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d9e7ea83
编写于
6月 30, 2018
作者:
Y
Yancey
提交者:
GitHub
6月 30, 2018
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Merge pull request #11790 from Yancey1989/dist_test_word2vec
add dist word2vec unit test
上级
c318aa5f
2a51e8e2
变更
2
隐藏空白更改
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Showing
2 changed file
with
204 addition
and
0 deletion
+204
-0
python/paddle/fluid/tests/unittests/CMakeLists.txt
python/paddle/fluid/tests/unittests/CMakeLists.txt
+1
-0
python/paddle/fluid/tests/unittests/test_dist_word2vec.py
python/paddle/fluid/tests/unittests/test_dist_word2vec.py
+203
-0
未找到文件。
python/paddle/fluid/tests/unittests/CMakeLists.txt
浏览文件 @
d9e7ea83
...
...
@@ -52,3 +52,4 @@ py_test_modules(test_parallel_executor_crf MODULES test_parallel_executor_crf SE
py_test_modules
(
test_parallel_executor_fetch_feed MODULES test_parallel_executor_fetch_feed SERIAL
)
set_tests_properties
(
test_listen_and_serv_op PROPERTIES TIMEOUT 20
)
set_tests_properties
(
test_dist_mnist PROPERTIES TIMEOUT 180
)
set_tests_properties
(
test_dist_word2vec PROPERTIES TIMEOUT 180
)
python/paddle/fluid/tests/unittests/test_dist_word2vec.py
0 → 100644
浏览文件 @
d9e7ea83
# Copyright (c) 2018 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.
import
numpy
as
np
import
argparse
import
time
import
math
import
paddle
import
paddle.fluid
as
fluid
import
paddle.fluid.profiler
as
profiler
from
paddle.fluid
import
core
import
unittest
from
multiprocessing
import
Process
import
os
import
signal
IS_SPARSE
=
True
EMBED_SIZE
=
32
HIDDEN_SIZE
=
256
N
=
5
BATCH_SIZE
=
32
ExecutionStrategy
=
core
.
ParallelExecutor
.
ExecutionStrategy
def
get_model
():
def
__network__
(
words
):
embed_first
=
fluid
.
layers
.
embedding
(
input
=
words
[
0
],
size
=
[
dict_size
,
EMBED_SIZE
],
dtype
=
'float32'
,
is_sparse
=
IS_SPARSE
,
param_attr
=
'shared_w'
)
embed_second
=
fluid
.
layers
.
embedding
(
input
=
words
[
1
],
size
=
[
dict_size
,
EMBED_SIZE
],
dtype
=
'float32'
,
is_sparse
=
IS_SPARSE
,
param_attr
=
'shared_w'
)
embed_third
=
fluid
.
layers
.
embedding
(
input
=
words
[
2
],
size
=
[
dict_size
,
EMBED_SIZE
],
dtype
=
'float32'
,
is_sparse
=
IS_SPARSE
,
param_attr
=
'shared_w'
)
embed_forth
=
fluid
.
layers
.
embedding
(
input
=
words
[
3
],
size
=
[
dict_size
,
EMBED_SIZE
],
dtype
=
'float32'
,
is_sparse
=
IS_SPARSE
,
param_attr
=
'shared_w'
)
concat_embed
=
fluid
.
layers
.
concat
(
input
=
[
embed_first
,
embed_second
,
embed_third
,
embed_forth
],
axis
=
1
)
hidden1
=
fluid
.
layers
.
fc
(
input
=
concat_embed
,
size
=
HIDDEN_SIZE
,
act
=
'sigmoid'
)
predict_word
=
fluid
.
layers
.
fc
(
input
=
hidden1
,
size
=
dict_size
,
act
=
'softmax'
)
cost
=
fluid
.
layers
.
cross_entropy
(
input
=
predict_word
,
label
=
words
[
4
])
avg_cost
=
fluid
.
layers
.
mean
(
cost
)
return
avg_cost
,
predict_word
word_dict
=
paddle
.
dataset
.
imikolov
.
build_dict
()
dict_size
=
len
(
word_dict
)
first_word
=
fluid
.
layers
.
data
(
name
=
'firstw'
,
shape
=
[
1
],
dtype
=
'int64'
)
second_word
=
fluid
.
layers
.
data
(
name
=
'secondw'
,
shape
=
[
1
],
dtype
=
'int64'
)
third_word
=
fluid
.
layers
.
data
(
name
=
'thirdw'
,
shape
=
[
1
],
dtype
=
'int64'
)
forth_word
=
fluid
.
layers
.
data
(
name
=
'forthw'
,
shape
=
[
1
],
dtype
=
'int64'
)
next_word
=
fluid
.
layers
.
data
(
name
=
'nextw'
,
shape
=
[
1
],
dtype
=
'int64'
)
avg_cost
,
predict_word
=
__network__
(
[
first_word
,
second_word
,
third_word
,
forth_word
,
next_word
])
inference_program
=
paddle
.
fluid
.
default_main_program
().
clone
()
sgd_optimizer
=
fluid
.
optimizer
.
SGD
(
learning_rate
=
0.001
)
sgd_optimizer
.
minimize
(
avg_cost
)
train_reader
=
paddle
.
batch
(
paddle
.
dataset
.
imikolov
.
train
(
word_dict
,
N
),
BATCH_SIZE
)
test_reader
=
paddle
.
batch
(
paddle
.
dataset
.
imikolov
.
test
(
word_dict
,
N
),
BATCH_SIZE
)
return
inference_program
,
avg_cost
,
train_reader
,
test_reader
,
predict_word
def
get_transpiler
(
trainer_id
,
main_program
,
pserver_endpoints
,
trainers
):
t
=
fluid
.
DistributeTranspiler
()
t
.
transpile
(
trainer_id
=
trainer_id
,
program
=
main_program
,
pservers
=
pserver_endpoints
,
trainers
=
trainers
)
return
t
def
run_pserver
(
pserver_endpoints
,
trainers
,
current_endpoint
):
get_model
()
t
=
get_transpiler
(
0
,
fluid
.
default_main_program
(),
pserver_endpoints
,
trainers
)
pserver_prog
=
t
.
get_pserver_program
(
current_endpoint
)
startup_prog
=
t
.
get_startup_program
(
current_endpoint
,
pserver_prog
)
place
=
fluid
.
CPUPlace
()
exe
=
fluid
.
Executor
(
place
)
exe
.
run
(
startup_prog
)
exe
.
run
(
pserver_prog
)
class
TestDistMnist
(
unittest
.
TestCase
):
def
setUp
(
self
):
self
.
_trainers
=
1
self
.
_pservers
=
1
self
.
_ps_endpoints
=
"127.0.0.1:9123"
def
start_pserver
(
self
,
endpoint
):
p
=
Process
(
target
=
run_pserver
,
args
=
(
self
.
_ps_endpoints
,
self
.
_trainers
,
endpoint
))
p
.
start
()
return
p
.
pid
def
_wait_ps_ready
(
self
,
pid
):
retry_times
=
5
while
True
:
assert
retry_times
>=
0
,
"wait ps ready failed"
time
.
sleep
(
1
)
try
:
# the listen_and_serv_op would touch a file which contains the listen port
# on the /tmp directory until it was ready to process all the RPC call.
os
.
stat
(
"/tmp/paddle.%d.port"
%
pid
)
return
except
os
.
error
:
retry_times
-=
1
def
stop_pserver
(
self
,
pid
):
os
.
kill
(
pid
,
signal
.
SIGKILL
)
def
test_with_place
(
self
):
p
=
fluid
.
CUDAPlace
(
0
)
if
core
.
is_compiled_with_cuda
(
)
else
fluid
.
CPUPlace
()
pserver_pid
=
self
.
start_pserver
(
self
.
_ps_endpoints
)
self
.
_wait_ps_ready
(
pserver_pid
)
self
.
run_trainer
(
p
,
0
)
self
.
stop_pserver
(
pserver_pid
)
def
run_trainer
(
self
,
place
,
trainer_id
):
test_program
,
avg_cost
,
train_reader
,
test_reader
,
predict
=
get_model
()
t
=
get_transpiler
(
trainer_id
,
fluid
.
default_main_program
(),
self
.
_ps_endpoints
,
self
.
_trainers
)
trainer_prog
=
t
.
get_trainer_program
()
exe
=
fluid
.
Executor
(
place
)
exe
.
run
(
fluid
.
default_startup_program
())
use_gpu
=
True
if
core
.
is_compiled_with_cuda
()
else
False
exec_strategy
=
ExecutionStrategy
()
exec_strategy
.
use_cuda
=
use_gpu
train_exe
=
fluid
.
ParallelExecutor
(
use_cuda
=
use_gpu
,
main_program
=
trainer_prog
,
loss_name
=
avg_cost
.
name
,
exec_strategy
=
exec_strategy
)
feed_var_list
=
[
var
for
var
in
trainer_prog
.
global_block
().
vars
.
itervalues
()
if
var
.
is_data
]
feeder
=
fluid
.
DataFeeder
(
feed_var_list
,
place
)
for
pass_id
in
xrange
(
10
):
for
batch_id
,
data
in
enumerate
(
train_reader
()):
avg_loss_np
=
train_exe
.
run
(
feed
=
feeder
.
feed
(
data
),
fetch_list
=
[
avg_cost
.
name
])
loss
=
np
.
array
(
avg_loss_np
).
mean
()
if
float
(
loss
)
<
5.0
:
return
if
math
.
isnan
(
loss
):
assert
(
"Got Nan loss, training failed"
)
if
__name__
==
"__main__"
:
unittest
.
main
()
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