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体验新版 GitCode,发现更多精彩内容 >>
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f54efd02
编写于
8月 13, 2018
作者:
W
Wu Yi
提交者:
GitHub
8月 13, 2018
浏览文件
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差异文件
Merge pull request #12660 from typhoonzero/polish_dist_unittests
polish dist unitest
上级
51cc80cc
069ff147
变更
7
隐藏空白更改
内联
并排
Showing
7 changed file
with
377 addition
and
528 deletion
+377
-528
python/paddle/fluid/tests/unittests/dist_mnist.py
python/paddle/fluid/tests/unittests/dist_mnist.py
+103
-0
python/paddle/fluid/tests/unittests/dist_se_resnext.py
python/paddle/fluid/tests/unittests/dist_se_resnext.py
+39
-147
python/paddle/fluid/tests/unittests/dist_word2vec.py
python/paddle/fluid/tests/unittests/dist_word2vec.py
+119
-0
python/paddle/fluid/tests/unittests/test_dist_base.py
python/paddle/fluid/tests/unittests/test_dist_base.py
+107
-6
python/paddle/fluid/tests/unittests/test_dist_mnist.py
python/paddle/fluid/tests/unittests/test_dist_mnist.py
+4
-191
python/paddle/fluid/tests/unittests/test_dist_se_resnext.py
python/paddle/fluid/tests/unittests/test_dist_se_resnext.py
+1
-1
python/paddle/fluid/tests/unittests/test_dist_word2vec.py
python/paddle/fluid/tests/unittests/test_dist_word2vec.py
+4
-183
未找到文件。
python/paddle/fluid/tests/unittests/dist_mnist.py
0 → 100644
浏览文件 @
f54efd02
# 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
from
functools
import
reduce
from
test_dist_base
import
TestDistRunnerBase
,
runtime_main
DTYPE
=
"float32"
paddle
.
dataset
.
mnist
.
fetch
()
# Fix seed for test
fluid
.
default_startup_program
().
random_seed
=
1
fluid
.
default_main_program
().
random_seed
=
1
def
cnn_model
(
data
):
conv_pool_1
=
fluid
.
nets
.
simple_img_conv_pool
(
input
=
data
,
filter_size
=
5
,
num_filters
=
20
,
pool_size
=
2
,
pool_stride
=
2
,
act
=
"relu"
,
param_attr
=
fluid
.
ParamAttr
(
initializer
=
fluid
.
initializer
.
Constant
()))
conv_pool_2
=
fluid
.
nets
.
simple_img_conv_pool
(
input
=
conv_pool_1
,
filter_size
=
5
,
num_filters
=
50
,
pool_size
=
2
,
pool_stride
=
2
,
act
=
"relu"
,
param_attr
=
fluid
.
ParamAttr
(
initializer
=
fluid
.
initializer
.
Constant
()))
SIZE
=
10
input_shape
=
conv_pool_2
.
shape
param_shape
=
[
reduce
(
lambda
a
,
b
:
a
*
b
,
input_shape
[
1
:],
1
)]
+
[
SIZE
]
scale
=
(
2.0
/
(
param_shape
[
0
]
**
2
*
SIZE
))
**
0.5
predict
=
fluid
.
layers
.
fc
(
input
=
conv_pool_2
,
size
=
SIZE
,
act
=
"softmax"
,
param_attr
=
fluid
.
param_attr
.
ParamAttr
(
initializer
=
fluid
.
initializer
.
NormalInitializer
(
loc
=
0.0
,
scale
=
scale
,
seed
=
1
)))
return
predict
class
TestDistMnist2x2
(
TestDistRunnerBase
):
def
get_model
(
self
,
batch_size
=
2
):
# Input data
images
=
fluid
.
layers
.
data
(
name
=
'pixel'
,
shape
=
[
1
,
28
,
28
],
dtype
=
DTYPE
)
label
=
fluid
.
layers
.
data
(
name
=
'label'
,
shape
=
[
1
],
dtype
=
'int64'
)
# Train program
predict
=
cnn_model
(
images
)
cost
=
fluid
.
layers
.
cross_entropy
(
input
=
predict
,
label
=
label
)
avg_cost
=
fluid
.
layers
.
mean
(
x
=
cost
)
# Evaluator
batch_size_tensor
=
fluid
.
layers
.
create_tensor
(
dtype
=
'int64'
)
batch_acc
=
fluid
.
layers
.
accuracy
(
input
=
predict
,
label
=
label
,
total
=
batch_size_tensor
)
inference_program
=
fluid
.
default_main_program
().
clone
()
# Optimization
opt
=
fluid
.
optimizer
.
AdamOptimizer
(
learning_rate
=
0.001
,
beta1
=
0.9
,
beta2
=
0.999
)
# Reader
train_reader
=
paddle
.
batch
(
paddle
.
dataset
.
mnist
.
train
(),
batch_size
=
batch_size
)
test_reader
=
paddle
.
batch
(
paddle
.
dataset
.
mnist
.
test
(),
batch_size
=
batch_size
)
opt
.
minimize
(
avg_cost
)
return
inference_program
,
avg_cost
,
train_reader
,
test_reader
,
batch_acc
,
predict
if
__name__
==
"__main__"
:
runtime_main
(
TestDistMnist2x2
)
python/paddle/fluid/tests/unittests/dist_se_resnext.py
浏览文件 @
f54efd02
...
...
@@ -27,6 +27,7 @@ from multiprocessing import Process
import
os
import
sys
import
signal
from
test_dist_base
import
TestDistRunnerBase
,
runtime_main
# Fix seed for test
fluid
.
default_startup_program
().
random_seed
=
1
...
...
@@ -196,161 +197,52 @@ class SE_ResNeXt():
return
scale
def
get_model
(
batch_size
):
# Input data
image
=
fluid
.
layers
.
data
(
name
=
"data"
,
shape
=
[
3
,
224
,
224
],
dtype
=
'float32'
)
label
=
fluid
.
layers
.
data
(
name
=
"int64"
,
shape
=
[
1
],
dtype
=
'int64'
)
class
DistSeResneXt2x2
(
TestDistRunnerBase
):
def
get_model
(
self
,
batch_size
=
2
):
# Input data
image
=
fluid
.
layers
.
data
(
name
=
"data"
,
shape
=
[
3
,
224
,
224
],
dtype
=
'float32'
)
label
=
fluid
.
layers
.
data
(
name
=
"int64"
,
shape
=
[
1
],
dtype
=
'int64'
)
# Train program
model
=
SE_ResNeXt
(
layers
=
50
)
out
=
model
.
net
(
input
=
image
,
class_dim
=
102
)
cost
=
fluid
.
layers
.
cross_entropy
(
input
=
out
,
label
=
label
)
# Train program
model
=
SE_ResNeXt
(
layers
=
50
)
out
=
model
.
net
(
input
=
image
,
class_dim
=
102
)
cost
=
fluid
.
layers
.
cross_entropy
(
input
=
out
,
label
=
label
)
avg_cost
=
fluid
.
layers
.
mean
(
x
=
cost
)
acc_top1
=
fluid
.
layers
.
accuracy
(
input
=
out
,
label
=
label
,
k
=
1
)
acc_top5
=
fluid
.
layers
.
accuracy
(
input
=
out
,
label
=
label
,
k
=
5
)
avg_cost
=
fluid
.
layers
.
mean
(
x
=
cost
)
acc_top1
=
fluid
.
layers
.
accuracy
(
input
=
out
,
label
=
label
,
k
=
1
)
acc_top5
=
fluid
.
layers
.
accuracy
(
input
=
out
,
label
=
label
,
k
=
5
)
# Evaluator
test_program
=
fluid
.
default_main_program
().
clone
(
for_test
=
True
)
# Evaluator
test_program
=
fluid
.
default_main_program
().
clone
(
for_test
=
True
)
# Optimization
total_images
=
6149
# flowers
epochs
=
[
30
,
60
,
90
]
step
=
int
(
total_images
/
batch_size
+
1
)
# Optimization
total_images
=
6149
# flowers
epochs
=
[
30
,
60
,
90
]
step
=
int
(
total_images
/
batch_size
+
1
)
bd
=
[
step
*
e
for
e
in
epochs
]
base_lr
=
0.1
lr
=
[]
lr
=
[
base_lr
*
(
0.1
**
i
)
for
i
in
range
(
len
(
bd
)
+
1
)]
bd
=
[
step
*
e
for
e
in
epochs
]
base_lr
=
0.1
lr
=
[]
lr
=
[
base_lr
*
(
0.1
**
i
)
for
i
in
range
(
len
(
bd
)
+
1
)]
optimizer
=
fluid
.
optimizer
.
Momentum
(
# FIXME(typhoonzero): add back LR decay once ParallelExecutor fixed.
#learning_rate=fluid.layers.piecewise_decay(
# boundaries=bd, values=lr),
learning_rate
=
base_lr
,
momentum
=
0.9
,
regularization
=
fluid
.
regularizer
.
L2Decay
(
1e-4
))
optimizer
.
minimize
(
avg_cost
)
optimizer
=
fluid
.
optimizer
.
Momentum
(
# FIXME(typhoonzero): add back LR decay once ParallelExecutor fixed.
#learning_rate=fluid.layers.piecewise_decay(
# boundaries=bd, values=lr),
learning_rate
=
base_lr
,
momentum
=
0.9
,
regularization
=
fluid
.
regularizer
.
L2Decay
(
1e-4
))
optimizer
.
minimize
(
avg_cost
)
# Reader
train_reader
=
paddle
.
batch
(
paddle
.
dataset
.
flowers
.
train
(),
batch_size
=
batch_size
)
test_reader
=
paddle
.
batch
(
paddle
.
dataset
.
flowers
.
test
(
use_xmap
=
False
),
batch_size
=
batch_size
)
# Reader
train_reader
=
paddle
.
batch
(
paddle
.
dataset
.
flowers
.
train
(),
batch_size
=
batch_size
)
test_reader
=
paddle
.
batch
(
paddle
.
dataset
.
flowers
.
test
(
use_xmap
=
False
),
batch_size
=
batch_size
)
return
test_program
,
avg_cost
,
train_reader
,
test_reader
,
acc_top1
,
out
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
class
DistSeResneXt2x2
:
def
run_pserver
(
self
,
pserver_endpoints
,
trainers
,
current_endpoint
,
trainer_id
):
get_model
(
batch_size
=
2
)
t
=
get_transpiler
(
trainer_id
,
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
)
def
_wait_ps_ready
(
self
,
pid
):
retry_times
=
20
while
True
:
assert
retry_times
>=
0
,
"wait ps ready failed"
time
.
sleep
(
3
)
print
(
"waiting ps ready: "
,
pid
)
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
run_trainer
(
self
,
place
,
endpoints
,
trainer_id
,
trainers
,
is_dist
=
True
):
test_program
,
avg_cost
,
train_reader
,
test_reader
,
batch_acc
,
predict
=
get_model
(
batch_size
=
2
)
if
is_dist
:
t
=
get_transpiler
(
trainer_id
,
fluid
.
default_main_program
(),
endpoints
,
trainers
)
trainer_prog
=
t
.
get_trainer_program
()
else
:
trainer_prog
=
fluid
.
default_main_program
()
startup_exe
=
fluid
.
Executor
(
place
)
startup_exe
.
run
(
fluid
.
default_startup_program
())
strategy
=
fluid
.
ExecutionStrategy
()
strategy
.
num_threads
=
1
strategy
.
allow_op_delay
=
False
exe
=
fluid
.
ParallelExecutor
(
True
,
loss_name
=
avg_cost
.
name
,
exec_strategy
=
strategy
)
feed_var_list
=
[
var
for
var
in
trainer_prog
.
global_block
().
vars
.
values
()
if
var
.
is_data
]
feeder
=
fluid
.
DataFeeder
(
feed_var_list
,
place
)
reader_generator
=
test_reader
()
data
=
next
(
reader_generator
)
first_loss
,
=
exe
.
run
(
fetch_list
=
[
avg_cost
.
name
],
feed
=
feeder
.
feed
(
data
))
print
(
first_loss
)
for
i
in
six
.
moves
.
xrange
(
5
):
data
=
next
(
reader_generator
)
loss
,
=
exe
.
run
(
fetch_list
=
[
avg_cost
.
name
],
feed
=
feeder
.
feed
(
data
))
data
=
next
(
reader_generator
)
last_loss
,
=
exe
.
run
(
fetch_list
=
[
avg_cost
.
name
],
feed
=
feeder
.
feed
(
data
))
print
(
last_loss
)
def
main
(
role
=
"pserver"
,
endpoints
=
"127.0.0.1:9123"
,
trainer_id
=
0
,
current_endpoint
=
"127.0.0.1:9123"
,
trainers
=
1
,
is_dist
=
True
):
model
=
DistSeResneXt2x2
()
if
role
==
"pserver"
:
model
.
run_pserver
(
endpoints
,
trainers
,
current_endpoint
,
trainer_id
)
else
:
p
=
fluid
.
CUDAPlace
(
0
)
if
core
.
is_compiled_with_cuda
(
)
else
fluid
.
CPUPlace
()
model
.
run_trainer
(
p
,
endpoints
,
trainer_id
,
trainers
,
is_dist
)
return
test_program
,
avg_cost
,
train_reader
,
test_reader
,
acc_top1
,
out
if
__name__
==
"__main__"
:
if
len
(
sys
.
argv
)
!=
7
:
print
(
"Usage: python dist_se_resnext.py [pserver/trainer] [endpoints] [trainer_id] [current_endpoint] [trainers] [is_dist]"
)
role
=
sys
.
argv
[
1
]
endpoints
=
sys
.
argv
[
2
]
trainer_id
=
int
(
sys
.
argv
[
3
])
current_endpoint
=
sys
.
argv
[
4
]
trainers
=
int
(
sys
.
argv
[
5
])
is_dist
=
True
if
sys
.
argv
[
6
]
==
"TRUE"
else
False
main
(
role
=
role
,
endpoints
=
endpoints
,
trainer_id
=
trainer_id
,
current_endpoint
=
current_endpoint
,
trainers
=
trainers
,
is_dist
=
is_dist
)
runtime_main
(
DistSeResneXt2x2
)
python/paddle/fluid/tests/unittests/dist_word2vec.py
0 → 100644
浏览文件 @
f54efd02
# 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
from
test_dist_base
import
TestDistRunnerBase
,
runtime_main
IS_SPARSE
=
True
EMBED_SIZE
=
32
HIDDEN_SIZE
=
256
N
=
5
# Fix seed for test
fluid
.
default_startup_program
().
random_seed
=
1
fluid
.
default_main_program
().
random_seed
=
1
class
TestDistWord2vec2x2
(
TestDistRunnerBase
):
def
get_model
(
self
,
batch_size
=
2
):
BATCH_SIZE
=
batch_size
def
__network__
(
words
):
embed_first
=
fluid
.
layers
.
embedding
(
input
=
words
[
0
],
size
=
[
dict_size
,
EMBED_SIZE
],
dtype
=
'float32'
,
is_sparse
=
IS_SPARSE
,
param_attr
=
fluid
.
ParamAttr
(
name
=
'shared_w'
,
initializer
=
fluid
.
initializer
.
Constant
()))
embed_second
=
fluid
.
layers
.
embedding
(
input
=
words
[
1
],
size
=
[
dict_size
,
EMBED_SIZE
],
dtype
=
'float32'
,
is_sparse
=
IS_SPARSE
,
param_attr
=
fluid
.
ParamAttr
(
name
=
'shared_w'
,
initializer
=
fluid
.
initializer
.
Constant
()))
embed_third
=
fluid
.
layers
.
embedding
(
input
=
words
[
2
],
size
=
[
dict_size
,
EMBED_SIZE
],
dtype
=
'float32'
,
is_sparse
=
IS_SPARSE
,
param_attr
=
fluid
.
ParamAttr
(
name
=
'shared_w'
,
initializer
=
fluid
.
initializer
.
Constant
()))
embed_forth
=
fluid
.
layers
.
embedding
(
input
=
words
[
3
],
size
=
[
dict_size
,
EMBED_SIZE
],
dtype
=
'float32'
,
is_sparse
=
IS_SPARSE
,
param_attr
=
fluid
.
ParamAttr
(
name
=
'shared_w'
,
initializer
=
fluid
.
initializer
.
Constant
()))
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'
,
param_attr
=
fluid
.
ParamAttr
(
initializer
=
fluid
.
initializer
.
Constant
()))
predict_word
=
fluid
.
layers
.
fc
(
input
=
hidden1
,
size
=
dict_size
,
act
=
'softmax'
,
param_attr
=
fluid
.
ParamAttr
(
initializer
=
fluid
.
initializer
.
Constant
()))
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
,
None
,
predict_word
if
__name__
==
"__main__"
:
runtime_main
(
TestDistWord2vec2x2
)
python/paddle/fluid/tests/unittests/test_dist_base.py
浏览文件 @
f54efd02
...
...
@@ -18,6 +18,109 @@ import os
import
sys
import
signal
import
subprocess
import
six
class
TestDistRunnerBase
(
object
):
def
get_model
(
self
,
batch_size
=
2
):
raise
NotImplementedError
(
"get_model should be implemented by child classes."
)
def
get_transpiler
(
self
,
trainer_id
,
main_program
,
pserver_endpoints
,
trainers
):
# NOTE: import fluid until runtime, or else forking processes will cause error.
import
paddle
import
paddle.fluid
as
fluid
t
=
fluid
.
DistributeTranspiler
()
t
.
transpile
(
trainer_id
=
trainer_id
,
program
=
main_program
,
pservers
=
pserver_endpoints
,
trainers
=
trainers
)
return
t
def
run_pserver
(
self
,
pserver_endpoints
,
trainers
,
current_endpoint
,
trainer_id
):
import
paddle
import
paddle.fluid
as
fluid
self
.
get_model
(
batch_size
=
2
)
t
=
self
.
get_transpiler
(
trainer_id
,
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
)
def
run_trainer
(
self
,
place
,
endpoints
,
trainer_id
,
trainers
,
is_dist
=
True
):
import
paddle
import
paddle.fluid
as
fluid
test_program
,
avg_cost
,
train_reader
,
test_reader
,
batch_acc
,
predict
=
\
self
.
get_model
(
batch_size
=
2
)
if
is_dist
:
t
=
self
.
get_transpiler
(
trainer_id
,
fluid
.
default_main_program
(),
endpoints
,
trainers
)
trainer_prog
=
t
.
get_trainer_program
()
else
:
trainer_prog
=
fluid
.
default_main_program
()
startup_exe
=
fluid
.
Executor
(
place
)
startup_exe
.
run
(
fluid
.
default_startup_program
())
strategy
=
fluid
.
ExecutionStrategy
()
strategy
.
num_threads
=
1
strategy
.
allow_op_delay
=
False
exe
=
fluid
.
ParallelExecutor
(
True
,
loss_name
=
avg_cost
.
name
,
exec_strategy
=
strategy
)
feed_var_list
=
[
var
for
var
in
trainer_prog
.
global_block
().
vars
.
values
()
if
var
.
is_data
]
feeder
=
fluid
.
DataFeeder
(
feed_var_list
,
place
)
reader_generator
=
test_reader
()
data
=
next
(
reader_generator
)
first_loss
,
=
exe
.
run
(
fetch_list
=
[
avg_cost
.
name
],
feed
=
feeder
.
feed
(
data
))
print
(
first_loss
)
for
i
in
six
.
moves
.
xrange
(
5
):
data
=
next
(
reader_generator
)
loss
,
=
exe
.
run
(
fetch_list
=
[
avg_cost
.
name
],
feed
=
feeder
.
feed
(
data
))
data
=
next
(
reader_generator
)
last_loss
,
=
exe
.
run
(
fetch_list
=
[
avg_cost
.
name
],
feed
=
feeder
.
feed
(
data
))
print
(
last_loss
)
def
runtime_main
(
test_class
):
import
paddle
import
paddle.fluid
as
fluid
import
paddle.fluid.core
as
core
if
len
(
sys
.
argv
)
!=
7
:
print
(
"Usage: python dist_se_resnext.py [pserver/trainer] [endpoints] [trainer_id] [current_endpoint] [trainers] [is_dist]"
)
role
=
sys
.
argv
[
1
]
endpoints
=
sys
.
argv
[
2
]
trainer_id
=
int
(
sys
.
argv
[
3
])
current_endpoint
=
sys
.
argv
[
4
]
trainers
=
int
(
sys
.
argv
[
5
])
is_dist
=
True
if
sys
.
argv
[
6
]
==
"TRUE"
else
False
model
=
test_class
()
if
role
==
"pserver"
:
model
.
run_pserver
(
endpoints
,
trainers
,
current_endpoint
,
trainer_id
)
else
:
p
=
fluid
.
CUDAPlace
(
0
)
if
core
.
is_compiled_with_cuda
(
)
else
fluid
.
CPUPlace
()
model
.
run_trainer
(
p
,
endpoints
,
trainer_id
,
trainers
,
is_dist
)
class
TestDistBase
(
unittest
.
TestCase
):
...
...
@@ -127,12 +230,10 @@ class TestDistBase(unittest.TestCase):
local_first_loss
=
eval
(
local_lines
[
0
])[
0
]
local_last_loss
=
eval
(
local_lines
[
1
])[
0
]
self
.
assertAlmostEqual
(
local_first_loss
,
dist_first_loss
,
delta
=
delta
)
self
.
assertAlmostEqual
(
local_last_loss
,
dist_last_loss
,
delta
=
delta
)
# check tr0_out
# FIXME: ensure the server process is killed
# replace with ps0.terminate()
# FIXME: use terminate() instead of sigkill.
os
.
kill
(
ps0
.
pid
,
signal
.
SIGKILL
)
os
.
kill
(
ps1
.
pid
,
signal
.
SIGKILL
)
FNULL
.
close
()
self
.
assertAlmostEqual
(
local_first_loss
,
dist_first_loss
,
delta
=
delta
)
self
.
assertAlmostEqual
(
local_last_loss
,
dist_last_loss
,
delta
=
delta
)
python/paddle/fluid/tests/unittests/test_dist_mnist.py
浏览文件 @
f54efd02
...
...
@@ -11,200 +11,13 @@
# 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
from
functools
import
reduce
SEED
=
1
DTYPE
=
"float32"
paddle
.
dataset
.
mnist
.
fetch
()
# random seed must set before configuring the network.
# fluid.default_startup_program().random_seed = SEED
def
cnn_model
(
data
):
conv_pool_1
=
fluid
.
nets
.
simple_img_conv_pool
(
input
=
data
,
filter_size
=
5
,
num_filters
=
20
,
pool_size
=
2
,
pool_stride
=
2
,
act
=
"relu"
)
conv_pool_2
=
fluid
.
nets
.
simple_img_conv_pool
(
input
=
conv_pool_1
,
filter_size
=
5
,
num_filters
=
50
,
pool_size
=
2
,
pool_stride
=
2
,
act
=
"relu"
)
# TODO(dzhwinter) : refine the initializer and random seed settting
SIZE
=
10
input_shape
=
conv_pool_2
.
shape
param_shape
=
[
reduce
(
lambda
a
,
b
:
a
*
b
,
input_shape
[
1
:],
1
)]
+
[
SIZE
]
scale
=
(
2.0
/
(
param_shape
[
0
]
**
2
*
SIZE
))
**
0.5
predict
=
fluid
.
layers
.
fc
(
input
=
conv_pool_2
,
size
=
SIZE
,
act
=
"softmax"
,
param_attr
=
fluid
.
param_attr
.
ParamAttr
(
initializer
=
fluid
.
initializer
.
NormalInitializer
(
loc
=
0.0
,
scale
=
scale
)))
return
predict
def
get_model
(
batch_size
):
# Input data
images
=
fluid
.
layers
.
data
(
name
=
'pixel'
,
shape
=
[
1
,
28
,
28
],
dtype
=
DTYPE
)
label
=
fluid
.
layers
.
data
(
name
=
'label'
,
shape
=
[
1
],
dtype
=
'int64'
)
# Train program
predict
=
cnn_model
(
images
)
cost
=
fluid
.
layers
.
cross_entropy
(
input
=
predict
,
label
=
label
)
avg_cost
=
fluid
.
layers
.
mean
(
x
=
cost
)
# Evaluator
batch_size_tensor
=
fluid
.
layers
.
create_tensor
(
dtype
=
'int64'
)
batch_acc
=
fluid
.
layers
.
accuracy
(
input
=
predict
,
label
=
label
,
total
=
batch_size_tensor
)
inference_program
=
fluid
.
default_main_program
().
clone
()
# Optimization
opt
=
fluid
.
optimizer
.
AdamOptimizer
(
learning_rate
=
0.001
,
beta1
=
0.9
,
beta2
=
0.999
)
# Reader
train_reader
=
paddle
.
batch
(
paddle
.
dataset
.
mnist
.
train
(),
batch_size
=
batch_size
)
test_reader
=
paddle
.
batch
(
paddle
.
dataset
.
mnist
.
test
(),
batch_size
=
batch_size
)
opt
.
minimize
(
avg_cost
)
return
inference_program
,
avg_cost
,
train_reader
,
test_reader
,
batch_acc
,
predict
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
(
batch_size
=
20
)
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
.
SIGTERM
)
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
,
batch_acc
,
predict
=
get_model
(
batch_size
=
20
)
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
())
feed_var_list
=
[
var
for
var
in
trainer_prog
.
global_block
().
vars
.
values
()
if
var
.
is_data
]
from
test_dist_base
import
TestDistBase
feeder
=
fluid
.
DataFeeder
(
feed_var_list
,
place
)
for
pass_id
in
range
(
10
):
for
batch_id
,
data
in
enumerate
(
train_reader
()):
exe
.
run
(
trainer_prog
,
feed
=
feeder
.
feed
(
data
))
if
(
batch_id
+
1
)
%
10
==
0
:
acc_set
=
[]
avg_loss_set
=
[]
for
test_data
in
test_reader
():
acc_np
,
avg_loss_np
=
exe
.
run
(
program
=
test_program
,
feed
=
feeder
.
feed
(
test_data
),
fetch_list
=
[
batch_acc
,
avg_cost
])
acc_set
.
append
(
float
(
acc_np
))
avg_loss_set
.
append
(
float
(
avg_loss_np
))
# get test acc and loss
acc_val
=
np
.
array
(
acc_set
).
mean
()
avg_loss_val
=
np
.
array
(
avg_loss_set
).
mean
()
if
float
(
acc_val
)
>
0.8
:
# Smaller value to increase CI speed
return
else
:
print
(
'PassID {0:1}, BatchID {1:04}, Test Loss {2:2.2}, Acc {3:2.2}'
.
format
(
pass_id
,
batch_id
+
1
,
float
(
avg_loss_val
),
float
(
acc_val
)))
if
math
.
isnan
(
float
(
avg_loss_val
)):
assert
(
"got Nan loss, training failed."
)
class
TestDistSeResneXt2x2
(
TestDistBase
):
def
test_se_resnext
(
self
):
self
.
check_with_place
(
"dist_mnist.py"
,
delta
=
1e-7
)
if
__name__
==
"__main__"
:
...
...
python/paddle/fluid/tests/unittests/test_dist_se_resnext.py
浏览文件 @
f54efd02
...
...
@@ -17,7 +17,7 @@ from test_dist_base import TestDistBase
class
TestDistSeResneXt2x2
(
TestDistBase
):
def
test_se_resnext
(
self
):
self
.
check_with_place
(
"dist_se_resnext.py"
)
self
.
check_with_place
(
"dist_se_resnext.py"
,
delta
=
1e-7
)
if
__name__
==
"__main__"
:
...
...
python/paddle/fluid/tests/unittests/test_dist_word2vec.py
浏览文件 @
f54efd02
...
...
@@ -11,192 +11,13 @@
# 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
)
from
test_dist_base
import
TestDistBase
feed_var_list
=
[
var
for
var
in
trainer_prog
.
global_block
().
vars
.
values
()
if
var
.
is_data
]
feeder
=
fluid
.
DataFeeder
(
feed_var_list
,
place
)
for
pass_id
in
range
(
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"
)
class
TestDistSeResneXt2x2
(
TestDistBase
):
def
test_se_resnext
(
self
):
self
.
check_with_place
(
"dist_word2vec.py"
,
delta
=
1e-7
)
if
__name__
==
"__main__"
:
...
...
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