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enable mkldnn_softmax

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update-doc-pybind
tensor-tang 7 years ago
parent 330e9929ec
commit 7483087c8c
4 changed files with 123 additions and 44 deletions
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24
paddle/gserver/activations/MKLDNNActivation.cpp
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@ -26,6 +26,25 @@ static ClassRegistrar<ActivationFunction> gMKLDNNActivationRegistrar;
*/
#define MKLDNN_ACTIVATION_CLASS_NAME(ACT_TYPE) mkldnn_##ACT_TYPE##Activation
/**
* @def DEFINE_MKLDNN_ACTIVATION
*/
#define DEFINE_MKLDNN_ACTIVATION(ACT_TYPE, BASE_CLASS) \
class MKLDNN_ACTIVATION_CLASS_NAME(ACT_TYPE) : public BASE_CLASS { \
private: \
static const std::string name; \
\
public: \
const std::string& getName() const { return name; } \
}; \
const std::string MKLDNN_ACTIVATION_CLASS_NAME(ACT_TYPE)::name = \
"mkldnn_" #ACT_TYPE; \
static InitFunction __reg_activation__mkldnn_##ACT_TYPE([] { \
gMKLDNNActivationRegistrar \
.registerClass<MKLDNN_ACTIVATION_CLASS_NAME(ACT_TYPE)>( \
"mkldnn_" #ACT_TYPE); \
});
/**
* @def DEFINE_MKLDNN_ELTWISE_ACTIVATION
*/
@ -73,6 +92,11 @@ DEFINE_MKLDNN_ELTWISE_ACTIVATION(tanh, 0.f, 0.f)
*/
DEFINE_MKLDNN_ELTWISE_ACTIVATION(elu, 0.f, 0.f)
/**
* @brief MKLDNN Softmax Activation
*/
DEFINE_MKLDNN_ACTIVATION(softmax, MKLDNNSoftmaxActivation)
ActivationFunction* MKLDNNActivation::create(const std::string& type) {
return gMKLDNNActivationRegistrar.createByType(type);
}

137
paddle/gserver/activations/MKLDNNActivation.h
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@ -36,6 +36,7 @@ protected:
// mkldnn matrix, primitive, stream and pipeline
MKLDNNMatrixPtr val_;
MKLDNNMatrixPtr grad_;
std::shared_ptr<mkldnn::engine> engine_;
std::shared_ptr<MKLDNNStream> stream_;
std::shared_ptr<mkldnn::primitive> fwd_;
std::shared_ptr<mkldnn::primitive> bwd_;
@ -48,8 +49,44 @@ public:
static ActivationFunction* create(const std::string& type);
static std::vector<std::string> getAllRegisteredTypes();
virtual const std::string& getName() const = 0;
virtual Error __must_check forward(Argument& act) = 0;
virtual Error __must_check backward(Argument& act) = 0;
/**
* reset the forward primitives
*/
virtual void resetFwd(Argument& act) {
VLOG(MKLDNN_BASE) << getName() << " reset mkldnn forward";
cnt_ = act.value->getElementCnt();
pipelineFwd_.clear();
stream_.reset(new MKLDNNStream());
engine_.reset(new mkldnn::engine(mkldnn::engine::cpu, 0));
val_ = std::dynamic_pointer_cast<MKLDNNMatrix>(act.value);
if (val_ == nullptr) {
int bs = act.getBatchSize();
int ih = act.getFrameHeight() > 0 ? act.getFrameHeight() : 1;
int iw = act.getFrameWidth() > 0 ? act.getFrameWidth() : 1;
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_);
CHECK(val_);
val_->downSpatial();
}
}
/**
* reset the backward primitives,
* can not merge this functions into resetFwd as the grad data
* would be changing before backward.
*/
virtual void resetBwd(Argument& act) {}
virtual Error __must_check forward(Argument& act) {
resetFwd(act);
stream_->submit(pipelineFwd_);
return Error();
}
virtual Error __must_check backward(Argument& act) {
resetBwd(act);
stream_->submit(pipelineBwd_);
return Error();
}
};
/**
@ -70,9 +107,7 @@ protected:
public:
MKLDNNEltwiseActivation() {}
~MKLDNNEltwiseActivation() {}
virtual const std::string& getName() const = 0;
// in common, the alpha of forward and backward should be equal.
@ -93,42 +128,21 @@ public:
return (mkldnn::algorithm)0;
}
/**
* reshape and reset the forward primitives
*/
void resetFwd(Argument& act) {
void resetFwd(Argument& act) override {
if (cnt_ == act.value->getElementCnt()) {
return;
}
VLOG(MKLDNN_BASE) << getName() << " reset mkldnn forward";
cnt_ = act.value->getElementCnt();
stream_.reset(new MKLDNNStream());
auto eng = CPUEngine::Instance().getEngine();
// get algo setting
mkldnn::algorithm algo = getAlgo(this->getName());
MKLDNNActivation::resetFwd(act);
// note: alpha represents the NegativeSlope when used in relu.
float alpha = getAlpha();
float beta = getBeta();
pipelineFwd_.clear();
val_ = std::dynamic_pointer_cast<MKLDNNMatrix>(act.value);
if (val_ == nullptr) {
int bs = act.getBatchSize();
int ih = act.getFrameHeight() > 0 ? act.getFrameHeight() : 1;
int iw = act.getFrameWidth() > 0 ? act.getFrameWidth() : 1;
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, eng);
CHECK(val_);
}
mkldnn::algorithm algo = getAlgo(this->getName());
auto fwdDesc = eltwise_fwd::desc(mkldnn::prop_kind::forward_training,
algo,
val_->getMemoryDesc(),
alpha,
beta);
fwdPD_.reset(new eltwise_fwd::primitive_desc(fwdDesc, eng));
fwdPD_.reset(new eltwise_fwd::primitive_desc(fwdDesc, *engine_));
// use inplace for forward but save input value before submit
inVal_ = val_;
copyInVal_ = nullptr;
@ -144,11 +158,7 @@ public:
needResetBwd_ = true;
}
/**
* reset the backward primitives, can not merge into resetFwd as the grad data
* would be changing before backward.
*/
void resetBwd(Argument& act) {
void resetBwd(Argument& act) override {
if (!needResetBwd_) {
return;
}
@ -167,16 +177,61 @@ public:
pipelineBwd_.clear();
pipelineBwd_.push_back(*bwd_);
}
};
Error __must_check forward(Argument& act) {
resetFwd(act);
stream_->submit(pipelineFwd_);
return Error();
/**
* @brief Base class of MKLDNN softmax Activation,
* only have mkldnn forward, use cpu implement for backward.
*/
class MKLDNNSoftmaxActivation : public MKLDNNActivation {
typedef mkldnn::softmax_forward softmax_fwd;
private:
// for backward
MatrixPtr sftMaxSum_;
MatrixPtr sftMaxDot_;
public:
MKLDNNSoftmaxActivation() {}
~MKLDNNSoftmaxActivation() {}
virtual const std::string& getName() const = 0;
void resetFwd(Argument& act) override {
if (cnt_ == act.value->getElementCnt()) {
return;
}
MKLDNNActivation::resetFwd(act);
int axis = 1;
auto fwdDesc = softmax_fwd::desc(
mkldnn::prop_kind::forward_scoring, val_->getMemoryDesc(), axis);
auto fwdPD = softmax_fwd::primitive_desc(fwdDesc, *engine_);
fwd_.reset(new softmax_fwd(fwdPD, *val_, *val_));
pipelineFwd_.push_back(*fwd_);
}
Error __must_check backward(Argument& act) {
resetBwd(act);
stream_->submit(pipelineBwd_);
Error __must_check backward(Argument& act) override {
MatrixPtr outputV = act.value;
MatrixPtr outputG = act.grad;
if (outputG->useGpu()) {
outputG->softmaxBackward(*outputV);
} else {
SetDevice device(act.deviceId);
Matrix::resizeOrCreate(sftMaxDot_,
outputG->getHeight(),
outputG->getWidth(),
/* trans */ false,
useGpu(act.deviceId));
Matrix::resizeOrCreate(sftMaxSum_,
outputG->getHeight(),
1,
/* trans */ false,
useGpu(act.deviceId));
sftMaxDot_->dotMul(*outputG, *outputV);
sftMaxSum_->colMerge(*sftMaxDot_);
act.grad->softmaxDerivative(*act.value, *sftMaxSum_);
}
return Error();
}
};

4
paddle/gserver/tests/test_MKLDNN.cpp
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@ -222,8 +222,8 @@ static void getAddtoConfig(TestConfig& cfg, const testActDesc& pm) {
}
void testActivation(std::string& actType, const testActDesc& pm) {
// TODO(TJ): mkldnn_softmax not implemented, paddle do not have elu activation
if (actType == "mkldnn_softmax" || actType == "mkldnn_elu") {
// TODO(TJ): remove me when paddle support elu activation
if (actType == "mkldnn_elu") {
return;
}
const std::string compareTypes[] = {actType, actType.erase(0, 7)};

2
python/paddle/trainer/config_parser.py
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@ -1566,7 +1566,7 @@ class LayerBase(object):
self.config = g_config.model_config.layers.add()
assert isinstance(self.config, LayerConfig)
use_mkldnn = bool(int(g_command_config_args.get("use_mkldnn", 0)))
mkldnn_acts = ['relu', 'tanh']
mkldnn_acts = ['relu', 'tanh', 'softmax']
if use_mkldnn and active_type in mkldnn_acts:
active_type = "mkldnn_" + active_type
self.config.name = name

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