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2c6ac629
编写于
9月 25, 2017
作者:
T
tensor-tang
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操作
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电子邮件补丁
差异文件
refine code and remove min clip
上级
799f80ad
变更
3
隐藏空白更改
内联
并排
Showing
3 changed file
with
198 addition
and
172 deletion
+198
-172
paddle/gserver/activations/MKLDNNActivation.cpp
paddle/gserver/activations/MKLDNNActivation.cpp
+186
-40
paddle/gserver/activations/MKLDNNActivation.h
paddle/gserver/activations/MKLDNNActivation.h
+10
-130
paddle/math/Matrix.cpp
paddle/math/Matrix.cpp
+2
-2
未找到文件。
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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