iw, int oc, int& oh, int& ow) = 0;
/**
* reset the mkldnn forward primitve and memory
* reset the mkldnn forward primitve and memories
* only would be called when input size changes
*/
virtual void resetFwd(std::vector<mkldnn::primitive>& pipeline,
......@@ -205,7 +143,7 @@ public:
MKLDNNMatrixPtr& out) = 0;
/**
* reset the mkldnn backward primitve and memory for mkldnn fc
* reset the mkldnn backward primitve and memories
* only would be called when needed
*/
virtual void resetBwd(std::vector<mkldnn::primitive>& pipeline,
......@@ -214,12 +152,6 @@ public:
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out) = 0;
/**
* Update input value data when input layer is "data" type.
* Since the input value data address might be changed.
*/
virtual void updateInputData() {}
/**
* Update weights and biases if necessary.
*/
......@@ -246,131 +178,78 @@ protected:
/**
* reshape the input image sizes and input batchsize
*/
virtual void reshapeInput(int& batchsize, int& height, int& width) {
const Argument& input = inputLayers_[0]->getOutput();
batchsize = input.getBatchSize();
int h = input.getFrameHeight();
int w = input.getFrameWidth();
if (h != 0) {
height = h;
}
if (w != 0) {
width = w;
}
}
void reshapeInput(int& batchsize, int& height, int& width);
/**
* reshape output image sizes
*/
virtual void reshapeOutput(size_t height, size_t width) {
output_.setFrameHeight(height);
output_.setFrameWidth(width);
for (size_t i = 0; i < outputOtherDevice_.size(); i++) {
outputOtherDevice_[i].setFrameHeight(height);
outputOtherDevice_[i].setFrameWidth(width);
}
}
void reshapeOutput(size_t height, size_t width);
/**
* reset the output grad matrix from primitive desc.
* and reset the merge grad primitive if needed.
* note: when this layer has serval outputs,
* it could not be mixed with cpu device,
* since it can not get memory desc from cpu device.
* reset MKLDNNMatrix from Matrix and internal primitive desc.
* reset nullptr if matrix or primitive desc is empty
*/
virtual void resetOutGrad(MKLDNNMatrixPtr& out,
mkldnn::memory::primitive_desc pd) {
CHECK(outputIsOnlyMKLDNN()) << "do not support mixed with other device yet";
mergeGrad_ = nullptr;
pipelineMergeGrad_.clear();
out = MKLDNNMatrix::create(output_.grad, pd);
if (outputMap_.size() <= 1) {
return;
}
std::vector<double> scales(outputMap_.size(), 1.0);
std::vector<mkldnn::memory::primitive_desc> srcPDs;
std::vector<mkldnn::primitive::at> srcs;
for (auto it = outputMap_.begin(); it != outputMap_.end(); ++it) {
MKLDNNMatrixPtr src =
std::dynamic_pointer_cast<MKLDNNMatrix>(it->second->grad);
VLOG(MKLDNN_BASE) << getName() << " has output grad " << it->first;
CHECK(src) << "should be MKLDNNMatrix";
auto srcDims = src->getDims();
auto dstDims = out->getDims();
CHECK_EQ(srcDims.size(), dstDims.size());
for (size_t i = 0; i < srcDims.size(); ++i) {
CHECK_EQ(srcDims[i], dstDims[i]);
}
srcPDs.push_back(src->getPrimitiveDesc());
srcs.push_back(*src);
}
void resetWithMatrix(MKLDNNMatrixPtr& dnn,
const MatrixPtr& mat,
mkldnn::memory::primitive_desc pd);
// TODO(TJ): remove me when mkldnn sum support different formats
for (size_t i = 1; i < srcPDs.size(); ++i) {
CHECK(srcPDs[0] == srcPDs[i]);
}
tmpOutGrad_ = nullptr;
tmpCvt_ = nullptr;
if (out->getPrimitiveDesc() != srcPDs[0]) {
tmpOutGrad_ = MKLDNNMatrix::create(nullptr, srcPDs[0]);
tmpCvt_ = MKLDNNMatrix::createReorder(tmpOutGrad_, out);
CHECK(tmpCvt_);
pipelineMergeGrad_.push_back(*tmpCvt_);
} else {
tmpOutGrad_ = out;
}
/**
* reset input value from input MKLDNNMatrix and internal primitive desc.
* reset both internal and external buffer and create reorder if necessary.
*/
void resetInValue(
MKLDNNMatrixPtr& in,
const std::shared_ptr<mkldnn::memory::primitive_desc>& intPD = nullptr);
auto sumPD = mkldnn::sum::primitive_desc(
tmpOutGrad_->getMemoryDesc(), scales, srcPDs);
mergeGrad_.reset(new mkldnn::sum(sumPD, srcs, *tmpOutGrad_));
pipelineMergeGrad_.insert(pipelineMergeGrad_.begin(), *mergeGrad_);
}
/**
* reset output value from internal primitive desc.
* reset both internal and external buffer and create reorder if necessary.
*/
void resetOutValue(MKLDNNMatrixPtr& out,
mkldnn::memory::primitive_desc intPD);
/**
* reset input grad from primitive desc.
* this function is avaiable for input is only mkldnn
* or input do not care cpu device
* reset input grad from internal primitive desc.
* reset both internal and external buffer and create reorder if necessary.
*/
virtual void resetInGrad(MKLDNNMatrixPtr& in,
mkldnn::memory::primitive_desc pd) {
LayerPtr& input = inputLayers_[0];
const MatrixPtr& grad =
input->getOutputMapSize() > 1 ? nullptr : input->getOutput().grad;
in = MKLDNNMatrix::create(grad, pd);
Argument& arg = input->getOutput(this->getName());
arg.grad = std::dynamic_pointer_cast<Matrix>(in);
}
void resetInGrad(MKLDNNMatrixPtr& in, mkldnn::memory::primitive_desc intPD);
/**
* print info about sizes
* reset output grad from internal primitive desc.
* merge grad if necessary.
* reset both internal and external buffer and create reorder if necessary.
* note: about merge grad, when this layer has several outputs,
* it could not be mixed with cpu device,
* since it can not get memory desc from cpu device.
*/
virtual void printSizeInfo() {
VLOG(MKLDNN_SIZES) << getName() << ": bs: " << bs_ << ", ic: " << ic_
<< ", ih: " << ih_ << ", iw: " << iw_ << ", oc: " << oc_
<< ", oh: " << oh_ << ", ow: " << ow_;
}
void resetOutGrad(MKLDNNMatrixPtr& out, mkldnn::memory::primitive_desc intPD);
/**
* Print the mkldnn memory format flow of value
* reset the merge grad primitive if necessary.
* note: do not support the grads mixed with cpu device,
* since it can not get memory desc from cpu device.
*/
virtual void printValueFormatFlow() {
if (inVal_ && outVal_) {
VLOG(MKLDNN_FMTS) << inVal_->getFormat() << " >>> "
<< outVal_->getFormat();
}
}
void resetMergeGrad(MKLDNNMatrixPtr& out);
protected:
/**
* Print the mkldnn memory format flow of grad
* Set deviceId of this layer.
*/
virtual void printGradFormatFlow() {
if (inGrad_ && outGrad_) {
VLOG(MKLDNN_FMTS) << inGrad_->getFormat() << " <<< "
<< outGrad_->getFormat();
void setDevice(int id) { deviceId_ = id; }
/**
* check the format is nchw or nc,
* which is supported by Paddle default memory layout
*/
bool isPaddleFormat(mkldnn::memory::format fmt) {
if (fmt == mkldnn::memory::format::nchw ||
fmt == mkldnn::memory::format::nc) {
return true;
} else {
return false;
}
}
protected:
/**
* If input only has MKLDNN device.
* Otherwise, only support the previous layer using CPU device.
......@@ -380,7 +259,6 @@ protected:
if (prevDevice == MKLDNN_DEVICE) {
return true;
} else {
// do not support GPU yet
CHECK_EQ(prevDevice, CPU_DEVICE) << "Only support CPU yet";
return false;
}
......@@ -400,9 +278,61 @@ protected:
}
/**
* Set deviceId of this layer.
* print info about sizes
*/
void setDevice(int id) { deviceId_ = id; }
virtual void printSizeInfo() {
VLOG(MKLDNN_SIZES) << getName() << ": bs: " << bs_ << ", ic: " << ic_
<< ", ih: " << ih_ << ", iw: " << iw_ << ", oc: " << oc_
<< ", oh: " << oh_ << ", ow: " << ow_;
}
/**
* print the mkldnn memory format of value
*/
virtual void printValueFormat() {
if (extInVal_) {
VLOG(MKLDNN_FMTS) << extInVal_->getFormat() << " >>> ";
}
if (inVal_) {
VLOG(MKLDNN_FMTS) << inVal_->getFormat() << " >>>";
}
if (outVal_) {
VLOG(MKLDNN_FMTS) << outVal_->getFormat() << " >>> ";
}
if (extOutVal_) {
VLOG(MKLDNN_FMTS) << extOutVal_->getFormat();
}
if (wgtVal_) {
VLOG(MKLDNN_FMTS) << "Weight value format: " << wgtVal_->getFormat();
}
if (biasVal_) {
VLOG(MKLDNN_FMTS) << "Bias value format: " << biasVal_->getFormat();
}
}
/**
* print the mkldnn memory format of grad
*/
virtual void printGradFormat() {
if (extOutGrad_) {
VLOG(MKLDNN_FMTS) << extOutGrad_->getFormat();
}
if (outGrad_) {
VLOG(MKLDNN_FMTS) << outGrad_->getFormat() << " <<< ";
}
if (inGrad_) {
VLOG(MKLDNN_FMTS) << inGrad_->getFormat() << " <<<";
}
if (extInGrad_) {
VLOG(MKLDNN_FMTS) << extInGrad_->getFormat() << " <<< ";
}
if (wgtGrad_) {
VLOG(MKLDNN_FMTS) << "Weight grad format: " << wgtGrad_->getFormat();
}
if (biasGrad_) {
VLOG(MKLDNN_FMTS) << "Bias grad format: " << biasGrad_->getFormat();
}
}
private:
/**
......@@ -449,6 +379,19 @@ private:
}
}
/**
* if have cpu device, share value and grad data with output_
*/
void shareCPUDevice() {
if (outputIsOnlyMKLDNN()) {
return;
}
for (size_t i = 0; i < outputOtherDevice_.size(); i++) {
outputOtherDevice_[i].value = output_.value;
outputOtherDevice_[i].grad = output_.grad;
}
}
/**
* Check the cpu device number of outputOtherDevice_.
* should have only one at most.
......
paddle/gserver/layers/MKLDNNPoolLayer.cpp
浏览文件 @ abce9eb7
......@@ -85,8 +85,6 @@ void MKLDNNPoolLayer::resetFwd(std::vector<primitive>& pipeline,
resetFwdPD(fwdPD_, in, out);
resetFwdPipeline(pipeline, fwdPD_, in, out);
printValueFormatFlow();
}
void MKLDNNPoolLayer::resetBwd(std::vector<primitive>& pipeline,
......@@ -101,65 +99,22 @@ void MKLDNNPoolLayer::resetBwd(std::vector<primitive>& pipeline,
resetBwdPD(pd, in, out);
resetBwdPipeline(pipeline, pd, in, out);
printGradFormatFlow();
}
void MKLDNNPoolLayer::updateInputData() {
inVal_->setData(getInputValue(0, CPU_DEVICE)->getData());
}
void MKLDNNPoolLayer::resetFwdBuffers(MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& out) {
resetInValue(in);
resetOutValue(out);
}
void MKLDNNPoolLayer::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_);
}
}
void MKLDNNPoolLayer::resetOutValue(MKLDNNMatrixPtr& out) {
CHECK(inVal_) << "Should reset input value first";
memory::dims outDims = memory::dims{bs_, oc_, oh_, ow_};
out = MKLDNNMatrix::create(
output_.value, outDims, inVal_->getFormat(), engine_);
// create reorder if output value has cpu device and pd do not match
cpuOutVal_ = nullptr;
cvtOutVal_ = nullptr;
if (!outputIsOnlyMKLDNN()) {
const MatrixPtr& cpuOut = getOutput(CPU_DEVICE).value;
cpuOutVal_ = MKLDNNMatrix::create(cpuOut, outDims, format::nchw, engine_);
if (cpuOutVal_->getPrimitiveDesc() != out->getPrimitiveDesc()) {
out = MKLDNNMatrix::create(nullptr, out->getPrimitiveDesc());
cvtOutVal_ = MKLDNNMatrix::createReorder(out, cpuOutVal_);
CHECK(cvtOutVal_) << "should not be emptry";
} else {
cpuOut->setData(output_.value->getData());
cpuOutVal_ = out;
}
output_.value = std::dynamic_pointer_cast<Matrix>(cpuOutVal_);
return;
}
output_.value = std::dynamic_pointer_cast<Matrix>(outVal_);
CHECK(in);
auto outPD =
MKLDNNMatrix::createPrimitiveDesc(outDims, in->getFormat(), engine_);
resetOutValue(out, outPD);
}
void MKLDNNPoolLayer::resetFwdPD(std::shared_ptr<pool_fwd::primitive_desc>& pd,
MKLDNNMatrixPtr in,
MKLDNNMatrixPtr out) {
memory::dims inDims = memory::dims{bs_, ic_, ih_, iw_};
memory::dims outDims = memory::dims{bs_, oc_, oh_, ow_};
memory::dims kernels = memory::dims{fh_, fw_};
memory::dims strides = memory::dims{sh_, sw_};
memory::dims padL = memory::dims{ph_, pw_};
......@@ -194,58 +149,26 @@ void MKLDNNPoolLayer::resetFwdPipeline(
? std::make_shared<pool_fwd>(pool_fwd(*pd, *in, *out, *workspace_))
: std::make_shared<pool_fwd>(pool_fwd(*pd, *in, *out));
pipeline.push_back(*fwd_);
if (cvtOutVal_) {
pipeline.push_back(*cvtOutVal_);
}
}
void MKLDNNPoolLayer::resetBwdBuffers(MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& out) {
resetOutGrad(out);
resetInGrad(in);
}
void MKLDNNPoolLayer::resetOutGrad(MKLDNNMatrixPtr& out) {
cpuOutGrad_ = nullptr;
cvtOutGrad_ = nullptr;
CHECK(outVal_);
if (outputIsOnlyMKLDNN()) {
MKLDNNLayer::resetOutGrad(out, outVal_->getPrimitiveDesc());
} else {
const MatrixPtr& cpuOut = getOutput(CPU_DEVICE).grad;
// always share the same grad data of CPU output
// then the activation can get the right grad from output_.grad
output_.grad->setData(cpuOut->getData());
cpuOutGrad_ = MKLDNNMatrix::create(
cpuOut, memory::dims{bs_, oc_, oh_, ow_}, format::nchw, engine_);
if (cpuOutGrad_->getPrimitiveDesc() != outVal_->getPrimitiveDesc()) {
out = MKLDNNMatrix::create(nullptr, outVal_->getPrimitiveDesc());
cvtOutGrad_ = MKLDNNMatrix::createReorder(cpuOutGrad_, out);
CHECK(cvtOutGrad_) << "should not be emptry";
} else {
out = cpuOutGrad_;
}
}
}
void MKLDNNPoolLayer::resetInGrad(MKLDNNMatrixPtr& in) {
in = nullptr;
if (inputLayers_[0]->getOutput().grad == nullptr) {
return;
}
CHECK(inVal_);
MKLDNNLayer::resetInGrad(in, inVal_->getPrimitiveDesc());
CHECK(inVal_ && outVal_);
resetOutGrad(out, outVal_->getPrimitiveDesc());
resetInGrad(in, inVal_->getPrimitiveDesc());
}
void MKLDNNPoolLayer::resetBwdPD(std::shared_ptr<pool_bwd::primitive_desc>& pd,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& out) {
pd = nullptr;
if (in == nullptr) {
return;
}
memory::dims kernels = memory::dims{fh_, fw_};
memory::dims strides = memory::dims{sh_, sw_};
memory::dims padL = memory::dims{ph_, pw_};
memory::dims padR = getPaddingR();
CHECK(in);
CHECK(out);
auto bwdDesc = pool_bwd::desc(poolAlgo_,
in->getMemoryDesc(),
......@@ -263,8 +186,8 @@ void MKLDNNPoolLayer::resetBwdPipeline(
std::shared_ptr<pool_bwd::primitive_desc>& pd,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& out) {
if (cvtOutGrad_) {
pipeline.push_back(*cvtOutGrad_);
if (pd == nullptr) {
return;
}
bwdData_ =
......
paddle/gserver/layers/MKLDNNPoolLayer.h
浏览文件 @ abce9eb7
......@@ -38,13 +38,6 @@ protected:
// pooling_avg or pooling_max
mkldnn::algorithm poolAlgo_;
// MKLDNNMatrixPtr which should be created from CPU Device
MKLDNNMatrixPtr cpuOutVal_;
MKLDNNMatrixPtr cpuOutGrad_;
// convert handle between CPU device and MKLDNN device
std::shared_ptr<mkldnn::reorder> cvtOutVal_;
std::shared_ptr<mkldnn::reorder> cvtOutGrad_;
// save forward primitive_desc, which can be used backward
std::shared_ptr<pool_fwd::primitive_desc> fwdPD_;
// according to https://github.com/01org/mkl-dnn/blob/master/tests/gtests/
......@@ -74,8 +67,6 @@ public:
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out) override;
void updateInputData() override;
void printSizeInfo() override {
MKLDNNLayer::printSizeInfo();
VLOG(MKLDNN_SIZES) << getName() << ": fh: " << fh_ << ", fw: " << fw_
......@@ -90,8 +81,6 @@ protected:
* reset pipeline.
*/
void resetFwdBuffers(MKLDNNMatrixPtr& in, MKLDNNMatrixPtr& out);
void resetInValue(MKLDNNMatrixPtr& in);
void resetOutValue(MKLDNNMatrixPtr& out);
void resetFwdPD(std::shared_ptr<pool_fwd::primitive_desc>& pd,
MKLDNNMatrixPtr in,
MKLDNNMatrixPtr out);
......@@ -106,8 +95,6 @@ protected:
* reset pipeline.
*/
void resetBwdBuffers(MKLDNNMatrixPtr& in, MKLDNNMatrixPtr& out);
void resetOutGrad(MKLDNNMatrixPtr& out);
void resetInGrad(MKLDNNMatrixPtr& in);
void resetBwdPD(std::shared_ptr<pool_bwd::primitive_desc>& pd,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& out);
......
paddle/gserver/tests/MKLDNNTester.cpp
浏览文件 @ abce9eb7
......@@ -97,7 +97,7 @@ void MKLDNNTester::randomWgtDatas() {
parameters_[REF][i]->randomize();
dnnValue->copyFrom(*refValue);
VLOG(lvl_) << "Random weight data " << parameters_[DNN][i]->getName();
VLOG(MKLDNN_TESTS) << "Random weight " << parameters_[DNN][i]->getName();
printVector(dnnValue);
}
}
......@@ -109,7 +109,7 @@ void MKLDNNTester::randomBotDatas() {
dataLayers_[REF][i]->getOutputValue()->randomizeUniform();
dataLayers_[DNN][i]->getOutputValue()->copyFrom(
*(dataLayers_[REF][i]->getOutputValue()));
VLOG(lvl_) << "Input " << i << " data:";
VLOG(MKLDNN_TESTS) << "Random Foward, InputValue " << i;
printMatrix(dataLayers_[REF][i]->getOutputValue());
}
}
......@@ -118,12 +118,12 @@ void MKLDNNTester::randomTopDiffs() {
refLayer_->getOutputGrad()->randomizeUniform();
dnnLayer_->getOutput(CPU_DEVICE)
.grad->copyFrom(*(refLayer_->getOutputGrad()));
VLOG(lvl_) << "Random Backward Input, TopDiff: ";
VLOG(MKLDNN_TESTS) << "Random Backward, OutputGrad";
printMatrix(refLayer_->getOutputGrad());
}
void MKLDNNTester::checkForward() {
VLOG(MKLDNN_ALL) << "Check Forward";
VLOG(MKLDNN_TESTS) << "Check Forward";
printTopDatas();
double delta =
compareMatrix(dnnLayer_->getOutputValue(), refLayer_->getOutputValue());
......@@ -131,15 +131,15 @@ void MKLDNNTester::checkForward() {
}
void MKLDNNTester::checkBackwardData() {
VLOG(MKLDNN_ALL) << "Check Backward Data";
VLOG(MKLDNN_TESTS) << "Check Backward Data";
// TODO(TJ): uncomment me when batch norm ready
// const bool isBN = dnnLayer_->getType() == "mkldnn_batch_norm";
for (size_t i = 0; i < dataLayers_[DNN].size(); ++i) {
const MatrixPtr& dnnDiff = dataLayers_[DNN][i]->getOutputGrad();
const MatrixPtr& refDiff = dataLayers_[REF][i]->getOutputGrad();
VLOG(lvl_) << "Mkldnn Backward Output BotDiff " << i;
VLOG(MKLDNN_ALL) << "MKLDNN Backward Result: InputGrad " << i;
printMatrix(dnnDiff);
VLOG(lvl_) << "Reference Backward Output BotDiff " << i;
VLOG(MKLDNN_ALL) << "Reference Backward Result: InputGrad " << i;
printMatrix(refDiff);
double delta = compareMatrix(dnnDiff, refDiff);
......@@ -153,7 +153,7 @@ void MKLDNNTester::checkBackwardData() {
}
void MKLDNNTester::checkBackwardWgts() {
VLOG(MKLDNN_ALL) << "Check Backward Weight";
VLOG(MKLDNN_TESTS) << "Check Backward Weight";
CHECK_EQ(parameters_[DNN].size(), parameters_[REF].size());
vector<VectorPtr> dnnWgts; // used to temply save mkldnn weights
saveWgt(parameters_[DNN], dnnWgts);
......@@ -165,9 +165,11 @@ void MKLDNNTester::checkBackwardWgts() {
for (size_t i = 0; i < parameters_[DNN].size(); ++i) {
const VectorPtr& dnn = parameters_[DNN][i]->getBuf(PARAMETER_VALUE);
const VectorPtr& ref = parameters_[REF][i]->getBuf(PARAMETER_VALUE);
VLOG(lvl_) << "Mkldnn Output weight " << parameters_[DNN][i]->getName();
VLOG(MKLDNN_ALL) << "MKLDNN Result: weight value"
<< parameters_[DNN][i]->getName();
printVector(dnn);
VLOG(lvl_) << "Reference Output weight " << parameters_[REF][i]->getName();
VLOG(MKLDNN_ALL) << "Reference Result: weight value "
<< parameters_[REF][i]->getName();
printVector(ref);
double delta = compareVector(dnn, ref);
......@@ -240,7 +242,8 @@ void MKLDNNTester::printTopDatas() {
}
for (int n = 0; n < NUM; ++n) {
VLOG(lvl_) << testLayers_[n]->getType() << " forward output TopData: ";
VLOG(MKLDNN_ALL) << testLayers_[n]->getType()
<< " Forward Result: OutputValue";
printMatrix(testLayers_[n]->getOutputValue());
}
}
......@@ -252,7 +255,7 @@ void MKLDNNTester::printMatrix(const MatrixPtr& m) {
std::ostringstream ostr;
m->print(ostr);
VLOG(lvl_) << std::endl << ostr.str();
VLOG(MKLDNN_ALL) << std::endl << ostr.str();
}
void MKLDNNTester::printVector(const VectorPtr& v) {
......@@ -262,7 +265,7 @@ void MKLDNNTester::printVector(const VectorPtr& v) {
std::ostringstream ostr;
v->print(ostr, v->getSize());
VLOG(lvl_) << std::endl << ostr.str();
VLOG(MKLDNN_ALL) << std::endl << ostr.str();
}
double MKLDNNTester::getDelta(const real* d1,
......@@ -314,7 +317,7 @@ void MKLDNNTester::runOnce() {
UpdateCallback updateCallback = [](Parameter* para) {
auto& grad = para->getBuf(PARAMETER_GRADIENT);
auto& value = para->getBuf(PARAMETER_VALUE);
real lr = 1e-3;
real lr = 1e-2;
value->add(*grad, lr);
grad->zeroMem();
};
......@@ -340,10 +343,9 @@ void MKLDNNTester::run(const TestConfig& dnn,
size_t batchSize,
size_t inputImgH,
size_t inputImgW,
bool printDetails,
size_t iter,
float epsilon,
bool log,
int level) {
float epsilon) {
CHECK(dnn.layerConfig.type().compare(0, 7, "mkldnn_") == 0 ||
dnn.layerConfig.active_type().compare(0, 7, "mkldnn_") == 0)
<< "should be MKLDNN layer or MKLDNN activation";
......@@ -359,10 +361,9 @@ void MKLDNNTester::run(const TestConfig& dnn,
ih_ = inputImgH;
iw_ = inputImgW;
log_ = printDetails;
iter_ = iter;
eps_ = epsilon;
log_ = log;
lvl_ = level;
// Firstly test mkldnn init from PARAM_FORMAT_ORIGINAL weight
reset(dnn, ref, batchSize);
......@@ -531,9 +532,11 @@ void MKLDNNTester::getOutResult(const std::string& configPath,
void MKLDNNTester::compareResult(DataOut& ref, DataOut& dnn, float eps) {
CHECK_EQ(ref.outValues.size(), dnn.outValues.size());
CHECK_EQ(ref.paraValues.size(), dnn.paraValues.size());
VLOG(MKLDNN_TESTS) << "compare value size: " << ref.outValues.size();
for (size_t i = 0; i < ref.outValues.size(); i++) {
EXPECT_LE(fabs(compareMatrix(ref.outValues[i], dnn.outValues[i])), eps);
}
VLOG(MKLDNN_TESTS) << "compare param size: " << ref.outValues.size();
for (size_t i = 0; i < ref.paraValues.size(); i++) {
EXPECT_LE(fabs(compareVector(ref.paraValues[i], dnn.paraValues[i])), eps);
}
......@@ -544,9 +547,10 @@ void MKLDNNTester::runBranchesTest(const std::string& configPath,
float eps) {
DataIn in;
initArgument(in, configPath, iter);
DataOut outCpu, outDnn;
VLOG(MKLDNN_TESTS) << "runing cpu network";
getOutResult(configPath, in, outCpu, false, iter);
VLOG(MKLDNN_TESTS) << "runing mkldnn network";
getOutResult(configPath, in, outDnn, true, iter);
compareResult(outCpu, outDnn, eps);
......
paddle/gserver/tests/MKLDNNTester.h
浏览文件 @ abce9eb7
......@@ -58,8 +58,6 @@ protected:
size_t iter_;
/// whether to print out the details
bool log_;
/// vlog level to print the matrix details datas
int lvl_;
/// epsilon
float eps_;
/// input image size, default 1
......@@ -70,7 +68,6 @@ public:
iter_ = iter;
eps_ = epsilon;
log_ = false;
lvl_ = MKLDNN_ALL;
}
~MKLDNNTester() {}
......@@ -81,10 +78,9 @@ public:
size_t batchSize,
size_t inputImgH = 1,
size_t inputImgW = 1,
bool printDetails = false,
size_t iter = 3,
float epsilon = 1e-4,
bool log = false,
int level = MKLDNN_ALL);
float epsilon = 1e-4);
static void runBranchesTest(const std::string& configPath,
size_t iter = 3,
float eps = 1e-4);
......
paddle/math/MKLDNNMatrix.cpp
浏览文件 @ abce9eb7
......@@ -18,7 +18,7 @@ using namespace mkldnn; // NOLINT
namespace paddle {
MKLDNNMatrixPtr MKLDNNMatrix::create(MatrixPtr m, memory::primitive_desc pd) {
MKLDNNMatrixPtr MKLDNNMatrix::create(memory::primitive_desc pd, MatrixPtr m) {
memory::desc md = pd.desc();
size_t ndims = md.data.ndims;
int* dims = md.data.dims;
......@@ -41,12 +41,12 @@ MKLDNNMatrixPtr MKLDNNMatrix::create(MatrixPtr m, memory::primitive_desc pd) {
return std::make_shared<MKLDNNMatrix>(cpuMatrix, pd);
}
MKLDNNMatrixPtr MKLDNNMatrix::create(MatrixPtr m,
memory::dims dims,
MKLDNNMatrixPtr MKLDNNMatrix::create(memory::dims dims,
memory::format fmt,
engine& eg,
MatrixPtr m,
mkldnn::memory::data_type dtype) {
return create(m, memory::primitive_desc(memory::desc(dims, dtype, fmt), eg));
return create(createPrimitiveDesc(dims, fmt, eg, dtype), m);
}
std::shared_ptr<reorder> MKLDNNMatrix::createReorder(const MKLDNNMatrixPtr& src,
......
paddle/math/MKLDNNMatrix.h
浏览文件 @ abce9eb7
......@@ -40,24 +40,37 @@ public:
/**
* Create MKLDNNMatrix from a MatrixPtr and memory primitive_desc
*/
static MKLDNNMatrixPtr create(MatrixPtr m, mkldnn::memory::primitive_desc pd);
static MKLDNNMatrixPtr create(mkldnn::memory::primitive_desc pd,
MatrixPtr m = nullptr);
/**
* Create MKLDNNMatrix from a MatrixPtr and memory details info
*/
static MKLDNNMatrixPtr create(
MatrixPtr m,
mkldnn::memory::dims dims,
mkldnn::memory::format fmt,
mkldnn::engine& eg,
MatrixPtr m = nullptr,
mkldnn::memory::data_type dtype = mkldnn::memory::data_type::f32);
/**
* Create primitive descriptor.
* default with f32 dtype
*/
static mkldnn::memory::primitive_desc createPrimitiveDesc(
const mkldnn::memory::dims dims,
const mkldnn::memory::format& fmt,
const mkldnn::engine& eg,
const mkldnn::memory::data_type& dtype = mkldnn::memory::data_type::f32) {
return mkldnn::memory::primitive_desc(memory::desc(dims, dtype, fmt), eg);
}
/**
* Create Memory descriptor.
* default with any format and f32 dtype
*/
static mkldnn::memory::desc createMemoryDesc(
const mkldnn::memory::dims& dims,
const mkldnn::memory::dims dims,
const mkldnn::memory::format& fmt = mkldnn::memory::format::any,
const mkldnn::memory::data_type& dtype = mkldnn::memory::data_type::f32) {
return mkldnn::memory::desc(dims, dtype, fmt);
......
paddle/trainer/tests/sample_trainer_config_branch_net.conf
浏览文件 @ abce9eb7
......@@ -17,7 +17,7 @@ from paddle.trainer_config_helpers import *
################################### Data Configuration ###################################
TrainData(ProtoData(files = "trainer/tests/mnist.list"))
################################### Algorithm Configuration ###################################
settings(batch_size = 256,
settings(batch_size = 128,
learning_method = MomentumOptimizer(momentum=0.5, sparse=False))
################################### Network Configuration ###################################
data = data_layer(name ="input", size=784)
......@@ -44,10 +44,11 @@ a2 = img_conv_layer(input=tmp,
shared_biases=True,
act=ReluActivation())
tmp = concat_layer(input=[a1, a2])
tmp = addto_layer(input=[a1, a2],
act=ReluActivation(),
bias_attr=False)
tmp = img_pool_layer(input=tmp,
num_channels=64,
pool_size=3,
stride=2,
padding=1,
......@@ -55,35 +56,34 @@ tmp = img_pool_layer(input=tmp,
b1 = img_conv_layer(input=tmp,
filter_size=3,
num_filters=64,
num_filters=32,
padding=1,
shared_biases=True,
act=ReluActivation())
b1 = img_pool_layer(input=b1,
pool_size=3,
stride=1,
padding=1,
stride=2,
padding=0,
pool_type=MaxPooling())
b2 = img_conv_layer(input=tmp,
filter_size=5,
filter_size=3,
num_filters=64,
padding=2,
padding=1,
shared_biases=True,
act=ReluActivation())
b2 = img_pool_layer(input=b2,
pool_size=5,
stride=1,
padding=2,
stride=2,
padding=1,
pool_type=MaxPooling())
tmp = addto_layer(input=[b1, b2],
act=ReluActivation(),
bias_attr=False)
tmp = concat_layer(input=[b1, b2])
tmp = img_pool_layer(input=tmp,
num_channels=96,
pool_size=3,
stride=2,
padding=1,
......
paddle/trainer/tests/sample_trainer_config_simple_net.conf
浏览文件 @ abce9eb7
......@@ -17,7 +17,7 @@ from paddle.trainer_config_helpers import *
################################### Data Configuration ###################################
TrainData(ProtoData(files = "trainer/tests/mnist.list"))
################################### Algorithm Configuration ###################################
settings(batch_size = 1000,
settings(batch_size = 128,
learning_method = MomentumOptimizer(momentum=0.5, sparse=False))
################################### Network Configuration ###################################
data = data_layer(name ="input", size=784)
......
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