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24f13b1a
编写于
9月 19, 2017
作者:
T
tensor-tang
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差异文件
add MKLDNN activation files
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paddle/gserver/activations/MKLDNNActivation.cpp
paddle/gserver/activations/MKLDNNActivation.cpp
+84
-0
paddle/gserver/activations/MKLDNNActivation.h
paddle/gserver/activations/MKLDNNActivation.h
+144
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未找到文件。
paddle/gserver/activations/MKLDNNActivation.cpp
0 → 100644
浏览文件 @
24f13b1a
/* Copyright (c) 2017 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "MKLDNNActivation.h"
#include "mkldnn.hpp"
#include "paddle/utils/ClassRegistrar.h"
namespace
paddle
{
static
ClassRegistrar
<
ActivationFunction
>
gMKLDNNActivationRegistrar
;
/**
* @def MKLDNN_ACTIVATION_CLASS_NAME
* @note MKLDNN_ACTIVATION_CLASS_NAME(relu) relu_;
* means mkldnn_reluActivation relu_;
*/
#define MKLDNN_ACTIVATION_CLASS_NAME(ACT_TYPE) mkldnn_##ACT_TYPE##Activation
/**
* @def DEFINE_MKLDNN_ELTWISE_ACTIVATION
*/
#define DEFINE_MKLDNN_ELTWISE_ACTIVATION(ACT_TYPE, ALPHA) \
class MKLDNN_ACTIVATION_CLASS_NAME(ACT_TYPE) \
: public MKLDNNEltwiseActivation { \
private: \
static const std::string name; \
static const float alpha; \
\
public: \
const std::string& getName() const { return name; } \
float getAlpha() const { return alpha; } \
}; \
const std::string MKLDNN_ACTIVATION_CLASS_NAME(ACT_TYPE)::name = \
"mkldnn_" #ACT_TYPE; \
const float MKLDNN_ACTIVATION_CLASS_NAME(ACT_TYPE)::alpha = ALPHA; \
static InitFunction __reg_activation__mkldnn_##ACT_TYPE([] { \
gMKLDNNActivationRegistrar \
.registerClass<MKLDNN_ACTIVATION_CLASS_NAME(ACT_TYPE)>( \
"mkldnn_" #ACT_TYPE); \
});
/**
* @brief MKLDNN Relu Activation.
* Actually mkldnn_relu is Leaky Relu.
* f(x) = x (x >= 0)
* f(x) = negative_slope * x (x < 0)
* @note the negative_slope should be -0.f
*/
DEFINE_MKLDNN_ELTWISE_ACTIVATION
(
relu
,
-
0.
f
)
/**
* @brief MKLDNN Tanh Activation.
*/
DEFINE_MKLDNN_ELTWISE_ACTIVATION
(
tanh
,
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
)
ActivationFunction
*
MKLDNNActivation
::
create
(
const
std
::
string
&
type
)
{
return
gMKLDNNActivationRegistrar
.
createByType
(
type
);
}
std
::
vector
<
std
::
string
>
MKLDNNActivation
::
getAllRegisteredTypes
()
{
std
::
vector
<
std
::
string
>
types
;
gMKLDNNActivationRegistrar
.
forEachType
(
[
&
](
const
std
::
string
&
type
)
{
types
.
push_back
(
type
);
});
return
types
;
}
}
// namespace paddle
paddle/gserver/activations/MKLDNNActivation.h
0 → 100644
浏览文件 @
24f13b1a
/* Copyright (c) 2017 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include "ActivationFunction.h"
#include "mkldnn.hpp"
#include "paddle/gserver/layers/MKLDNNBase.h"
#include "paddle/math/MKLDNNMatrix.h"
#include "paddle/parameter/Argument.h"
namespace
paddle
{
/**
* @brief Base class of MKLDNN Activation.
* Common activation function are provieded,
* including mkldnn_relu, mkldnn_elu, mkldnn_tanh, mkldnn_softmax
*/
class
MKLDNNActivation
:
public
ActivationFunction
{
protected:
// input value element count
size_t
cnt_
;
// mkldnn matrix, primitive, stream and pipeline
MKLDNNMatrixPtr
val_
;
MKLDNNMatrixPtr
grad_
;
std
::
shared_ptr
<
MKLDNNStream
>
stream_
;
std
::
shared_ptr
<
mkldnn
::
primitive
>
fwd_
;
std
::
shared_ptr
<
mkldnn
::
primitive
>
bwd_
;
std
::
vector
<
mkldnn
::
primitive
>
pipelineFwd_
;
std
::
vector
<
mkldnn
::
primitive
>
pipelineBwd_
;
public:
MKLDNNActivation
()
:
cnt_
(
0
)
{}
~
MKLDNNActivation
()
{}
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
;
};
/**
* @brief Base class of MKLDNN Eltwise Activation,
* includes mkldnn_relu, mkldnn_elu and mkldnn_tanh.
*/
class
MKLDNNEltwiseActivation
:
public
MKLDNNActivation
{
typedef
mkldnn
::
eltwise_forward
eltwise_fwd
;
typedef
mkldnn
::
eltwise_backward
eltwise_bwd
;
public:
MKLDNNEltwiseActivation
()
{}
~
MKLDNNEltwiseActivation
()
{}
virtual
const
std
::
string
&
getName
()
const
=
0
;
virtual
float
getAlpha
()
const
=
0
;
virtual
float
getBeta
()
const
{
return
0.
f
;
}
/**
* reshape and reset the forward and backward primitives
*/
void
resetPrimitives
(
Argument
&
act
)
{
if
(
cnt_
==
act
.
value
->
getElementCnt
())
{
return
;
}
cnt_
=
act
.
value
->
getElementCnt
();
stream_
.
reset
(
new
MKLDNNStream
());
auto
eng
=
CPUEngine
::
Instance
().
getEngine
();
// get algo setting
mkldnn
::
algorithm
algo
;
if
(
this
->
getName
()
==
"mkldnn_relu"
)
{
algo
=
mkldnn
::
algorithm
::
eltwise_relu
;
}
else
if
(
this
->
getName
()
==
"mkldnn_tanh"
)
{
algo
=
mkldnn
::
algorithm
::
eltwise_tanh
;
}
else
if
(
this
->
getName
()
==
"mkldnn_elu"
)
{
algo
=
mkldnn
::
algorithm
::
eltwise_elu
;
}
else
{
LOG
(
FATAL
)
<<
"Unkown eltwise activation type: "
<<
this
->
getName
();
}
// note: alpha represents the NegativeSlope when used in relu.
float
alpha
=
getAlpha
();
float
beta
=
getBeta
();
/// forward
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
);
auto
fwdPD
=
eltwise_fwd
::
primitive_desc
(
fwdDesc
,
eng
);
// inplace buffer, dst = src
fwd_
.
reset
(
new
eltwise_fwd
(
fwdPD
,
*
val_
,
*
val_
));
pipelineFwd_
.
clear
();
pipelineFwd_
.
push_back
(
*
fwd_
);
/// backward
if
(
act
.
grad
==
nullptr
)
{
grad_
=
nullptr
;
return
;
}
grad_
=
MKLDNNMatrix
::
create
(
act
.
grad
,
val_
->
getPrimitiveDesc
());
auto
bwdDesc
=
eltwise_bwd
::
desc
(
algo
,
grad_
->
getMemoryDesc
(),
val_
->
getMemoryDesc
(),
alpha
,
beta
);
auto
bwdPD
=
eltwise_bwd
::
primitive_desc
(
bwdDesc
,
eng
,
fwdPD
);
bwd_
.
reset
(
new
eltwise_bwd
(
bwdPD
,
*
val_
,
*
grad_
,
*
grad_
));
pipelineBwd_
.
clear
();
pipelineBwd_
.
push_back
(
*
bwd_
);
}
Error
__must_check
forward
(
Argument
&
act
)
{
resetPrimitives
(
act
);
stream_
->
submit
(
pipelineFwd_
);
return
Error
();
}
Error
__must_check
backward
(
Argument
&
act
)
{
stream_
->
submit
(
pipelineBwd_
);
return
Error
();
}
};
}
// namespace paddle
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