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体验新版 GitCode,发现更多精彩内容 >>
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a9dbdab5
编写于
1月 11, 2018
作者:
Y
Yu Yang
提交者:
GitHub
1月 11, 2018
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差异文件
Merge pull request #7396 from reyoung/feature/parallel_for_unittest
Feature/parallel for unittest
上级
95c0c126
83c72536
变更
2
隐藏空白更改
内联
并排
Showing
2 changed file
with
155 addition
and
41 deletion
+155
-41
paddle/framework/init.cc
paddle/framework/init.cc
+4
-1
python/paddle/v2/fluid/tests/test_parallel_op.py
python/paddle/v2/fluid/tests/test_parallel_op.py
+151
-40
未找到文件。
paddle/framework/init.cc
浏览文件 @
a9dbdab5
...
...
@@ -11,6 +11,7 @@ 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 <string.h> // for strdup
#include <algorithm>
#include <string>
...
...
@@ -60,7 +61,9 @@ void InitDevices() {
}
void
InitGLOG
(
const
std
::
string
&
prog_name
)
{
google
::
InitGoogleLogging
(
prog_name
.
c_str
());
// glog will not hold the ARGV[0] inside.
// Use strdup to alloc a new string.
google
::
InitGoogleLogging
(
strdup
(
prog_name
.
c_str
()));
google
::
InstallFailureSignalHandler
();
}
...
...
python/paddle/v2/fluid/tests/test_parallel_op.py
浏览文件 @
a9dbdab5
import
unittest
import
paddle.v2.fluid.layers
as
layers
import
paddle.v2.fluid
as
fluid
from
paddle.v2.fluid.framework
import
Program
from
paddle.v2.fluid.executor
import
Executor
from
paddle.v2.fluid.backward
import
append_backward
import
numpy
as
np
import
paddle.v2.fluid.core
as
core
class
ParallelOpTest
(
unittest
.
TestCase
):
def
setUp
(
self
):
x
=
layers
.
data
(
shape
=
[
-
1
,
30
,
40
],
dtype
=
'float32'
,
name
=
'x'
,
append_batch_size
=
False
,
stop_gradient
=
False
)
places
=
layers
.
get_places
(
device_count
=
4
)
pd
=
layers
.
ParallelDo
(
places
=
places
)
with
pd
.
do
():
data
=
pd
.
read_input
(
x
)
hidden
=
layers
.
fc
(
input
=
data
,
size
=
7
)
pd
.
write_output
(
hidden
)
data
=
pd
()
loss
=
layers
.
mean
(
x
=
data
)
sgd_optimizer
=
fluid
.
optimizer
.
SGD
(
learning_rate
=
0.001
)
sgd_optimizer
.
minimize
(
loss
)
exe
=
fluid
.
Executor
(
fluid
.
CPUPlace
())
exe
.
run
(
fluid
.
default_startup_program
())
exe
.
run
(
fluid
.
default_main_program
(),
feed
=
{
x
.
name
:
np
.
random
.
uniform
(
0.1
,
0.6
,
(
20
,
30
,
40
)).
astype
(
"float32"
)
})
def
test_forward
(
self
):
pass
import
numpy
class
BaseParallelForTest
(
unittest
.
TestCase
):
def
run_test
(
self
,
callback
,
feed
,
fetch
):
"""
Run the unittest for parallel.for
Args:
callback(callable): A callable function returns a generator. There
are two yields in the generator function. The first yield
returns the data layers, and the second yield returns the loss.
The modified data variables will be sent back during the first
yield.
feed(dict): The executor feeding dictionary.
fetch(list|basestr): The fetch name lists.
Returns:
None
Raises:
AssertionError when the computation of cpu, parallel.for in cpu,
gpu, parallel.for in gpu are different.
"""
cpu
=
fluid
.
CPUPlace
()
result_cpu
=
self
.
_run_test_impl_
(
callback
=
callback
,
feed
=
feed
,
fetch
=
fetch
,
place
=
cpu
,
use_parallel
=
False
)
result_cpu_parallel
=
self
.
_run_test_impl_
(
callback
=
callback
,
feed
=
feed
,
fetch
=
fetch
,
place
=
cpu
,
use_parallel
=
True
)
if
fluid
.
core
.
is_compile_gpu
():
gpu
=
fluid
.
CUDAPlace
(
0
)
result_gpu
=
self
.
_run_test_impl_
(
callback
=
callback
,
feed
=
feed
,
fetch
=
fetch
,
place
=
gpu
,
use_parallel
=
False
)
result_gpu_parallel
=
self
.
_run_test_impl_
(
callback
=
callback
,
feed
=
feed
,
fetch
=
fetch
,
place
=
gpu
,
use_parallel
=
True
)
self
.
_assert_same_
(
fetch
,
result_cpu
,
result_cpu_parallel
,
result_gpu
,
result_gpu_parallel
)
else
:
self
.
_assert_same_
(
fetch
,
result_cpu
,
result_cpu_parallel
)
def
_run_test_impl_
(
self
,
callback
,
feed
,
fetch
,
place
,
use_parallel
=
False
):
"""
Run a single test, returns the fetch values
Args:
place(Place): the computation place.
use_parallel(bool): Whether use parallel.for or not.
Returns:
Fetched numpy arrays.
"""
if
isinstance
(
fetch
,
basestring
):
fetch
=
[
fetch
]
main
=
fluid
.
Program
()
startup
=
fluid
.
Program
()
# Fix seed
main
.
random_seed
=
10
startup
.
random_seed
=
10
with
fluid
.
program_guard
(
main
,
startup
):
generator
=
callback
()
# Automatically insert parallel do if use_parallel = True
if
use_parallel
:
places
=
fluid
.
layers
.
get_places
()
pd
=
fluid
.
layers
.
ParallelDo
(
places
)
data
=
next
(
generator
)
if
isinstance
(
data
,
fluid
.
Variable
):
data
=
[
data
]
with
pd
.
do
():
ins
=
map
(
pd
.
read_input
,
data
)
if
len
(
ins
)
==
1
:
ins
=
ins
[
0
]
loss
=
generator
.
send
(
ins
)
# patch input
pd
.
write_output
(
loss
)
loss
=
pd
()
else
:
data
=
next
(
generator
)
loss
=
generator
.
send
(
data
)
self
.
assertIsNotNone
(
loss
)
avg_loss
=
fluid
.
layers
.
mean
(
x
=
loss
)
fluid
.
backward
.
append_backward
(
loss
=
avg_loss
)
exe
=
fluid
.
Executor
(
place
)
exe
.
run
(
startup
)
return
exe
.
run
(
main
,
feed
=
feed
,
fetch_list
=
fetch
)
def
_assert_same_
(
self
,
fetch
,
*
args
):
"""
Assert the return values of `run_test` are same.
Args:
fetch: Fetch list. Used for print error message
*args: The fetch result lists of each situations.
Returns:
None
Raises:
AssertionError
"""
def
_impl_
(
a
,
b
,
fetch_id
,
item_id
):
item_str
=
[
'CPU'
,
'ParallelCPU'
,
'GPU'
,
'ParallelGPU'
]
flag
=
numpy
.
allclose
(
a
,
b
,
rtol
=
0.1
)
self
.
assertTrue
(
flag
,
"The {0} are different in {1}"
.
format
(
fetch
[
fetch_id
],
item_str
[
item_id
]))
for
i
,
items
in
enumerate
(
zip
(
*
args
)):
self
.
assertGreater
(
len
(
items
),
0
)
for
j
in
range
(
1
,
len
(
items
)):
_impl_
(
items
[
0
],
items
[
j
],
fetch_id
=
i
,
item_id
=
j
)
class
ParallelOpTest
(
BaseParallelForTest
):
def
test_simple_fc
(
self
):
def
__network__
():
x
=
fluid
.
layers
.
data
(
shape
=
[
784
],
dtype
=
'float32'
,
name
=
'img'
)
# FIXME: This is a bug of parallel.do
x
.
stop_gradient
=
False
x
=
yield
x
hidden
=
fluid
.
layers
.
fc
(
input
=
x
,
size
=
200
,
param_attr
=
'fc1.w'
)
loss
=
fluid
.
layers
.
mean
(
x
=
hidden
)
yield
loss
self
.
run_test
(
callback
=
__network__
,
feed
=
{
'img'
:
numpy
.
random
.
random
(
size
=
(
128
*
3
,
784
)).
astype
(
'float32'
)
},
fetch
=
'fc1.w@GRAD'
)
if
__name__
==
'__main__'
:
...
...
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