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提交 2c6ac629 编写于 作者: T tensor-tang
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refine code and remove min clip

上级 799f80ad
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Showing with 198 addition and 172 deletion
paddle/gserver/activations/MKLDNNActivation.cpp
浏览文件 @ 2c6ac629
......@@ -27,49 +27,52 @@ static ClassRegistrar<ActivationFunction> gMKLDNNActivationRegistrar;
#define MKLDNN_ACTIVATION_CLASS_NAME(ACT_TYPE) mkldnn_##ACT_TYPE##Activation
/**
* @def DEFINE_MKLDNN_ACTIVATION
* @def BEGIN_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); \
#define BEGIN_MKLDNN_ACTIVATION(ACT_TYPE, BASE_CLASS) \
class MKLDNN_ACTIVATION_CLASS_NAME(ACT_TYPE) : public BASE_CLASS {
/**
* @def END_MKLDNN_ACTIVATION
*/
#define END_MKLDNN_ACTIVATION(ACT_TYPE) \
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_ACTIVATION
*/
#define DEFINE_MKLDNN_ACTIVATION(ACT_TYPE, BASE_CLASS) \
BEGIN_MKLDNN_ACTIVATION(ACT_TYPE, BASE_CLASS) \
END_MKLDNN_ACTIVATION(ACT_TYPE)
/**
* @def DEFINE_MKLDNN_ELTWISE_ACTIVATION
*/
#define DEFINE_MKLDNN_ELTWISE_ACTIVATION(ACT_TYPE, ALPHA, BWD_ALPHA) \
class MKLDNN_ACTIVATION_CLASS_NAME(ACT_TYPE) \
: public MKLDNNEltwiseActivation { \
private: \
static const std::string name; \
static const float alpha; \
static const float bwdAlpha; \
\
public: \
const std::string& getName() const { return name; } \
float getAlpha() const { return alpha; } \
float getBwdAlpha() const { return bwdAlpha; } \
}; \
const std::string MKLDNN_ACTIVATION_CLASS_NAME(ACT_TYPE)::name = \
"mkldnn_" #ACT_TYPE; \
const float MKLDNN_ACTIVATION_CLASS_NAME(ACT_TYPE)::alpha = ALPHA; \
const float MKLDNN_ACTIVATION_CLASS_NAME(ACT_TYPE)::bwdAlpha = BWD_ALPHA; \
static InitFunction __reg_activation__mkldnn_##ACT_TYPE([] { \
gMKLDNNActivationRegistrar \
.registerClass<MKLDNN_ACTIVATION_CLASS_NAME(ACT_TYPE)>( \
"mkldnn_" #ACT_TYPE); \
});
#define DEFINE_MKLDNN_ELTWISE_ACTIVATION( \
ACT_TYPE, BASE_CLASS, ALPHA, BWD_ALPHA) \
BEGIN_MKLDNN_ACTIVATION(ACT_TYPE, BASE_CLASS) \
private: \
static const float alpha; \
static const float bwdAlpha; \
\
public: \
float getAlpha() const { return alpha; } \
float getBwdAlpha() const { return bwdAlpha; } \
END_MKLDNN_ACTIVATION(ACT_TYPE) \
const float MKLDNN_ACTIVATION_CLASS_NAME(ACT_TYPE)::alpha = ALPHA; \
const float MKLDNN_ACTIVATION_CLASS_NAME(ACT_TYPE)::bwdAlpha = BWD_ALPHA;
/**
* @brief MKLDNN Relu Activation.
......@@ -78,25 +81,138 @@ static ClassRegistrar<ActivationFunction> gMKLDNNActivationRegistrar;
* f(x) = negative_slope * x (x < 0)
* @note the negative_slope should be -0.f in forward
*/
DEFINE_MKLDNN_ELTWISE_ACTIVATION(relu, -0.f, 0.f)
DEFINE_MKLDNN_ELTWISE_ACTIVATION(relu, MKLDNNEltwiseActivation, -0.f, 0.f)
/**
* @brief MKLDNN Tanh Activation.
*/
DEFINE_MKLDNN_ELTWISE_ACTIVATION(tanh, 0.f, 0.f)
DEFINE_MKLDNN_ELTWISE_ACTIVATION(tanh, MKLDNNEltwiseActivation, 0.f, 0.f)
/**
* @brief MKLDNN ELU(Exponential Linear Unit) Activation.
* f(x) = x (x >= 0)
* f(x) = negative_slope * (exp(x) - 1) (x < 0)
*/
DEFINE_MKLDNN_ELTWISE_ACTIVATION(elu, 0.f, 0.f)
DEFINE_MKLDNN_ELTWISE_ACTIVATION(elu, MKLDNNEltwiseActivation, 0.f, 0.f)
mkldnn::algorithm MKLDNNEltwiseActivation::getAlgo(std::string type) const {
const std::map<std::string, mkldnn::algorithm> algoMap = {
{"relu", algorithm::eltwise_relu},
{"tanh", algorithm::eltwise_tanh},
{"elu", algorithm::eltwise_elu}};
type.erase(0, 7); // remove mkldnn_
algorithm algo = (algorithm)0;
mapGet(type, algoMap, &algo);
return algo;
}
void MKLDNNEltwiseActivation::resetFwd(Argument& act) {
if (cnt_ == act.value->getElementCnt()) {
return;
}
MKLDNNActivation::resetFwd(act);
// note: alpha represents the NegativeSlope when used in relu.
float alpha = getAlpha();
float beta = getBeta();
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, *engine_));
// use inplace for forward but save input value before submit
inVal_ = val_;
copyInVal_ = nullptr;
if (act.grad && algo == algorithm::eltwise_tanh) {
// tanh need save src input for backward
inVal_ = MKLDNNMatrix::create(nullptr, val_->getPrimitiveDesc());
copyInVal_ = std::make_shared<mkldnn::reorder>(*val_, *inVal_);
CHECK(copyInVal_) << "should not be emptry";
pipelineFwd_.push_back(*copyInVal_);
}
fwd_.reset(new eltwise_fwd(*fwdPD_, *val_, *val_));
pipelineFwd_.push_back(*fwd_);
needResetBwd_ = true;
}
void MKLDNNEltwiseActivation::resetBwd(Argument& act) {
if (!needResetBwd_) {
return;
}
VLOG(MKLDNN_BASE) << getName() << " reset mkldnn backward";
needResetBwd_ = false;
algorithm algo = getAlgo(this->getName());
float alpha = getBwdAlpha();
float beta = getBeta();
grad_ = MKLDNNMatrix::create(act.grad, val_->getPrimitiveDesc());
auto eng = CPUEngine::Instance().getEngine();
auto bwdDesc = eltwise_bwd::desc(
algo, grad_->getMemoryDesc(), val_->getMemoryDesc(), alpha, beta);
auto bwdPD = eltwise_bwd::primitive_desc(bwdDesc, eng, *fwdPD_);
CHECK(inVal_);
bwd_.reset(new eltwise_bwd(bwdPD, *inVal_, *grad_, *grad_));
pipelineBwd_.clear();
pipelineBwd_.push_back(*bwd_);
}
/**
* @brief MKLDNN Softmax Activation
*/
DEFINE_MKLDNN_ACTIVATION(softmax, MKLDNNSoftmaxActivation)
void MKLDNNSoftmaxActivation::resetFwd(Argument& act) {
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 MKLDNNSoftmaxActivation::forward(Argument& act) {
resetFwd(act);
stream_->submit(pipelineFwd_);
real* v = act.value->getData();
real threshold = exp(-64);
#pragma omp parallel for
for (size_t i = 0; i < act.value->getElementCnt(); ++i) {
v[i] = v[i] < threshold ? threshold : v[i];
}
return Error();
}
Error __must_check MKLDNNSoftmaxActivation::backward(Argument& act) {
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();
}
ActivationFunction* MKLDNNActivation::create(const std::string& type) {
return gMKLDNNActivationRegistrar.createByType(type);
}
......@@ -108,4 +224,34 @@ std::vector<std::string> MKLDNNActivation::getAllRegisteredTypes() {
return types;
}
void MKLDNNActivation::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();
}
}
Error __must_check MKLDNNActivation::forward(Argument& act) {
resetFwd(act);
stream_->submit(pipelineFwd_);
return Error();
}
Error __must_check MKLDNNActivation::backward(Argument& act) {
resetBwd(act);
stream_->submit(pipelineBwd_);
return Error();
}
} // namespace paddle
paddle/gserver/activations/MKLDNNActivation.h
浏览文件 @ 2c6ac629
......@@ -52,41 +52,15 @@ public:
/**
* 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();
}
}
virtual void resetFwd(Argument& act);
/**
* 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();
}
virtual Error __must_check forward(Argument& act);
virtual Error __must_check backward(Argument& act);
};
/**
......@@ -96,6 +70,7 @@ public:
class MKLDNNEltwiseActivation : public MKLDNNActivation {
typedef mkldnn::eltwise_forward eltwise_fwd;
typedef mkldnn::eltwise_backward eltwise_bwd;
typedef mkldnn::algorithm algorithm;
protected:
// save the forward primitive desc, which can be used backward
......@@ -115,68 +90,9 @@ public:
virtual float getAlpha() const = 0;
virtual float getBwdAlpha() const = 0;
virtual float getBeta() const { return 0.f; }
virtual mkldnn::algorithm getAlgo(const std::string& type) const {
if (type == "mkldnn_relu") {
return mkldnn::algorithm::eltwise_relu;
} else if (type == "mkldnn_tanh") {
return mkldnn::algorithm::eltwise_tanh;
} else if (type == "mkldnn_elu") {
return mkldnn::algorithm::eltwise_elu;
} else {
LOG(FATAL) << "Unkown eltwise activation type: " << type;
}
return (mkldnn::algorithm)0;
}
void resetFwd(Argument& act) override {
if (cnt_ == act.value->getElementCnt()) {
return;
}
MKLDNNActivation::resetFwd(act);
// note: alpha represents the NegativeSlope when used in relu.
float alpha = getAlpha();
float beta = getBeta();
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, *engine_));
// use inplace for forward but save input value before submit
inVal_ = val_;
copyInVal_ = nullptr;
if (act.grad && algo == mkldnn::algorithm::eltwise_tanh) {
// tanh need save src input for backward
inVal_ = MKLDNNMatrix::create(nullptr, val_->getPrimitiveDesc());
copyInVal_ = std::make_shared<mkldnn::reorder>(*val_, *inVal_);
CHECK(copyInVal_) << "should not be emptry";
pipelineFwd_.push_back(*copyInVal_);
}
fwd_.reset(new eltwise_fwd(*fwdPD_, *val_, *val_));
pipelineFwd_.push_back(*fwd_);
needResetBwd_ = true;
}
void resetBwd(Argument& act) override {
if (!needResetBwd_) {
return;
}
VLOG(MKLDNN_BASE) << getName() << " reset mkldnn backward";
needResetBwd_ = false;
mkldnn::algorithm algo = getAlgo(this->getName());
float alpha = getBwdAlpha();
float beta = getBeta();
grad_ = MKLDNNMatrix::create(act.grad, val_->getPrimitiveDesc());
auto eng = CPUEngine::Instance().getEngine();
auto bwdDesc = eltwise_bwd::desc(
algo, grad_->getMemoryDesc(), val_->getMemoryDesc(), alpha, beta);
auto bwdPD = eltwise_bwd::primitive_desc(bwdDesc, eng, *fwdPD_);
CHECK(inVal_);
bwd_.reset(new eltwise_bwd(bwdPD, *inVal_, *grad_, *grad_));
pipelineBwd_.clear();
pipelineBwd_.push_back(*bwd_);
}
virtual algorithm getAlgo(std::string type) const;
void resetFwd(Argument& act) override;
void resetBwd(Argument& act) override;
};
/**
......@@ -195,45 +111,9 @@ 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) 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();
}
void resetFwd(Argument& act) override;
Error __must_check forward(Argument& act) override;
Error __must_check backward(Argument& act) override;
};
} // namespace paddle
paddle/math/Matrix.cpp
浏览文件 @ 2c6ac629
......@@ -3637,7 +3637,7 @@ void CpuMatrix::oneHotCrossEntropy(Matrix& output, IVector& label) {
for (size_t i = 0; i < numSamples; ++i, out += dim) {
CHECK_GE(lbl[i], 0);
CHECK_LT((size_t)lbl[i], dim);
cost[i] = -std::log(std::max(out[lbl[i]], real(FLT_MIN)));
cost[i] = -std::log(out[lbl[i]]);
}
}
......@@ -3652,7 +3652,7 @@ void CpuMatrix::oneHotCrossEntropyBp(Matrix& output, IVector& label) {
real* grad = getData();
int* lbl = label.getData();
for (size_t i = 0; i < numSamples; ++i, out += dim, grad += dim) {
grad[lbl[i]] -= 1 / std::max(out[lbl[i]], real(FLT_MIN));
grad[lbl[i]] -= 1 / out[lbl[i]];
}
}
......
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