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体验新版 GitCode,发现更多精彩内容 >>
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9d692e3b
编写于
9月 19, 2017
作者:
T
tensor-tang
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
add gtest for MKLDNN activation and pass them
上级
24f13b1a
变更
6
隐藏空白更改
内联
并排
Showing
6 changed file
with
172 addition
and
59 deletion
+172
-59
paddle/gserver/activations/ActivationFunction.cpp
paddle/gserver/activations/ActivationFunction.cpp
+10
-1
paddle/gserver/activations/MKLDNNActivation.cpp
paddle/gserver/activations/MKLDNNActivation.cpp
+25
-22
paddle/gserver/activations/MKLDNNActivation.h
paddle/gserver/activations/MKLDNNActivation.h
+61
-23
paddle/gserver/tests/MKLDNNTester.cpp
paddle/gserver/tests/MKLDNNTester.cpp
+30
-10
paddle/gserver/tests/MKLDNNTester.h
paddle/gserver/tests/MKLDNNTester.h
+1
-2
paddle/gserver/tests/test_MKLDNN.cpp
paddle/gserver/tests/test_MKLDNN.cpp
+45
-1
未找到文件。
paddle/gserver/activations/ActivationFunction.cpp
浏览文件 @
9d692e3b
...
...
@@ -22,9 +22,12 @@ limitations under the License. */
#include <type_traits>
#include "paddle/parameter/Argument.h"
#include "paddle/utils/ClassRegistrar.h"
#include "paddle/utils/Logging.h"
#ifdef PADDLE_USE_MKLDNN
#include "MKLDNNActivation.h"
#endif
namespace
paddle
{
static
ClassRegistrar
<
ActivationFunction
>
gActivationRegistrar
;
...
...
@@ -456,6 +459,12 @@ Error __must_check backward(Argument& act) {
END_DEFINE_ACTIVATION
(
log
)
ActivationFunction
*
ActivationFunction
::
create
(
const
std
::
string
&
type
)
{
#ifdef PADDLE_USE_MKLDNN
if
(
!
type
.
empty
()
&&
type
.
compare
(
0
,
7
,
"mkldnn_"
)
==
0
)
{
return
MKLDNNActivation
::
create
(
type
);
}
#endif
return
gActivationRegistrar
.
createByType
(
type
);
}
...
...
paddle/gserver/activations/MKLDNNActivation.cpp
浏览文件 @
9d692e3b
...
...
@@ -29,24 +29,27 @@ static ClassRegistrar<ActivationFunction> gMKLDNNActivationRegistrar;
/**
* @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); \
#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); \
});
/**
...
...
@@ -54,21 +57,21 @@ static ClassRegistrar<ActivationFunction> gMKLDNNActivationRegistrar;
* 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
* @note the negative_slope should be -0.f
in forward
*/
DEFINE_MKLDNN_ELTWISE_ACTIVATION
(
relu
,
-
0.
f
)
DEFINE_MKLDNN_ELTWISE_ACTIVATION
(
relu
,
-
0.
f
,
0.
f
)
/**
* @brief MKLDNN Tanh Activation.
*/
DEFINE_MKLDNN_ELTWISE_ACTIVATION
(
tanh
,
0.
f
)
DEFINE_MKLDNN_ELTWISE_ACTIVATION
(
tanh
,
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
)
DEFINE_MKLDNN_ELTWISE_ACTIVATION
(
elu
,
0.
f
,
0.
f
)
ActivationFunction
*
MKLDNNActivation
::
create
(
const
std
::
string
&
type
)
{
return
gMKLDNNActivationRegistrar
.
createByType
(
type
);
...
...
paddle/gserver/activations/MKLDNNActivation.h
浏览文件 @
9d692e3b
...
...
@@ -30,6 +30,9 @@ class MKLDNNActivation : public ActivationFunction {
protected:
// input value element count
size_t
cnt_
;
// should not merge the resetBwd into resetFwd,
// because the grad data would be changing before backward.
bool
needResetBwd_
;
// mkldnn matrix, primitive, stream and pipeline
MKLDNNMatrixPtr
val_
;
MKLDNNMatrixPtr
grad_
;
...
...
@@ -40,7 +43,7 @@ protected:
std
::
vector
<
mkldnn
::
primitive
>
pipelineBwd_
;
public:
MKLDNNActivation
()
:
cnt_
(
0
)
{}
MKLDNNActivation
()
:
cnt_
(
0
)
,
needResetBwd_
(
true
)
{}
~
MKLDNNActivation
()
{}
static
ActivationFunction
*
create
(
const
std
::
string
&
type
);
static
std
::
vector
<
std
::
string
>
getAllRegisteredTypes
();
...
...
@@ -57,19 +60,43 @@ class MKLDNNEltwiseActivation : public MKLDNNActivation {
typedef
mkldnn
::
eltwise_forward
eltwise_fwd
;
typedef
mkldnn
::
eltwise_backward
eltwise_bwd
;
protected:
// save the forward primitive desc, which can be used backward
std
::
shared_ptr
<
eltwise_fwd
::
primitive_desc
>
fwdPD_
;
// eltwise_bwd need src input value
MKLDNNMatrixPtr
inVal_
;
// use for copy data
std
::
shared_ptr
<
mkldnn
::
reorder
>
copyInVal_
;
public:
MKLDNNEltwiseActivation
()
{}
~
MKLDNNEltwiseActivation
()
{}
virtual
const
std
::
string
&
getName
()
const
=
0
;
// in common, the alpha of forward and backward should be equal.
// but for relu, to avoid negative value, they should be opposite
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
and backward
primitives
* reshape and reset the forward primitives
*/
void
reset
Primitives
(
Argument
&
act
)
{
void
reset
Fwd
(
Argument
&
act
)
{
if
(
cnt_
==
act
.
value
->
getElementCnt
())
{
return
;
}
...
...
@@ -78,21 +105,13 @@ public:
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
();
}
mkldnn
::
algorithm
algo
=
getAlgo
(
this
->
getName
());
// note: alpha represents the NegativeSlope when used in relu.
float
alpha
=
getAlpha
();
float
beta
=
getBeta
();
/// forward
pipelineFwd_
.
clear
();
val_
=
std
::
dynamic_pointer_cast
<
MKLDNNMatrix
>
(
act
.
value
);
if
(
val_
==
nullptr
)
{
int
bs
=
act
.
getBatchSize
();
...
...
@@ -109,33 +128,52 @@ public:
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
();
fwdPD_
.
reset
(
new
eltwise_fwd
::
primitive_desc
(
fwdDesc
,
eng
));
// use inplace for forward but save input value before submit
inVal_
=
val_
;
if
(
act
.
grad
)
{
// only copy when need do 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
;
}
/// backward
if
(
act
.
grad
==
nullptr
)
{
grad_
=
nullptr
;
/**
* 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
;
}
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
);
bwd_
.
reset
(
new
eltwise_bwd
(
bwdPD
,
*
val_
,
*
grad_
,
*
grad_
));
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_
);
}
Error
__must_check
forward
(
Argument
&
act
)
{
reset
Primitives
(
act
);
reset
Fwd
(
act
);
stream_
->
submit
(
pipelineFwd_
);
return
Error
();
}
Error
__must_check
backward
(
Argument
&
act
)
{
resetBwd
(
act
);
stream_
->
submit
(
pipelineBwd_
);
return
Error
();
}
...
...
paddle/gserver/tests/MKLDNNTester.cpp
浏览文件 @
9d692e3b
...
...
@@ -64,15 +64,17 @@ void MKLDNNTester::reset(const TestConfig& dnn,
configs_
[
i
],
&
(
layerMaps_
[
i
]),
&
(
parameters_
[
i
]),
&
(
testLayers_
[
i
]));
}
refLayer_
=
testLayers_
[
REF
];
dnnLayer_
=
std
::
dynamic_pointer_cast
<
MKLDNNLayer
>
(
testLayers_
[
DNN
]);
CHECK
(
dnnLayer_
);
// for comparison with Paddle reference results,
// need manually add cpu device output for test
dnnLayer_
->
addOutputArgument
(
CPU_DEVICE
);
dnnLayer_
=
testLayers_
[
DNN
];
EXPECT_EQ
(
dataLayers_
[
DNN
].
size
(),
dataLayers_
[
REF
].
size
());
EXPECT_EQ
(
parameters_
[
DNN
].
size
(),
parameters_
[
REF
].
size
());
setInputImgSize
();
// for comparison with Paddle reference results,
// need manually add cpu device output for test
MKLDNNLayerPtr
dnnLayer
=
std
::
dynamic_pointer_cast
<
MKLDNNLayer
>
(
dnnLayer_
);
if
(
dnnLayer
)
{
dnnLayer
->
addOutputArgument
(
CPU_DEVICE
);
}
}
void
MKLDNNTester
::
setInputImgSize
()
{
...
...
@@ -122,7 +124,7 @@ void MKLDNNTester::randomTopDiffs() {
void
MKLDNNTester
::
checkForward
()
{
VLOG
(
MKLDNN_ALL
)
<<
"Check Forward"
;
printTopDatas
();
double
delta
=
compareMatrix
(
dnnLayer_
->
getOutput
(
-
1
).
value
,
double
delta
=
compareMatrix
(
dnnLayer_
->
getOutput
(
CPU_DEVICE
).
value
,
refLayer_
->
getOutputValue
());
EXPECT_LE
(
fabs
(
delta
),
eps_
);
}
...
...
@@ -155,7 +157,10 @@ void MKLDNNTester::checkBackwardWgts() {
vector
<
VectorPtr
>
dnnWgts
;
// used to temply save mkldnn weights
saveWgt
(
parameters_
[
DNN
],
dnnWgts
);
dnnLayer_
->
convertWeightsToPaddle
();
MKLDNNLayerPtr
dnnLayer
=
std
::
dynamic_pointer_cast
<
MKLDNNLayer
>
(
dnnLayer_
);
if
(
dnnLayer
)
{
dnnLayer
->
convertWeightsToPaddle
();
}
for
(
size_t
i
=
0
;
i
<
parameters_
[
DNN
].
size
();
++
i
)
{
const
VectorPtr
&
dnn
=
parameters_
[
DNN
][
i
]
->
getBuf
(
PARAMETER_VALUE
);
const
VectorPtr
&
ref
=
parameters_
[
REF
][
i
]
->
getBuf
(
PARAMETER_VALUE
);
...
...
@@ -322,6 +327,10 @@ void MKLDNNTester::runOnce() {
// and clearTopDatas(REF) should be coverd by ref layers
clearBotDiffs
(
REF
);
clearWgtDiffs
(
REF
);
// it is necessary to clear bottom diffs when only activation is dnn type
if
(
configs_
[
DNN
].
layerConfig
.
active_type
().
compare
(
0
,
7
,
"mkldnn_"
)
==
0
)
{
clearBotDiffs
(
DNN
);
}
}
void
MKLDNNTester
::
run
(
const
TestConfig
&
dnn
,
...
...
@@ -333,8 +342,19 @@ void MKLDNNTester::run(const TestConfig& dnn,
float
epsilon
,
bool
log
,
int
level
)
{
VLOG
(
MKLDNN_TESTS
)
<<
"Test MKLDNN functionality: "
<<
dnn
.
layerConfig
.
type
()
<<
" vs "
<<
ref
.
layerConfig
.
type
();
CHECK
(
dnn
.
layerConfig
.
type
().
compare
(
0
,
7
,
"mkldnn_"
)
==
0
||
dnn
.
layerConfig
.
active_type
().
compare
(
0
,
7
,
"mkldnn_"
)
==
0
)
<<
"should be MKLDNN layer or MKLDNN activation"
;
if
(
dnn
.
layerConfig
.
type
()
==
ref
.
layerConfig
.
type
())
{
VLOG
(
MKLDNN_TESTS
)
<<
"Test MKLDNN functionality: "
<<
dnn
.
layerConfig
.
active_type
()
<<
" vs "
<<
ref
.
layerConfig
.
active_type
();
}
else
{
VLOG
(
MKLDNN_TESTS
)
<<
"Test MKLDNN functionality: "
<<
dnn
.
layerConfig
.
type
()
<<
" vs "
<<
ref
.
layerConfig
.
type
();
}
ih_
=
inputImgH
;
iw_
=
inputImgW
;
iter_
=
iter
;
...
...
paddle/gserver/tests/MKLDNNTester.h
浏览文件 @
9d692e3b
...
...
@@ -41,8 +41,7 @@ protected:
vector
<
LayerMap
>
layerMaps_
;
vector
<
vector
<
ParameterPtr
>>
parameters_
;
vector
<
LayerPtr
>
testLayers_
;
LayerPtr
refLayer_
;
MKLDNNLayerPtr
dnnLayer_
;
LayerPtr
refLayer_
,
dnnLayer_
;
/// run some iterations, all the result should pass
size_t
iter_
;
...
...
paddle/gserver/tests/test_MKLDNN.cpp
浏览文件 @
9d692e3b
...
...
@@ -17,6 +17,7 @@ limitations under the License. */
#include <vector>
#include "MKLDNNTester.h"
#include "ModelConfig.pb.h"
#include "paddle/gserver/activations/MKLDNNActivation.h"
#include "paddle/math/MathUtils.h"
using
namespace
paddle
;
// NOLINT
...
...
@@ -190,7 +191,7 @@ void testPoolLayer(const testPoolDesc& pm) {
}
}
TEST
(
M
kldnn
Layer
,
PoolLayer
)
{
TEST
(
M
KLDNN
Layer
,
PoolLayer
)
{
/* bs, ch, ih, iw, oh, ow, fh, fw, ph, pw, sh, sw*/
testPoolLayer
({
2
,
1
,
4
,
4
,
2
,
2
,
3
,
3
,
0
,
0
,
2
,
2
});
testPoolLayer
({
10
,
8
,
16
,
16
,
8
,
8
,
2
,
2
,
0
,
0
,
2
,
2
});
...
...
@@ -202,6 +203,49 @@ TEST(MkldnnLayer, PoolLayer) {
testPoolLayer
({
2
,
8
,
56
,
56
,
29
,
29
,
3
,
3
,
1
,
1
,
2
,
2
});
}
struct
testActDesc
{
int
bs
,
ch
;
int
ih
,
iw
;
};
static
void
getAddtoConfig
(
TestConfig
&
cfg
,
const
testActDesc
&
pm
)
{
cfg
.
biasSize
=
0
;
cfg
.
layerConfig
.
set_type
(
"addto"
);
cfg
.
layerConfig
.
set_size
(
pm
.
ch
*
pm
.
ih
*
pm
.
iw
);
cfg
.
inputDefs
.
push_back
(
{
INPUT_DATA
,
"layer_0"
,
/* size of input layer= */
size_t
(
pm
.
ch
*
pm
.
ih
*
pm
.
iw
),
0
});
cfg
.
layerConfig
.
add_inputs
();
}
void
testActivation
(
std
::
string
&
type
,
const
testActDesc
&
pm
)
{
const
std
::
string
compareTypes
[]
=
{
type
,
type
.
erase
(
0
,
7
)};
TestConfig
cfg
;
getAddtoConfig
(
cfg
,
pm
);
TestConfig
ref
=
cfg
;
cfg
.
layerConfig
.
set_active_type
(
compareTypes
[
0
]);
ref
.
layerConfig
.
set_active_type
(
compareTypes
[
1
]);
MKLDNNTester
tester
;
for
(
auto
bs
:
{
pm
.
bs
,
1
})
{
tester
.
run
(
cfg
,
ref
,
bs
,
pm
.
ih
,
pm
.
iw
);
}
}
TEST
(
MKLDNNActivation
,
Activations
)
{
auto
types
=
MKLDNNActivation
::
getAllRegisteredTypes
();
// TODO(TJ): mkldnn_softmax not implemented, paddle do not have elu activation
std
::
set
<
string
>
excluded
{
"mkldnn_softmax"
,
"mkldnn_elu"
};
for
(
auto
type
:
types
)
{
if
(
excluded
.
count
(
type
))
{
continue
;
}
testActivation
(
type
,
{
16
,
64
,
32
,
32
});
}
}
// TODO(TJ): add branch test
int
main
(
int
argc
,
char
**
argv
)
{
...
...
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