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体验新版 GitCode,发现更多精彩内容 >>
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提交
2e74cf46
编写于
11月 01, 2017
作者:
T
Tao Luo
提交者:
GitHub
11月 01, 2017
浏览文件
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差异文件
Merge pull request #5273 from tensor-tang/merge
refine mkldnn unit test
上级
8401039f
1e8300fa
变更
11
隐藏空白更改
内联
并排
Showing
11 changed file
with
220 addition
and
269 deletion
+220
-269
paddle/gserver/tests/MKLDNNTester.cpp
paddle/gserver/tests/MKLDNNTester.cpp
+30
-20
paddle/gserver/tests/MKLDNNTester.h
paddle/gserver/tests/MKLDNNTester.h
+10
-10
paddle/gserver/tests/mkldnn_branch_net.conf
paddle/gserver/tests/mkldnn_branch_net.conf
+142
-0
paddle/gserver/tests/mkldnn_branches_fc.conf
paddle/gserver/tests/mkldnn_branches_fc.conf
+0
-58
paddle/gserver/tests/mkldnn_branches_pool.conf
paddle/gserver/tests/mkldnn_branches_pool.conf
+0
-60
paddle/gserver/tests/mkldnn_simple_net.conf
paddle/gserver/tests/mkldnn_simple_net.conf
+28
-20
paddle/gserver/tests/test_MKLDNN.cpp
paddle/gserver/tests/test_MKLDNN.cpp
+5
-6
paddle/math/MKLDNNMatrix.h
paddle/math/MKLDNNMatrix.h
+5
-0
paddle/trainer/tests/CMakeLists.txt
paddle/trainer/tests/CMakeLists.txt
+0
-16
paddle/trainer/tests/sample_trainer_config_simple_net.conf
paddle/trainer/tests/sample_trainer_config_simple_net.conf
+0
-68
paddle/trainer/tests/test_CompareTwoNets.cpp
paddle/trainer/tests/test_CompareTwoNets.cpp
+0
-11
未找到文件。
paddle/gserver/tests/MKLDNNTester.cpp
浏览文件 @
2e74cf46
...
...
@@ -273,31 +273,37 @@ void MKLDNNTester::printVector(const VectorPtr& v) {
VLOG
(
MKLDNN_ALL
)
<<
std
::
endl
<<
ostr
.
str
();
}
double
MKLDNNTester
::
getDelta
(
const
real
*
d1
,
const
real
*
d2
,
double
MKLDNNTester
::
getDelta
(
const
real
*
refer
,
const
real
*
value
,
size_t
len
,
const
float
failRate
,
const
float
thres
)
{
double
delta
=
0
,
sum
=
0
;
int
failCnt
=
0
;
const
double
eps
=
1e-5
;
double
max
Out
=
0
;
double
max
Ratio
=
0
;
for
(
size_t
i
=
0
;
i
<
len
;
++
i
)
{
double
ref
=
fabs
(
d2
[
i
]);
double
diff
=
fabs
(
d1
[
i
]
-
d2
[
i
]);
double
ref
=
fabs
(
refer
[
i
]);
double
val
=
fabs
(
value
[
i
]);
double
diff
=
fabs
(
refer
[
i
]
-
value
[
i
]);
delta
+=
diff
;
sum
+=
ref
;
if
(
ref
>
eps
&&
fabs
(
d1
[
i
])
>
eps
&&
diff
/
ref
>
thres
)
{
maxOut
=
std
::
max
(
maxOut
,
diff
/
ref
);
if
(
ref
<
eps
&&
val
<
eps
)
{
// both values are very small
continue
;
}
double
ratio
=
diff
/
ref
;
if
(
ratio
>
thres
)
{
maxRatio
=
std
::
max
(
maxRatio
,
ratio
);
failCnt
++
;
}
}
EXPECT_TRUE
(
std
::
isnormal
(
sum
));
EXPECT_FALSE
(
std
::
isinf
(
sum
));
EXPECT_FALSE
(
std
::
isnan
(
sum
));
EXPECT_FALSE
(
std
::
isnan
(
delta
));
VLOG
(
MKLDNN_ALL
)
<<
"reference avg data: "
<<
sum
/
len
<<
", delta: "
<<
delta
/
sum
<<
", failCnt:"
<<
failCnt
;
return
(
failCnt
/
(
float
)
len
)
>
failRate
?
maxOut
:
delta
/
sum
;
double
res
=
sum
>
eps
?
delta
/
sum
:
eps
;
return
(
failCnt
/
(
float
)
len
)
>
failRate
?
maxRatio
:
res
;
}
double
MKLDNNTester
::
compareMatrix
(
const
MatrixPtr
&
m1
,
const
MatrixPtr
&
m2
)
{
...
...
@@ -515,12 +521,16 @@ void MKLDNNTester::getOutResult(const std::string& configPath,
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
);
const
MatrixPtr
&
src
=
outArgs
[
k
].
value
;
MatrixPtr
dst
=
Matrix
::
create
(
src
->
getHeight
(),
src
->
getWidth
(),
false
,
false
);
if
(
typeid
(
*
src
)
==
typeid
(
MKLDNNMatrix
))
{
MKLDNNMatrixPtr
dnnSrc
=
std
::
dynamic_pointer_cast
<
MKLDNNMatrix
>
(
src
);
dnnSrc
->
copyTo
(
*
dst
);
}
else
{
dst
->
copyFrom
(
*
src
);
}
out
.
outValues
.
push_back
(
dst
);
}
// random backward input
...
...
@@ -543,19 +553,19 @@ void MKLDNNTester::getOutResult(const std::string& configPath,
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
());
VLOG
(
MKLDNN_TESTS
)
<<
"compare value size: "
<<
ref
.
outValues
.
size
();
for
(
size_t
i
=
0
;
i
<
ref
.
outValues
.
size
();
i
++
)
{
VLOG
(
MKLDNN_TESTS
)
<<
"compare value index: "
<<
i
;
EXPECT_LE
(
fabs
(
compareMatrix
(
ref
.
outValues
[
i
],
dnn
.
outValues
[
i
])),
eps
);
}
VLOG
(
MKLDNN_TESTS
)
<<
"compare param size: "
<<
ref
.
outValues
.
size
();
for
(
size_t
i
=
0
;
i
<
ref
.
paraValues
.
size
();
i
++
)
{
VLOG
(
MKLDNN_TESTS
)
<<
"compare param index: "
<<
i
;
EXPECT_LE
(
fabs
(
compareVector
(
ref
.
paraValues
[
i
],
dnn
.
paraValues
[
i
])),
eps
);
}
}
void
MKLDNNTester
::
run
Branches
Test
(
const
std
::
string
&
configPath
,
size_t
iter
,
float
eps
)
{
void
MKLDNNTester
::
run
Net
Test
(
const
std
::
string
&
configPath
,
size_t
iter
,
float
eps
)
{
DataIn
in
;
initArgument
(
in
,
configPath
,
iter
);
DataOut
outCpu
,
outDnn
;
...
...
paddle/gserver/tests/MKLDNNTester.h
浏览文件 @
2e74cf46
...
...
@@ -85,17 +85,17 @@ public:
bool
printDetails
=
false
,
size_t
iter
=
3
,
float
epsilon
=
1e-4
);
static
void
run
Branches
Test
(
const
std
::
string
&
configPath
,
size_t
iter
=
3
,
float
eps
=
1e-4
);
static
void
run
Net
Test
(
const
std
::
string
&
configPath
,
size_t
iter
=
2
,
float
eps
=
1e-4
);
static
void
initArgument
(
DataIn
&
data
,
const
std
::
string
&
configPath
,
size_t
iter
=
3
);
size_t
iter
=
2
);
static
void
getOutResult
(
const
std
::
string
&
configPath
,
DataIn
&
in
,
DataOut
&
out
,
bool
use_mkldnn
,
size_t
iter
=
3
);
size_t
iter
=
2
);
private:
void
reset
(
const
TestConfig
&
dnn
,
const
TestConfig
&
ref
,
size_t
batchSize
);
...
...
@@ -128,13 +128,13 @@ private:
/**
* Get delta percent
* if many(>failRate) wrong(abs(
dnn-ref)/abs(ref)>thres) points return the
* max(diff/ref)
* else return sum(abs(
a-b)) / sum(abs(b
))
* if many(>failRate) wrong(abs(
val-ref)/abs(ref) > thres) points
*
return the
max(diff/ref)
* else return sum(abs(
diff)) / sum(abs(ref
))
* The return value should be smaller than eps when passing.
*/
static
double
getDelta
(
const
real
*
d1
,
const
real
*
d2
,
static
double
getDelta
(
const
real
*
refer
,
const
real
*
value
,
size_t
len
,
const
float
failRate
=
1e-3
,
const
float
thres
=
0.1
);
...
...
paddle/
trainer/tests/sample_trainer_config
_branch_net.conf
→
paddle/
gserver/tests/mkldnn
_branch_net.conf
浏览文件 @
2e74cf46
...
...
@@ -14,36 +14,82 @@
from
paddle
.
trainer_config_helpers
import
*
################################### Data Configuration ###################################
TrainData
(
ProtoData
(
files
=
"trainer/tests/mnist.list"
))
################################### Algorithm Configuration ###################################
settings
(
batch_size
=
128
,
learning_method
=
MomentumOptimizer
(
momentum
=
0
.
5
,
sparse
=
False
))
################################### Network Configuration ###################################
data
=
data_layer
(
name
=
"input"
,
size
=
784
)
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
def
two_conv_bn
(
input
,
group_name
):
out1
,
out2
=
two_conv
(
input
,
group_name
)
out1
=
batch_norm_layer
(
input
=
out1
,
name
=
group_name
+
'_bn1_'
,
use_global_stats
=
False
,
act
=
ReluActivation
())
out2
=
batch_norm_layer
(
input
=
out2
,
name
=
group_name
+
'_bn2_'
,
use_global_stats
=
False
,
act
=
ReluActivation
())
return
out1
,
out2
def
two_conv_pool
(
input
,
group_name
):
out1
,
out2
=
two_conv
(
input
,
group_name
)
out1
=
img_pool_layer
(
input
=
out1
,
name
=
group_name
+
'_pool1_'
,
pool_size
=
3
,
stride
=
2
,
padding
=
0
,
pool_type
=
MaxPooling
())
out2
=
img_pool_layer
(
input
=
out2
,
name
=
group_name
+
'_pool2_'
,
pool_size
=
5
,
stride
=
2
,
padding
=
1
,
pool_type
=
MaxPooling
())
return
out1
,
out2
def
two_fc
(
input
,
group_name
):
out1
=
fc_layer
(
input
=
input
,
name
=
group_name
+
'_fc1_'
,
size
=
channels
,
bias_attr
=
False
,
act
=
LinearActivation
())
tmp
=
img_conv_layer
(
input
=
data
,
num_channels
=
1
,
filter_size
=
3
,
num_filters
=
32
,
padding
=
1
,
shared_biases
=
True
,
act
=
ReluActivation
())
out2
=
fc_layer
(
input
=
input
,
name
=
group_name
+
'_fc2_'
,
size
=
channels
,
bias_attr
=
False
,
act
=
LinearActivation
())
return
out1
,
out2
a1
=
img_conv_layer
(
input
=
tmp
,
filter_size
=
1
,
num_filters
=
32
,
padding
=
0
,
shared_biases
=
True
,
act
=
ReluActivation
())
data
=
data_layer
(
name
=
"input"
,
size
=
channels
*
16
*
16
)
a2
=
img_conv_layer
(
input
=
tmp
,
tmp
=
img_conv_layer
(
input
=
data
,
num_channels
=
channels
,
filter_size
=
3
,
num_filters
=
32
,
num_filters
=
channels
,
padding
=
1
,
shared_biases
=
True
,
act
=
ReluActivation
())
a1
,
a2
=
two_conv
(
tmp
,
'conv_branch'
)
tmp
=
addto_layer
(
input
=[
a1
,
a2
],
act
=
ReluActivation
(),
bias_attr
=
False
)
...
...
@@ -54,36 +100,11 @@ tmp = img_pool_layer(input=tmp,
padding
=
1
,
pool_type
=
AvgPooling
())
b1
=
img_conv_layer
(
input
=
tmp
,
filter_size
=
3
,
num_filters
=
32
,
padding
=
1
,
shared_biases
=
True
,
act
=
ReluActivation
())
b1
=
img_pool_layer
(
input
=
b1
,
pool_size
=
3
,
stride
=
2
,
padding
=
0
,
pool_type
=
MaxPooling
())
b2
=
img_conv_layer
(
input
=
tmp
,
filter_size
=
3
,
num_filters
=
64
,
padding
=
1
,
shared_biases
=
True
,
act
=
ReluActivation
())
b2
=
img_pool_layer
(
input
=
b2
,
pool_size
=
5
,
stride
=
2
,
padding
=
1
,
pool_type
=
MaxPooling
())
b1
,
b2
=
two_conv_pool
(
tmp
,
'pool_branch'
)
tmp
=
concat_layer
(
input
=[
b1
,
b2
])
tmp
=
img_pool_layer
(
input
=
tmp
,
num_channels
=
96
,
num_channels
=
channels
*
2
,
pool_size
=
3
,
stride
=
2
,
padding
=
1
,
...
...
@@ -91,8 +112,9 @@ tmp = img_pool_layer(input=tmp,
tmp
=
img_conv_layer
(
input
=
tmp
,
filter_size
=
3
,
num_filters
=
32
,
num_filters
=
channels
,
padding
=
1
,
stride
=
2
,
shared_biases
=
True
,
act
=
LinearActivation
(),
bias_attr
=
False
)
...
...
@@ -101,33 +123,20 @@ tmp = batch_norm_layer(input=tmp,
use_global_stats
=
False
,
act
=
ReluActivation
())
c1
=
img_conv_layer
(
input
=
tmp
,
filter_size
=
1
,
num_filters
=
32
,
padding
=
0
,
shared_biases
=
True
,
act
=
ReluActivation
())
c2
=
img_conv_layer
(
input
=
tmp
,
filter_size
=
3
,
num_filters
=
32
,
padding
=
1
,
shared_biases
=
True
,
act
=
ReluActivation
())
c1
,
c2
=
two_conv_bn
(
tmp
,
'bn_branch'
)
tmp
=
addto_layer
(
input
=[
c1
,
c2
],
act
=
ReluActivation
(),
bias_attr
=
False
)
tmp
=
fc_layer
(
input
=
tmp
,
size
=
64
,
bias_attr
=
Fals
e
,
act
=
Tanh
Activation
())
tmp
=
fc_layer
(
input
=
tmp
,
size
=
channels
,
bias_attr
=
Tru
e
,
act
=
Relu
Activation
())
output
=
fc_layer
(
input
=
tmp
,
size
=
10
,
d1
,
d2
=
two_fc
(
tmp
,
'fc_branch'
)
tmp
=
addto_layer
(
input
=[
d1
,
d2
])
out
=
fc_layer
(
input
=
tmp
,
size
=
10
,
bias_attr
=
True
,
act
=
SoftmaxActivation
())
lbl
=
data_layer
(
name
=
"label"
,
size
=
10
)
cost
=
classification_cost
(
input
=
output
,
label
=
lbl
)
outputs
(
cost
)
outputs
(
out
)
paddle/gserver/tests/mkldnn_branches_fc.conf
已删除
100644 → 0
浏览文件 @
8401039f
# 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_fc
(
input
,
group_name
):
out1
=
fc_layer
(
input
=
input
,
name
=
group_name
+
'_fc1'
,
size
=
channels
,
bias_attr
=
False
,
act
=
LinearActivation
())
out2
=
fc_layer
(
input
=
input
,
name
=
group_name
+
'_fc2'
,
size
=
channels
,
bias_attr
=
False
,
act
=
LinearActivation
())
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
=
LinearActivation
())
pool
=
img_pool_layer
(
input
=
conv
,
pool_size
=
3
,
stride
=
2
,
padding
=
1
,
pool_type
=
AvgPooling
())
a1
,
a2
=
two_fc
(
input
=
pool
,
group_name
=
'a'
)
concat
=
concat_layer
(
input
=[
a1
,
a2
])
b1
,
b2
=
two_fc
(
input
=
pool
,
group_name
=
'b'
)
addto
=
addto_layer
(
input
=[
b1
,
b2
])
outputs
([
concat
,
addto
])
paddle/gserver/tests/mkldnn_branches_pool.conf
已删除
100644 → 0
浏览文件 @
8401039f
# 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_pool
(
input
,
group_name
):
out1
=
img_pool_layer
(
input
=
input
,
name
=
group_name
+
'_pool1'
,
pool_size
=
3
,
stride
=
2
,
padding
=
0
,
pool_type
=
MaxPooling
())
out2
=
img_pool_layer
(
input
=
input
,
name
=
group_name
+
'_pool2'
,
pool_size
=
5
,
stride
=
2
,
padding
=
1
,
pool_type
=
MaxPooling
())
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
=
LinearActivation
())
pool
=
img_pool_layer
(
input
=
conv
,
pool_size
=
3
,
stride
=
1
,
padding
=
1
,
pool_type
=
AvgPooling
())
a1
,
a2
=
two_pool
(
input
=
pool
,
group_name
=
'a'
)
concat
=
concat_layer
(
input
=[
a1
,
a2
])
b1
,
b2
=
two_pool
(
input
=
pool
,
group_name
=
'b'
)
addto
=
addto_layer
(
input
=[
b1
,
b2
])
outputs
([
concat
,
addto
])
paddle/gserver/tests/mkldnn_
branches_conv
.conf
→
paddle/gserver/tests/mkldnn_
simple_net
.conf
浏览文件 @
2e74cf46
...
...
@@ -17,40 +17,48 @@ 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
())
data
=
data_layer
(
name
=
"input"
,
size
=
channels
*
16
*
16
)
out2
=
img_conv_layer
(
input
=
input
,
n
ame
=
group_name
+
'_conv2'
,
tmp
=
img_conv_layer
(
input
=
data
,
n
um_channels
=
channels
,
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
)
tmp
=
img_pool_layer
(
input
=
tmp
,
pool_size
=
3
,
stride
=
1
,
padding
=
0
,
pool_type
=
AvgPooling
())
conv
=
img_conv_layer
(
input
=
data
,
num_channels
=
channels
,
tmp
=
img_conv_layer
(
input
=
tmp
,
filter_size
=
3
,
num_filters
=
channels
,
padding
=
1
,
shared_biases
=
True
,
act
=
ReluActivation
())
act
=
LinearActivation
(),
bias_attr
=
False
)
a1
,
a2
=
two_conv
(
input
=
conv
,
group_name
=
'a'
)
tmp
=
batch_norm_layer
(
input
=
tmp
,
use_global_stats
=
False
,
act
=
ReluActivation
())
concat
=
concat_layer
(
input
=[
a1
,
a2
])
tmp
=
img_pool_layer
(
input
=
tmp
,
pool_size
=
3
,
stride
=
2
,
padding
=
1
,
pool_type
=
MaxPooling
())
b1
,
b2
=
two_conv
(
input
=
conv
,
group_name
=
'b'
)
tmp
=
fc_layer
(
input
=
tmp
,
size
=
channels
,
bias_attr
=
False
,
act
=
ReluActivation
())
addto
=
addto_layer
(
input
=[
b1
,
b2
])
out
=
fc_layer
(
input
=
tmp
,
size
=
10
,
bias_attr
=
True
,
act
=
SoftmaxActivation
())
outputs
(
[
concat
,
addto
]
)
outputs
(
out
)
paddle/gserver/tests/test_MKLDNN.cpp
浏览文件 @
2e74cf46
...
...
@@ -234,8 +234,7 @@ static void getMKLDNNBatchNormConfig(TestConfig& cfg,
cfg
.
inputDefs
.
push_back
({
INPUT_DATA
,
"layer_2_moving_var"
,
1
,
size_t
(
pm
.
ic
)});
cfg
.
inputDefs
.
back
().
isStatic
=
true
;
LayerInputConfig
*
input
=
cfg
.
layerConfig
.
add_inputs
();
// TODO(TJ): uncomment me when refine and support comparing all zeroes vector
// cfg.layerConfig.set_active_type("relu");
cfg
.
layerConfig
.
set_active_type
(
"relu"
);
cfg
.
layerConfig
.
add_inputs
();
cfg
.
layerConfig
.
add_inputs
();
ImageConfig
*
img_conf
=
input
->
mutable_image_conf
();
...
...
@@ -309,15 +308,15 @@ TEST(MKLDNNActivation, Activations) {
}
DECLARE_string
(
config_args
);
TEST
(
MKLDNN
Layer
,
branches
)
{
std
::
vector
<
std
::
string
>
cases
=
{
"
conv"
,
"pool"
,
"fc
"
};
TEST
(
MKLDNN
Net
,
net
)
{
std
::
vector
<
std
::
string
>
cases
=
{
"
simple"
,
"branch
"
};
for
(
auto
name
:
cases
)
{
std
::
string
config
=
"./gserver/tests/mkldnn_
branches_"
+
name
+
"
.conf"
;
std
::
string
config
=
"./gserver/tests/mkldnn_
"
+
name
+
"_net
.conf"
;
for
(
auto
channels
:
{
2
,
32
})
{
std
::
ostringstream
oss
;
oss
<<
"channels="
<<
channels
;
FLAGS_config_args
=
oss
.
str
();
MKLDNNTester
::
run
Branches
Test
(
config
);
MKLDNNTester
::
run
Net
Test
(
config
);
}
}
}
...
...
paddle/math/MKLDNNMatrix.h
浏览文件 @
2e74cf46
...
...
@@ -102,6 +102,11 @@ public:
m_
->
copyFrom
(
src
);
}
void
copyTo
(
Matrix
&
dst
)
{
// TODO(TJ): reorder data if this format is not nchw or x
dst
.
copyFrom
(
*
m_
);
}
public:
/**
* Reorder this MKLDNNMatrix from other format.
...
...
paddle/trainer/tests/CMakeLists.txt
浏览文件 @
2e74cf46
...
...
@@ -37,22 +37,6 @@ add_test(NAME test_CompareTwoNets
--config_file_a=trainer/tests/sample_trainer_config_qb_rnn.conf --config_file_b=trainer/tests/sample_trainer_config_rnn.conf
WORKING_DIRECTORY
${
PADDLE_SOURCE_DIR
}
/paddle/
)
################ test_CompareMKLDNNandCPU ######################
if
(
WITH_MKLDNN
)
macro
(
gen_command VAR_NAME CONFIG_FILE
)
set
(
${
VAR_NAME
}
"
${
PADDLE_SOURCE_DIR
}
/paddle/.set_python_path.sh"
"-d"
"
${
PADDLE_SOURCE_DIR
}
/python/"
"
${
CMAKE_CURRENT_BINARY_DIR
}
/test_CompareMKLDNNandCPU --use_gpu=False"
"--config_file_a=trainer/tests/
${
CONFIG_FILE
}
--use_mkldnn_a=True"
"--config_file_b=trainer/tests/
${
CONFIG_FILE
}
--use_mkldnn_b=False"
"WORKING_DIRECTORY"
"
${
PADDLE_SOURCE_DIR
}
/paddle/"
)
endmacro
()
add_unittest_without_exec
(
test_CompareMKLDNNandCPU test_CompareTwoNets.cpp
)
gen_command
(
compare_simple_net
"sample_trainer_config_simple_net.conf"
)
gen_command
(
compare_branch_net
"sample_trainer_config_branch_net.conf"
)
add_test
(
NAME test_CompareMKLDNNandCPU_simple_net COMMAND
${
compare_simple_net
}
)
add_test
(
NAME test_CompareMKLDNNandCPU_branch_net COMMAND
${
compare_branch_net
}
)
endif
()
############### test_CompareTwoOpts ###################
add_unittest_without_exec
(
test_CompareTwoOpts
test_CompareTwoOpts.cpp
)
...
...
paddle/trainer/tests/sample_trainer_config_simple_net.conf
已删除
100644 → 0
浏览文件 @
8401039f
# 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
*
################################### Data Configuration ###################################
TrainData
(
ProtoData
(
files
=
"trainer/tests/mnist.list"
))
################################### Algorithm Configuration ###################################
settings
(
batch_size
=
128
,
learning_method
=
MomentumOptimizer
(
momentum
=
0
.
5
,
sparse
=
False
))
################################### Network Configuration ###################################
data
=
data_layer
(
name
=
"input"
,
size
=
784
)
tmp
=
img_conv_layer
(
input
=
data
,
num_channels
=
1
,
filter_size
=
3
,
num_filters
=
32
,
padding
=
1
,
shared_biases
=
True
,
act
=
ReluActivation
())
tmp
=
img_pool_layer
(
input
=
tmp
,
pool_size
=
3
,
stride
=
2
,
padding
=
1
,
pool_type
=
AvgPooling
())
tmp
=
img_conv_layer
(
input
=
tmp
,
filter_size
=
3
,
num_filters
=
32
,
padding
=
1
,
shared_biases
=
True
,
act
=
LinearActivation
(),
bias_attr
=
False
)
tmp
=
batch_norm_layer
(
input
=
tmp
,
use_global_stats
=
False
,
act
=
ReluActivation
())
tmp
=
img_pool_layer
(
input
=
tmp
,
pool_size
=
3
,
stride
=
2
,
padding
=
1
,
pool_type
=
MaxPooling
())
tmp
=
fc_layer
(
input
=
tmp
,
size
=
64
,
bias_attr
=
True
,
act
=
ReluActivation
())
output
=
fc_layer
(
input
=
tmp
,
size
=
10
,
bias_attr
=
True
,
act
=
SoftmaxActivation
())
lbl
=
data_layer
(
name
=
"label"
,
size
=
10
)
cost
=
classification_cost
(
input
=
output
,
label
=
lbl
)
outputs
(
cost
)
paddle/trainer/tests/test_CompareTwoNets.cpp
浏览文件 @
2e74cf46
...
...
@@ -26,15 +26,12 @@ DECLARE_int32(gpu_id);
DECLARE_bool
(
local
);
DECLARE_bool
(
use_gpu
);
DECLARE_bool
(
use_mkldnn
);
DECLARE_string
(
config
);
DECLARE_string
(
nics
);
DEFINE_string
(
config_file_a
,
""
,
"config of one network to compare"
);
DEFINE_string
(
config_file_b
,
""
,
"config of another network to compare"
);
DEFINE_bool
(
use_mkldnn_a
,
false
,
"whether to use mkldnn to run config_file_a"
);
DEFINE_bool
(
use_mkldnn_b
,
false
,
"whether to use mkldnn to run config_file_b"
);
DEFINE_bool
(
need_high_accuracy
,
false
,
"whether need to run in double accuracy"
);
...
...
@@ -131,12 +128,6 @@ void compareGradient(ComData& comDataA, ComData& comDataB) {
matA
.
getWidth
());
}
if
(
FLAGS_use_mkldnn_a
||
FLAGS_use_mkldnn_b
)
{
// some format of mkldnn parameter is different with cpu
// test_MKLDNN will check the parameters
return
;
}
vector
<
ParameterPtr
>&
parametersA
=
comDataA
.
parameters
;
vector
<
ParameterPtr
>&
parametersB
=
comDataB
.
parameters
;
...
...
@@ -176,12 +167,10 @@ void compareGradient(ComData& comDataA, ComData& comDataB) {
TEST
(
Trainer
,
create
)
{
ComData
dataA
;
FLAGS_use_mkldnn
=
FLAGS_use_mkldnn_a
;
calcGradient
(
dataA
,
FLAGS_config_file_a
);
LOG
(
INFO
)
<<
"
\n\n
forwardBackward of Network A is finished
\n\n
"
;
ComData
dataB
;
FLAGS_use_mkldnn
=
FLAGS_use_mkldnn_b
;
calcGradient
(
dataB
,
FLAGS_config_file_b
);
LOG
(
INFO
)
<<
"
\n\n
forwardBackward of the Network B is finished
\n\n
"
;
...
...
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