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pass mkldnn gtest

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revert-3824-remove_grad_op_type
tensor-tang 8 years ago
parent ec9009f320
commit 0c951176bd
6 changed files with 112 additions and 54 deletions
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24
paddle/gserver/layers/MkldnnFcLayer.cpp
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@ -42,7 +42,6 @@ bool MkldnnFcLayer::init(const LayerMap& layerMap,
// create weight
weight_ =
std::unique_ptr<Weight>(new Weight(oc_, iLayerSize_, parameters_[0], 0));
initWgt();
// create biases
if (biasParameter_.get() != NULL) {
@ -51,20 +50,36 @@ bool MkldnnFcLayer::init(const LayerMap& layerMap,
return true;
}
void MkldnnFcLayer::initWgt() {
void MkldnnFcLayer::cvtWgtFromPaddle() {
if (hasInitedWgt_) {
return;
}
// The weight_ is transposed from initial paddle weight
MatrixPtr paddleWgt = Matrix::create(
weight_->getW()->getData(), iLayerSize_, oc_, false, false);
std::ostringstream ostr;
paddleWgt->print(ostr);
VLOG(DNN_BASE) << ostr.str();
VLOG(DNN_ALL) << "Initial Weight from paddle: " << std::endl << ostr.str();
// Firstly in mkldnn, the matrix is transposed from initial paddle weight
// The mkldnn weight is transposed from initial paddle matrix
MatrixPtr paddleWgtT;
paddleWgt->transpose(paddleWgtT, true);
weight_->getW()->copyFrom(*paddleWgtT);
hasInitedWgt_ = true;
}
void MkldnnFcLayer::cvtWgtToPaddle() {
MatrixPtr dnnWgt = weight_->getW();
MatrixPtr paddleWgt;
dnnWgt->transpose(paddleWgt, true);
// copy paddle weight and override on weight_
MatrixPtr dnnWgtT = Matrix::create(
dnnWgt->getData(), dnnWgt->getWidth(), dnnWgt->getHeight(), false, false);
dnnWgtT->copyFrom(*paddleWgt);
}
void MkldnnFcLayer::reshape() {
@ -86,6 +101,7 @@ void MkldnnFcLayer::reshape() {
ic_ = iLayerSize_ / (ih_ * iw_);
CHECK_EQ(size_t(ic_ * ih_ * iw_), iLayerSize_) << "not divisible";
CHECK_EQ(size_t(oc_), getSize());
printSizeInfo();
// reset output
output_.setFrameHeight(oh_);

11
paddle/gserver/layers/MkldnnFcLayer.h
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@ -29,25 +29,30 @@ protected:
// input layer size, can not be change after init
size_t iLayerSize_; // == ic * ih * iw
bool hasInitedWgt_;
// fc weight and bias
std::unique_ptr<Weight> weight_;
std::unique_ptr<Weight> biases_;
public:
explicit MkldnnFcLayer(const LayerConfig& config) : MkldnnLayer(config) {}
explicit MkldnnFcLayer(const LayerConfig& config)
: MkldnnLayer(config), hasInitedWgt_(false) {}
~MkldnnFcLayer() {}
bool init(const LayerMap& layerMap,
const ParameterMap& parameterMap) override;
void initWgt();
void cvtWgtFromPaddle() override;
void reshape();
void cvtWgtToPaddle() override;
void forward(PassType passType) override;
void backward(const UpdateCallback& callback) override;
void reshape();
};
} // namespace paddle

62
paddle/gserver/layers/MkldnnLayer.cpp
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@ -25,11 +25,18 @@ namespace paddle {
bool MkldnnLayer::init(const LayerMap& layerMap,
const ParameterMap& parameterMap) {
if (!Layer::init(layerMap, parameterMap)) {
return false;
}
CHECK(FLAGS_use_mkldnn) << "MkldnnLayers only support use_mkldnn."
<< "Please set WITH_MKLDNN=ON "
<< "and set use_mkldnn=True";
stream_.reset(new MkldnnStream());
engine_ = CpuEngine::Instance().getEngine();
// TODO(TJ): deivecId
return Layer::init(layerMap, parameterMap);
return true;
}
void MkldnnLayer::resetForwardFC(int bs,
@ -42,7 +49,6 @@ void MkldnnLayer::resetForwardFC(int bs,
real* wgtData,
real* biasData) {
bool hasSpatial = ih == 1 && iw == 1 ? false : true;
engine_ = CpuEngine::Instance().getEngine();
mem::desc botMD = hasSpatial ? createMD({bs, ic, ih, iw}, format::nchw)
: createMD({bs, ic}, format::nc);
@ -52,21 +58,21 @@ void MkldnnLayer::resetForwardFC(int bs,
: createMD({}, format::format_undef);
mem::desc topMD = createMD({bs, oc}, format::nc);
inVal_.reset(new mem(mem::primitive_desc(botMD, engine_), botData));
wgtVal_.reset(new mem(mem::primitive_desc(wgtMD, engine_), wgtData));
outVal_.reset(new mem(mem::primitive_desc(topMD, engine_), topData));
mkldnn::prop_kind pk = mkldnn::prop_kind::forward;
fc_fwd::desc fwdDesc = biasData != NULL
? fc_fwd::desc(pk, botMD, wgtMD, biasMD, topMD)
: fc_fwd::desc(pk, botMD, wgtMD, topMD);
fc_fwd::primitive_desc fwdPD = fc_fwd::primitive_desc(fwdDesc, engine_);
mem bot = mem(mem::primitive_desc(botMD, engine_), botData);
mem wgt = mem(mem::primitive_desc(wgtMD, engine_), wgtData);
mem top = mem(mem::primitive_desc(topMD, engine_), topData);
if (biasData != NULL) {
mem bias = mem(mem::primitive_desc(biasMD, engine_), biasData);
fwd_.reset(new fc_fwd(fwdPD, bot, wgt, bias, top));
biasVal_.reset(new mem(mem::primitive_desc(biasMD, engine_), biasData));
fwd_.reset(new fc_fwd(fwdPD, *inVal_, *wgtVal_, *biasVal_, *outVal_));
} else {
fwd_.reset(new fc_fwd(fwdPD, bot, wgt, top));
fwd_.reset(new fc_fwd(fwdPD, *inVal_, *wgtVal_, *outVal_));
}
pipelineFwd_.clear();
pipelineFwd_.push_back(*fwd_);
@ -84,8 +90,12 @@ void MkldnnLayer::mkldnnForwardFC(int bs,
// if input size changed, reset it
resetForwardFC(bs, ic, ih, iw, botData, oc, topData, wgtData, biasData);
this->cvtWgtFromPaddle();
// update input, since the data might be changed if this is after data layer
inVal_->set_data_handle(botData);
// just forward
// update botdata
stream_->submit(pipelineFwd_);
}
@ -112,6 +122,10 @@ void MkldnnLayer::resetBackwardFC(int bs,
mem::desc biasMD = biasDiff != NULL ? createMD({oc}, format::x)
: createMD({}, format::format_undef);
inVal_.reset(new mem(mem::primitive_desc(botMD, engine_), botData));
wgtGrad_.reset(new mem(mem::primitive_desc(wgtMD, engine_), wgtDiff));
outGrad_.reset(new mem(mem::primitive_desc(topMD, engine_), topDiff));
fc_fwd::desc fwdDesc =
fc_fwd::desc(mkldnn::prop_kind::forward, botMD, wgtMD, topMD);
fc_fwd::primitive_desc fwdPD = fc_fwd::primitive_desc(fwdDesc, engine_);
@ -121,15 +135,12 @@ void MkldnnLayer::resetBackwardFC(int bs,
fc_bwdWgt::primitive_desc bwdWgtPD =
fc_bwdWgt::primitive_desc(bwdWgtDesc, engine_, fwdPD);
mem botVal = mem(mem::primitive_desc(botMD, engine_), botData);
mem wgtGrad = mem(mem::primitive_desc(wgtMD, engine_), wgtDiff);
mem topGrad = mem(mem::primitive_desc(topMD, engine_), topDiff);
if (biasDiff != NULL) {
mem biasGrad = mem(mem::primitive_desc(biasMD, engine_), biasDiff);
bwdWgt_.reset(new fc_bwdWgt(bwdWgtPD, botVal, topGrad, wgtGrad, biasGrad));
biasGrad_.reset(new mem(mem::primitive_desc(biasMD, engine_), biasDiff));
bwdWgt_.reset(
new fc_bwdWgt(bwdWgtPD, *inVal_, *outGrad_, *wgtGrad_, *biasGrad_));
} else {
bwdWgt_.reset(new fc_bwdWgt(bwdWgtPD, botVal, topGrad, wgtGrad));
bwdWgt_.reset(new fc_bwdWgt(bwdWgtPD, *inVal_, *outGrad_, *wgtGrad_));
}
pipelineBwd_.clear();
pipelineBwd_.push_back(*bwdWgt_);
@ -142,9 +153,9 @@ void MkldnnLayer::resetBackwardFC(int bs,
fc_bwdData::desc bwdDataDesc = fc_bwdData::desc(botMD, wgtMD, topMD);
fc_bwdData::primitive_desc bwdDataPD =
fc_bwdData::primitive_desc(bwdDataDesc, engine_, fwdPD);
mem botGrad = mem(mem::primitive_desc(botMD, engine_), botDiff);
mem wgtVal = mem(mem::primitive_desc(wgtMD, engine_), wgtData);
bwdData_.reset(new fc_bwdData(bwdDataPD, topGrad, wgtVal, botGrad));
inGrad_.reset(new mem(mem::primitive_desc(botMD, engine_), botDiff));
wgtVal_.reset(new mem(mem::primitive_desc(wgtMD, engine_), wgtData));
bwdData_.reset(new fc_bwdData(bwdDataPD, *outGrad_, *wgtVal_, *inGrad_));
pipelineBwd_.push_back(*bwdData_);
}
@ -172,11 +183,18 @@ void MkldnnLayer::mkldnnBackwardFC(int bs,
wgtData,
biasDiff);
// just forward
// update botdata
// update data
outGrad_->set_data_handle(topDiff);
stream_->submit(pipelineBwd_);
}
void MkldnnLayer::printSizeInfo() {
VLOG(DNN_SIZES) << "bs: " << bs_ << ", ic: " << ic_ << ", ih: " << ih_
<< ", iw: " << iw_ << ", oc: " << oc_ << ", oh: " << oh_
<< ", ow: " << ow_;
}
mem::desc MkldnnLayer::createMD(mem::dims dims,
mem::format fmt,
mem::data_type type) {

27
paddle/gserver/layers/MkldnnLayer.h
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@ -40,13 +40,24 @@ protected:
// mkldnn engine, stream and primivtives
mkldnn::engine engine_;
std::shared_ptr<MkldnnStream> stream_;
std::shared_ptr<mkldnn::primitive> fwd_;
std::shared_ptr<mkldnn::primitive> bwdWgt_;
std::shared_ptr<mkldnn::primitive> bwdData_;
std::vector<mkldnn::primitive> pipelineFwd_;
std::vector<mkldnn::primitive> pipelineBwd_;
// TODO(TJ): change below memory as MkldnnMatrixPtr type
// input == bottom, output == top
// value == data, grad == diff
std::shared_ptr<mkldnn::memory> inVal_;
std::shared_ptr<mkldnn::memory> inGrad_;
std::shared_ptr<mkldnn::memory> outVal_;
std::shared_ptr<mkldnn::memory> outGrad_;
std::shared_ptr<mkldnn::memory> wgtVal_;
std::shared_ptr<mkldnn::memory> wgtGrad_;
std::shared_ptr<mkldnn::memory> biasVal_;
std::shared_ptr<mkldnn::memory> biasGrad_;
public:
explicit MkldnnLayer(const LayerConfig& config)
: Layer(config),
@ -67,6 +78,20 @@ public:
virtual bool init(const LayerMap& layerMap, const ParameterMap& parameterMap);
virtual void printSizeInfo();
/**
* convert weight from paddle format to mkldnn format
* weight_ will be override
*/
virtual void cvtWgtFromPaddle() { ; }
/**
* convert mkldnn weight to paddle format
* weight_ will be override
*/
virtual void cvtWgtToPaddle() { ; }
void resetForwardFC(int bs,
int ic,
int ih,

30
paddle/gserver/tests/MkldnnTester.cpp
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@ -14,6 +14,7 @@ limitations under the License. */
#include "MkldnnTester.h"
#include "paddle/gserver/layers/MkldnnBase.h"
#include "paddle/gserver/layers/MkldnnLayer.h"
namespace paddle {
@ -145,7 +146,10 @@ void MkldnnTester::checkBackwardWgts() {
vector<VectorPtr> dnnWgts; // used to temply save mkldnn weights
saveWgt(parameters_[DNN], dnnWgts);
// TODO(TJ): cvtWgtToPaddle
const MkldnnLayerPtr dnnlayer =
std::dynamic_pointer_cast<MkldnnLayer>(dnnLayer_);
CHECK(dnnlayer);
dnnlayer->cvtWgtToPaddle();
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);
@ -233,11 +237,10 @@ void MkldnnTester::printMatrix(const MatrixPtr& m) {
if (!log_) {
return;
}
#ifdef _DEBUG
std::ostream str;
m->print(str);
VLOG(lvl_) << str;
#endif
std::ostringstream ostr;
m->print(ostr);
VLOG(lvl_) << std::endl << ostr.str();
}
void MkldnnTester::printVector(const VectorPtr& v) {
@ -245,15 +248,9 @@ void MkldnnTester::printVector(const VectorPtr& v) {
return;
}
CHECK(v);
CHECK(v->getData());
const real* pd = v->getData();
const size_t sz = v->getSize();
std::stringstream row;
for (size_t i = 0; i < sz; ++i) {
row << pd[i] << ", ";
}
VLOG(lvl_) << row.str();
std::ostringstream ostr;
v->print(ostr, v->getSize());
VLOG(lvl_) << std::endl << ostr.str();
}
double MkldnnTester::getDelta(const real* d1,
@ -335,7 +332,6 @@ void MkldnnTester::run(const TestConfig& dnn,
// Firstly always set flag false to initial from paddle weight
TestConfig first = dnn;
// first.layerConfig.set_init_wgt_from_mkldnn(false);
// reset and run once
reset(first, ref, batchSize);
@ -348,8 +344,6 @@ void MkldnnTester::run(const TestConfig& dnn,
// firstly get the flag
bool initWgtFromMkldnn = false;
// dnn.layerConfig.has_init_wgt_from_mkldnn() &&
// dnn.layerConfig.init_wgt_from_mkldnn();
if (initWgtFromMkldnn) {
// after run once the mkldnn weight has been stored in dnnlayer

12
paddle/gserver/tests/test_Mkldnn.cpp
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@ -55,12 +55,12 @@ void testFcLayer(const testFCDesc& pm) {
}
TEST(MkldnnLayer, fcLayer) {
testFcLayer({2, 2, 3, 1, 1}); /*
testFcLayer({16, 32, 64, 1, 1});
testFcLayer({8, 16, 32, 13, 13});
testFcLayer({4, 12, 18, 13, 11});
testFcLayer({2, 64, 32, 16, 16});
testFcLayer({15, 3, 6, 16, 16});*/
testFcLayer({2, 2, 3, 1, 1});
testFcLayer({3, 7, 19, 1, 1});
testFcLayer({8, 16, 32, 13, 13});
testFcLayer({4, 12, 18, 13, 11});
testFcLayer({2, 64, 32, 16, 16});
testFcLayer({15, 3, 6, 16, 16});
}
// TODO(TJ): add branch test

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