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体验新版 GitCode,发现更多精彩内容 >>
提交
3ae9aa93
编写于
10月 17, 2017
作者:
T
Tao Luo
提交者:
GitHub
10月 17, 2017
浏览文件
操作
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下载
差异文件
Merge pull request #4860 from tensor-tang/merge_grad_gtest
enable merge grad unit test
上级
f12f61d5
60b84856
变更
10
隐藏空白更改
内联
并排
Showing
10 changed file
with
296 addition
and
18 deletion
+296
-18
paddle/gserver/gradientmachines/NeuralNetwork.cpp
paddle/gserver/gradientmachines/NeuralNetwork.cpp
+15
-0
paddle/gserver/gradientmachines/NeuralNetwork.h
paddle/gserver/gradientmachines/NeuralNetwork.h
+3
-0
paddle/gserver/layers/MKLDNNConvLayer.cpp
paddle/gserver/layers/MKLDNNConvLayer.cpp
+5
-3
paddle/gserver/layers/MKLDNNLayer.h
paddle/gserver/layers/MKLDNNLayer.h
+18
-1
paddle/gserver/layers/MKLDNNPoolLayer.cpp
paddle/gserver/layers/MKLDNNPoolLayer.cpp
+5
-3
paddle/gserver/tests/CMakeLists.txt
paddle/gserver/tests/CMakeLists.txt
+4
-1
paddle/gserver/tests/MKLDNNTester.cpp
paddle/gserver/tests/MKLDNNTester.cpp
+141
-0
paddle/gserver/tests/MKLDNNTester.h
paddle/gserver/tests/MKLDNNTester.h
+30
-8
paddle/gserver/tests/mkldnn_branches_conv.conf
paddle/gserver/tests/mkldnn_branches_conv.conf
+56
-0
paddle/gserver/tests/test_MKLDNN.cpp
paddle/gserver/tests/test_MKLDNN.cpp
+19
-2
未找到文件。
paddle/gserver/gradientmachines/NeuralNetwork.cpp
浏览文件 @
3ae9aa93
...
@@ -21,6 +21,10 @@ limitations under the License. */
...
@@ -21,6 +21,10 @@ limitations under the License. */
#include "paddle/utils/Logging.h"
#include "paddle/utils/Logging.h"
#include "paddle/utils/Stat.h"
#include "paddle/utils/Stat.h"
#ifdef PADDLE_USE_MKLDNN
#include "paddle/gserver/layers/MKLDNNLayer.h"
#endif
#ifndef PADDLE_MOBILE_INFERENCE
#ifndef PADDLE_MOBILE_INFERENCE
#include "MultiNetwork.h"
#include "MultiNetwork.h"
#include "RecurrentGradientMachine.h"
#include "RecurrentGradientMachine.h"
...
@@ -300,6 +304,17 @@ void NeuralNetwork::backward(const UpdateCallback& callback) {
...
@@ -300,6 +304,17 @@ void NeuralNetwork::backward(const UpdateCallback& callback) {
}
}
}
}
void
NeuralNetwork
::
finish
()
{
#ifdef PADDLE_USE_MKLDNN
FOR_EACH_R
(
layer
,
layers_
)
{
MKLDNNLayerPtr
dnnLayer
=
std
::
dynamic_pointer_cast
<
MKLDNNLayer
>
(
*
layer
);
if
(
dnnLayer
)
{
dnnLayer
->
convertWeightsToPaddle
();
}
}
#endif
}
Argument
NeuralNetwork
::
getLayerOutput
(
const
std
::
string
&
layerName
)
{
Argument
NeuralNetwork
::
getLayerOutput
(
const
std
::
string
&
layerName
)
{
return
getLayer
(
layerName
)
->
getOutput
();
return
getLayer
(
layerName
)
->
getOutput
();
}
}
...
...
paddle/gserver/gradientmachines/NeuralNetwork.h
浏览文件 @
3ae9aa93
...
@@ -134,6 +134,9 @@ public:
...
@@ -134,6 +134,9 @@ public:
const
std
::
string
&
getName
()
const
{
return
subModelName_
;
}
const
std
::
string
&
getName
()
const
{
return
subModelName_
;
}
/// some finish work, like convert the weight format of MKLDNNLayers
void
finish
()
override
;
protected:
protected:
/**
/**
* The constructor of NeuralNetwork.
* The constructor of NeuralNetwork.
...
...
paddle/gserver/layers/MKLDNNConvLayer.cpp
浏览文件 @
3ae9aa93
...
@@ -313,6 +313,7 @@ void MKLDNNConvLayer::resetOutValue(
...
@@ -313,6 +313,7 @@ void MKLDNNConvLayer::resetOutValue(
cvtOutVal_
=
MKLDNNMatrix
::
createReorder
(
out
,
cpuOutVal_
);
cvtOutVal_
=
MKLDNNMatrix
::
createReorder
(
out
,
cpuOutVal_
);
CHECK
(
cvtOutVal_
)
<<
"should not be empty"
;
CHECK
(
cvtOutVal_
)
<<
"should not be empty"
;
}
else
{
}
else
{
cpuOut
->
setData
(
output_
.
value
->
getData
());
cpuOutVal_
=
out
;
cpuOutVal_
=
out
;
}
}
// when output is cpu device, change the mkldnn output value and make them
// when output is cpu device, change the mkldnn output value and make them
...
@@ -456,17 +457,18 @@ void MKLDNNConvLayer::resetOutGrad(
...
@@ -456,17 +457,18 @@ void MKLDNNConvLayer::resetOutGrad(
MKLDNNLayer
::
resetOutGrad
(
out
,
outVal_
->
getPrimitiveDesc
());
MKLDNNLayer
::
resetOutGrad
(
out
,
outVal_
->
getPrimitiveDesc
());
}
else
{
}
else
{
const
MatrixPtr
&
cpuOut
=
getOutput
(
CPU_DEVICE
).
grad
;
const
MatrixPtr
&
cpuOut
=
getOutput
(
CPU_DEVICE
).
grad
;
// always share the same grad data of CPU output
// then the activation can get the right grad from output_.grad
output_
.
grad
->
setData
(
cpuOut
->
getData
());
// same PrimitiveDesc with cpuInVal_
// same PrimitiveDesc with cpuInVal_
CHECK
(
cpuOutVal_
);
CHECK
(
cpuOutVal_
);
cpuOutGrad_
=
MKLDNNMatrix
::
create
(
cpuOut
,
cpuOutVal_
->
getPrimitiveDesc
());
cpuOutGrad_
=
MKLDNNMatrix
::
create
(
cpuOut
,
cpuOutVal_
->
getPrimitiveDesc
());
// create reorder if primitive desc does not match
// create reorder if primitive desc does not match
if
(
cpuOutGrad_
->
getPrimitiveDesc
()
!=
outVal_
->
getPrimitiveDesc
())
{
if
(
cpuOutGrad_
->
getPrimitiveDesc
()
!=
outVal_
->
getPrimitiveDesc
())
{
out
=
MKLDNNMatrix
::
create
(
output_
.
grad
,
outVal_
->
getPrimitiveDesc
());
out
=
MKLDNNMatrix
::
create
(
nullptr
,
outVal_
->
getPrimitiveDesc
());
cvtOutGrad_
=
MKLDNNMatrix
::
createReorder
(
cpuOutGrad_
,
out
);
cvtOutGrad_
=
MKLDNNMatrix
::
createReorder
(
cpuOutGrad_
,
out
);
CHECK
(
cvtOutGrad_
);
CHECK
(
cvtOutGrad_
);
}
else
{
}
else
{
// share the same data of CPU output
output_
.
grad
->
setData
(
cpuOut
->
getData
());
out
=
cpuOutGrad_
;
out
=
cpuOutGrad_
;
}
}
}
}
...
...
paddle/gserver/layers/MKLDNNLayer.h
浏览文件 @
3ae9aa93
...
@@ -46,6 +46,9 @@ protected:
...
@@ -46,6 +46,9 @@ protected:
// backward also need reset after reset forward handle
// backward also need reset after reset forward handle
bool
needResetBwd_
;
bool
needResetBwd_
;
// is output only mkldnn
bool
outputOnlyMKLDNN_
;
// mkldnn engine, stream and primivtives
// mkldnn engine, stream and primivtives
mkldnn
::
engine
engine_
;
mkldnn
::
engine
engine_
;
std
::
shared_ptr
<
MKLDNNStream
>
stream_
;
std
::
shared_ptr
<
MKLDNNStream
>
stream_
;
...
@@ -141,6 +144,9 @@ public:
...
@@ -141,6 +144,9 @@ public:
updateInputData
();
updateInputData
();
}
}
if
(
!
outputOnlyMKLDNN_
)
{
clearGrads
();
}
stream_
->
submit
(
pipelineFwd_
);
stream_
->
submit
(
pipelineFwd_
);
}
}
...
@@ -389,7 +395,8 @@ protected:
...
@@ -389,7 +395,8 @@ protected:
CHECK_EQ
(
outputOtherDevice_
[
i
].
deviceId
,
CPU_DEVICE
)
CHECK_EQ
(
outputOtherDevice_
[
i
].
deviceId
,
CPU_DEVICE
)
<<
"Only support other device is CPU yet"
;
<<
"Only support other device is CPU yet"
;
}
}
return
outputOtherDevice_
.
size
()
==
0
;
outputOnlyMKLDNN_
=
outputOtherDevice_
.
size
()
==
0
;
return
outputOnlyMKLDNN_
;
}
}
/**
/**
...
@@ -398,6 +405,16 @@ protected:
...
@@ -398,6 +405,16 @@ protected:
void
setDevice
(
int
id
)
{
deviceId_
=
id
;
}
void
setDevice
(
int
id
)
{
deviceId_
=
id
;
}
private:
private:
/**
* clear all grad
*/
void
clearGrads
()
{
output_
.
grad
->
zeroMem
();
for
(
size_t
i
=
0
;
i
<
outputOtherDevice_
.
size
();
i
++
)
{
outputOtherDevice_
[
i
].
grad
->
zeroMem
();
}
}
/**
/**
* Set deviceId of the params used in this layer.
* Set deviceId of the params used in this layer.
*/
*/
...
...
paddle/gserver/layers/MKLDNNPoolLayer.cpp
浏览文件 @
3ae9aa93
...
@@ -146,6 +146,7 @@ void MKLDNNPoolLayer::resetOutValue(MKLDNNMatrixPtr& out) {
...
@@ -146,6 +146,7 @@ void MKLDNNPoolLayer::resetOutValue(MKLDNNMatrixPtr& out) {
cvtOutVal_
=
MKLDNNMatrix
::
createReorder
(
out
,
cpuOutVal_
);
cvtOutVal_
=
MKLDNNMatrix
::
createReorder
(
out
,
cpuOutVal_
);
CHECK
(
cvtOutVal_
)
<<
"should not be emptry"
;
CHECK
(
cvtOutVal_
)
<<
"should not be emptry"
;
}
else
{
}
else
{
cpuOut
->
setData
(
output_
.
value
->
getData
());
cpuOutVal_
=
out
;
cpuOutVal_
=
out
;
}
}
output_
.
value
=
std
::
dynamic_pointer_cast
<
Matrix
>
(
cpuOutVal_
);
output_
.
value
=
std
::
dynamic_pointer_cast
<
Matrix
>
(
cpuOutVal_
);
...
@@ -213,15 +214,16 @@ void MKLDNNPoolLayer::resetOutGrad(MKLDNNMatrixPtr& out) {
...
@@ -213,15 +214,16 @@ void MKLDNNPoolLayer::resetOutGrad(MKLDNNMatrixPtr& out) {
MKLDNNLayer
::
resetOutGrad
(
out
,
outVal_
->
getPrimitiveDesc
());
MKLDNNLayer
::
resetOutGrad
(
out
,
outVal_
->
getPrimitiveDesc
());
}
else
{
}
else
{
const
MatrixPtr
&
cpuOut
=
getOutput
(
CPU_DEVICE
).
grad
;
const
MatrixPtr
&
cpuOut
=
getOutput
(
CPU_DEVICE
).
grad
;
// always share the same grad data of CPU output
// then the activation can get the right grad from output_.grad
output_
.
grad
->
setData
(
cpuOut
->
getData
());
cpuOutGrad_
=
MKLDNNMatrix
::
create
(
cpuOutGrad_
=
MKLDNNMatrix
::
create
(
cpuOut
,
memory
::
dims
{
bs_
,
oc_
,
oh_
,
ow_
},
format
::
nchw
,
engine_
);
cpuOut
,
memory
::
dims
{
bs_
,
oc_
,
oh_
,
ow_
},
format
::
nchw
,
engine_
);
if
(
cpuOutGrad_
->
getPrimitiveDesc
()
!=
outVal_
->
getPrimitiveDesc
())
{
if
(
cpuOutGrad_
->
getPrimitiveDesc
()
!=
outVal_
->
getPrimitiveDesc
())
{
out
=
MKLDNNMatrix
::
create
(
output_
.
grad
,
outVal_
->
getPrimitiveDesc
());
out
=
MKLDNNMatrix
::
create
(
nullptr
,
outVal_
->
getPrimitiveDesc
());
cvtOutGrad_
=
MKLDNNMatrix
::
createReorder
(
cpuOutGrad_
,
out
);
cvtOutGrad_
=
MKLDNNMatrix
::
createReorder
(
cpuOutGrad_
,
out
);
CHECK
(
cvtOutGrad_
)
<<
"should not be emptry"
;
CHECK
(
cvtOutGrad_
)
<<
"should not be emptry"
;
}
else
{
}
else
{
// share the same data of CPU output
output_
.
grad
->
setData
(
cpuOut
->
getData
());
out
=
cpuOutGrad_
;
out
=
cpuOutGrad_
;
}
}
}
}
...
...
paddle/gserver/tests/CMakeLists.txt
浏览文件 @
3ae9aa93
...
@@ -26,7 +26,10 @@ if(WITH_MKLDNN)
...
@@ -26,7 +26,10 @@ if(WITH_MKLDNN)
test_MKLDNN.cpp
test_MKLDNN.cpp
MKLDNNTester.cpp
MKLDNNTester.cpp
LayerGradUtil.cpp
)
LayerGradUtil.cpp
)
add_test
(
NAME test_MKLDNN COMMAND test_MKLDNN
)
add_test
(
NAME test_MKLDNN
COMMAND .set_python_path.sh -d
${
PADDLE_SOURCE_DIR
}
/python
${
CMAKE_CURRENT_BINARY_DIR
}
/test_MKLDNN
WORKING_DIRECTORY
${
PADDLE_SOURCE_DIR
}
/paddle
)
endif
()
endif
()
################ test_CRFLayerGrad ####################
################ test_CRFLayerGrad ####################
...
...
paddle/gserver/tests/MKLDNNTester.cpp
浏览文件 @
3ae9aa93
...
@@ -15,6 +15,7 @@ limitations under the License. */
...
@@ -15,6 +15,7 @@ limitations under the License. */
#include "MKLDNNTester.h"
#include "MKLDNNTester.h"
#include "paddle/gserver/layers/MKLDNNBase.h"
#include "paddle/gserver/layers/MKLDNNBase.h"
#include "paddle/gserver/layers/MKLDNNLayer.h"
#include "paddle/gserver/layers/MKLDNNLayer.h"
#include "paddle/trainer/Trainer.h"
namespace
paddle
{
namespace
paddle
{
...
@@ -315,6 +316,7 @@ void MKLDNNTester::runOnce() {
...
@@ -315,6 +316,7 @@ void MKLDNNTester::runOnce() {
auto
&
value
=
para
->
getBuf
(
PARAMETER_VALUE
);
auto
&
value
=
para
->
getBuf
(
PARAMETER_VALUE
);
real
lr
=
1e-3
;
real
lr
=
1e-3
;
value
->
add
(
*
grad
,
lr
);
value
->
add
(
*
grad
,
lr
);
grad
->
zeroMem
();
};
};
randomTopDiffs
();
randomTopDiffs
();
dnnLayer_
->
backward
(
updateCallback
);
dnnLayer_
->
backward
(
updateCallback
);
...
@@ -411,4 +413,143 @@ void MKLDNNTester::run(const TestConfig& dnn,
...
@@ -411,4 +413,143 @@ void MKLDNNTester::run(const TestConfig& dnn,
}
}
}
}
void
MKLDNNTester
::
initArgument
(
DataIn
&
data
,
const
std
::
string
&
configPath
,
const
size_t
iter
)
{
TrainerConfigHelper
config
(
configPath
);
size_t
batchSize
=
config
.
getOptConfig
().
batch_size
();
data
.
inArgs
.
resize
(
iter
);
data
.
outGrads
.
resize
(
iter
);
data
.
paraValues
.
clear
();
for
(
const
auto
&
layer_name
:
config
.
getModelConfig
().
input_layer_names
())
{
auto
layer_config
=
std
::
find_if
(
config
.
getModelConfig
().
layers
().
begin
(),
config
.
getModelConfig
().
layers
().
end
(),
[
=
](
const
LayerConfig
&
layer_config
)
{
return
layer_config
.
name
()
==
layer_name
;
});
CHECK
(
layer_config
!=
config
.
getModelConfig
().
layers
().
end
());
size_t
layerSize
=
layer_config
->
size
();
for
(
size_t
i
=
0
;
i
<
iter
;
++
i
)
{
Argument
arg
;
arg
.
value
=
Matrix
::
create
(
batchSize
,
layerSize
,
false
,
false
);
arg
.
grad
=
Matrix
::
create
(
batchSize
,
layerSize
,
false
,
false
);
arg
.
value
->
randomizeUniform
();
arg
.
value
->
add
(
-
0.5
);
arg
.
value
->
sigmoid
(
*
arg
.
value
);
arg
.
grad
->
zeroMem
();
arg
.
ids
=
VectorT
<
int
>::
create
(
batchSize
,
false
);
arg
.
ids
->
rand
(
layerSize
);
generateSequenceStartPositions
(
batchSize
,
arg
.
sequenceStartPositions
);
data
.
inArgs
[
i
].
push_back
(
arg
);
}
}
for
(
const
auto
&
layer_name
:
config
.
getModelConfig
().
output_layer_names
())
{
auto
layer_config
=
std
::
find_if
(
config
.
getModelConfig
().
layers
().
begin
(),
config
.
getModelConfig
().
layers
().
end
(),
[
=
](
const
LayerConfig
&
layer_config
)
{
return
layer_config
.
name
()
==
layer_name
;
});
CHECK
(
layer_config
!=
config
.
getModelConfig
().
layers
().
end
());
size_t
layerSize
=
layer_config
->
size
();
for
(
size_t
i
=
0
;
i
<
iter
;
++
i
)
{
MatrixPtr
grad
=
Matrix
::
create
(
batchSize
,
layerSize
,
false
,
false
);
grad
->
randomizeUniform
();
data
.
outGrads
[
i
].
push_back
(
grad
);
}
}
for
(
const
auto
&
para_config
:
config
.
getModelConfig
().
parameters
())
{
VectorPtr
value
=
Vector
::
create
(
para_config
.
size
(),
false
);
value
->
randnorm
(
0
,
2
);
data
.
paraValues
.
push_back
(
value
);
}
}
void
MKLDNNTester
::
getOutResult
(
const
std
::
string
&
configPath
,
DataIn
&
in
,
DataOut
&
out
,
bool
use_mkldnn
,
size_t
iter
)
{
FLAGS_use_gpu
=
false
;
FLAGS_use_mkldnn
=
use_mkldnn
;
*
ThreadLocalRand
::
getSeed
()
=
1
;
srand
(
1
);
Trainer
trainer
;
auto
config
=
std
::
make_shared
<
TrainerConfigHelper
>
(
configPath
);
trainer
.
init
(
config
,
false
);
auto
gradientMachine
=
trainer
.
getGradientMachine
();
std
::
vector
<
ParameterPtr
>
parameters
=
gradientMachine
->
getParameters
();
for
(
size_t
i
=
0
;
i
<
in
.
paraValues
.
size
();
i
++
)
{
parameters
[
i
]
->
getBuf
(
PARAMETER_VALUE
)
->
copyFrom
(
*
in
.
paraValues
[
i
]);
}
UpdateCallback
simpleUpdate
=
[](
Parameter
*
para
)
{
auto
&
grad
=
para
->
getBuf
(
PARAMETER_GRADIENT
);
auto
&
value
=
para
->
getBuf
(
PARAMETER_VALUE
);
real
lr
=
1e-2
;
value
->
add
(
*
grad
,
lr
);
grad
->
zeroMem
();
};
vector
<
Argument
>
outArgs
;
gradientMachine
->
start
();
out
.
outValues
.
clear
();
out
.
paraValues
.
clear
();
for
(
size_t
i
=
0
;
i
<
iter
;
++
i
)
{
VLOG
(
MKLDNN_TESTS
)
<<
"runing iteration "
<<
i
;
gradientMachine
->
forward
(
in
.
inArgs
[
i
],
&
outArgs
,
PASS_TRAIN
);
// save forward result
for
(
size_t
k
=
0
;
k
<
outArgs
.
size
();
k
++
)
{
MatrixPtr
value
=
Matrix
::
create
(
outArgs
[
k
].
value
->
getHeight
(),
outArgs
[
k
].
value
->
getWidth
(),
false
,
false
);
value
->
copyFrom
(
*
outArgs
[
k
].
value
);
out
.
outValues
.
push_back
(
value
);
}
// random backward input
for
(
size_t
k
=
0
;
k
<
outArgs
.
size
();
k
++
)
{
outArgs
[
k
].
grad
->
copyFrom
(
*
in
.
outGrads
[
i
][
k
]);
}
gradientMachine
->
backward
(
simpleUpdate
);
}
gradientMachine
->
finish
();
// save param value
for
(
size_t
i
=
0
;
i
<
in
.
paraValues
.
size
();
i
++
)
{
VectorPtr
val
=
Vector
::
create
(
parameters
[
i
]
->
getBuf
(
PARAMETER_VALUE
)
->
getSize
(),
false
);
val
->
copyFrom
(
*
parameters
[
i
]
->
getBuf
(
PARAMETER_VALUE
));
out
.
paraValues
.
push_back
(
val
);
}
}
void
MKLDNNTester
::
compareResult
(
DataOut
&
ref
,
DataOut
&
dnn
,
float
eps
)
{
CHECK_EQ
(
ref
.
outValues
.
size
(),
dnn
.
outValues
.
size
());
CHECK_EQ
(
ref
.
paraValues
.
size
(),
dnn
.
paraValues
.
size
());
for
(
size_t
i
=
0
;
i
<
ref
.
outValues
.
size
();
i
++
)
{
EXPECT_LE
(
fabs
(
compareMatrix
(
ref
.
outValues
[
i
],
dnn
.
outValues
[
i
])),
eps
);
}
for
(
size_t
i
=
0
;
i
<
ref
.
paraValues
.
size
();
i
++
)
{
EXPECT_LE
(
fabs
(
compareVector
(
ref
.
paraValues
[
i
],
dnn
.
paraValues
[
i
])),
eps
);
}
}
void
MKLDNNTester
::
runBranchesTest
(
const
std
::
string
&
configPath
,
size_t
iter
,
float
eps
)
{
DataIn
in
;
initArgument
(
in
,
configPath
,
iter
);
DataOut
outCpu
,
outDnn
;
getOutResult
(
configPath
,
in
,
outCpu
,
false
,
iter
);
getOutResult
(
configPath
,
in
,
outDnn
,
true
,
iter
);
compareResult
(
outCpu
,
outDnn
,
eps
);
}
}
// namespace paddle
}
// namespace paddle
paddle/gserver/tests/MKLDNNTester.h
浏览文件 @
3ae9aa93
...
@@ -33,6 +33,17 @@ class MKLDNNTester {
...
@@ -33,6 +33,17 @@ class MKLDNNTester {
NUM
=
2
,
// Number of total
NUM
=
2
,
// Number of total
};
};
struct
DataIn
{
std
::
vector
<
std
::
vector
<
Argument
>>
inArgs
;
std
::
vector
<
std
::
vector
<
MatrixPtr
>>
outGrads
;
std
::
vector
<
VectorPtr
>
paraValues
;
};
struct
DataOut
{
std
::
vector
<
MatrixPtr
>
outValues
;
std
::
vector
<
VectorPtr
>
paraValues
;
};
protected:
protected:
std
::
vector
<
TestConfig
>
configs_
;
std
::
vector
<
TestConfig
>
configs_
;
vector
<
string
>
layerNames_
;
vector
<
string
>
layerNames_
;
...
@@ -74,7 +85,17 @@ public:
...
@@ -74,7 +85,17 @@ public:
float
epsilon
=
1e-4
,
float
epsilon
=
1e-4
,
bool
log
=
false
,
bool
log
=
false
,
int
level
=
MKLDNN_ALL
);
int
level
=
MKLDNN_ALL
);
void
setLogLevel
(
int
lvl
)
{
lvl_
=
lvl
;
}
static
void
runBranchesTest
(
const
std
::
string
&
configPath
,
size_t
iter
=
3
,
float
eps
=
1e-4
);
static
void
initArgument
(
DataIn
&
data
,
const
std
::
string
&
configPath
,
size_t
iter
=
3
);
static
void
getOutResult
(
const
std
::
string
&
configPath
,
DataIn
&
in
,
DataOut
&
out
,
bool
use_mkldnn
,
size_t
iter
=
3
);
private:
private:
void
reset
(
const
TestConfig
&
dnn
,
const
TestConfig
&
ref
,
size_t
batchSize
);
void
reset
(
const
TestConfig
&
dnn
,
const
TestConfig
&
ref
,
size_t
batchSize
);
...
@@ -101,8 +122,9 @@ private:
...
@@ -101,8 +122,9 @@ private:
void
saveWgt
(
const
vector
<
ParameterPtr
>&
from
,
vector
<
VectorPtr
>&
to
);
void
saveWgt
(
const
vector
<
ParameterPtr
>&
from
,
vector
<
VectorPtr
>&
to
);
void
restoreWgt
(
const
vector
<
VectorPtr
>&
from
,
vector
<
ParameterPtr
>&
to
);
void
restoreWgt
(
const
vector
<
VectorPtr
>&
from
,
vector
<
ParameterPtr
>&
to
);
double
compareMatrix
(
const
MatrixPtr
&
m1
,
const
MatrixPtr
&
m2
);
static
double
compareMatrix
(
const
MatrixPtr
&
m1
,
const
MatrixPtr
&
m2
);
double
compareVector
(
const
VectorPtr
&
v1
,
const
VectorPtr
&
v2
);
static
double
compareVector
(
const
VectorPtr
&
v1
,
const
VectorPtr
&
v2
);
static
void
compareResult
(
DataOut
&
ref
,
DataOut
&
dnn
,
float
eps
=
1e-4
);
/**
/**
* Get delta percent
* Get delta percent
...
@@ -111,11 +133,11 @@ private:
...
@@ -111,11 +133,11 @@ private:
* else return sum(abs(a-b)) / sum(abs(b))
* else return sum(abs(a-b)) / sum(abs(b))
* The return value should be smaller than eps when passing.
* The return value should be smaller than eps when passing.
*/
*/
double
getDelta
(
const
real
*
d1
,
static
double
getDelta
(
const
real
*
d1
,
const
real
*
d2
,
const
real
*
d2
,
size_t
len
,
size_t
len
,
const
float
failRate
=
1e-3
,
const
float
failRate
=
1e-3
,
const
float
thres
=
0.1
);
const
float
thres
=
0.1
);
};
};
}
// namespace paddle
}
// namespace paddle
paddle/gserver/tests/mkldnn_branches_conv.conf
0 → 100644
浏览文件 @
3ae9aa93
# Copyright (c) 2017 PaddlePaddle Authors. All Rights Reserved
#
# 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.
from
paddle
.
trainer_config_helpers
import
*
settings
(
batch_size
=
16
)
channels
=
get_config_arg
(
"channels"
,
int
,
2
)
def
two_conv
(
input
,
group_name
):
out1
=
img_conv_layer
(
input
=
input
,
name
=
group_name
+
'_conv1'
,
filter_size
=
1
,
num_filters
=
channels
,
padding
=
0
,
shared_biases
=
True
,
act
=
ReluActivation
())
out2
=
img_conv_layer
(
input
=
input
,
name
=
group_name
+
'_conv2'
,
filter_size
=
3
,
num_filters
=
channels
,
padding
=
1
,
shared_biases
=
True
,
act
=
ReluActivation
())
return
out1
,
out2
data
=
data_layer
(
name
=
"input"
,
size
=
channels
*
16
*
16
)
conv
=
img_conv_layer
(
input
=
data
,
num_channels
=
channels
,
filter_size
=
3
,
num_filters
=
channels
,
padding
=
1
,
shared_biases
=
True
,
act
=
ReluActivation
())
a1
,
a2
=
two_conv
(
input
=
conv
,
group_name
=
'a'
)
concat
=
concat_layer
(
input
=[
a1
,
a2
])
b1
,
b2
=
two_conv
(
input
=
conv
,
group_name
=
'b'
)
addto
=
addto_layer
(
input
=[
b1
,
b2
])
outputs
([
concat
,
addto
])
paddle/gserver/tests/test_MKLDNN.cpp
浏览文件 @
3ae9aa93
...
@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
...
@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
limitations under the License. */
#include <gtest/gtest.h>
#include <gtest/gtest.h>
#include <paddle/utils/PythonUtil.h>
#include <string>
#include <string>
#include <vector>
#include <vector>
#include "MKLDNNTester.h"
#include "MKLDNNTester.h"
...
@@ -40,12 +41,13 @@ DECLARE_bool(use_mkldnn);
...
@@ -40,12 +41,13 @@ DECLARE_bool(use_mkldnn);
struct
testFcDesc
{
struct
testFcDesc
{
int
bs
;
int
bs
;
int
ic
;
int
ic
;
int
oc
;
int
ih
,
iw
;
// oh == ow == 1
int
ih
,
iw
;
// oh == ow == 1
int
oc
;
};
};
static
void
getMKLDNNFcConfig
(
TestConfig
&
cfg
,
const
testFcDesc
&
pm
)
{
static
void
getMKLDNNFcConfig
(
TestConfig
&
cfg
,
const
testFcDesc
&
pm
)
{
cfg
.
layerConfig
.
set_type
(
"mkldnn_fc"
);
cfg
.
layerConfig
.
set_type
(
"mkldnn_fc"
);
cfg
.
layerConfig
.
set_active_type
(
"relu"
);
cfg
.
layerConfig
.
set_size
(
pm
.
oc
);
cfg
.
layerConfig
.
set_size
(
pm
.
oc
);
cfg
.
inputDefs
.
push_back
(
cfg
.
inputDefs
.
push_back
(
{
INPUT_DATA
,
{
INPUT_DATA
,
...
@@ -86,6 +88,7 @@ struct testConvDesc {
...
@@ -86,6 +88,7 @@ struct testConvDesc {
static
void
getMKLDNNConvConfig
(
TestConfig
&
cfg
,
const
testConvDesc
&
pm
)
{
static
void
getMKLDNNConvConfig
(
TestConfig
&
cfg
,
const
testConvDesc
&
pm
)
{
cfg
.
layerConfig
.
set_type
(
"mkldnn_conv"
);
cfg
.
layerConfig
.
set_type
(
"mkldnn_conv"
);
cfg
.
layerConfig
.
set_active_type
(
"relu"
);
cfg
.
layerConfig
.
set_num_filters
(
pm
.
oc
);
cfg
.
layerConfig
.
set_num_filters
(
pm
.
oc
);
cfg
.
layerConfig
.
set_size
(
pm
.
oc
*
pm
.
oh
*
pm
.
ow
);
cfg
.
layerConfig
.
set_size
(
pm
.
oc
*
pm
.
oh
*
pm
.
ow
);
cfg
.
layerConfig
.
set_shared_biases
(
true
);
cfg
.
layerConfig
.
set_shared_biases
(
true
);
...
@@ -158,6 +161,7 @@ struct testPoolDesc {
...
@@ -158,6 +161,7 @@ struct testPoolDesc {
static
void
getMKLDNNPoolConfig
(
TestConfig
&
cfg
,
const
testPoolDesc
&
pm
)
{
static
void
getMKLDNNPoolConfig
(
TestConfig
&
cfg
,
const
testPoolDesc
&
pm
)
{
cfg
.
layerConfig
.
set_type
(
"mkldnn_pool"
);
cfg
.
layerConfig
.
set_type
(
"mkldnn_pool"
);
cfg
.
layerConfig
.
set_active_type
(
"relu"
);
cfg
.
layerConfig
.
set_size
(
pm
.
ic
*
pm
.
oh
*
pm
.
ow
);
cfg
.
layerConfig
.
set_size
(
pm
.
ic
*
pm
.
oh
*
pm
.
ow
);
cfg
.
inputDefs
.
push_back
(
cfg
.
inputDefs
.
push_back
(
{
INPUT_DATA
,
{
INPUT_DATA
,
...
@@ -244,13 +248,26 @@ TEST(MKLDNNActivation, Activations) {
...
@@ -244,13 +248,26 @@ TEST(MKLDNNActivation, Activations) {
}
}
}
}
// TODO(TJ): add branch test
DECLARE_string
(
config_args
);
TEST
(
MKLDNNLayer
,
branches
)
{
std
::
vector
<
std
::
string
>
cases
=
{
"conv"
};
for
(
auto
name
:
cases
)
{
std
::
string
config
=
"./gserver/tests/mkldnn_branches_"
+
name
+
".conf"
;
for
(
auto
channels
:
{
2
,
32
})
{
std
::
ostringstream
oss
;
oss
<<
"channels="
<<
channels
;
FLAGS_config_args
=
oss
.
str
();
MKLDNNTester
::
runBranchesTest
(
config
);
}
}
}
int
main
(
int
argc
,
char
**
argv
)
{
int
main
(
int
argc
,
char
**
argv
)
{
testing
::
InitGoogleTest
(
&
argc
,
argv
);
testing
::
InitGoogleTest
(
&
argc
,
argv
);
FLAGS_use_gpu
=
false
;
FLAGS_use_gpu
=
false
;
FLAGS_use_mkldnn
=
true
;
FLAGS_use_mkldnn
=
true
;
initMain
(
argc
,
argv
);
initMain
(
argc
,
argv
);
initPython
(
argc
,
argv
);
FLAGS_thread_local_rand_use_global_seed
=
true
;
FLAGS_thread_local_rand_use_global_seed
=
true
;
srand
(
1
);
srand
(
1
);
return
RUN_ALL_TESTS
();
return
RUN_ALL_TESTS
();
...
...
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