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体验新版 GitCode,发现更多精彩内容 >>
提交
0cc85d79
编写于
9月 26, 2017
作者:
T
Tao Luo
提交者:
GitHub
9月 26, 2017
浏览文件
操作
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差异文件
Merge pull request #4331 from tensor-tang/mkldnn_softmax
Add mkldnn_softmax
上级
7d653216
672c9681
变更
5
隐藏空白更改
内联
并排
Showing
5 changed file
with
226 addition
and
132 deletion
+226
-132
benchmark/paddle/image/run_mkldnn.sh
benchmark/paddle/image/run_mkldnn.sh
+1
-4
paddle/gserver/activations/MKLDNNActivation.cpp
paddle/gserver/activations/MKLDNNActivation.cpp
+187
-25
paddle/gserver/activations/MKLDNNActivation.h
paddle/gserver/activations/MKLDNNActivation.h
+35
-100
paddle/gserver/tests/test_MKLDNN.cpp
paddle/gserver/tests/test_MKLDNN.cpp
+2
-2
python/paddle/trainer/config_parser.py
python/paddle/trainer/config_parser.py
+1
-1
未找到文件。
benchmark/paddle/image/run_mkldnn.sh
浏览文件 @
0cc85d79
...
...
@@ -9,11 +9,9 @@ function train() {
bs
=
$2
use_mkldnn
=
$3
if
[
$3
==
"True"
]
;
then
use_mkldnn
=
$3
thread
=
1
log
=
"logs/
${
topology
}
-mkldnn-
${
bs
}
.log"
elif
[
$3
==
"False"
]
;
then
use_mkldnn
=
$3
thread
=
`
nproc
`
log
=
"logs/
${
topology
}
-
${
thread
}
mklml-
${
bs
}
.log"
else
...
...
@@ -39,8 +37,7 @@ if [ ! -d "logs" ]; then
mkdir
logs
fi
#========= mkldnn =========#
# vgg
#========== mkldnn ==========#
train vgg 64 True
train vgg 128 True
train vgg 256 True
...
...
paddle/gserver/activations/MKLDNNActivation.cpp
浏览文件 @
0cc85d79
...
...
@@ -27,31 +27,53 @@ static ClassRegistrar<ActivationFunction> gMKLDNNActivationRegistrar;
#define MKLDNN_ACTIVATION_CLASS_NAME(ACT_TYPE) mkldnn_##ACT_TYPE##Activation
/**
* @def DEFINE_MKLDNN_ELTWISE_ACTIVATION
* @def BEGIN_MKLDNN_ACTIVATION
*/
#define BEGIN_MKLDNN_ACTIVATION(ACT_TYPE, BASE_CLASS) \
class MKLDNN_ACTIVATION_CLASS_NAME(ACT_TYPE) : public BASE_CLASS {
/**
* @def END_MKLDNN_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 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, 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.
* Actually mkldnn_relu is Leaky Relu.
...
...
@@ -59,19 +81,129 @@ 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
;
Matrix
::
resizeOrCreate
(
sftMaxDot_
,
outputG
->
getHeight
(),
outputG
->
getWidth
(),
/* trans */
false
,
/* useGpu */
false
);
Matrix
::
resizeOrCreate
(
sftMaxSum_
,
outputG
->
getHeight
(),
1
,
/* trans */
false
,
/* useGpu */
false
);
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
);
...
...
@@ -84,4 +216,34 @@ 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 @
0cc85d79
...
...
@@ -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 +49,18 @@ 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
);
/**
* 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
);
virtual
Error
__must_check
backward
(
Argument
&
act
);
};
/**
...
...
@@ -59,6 +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
...
...
@@ -70,9 +82,7 @@ protected:
public:
MKLDNNEltwiseActivation
()
{}
~
MKLDNNEltwiseActivation
()
{}
virtual
const
std
::
string
&
getName
()
const
=
0
;
// in common, the alpha of forward and backward should be equal.
...
...
@@ -80,105 +90,30 @@ 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
;
}
/**
* reshape and reset the forward primitives
*/
void
resetFwd
(
Argument
&
act
)
{
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
());
// 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_
);
}
auto
fwdDesc
=
eltwise_fwd
::
desc
(
mkldnn
::
prop_kind
::
forward_training
,
algo
,
val_
->
getMemoryDesc
(),
alpha
,
beta
);
fwdPD_
.
reset
(
new
eltwise_fwd
::
primitive_desc
(
fwdDesc
,
eng
));
// 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
;
}
virtual
algorithm
getAlgo
(
std
::
string
type
)
const
;
void
resetFwd
(
Argument
&
act
)
override
;
void
resetBwd
(
Argument
&
act
)
override
;
};
/**
* reset the backward primitives, can not merge into resetFwd as the grad data
* would be changing before backward.
*/
void
resetBwd
(
Argument
&
act
)
{
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_
);
}
/**
* @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
;
Error
__must_check
forward
(
Argument
&
act
)
{
resetFwd
(
act
);
stream_
->
submit
(
pipelineFwd_
);
return
Error
();
}
private:
// for backward
MatrixPtr
sftMaxSum_
;
MatrixPtr
sftMaxDot_
;
Error
__must_check
backward
(
Argument
&
act
)
{
resetBwd
(
act
);
stream_
->
submit
(
pipelineBwd_
);
return
Error
();
}
public:
MKLDNNSoftmaxActivation
()
{}
~
MKLDNNSoftmaxActivation
()
{}
virtual
const
std
::
string
&
getName
()
const
=
0
;
void
resetFwd
(
Argument
&
act
)
override
;
Error
__must_check
forward
(
Argument
&
act
)
override
;
Error
__must_check
backward
(
Argument
&
act
)
override
;
};
}
// namespace paddle
paddle/gserver/tests/test_MKLDNN.cpp
浏览文件 @
0cc85d79
...
...
@@ -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
)};
...
...
python/paddle/trainer/config_parser.py
浏览文件 @
0cc85d79
...
...
@@ -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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