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6a1df469
编写于
6月 06, 2019
作者:
G
gongweibao
提交者:
GitHub
6月 06, 2019
浏览文件
操作
浏览文件
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电子邮件补丁
差异文件
Fine tuning launch.py (#17223)
上级
841553e1
变更
4
隐藏空白更改
内联
并排
Showing
4 changed file
with
260 addition
and
104 deletion
+260
-104
python/paddle/distributed/launch.py
python/paddle/distributed/launch.py
+169
-104
python/paddle/fluid/tests/unittests/CMakeLists.txt
python/paddle/fluid/tests/unittests/CMakeLists.txt
+26
-0
python/paddle/fluid/tests/unittests/multi_process.py
python/paddle/fluid/tests/unittests/multi_process.py
+35
-0
python/paddle/fluid/tests/unittests/test_launch.sh
python/paddle/fluid/tests/unittests/test_launch.sh
+30
-0
未找到文件。
python/paddle/distributed/launch.py
浏览文件 @
6a1df469
# Copyright (c) 201
8
PaddlePaddle Authors. All Rights Reserved.
# Copyright (c) 201
9
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.
...
...
@@ -11,100 +11,58 @@
# 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.
"""
paddle.distributed.launch is a module that spawns multiple distributed
process on each trainning node for gpu trainning.
Usage:
In both of single node training or multiple node training, this module
launch a process on each of the given gpu card.
1. for single node trainning with all visible gpu cards:
python -m paddle.distributed.launch
\
your_training_py (arg1 arg2 and all others)
2. for single node trainning with [0,4) cards
python -m paddle.distributed.launch --selected_gpus="0,1,2,3"
\
your_training_py (arg1 arg2 and all others)
3. for mulitple node training such as two node:192.168.0.16, 192.168.0.17
on 192.168.0.16:
python -m paddle.distributed.launch --cluster_node_ips="192.168.0.16,192.168.0.17"
\
--node_ip=192.168.0.16
\
your_training_py (arg1 arg2 and all others)
on 192.168.0.17:
python -m paddle.distributed.launch --cluster_node_ips="192.168.0.16,192.168.0.17"
\
--node_ip=192.168.0.17
\
your_training_py (arg1 arg2 and all others)
"""
from
__future__
import
print_function
import
sys
from
sys
import
version
import
subprocess
import
os
import
sys
import
time
import
argparse
default_envs
=
{
"PADDLE_TRAINER_ENDPOINTS"
:
"127.0.0.1:6170,127.0.0.1:6171,127.0.0.1:6172,127.0.0.1:6173,127.0.0.1:6174,127.0.0.1:6175,127.0.0.1:6176,127.0.0.1:6177"
,
"LD_LIBRARY_PATH"
:
os
.
getenv
(
"LD_LIBRARY_PATH"
,
""
),
"PATH"
:
os
.
getenv
(
"PATH"
),
"LD_PRELOAD"
:
os
.
getenv
(
"LD_PRELOAD"
,
""
),
"PADDLE_TRAINERS_NUM"
:
"8"
,
"NCCL_DEBUG"
:
"INFO"
,
"GLOG_v"
:
"0"
,
"NCCL_SOCKET_IFNAME"
:
"eth0"
,
"NCCL_IB_GID_INDEX"
:
"3"
,
"NCCL_IB_RETRY_CNT"
:
"0"
,
"PYTHONPATH"
:
os
.
getenv
(
"PYTHONPATH"
,
""
),
}
GPUS
=
8
def
get_gpu_ids
(
gpus
):
if
os
.
getenv
(
"CUDA_VISIBLE_DEVICES"
):
ids
=
[
int
(
i
)
for
i
in
os
.
getenv
(
"CUDA_VISIBLE_DEVICES"
).
split
(
","
)][:
gpus
]
if
gpus
>
len
(
ids
):
raise
EnvironmentError
(
"The count of env CUDA_VISIBLE_DEVICES should not greater than the passed gpus: %s"
%
gpus
)
return
ids
else
:
return
[
i
for
i
in
range
(
gpus
)]
import
six
import
copy
from
argparse
import
ArgumentParser
,
REMAINDER
import
paddle.fluid
as
fluid
def
start_procs
(
gpus
,
entrypoint
,
entrypoint_args
,
log_dir
):
procs
=
[]
log_fns
=
[]
os
.
system
(
"mkdir -p %s"
%
log_dir
)
# ======== update parent envs =======
for
k
,
v
in
os
.
environ
.
items
():
if
k
.
startswith
(
"FLAGS_"
)
or
k
.
startswith
(
"NCCL_"
)
or
\
k
.
startswith
(
"GLOG_"
):
default_envs
[
k
]
=
v
# ======== for dist training =======
node_trainer_id
=
int
(
os
.
getenv
(
"PADDLE_TRAINER_ID"
,
"0"
))
current_ip
=
os
.
getenv
(
"POD_IP"
,
"127.0.0.1"
)
trainer_ips
=
os
.
getenv
(
"PADDLE_TRAINERS"
,
current_ip
).
split
(
","
)
num_nodes
=
len
(
trainer_ips
)
all_nodes_devices_endpoints
=
""
for
n
in
trainer_ips
:
for
i
in
range
(
gpus
):
if
all_nodes_devices_endpoints
:
all_nodes_devices_endpoints
+=
","
all_nodes_devices_endpoints
+=
"%s:617%d"
%
(
n
,
i
)
nranks
=
num_nodes
*
gpus
# ======== for dist training =======
gpu_ids
=
get_gpu_ids
(
gpus
)
for
i
in
range
(
gpus
):
curr_env
=
{}
curr_env
.
update
(
default_envs
)
curr_env
.
update
({
"FLAGS_selected_gpus"
:
"%d"
%
gpu_ids
[
i
],
"PADDLE_TRAINER_ID"
:
"%d"
%
(
node_trainer_id
*
gpus
+
i
),
"PADDLE_CURRENT_ENDPOINT"
:
"%s:617%d"
%
(
current_ip
,
i
),
# nranks
"PADDLE_TRAINERS_NUM"
:
"%d"
%
nranks
,
"PADDLE_TRAINER_ENDPOINTS"
:
all_nodes_devices_endpoints
})
print
(
"starting process "
,
i
,
entrypoint
,
entrypoint_args
,
curr_env
)
fn
=
open
(
"%s/workerlog.%d"
%
(
log_dir
,
i
),
"w"
)
log_fns
.
append
(
fn
)
cmd
=
[
sys
.
executable
,
"-u"
,
entrypoint
]
+
entrypoint_args
procs
.
append
(
subprocess
.
Popen
(
cmd
,
stdout
=
fn
,
stderr
=
fn
,
env
=
curr_env
))
for
i
in
range
(
gpus
):
try
:
procs
[
i
].
communicate
()
procs
[
i
].
terminate
()
log_fns
[
i
].
close
()
except
:
pass
def
_print_arguments
(
args
):
print
(
"----------- Configuration Arguments -----------"
)
for
arg
,
value
in
sorted
(
six
.
iteritems
(
vars
(
args
))):
print
(
"%s: %s"
%
(
arg
,
value
))
print
(
"------------------------------------------------"
)
def
parse_args
():
parser
=
argparse
.
ArgumentParser
(
def
_parse_args
():
"""
Helper function parsing the command line options
@retval ArgumentParser
"""
parser
=
ArgumentParser
(
description
=
'''start paddle training using multi-process mode.
NOTE: your train program ***must*** run as distributed nccl2 mode,
see: http://www.paddlepaddle.org/documentation/docs/zh/1.2/user_guides/howto/training/cluster_howto.html#permalink-8--nccl2-
...
...
@@ -117,33 +75,140 @@ PADDLE_TRAINERS_NUM
PADDLE_TRAINER_ENDPOINTS
POD_IP (current node ip address, not needed for local training)
'''
)
# Optional arguments for the launch helper
parser
.
add_argument
(
'--gpus'
,
"--cluster_node_ips"
,
type
=
str
,
default
=
"127.0.0.1"
,
help
=
"Paddle cluster nodes ips, such as 192.168.0.16,192.168.0.17.."
)
parser
.
add_argument
(
"--node_ip"
,
type
=
str
,
default
=
"127.0.0.1"
,
help
=
"The current node ip. "
)
parser
.
add_argument
(
"--started_port"
,
type
=
int
,
default
=
8
,
help
=
'start number of processes for every gpu'
)
default
=
6170
,
help
=
"The trainer's started port on a single node"
)
parser
.
add_argument
(
"--print_config"
,
type
=
bool
,
default
=
True
,
help
=
"Print the config or not"
)
parser
.
add_argument
(
"--selected_gpus"
,
type
=
str
,
default
=
None
,
help
=
"It's for gpu trainning and the trainning process will run on the selected_gpus,"
"each process is bound to a single GPU. And if it's not setted, this module will use all the gpu cards for training."
)
parser
.
add_argument
(
'--log_dir'
,
"--log_dir"
,
type
=
str
,
default
=
"mylog"
,
help
=
'directory to put logs per process.'
)
help
=
"The path for each process's log.If it's not setted, the log will printed to default pipe."
)
# positional
parser
.
add_argument
(
'entrypoint_script'
,
"training_script"
,
type
=
str
,
help
=
"The entrypoint script to be launched in parallel,"
"followed by all the arguments for each process,"
"e.g. train.py --lr 0.1"
)
parser
.
add_argument
(
'entrypoint_args'
,
nargs
=
argparse
.
REMAINDER
)
help
=
"The full path to the single GPU training "
"program/script to be launched in parallel, "
"followed by all the arguments for the "
"training script"
)
# rest from the training program
parser
.
add_argument
(
'training_script_args'
,
nargs
=
REMAINDER
)
return
parser
.
parse_args
()
def
main
():
args
=
parse_args
()
def
start_procs
(
args
):
"""
"""
procs
=
[]
log_fns
=
[]
default_env
=
os
.
environ
.
copy
()
current_node_ip
=
args
.
node_ip
node_ips
=
[
x
.
strip
()
for
x
in
args
.
cluster_node_ips
.
split
(
','
)]
node_id
=
node_ips
.
index
(
current_node_ip
)
num_nodes
=
len
(
node_ips
)
if
args
.
selected_gpus
is
None
:
gpus_num
=
fluid
.
core
.
get_cuda_device_count
()
selected_gpus
=
[
str
(
x
)
for
x
in
range
(
0
,
gpus_num
)]
else
:
selected_gpus
=
[
x
.
strip
()
for
x
in
args
.
selected_gpus
.
split
(
','
)]
selected_gpus_num
=
len
(
selected_gpus
)
trainers_endpoints
=
""
for
ip
in
node_ips
:
for
i
in
range
(
selected_gpus_num
):
if
trainers_endpoints
!=
""
:
trainers_endpoints
+=
","
trainers_endpoints
+=
"%s:617%d"
%
(
ip
,
i
)
nranks
=
num_nodes
*
selected_gpus_num
if
args
.
print_config
:
print
(
"trainers_endpoints:"
,
trainers_endpoints
,
", node_id:"
,
node_id
,
", current_node_ip:"
,
current_node_ip
,
", num_nodes:"
,
num_nodes
,
", node_ips:"
,
node_ips
,
", nranks:"
,
nranks
)
current_env
=
copy
.
copy
(
default_env
)
procs
=
[]
cmds
=
[]
for
i
in
range
(
0
,
selected_gpus_num
):
current_env
.
update
({
"FLAGS_selected_gpus"
:
"%s"
%
selected_gpus
[
i
],
"PADDLE_TRAINER_ID"
:
"%d"
%
(
node_id
*
selected_gpus_num
+
i
),
"PADDLE_CURRENT_ENDPOINT"
:
"%s:%d"
%
(
current_node_ip
,
args
.
started_port
+
i
),
"PADDLE_TRAINERS_NUM"
:
"%d"
%
nranks
,
"PADDLE_TRAINER_ENDPOINTS"
:
trainers_endpoints
})
cmd
=
[
sys
.
executable
,
"-u"
,
args
.
training_script
]
+
args
.
training_script_args
cmds
.
append
(
cmd
)
if
args
.
log_dir
is
not
None
:
fn
=
open
(
"%s/workerlog.%d"
%
(
args
.
log_dir
,
i
),
"w"
)
log_fns
.
append
(
fn
)
proc
=
subprocess
.
Popen
(
cmd
,
env
=
current_env
,
stdout
=
fn
,
stderr
=
fn
)
else
:
proc
=
subprocess
.
Popen
(
cmd
,
env
=
current_env
)
procs
.
append
(
proc
)
for
i
in
range
(
0
,
len
(
procs
)):
proc
=
procs
[
i
]
proc
.
wait
()
if
len
(
log_fns
)
>
0
:
log_fns
[
i
].
close
()
if
proc
.
returncode
!=
0
:
raise
subprocess
.
CalledProcessError
(
returncode
=
procs
[
i
].
returncode
,
cmd
=
cmds
[
i
])
# launch multiple training process
start_procs
(
args
.
gpus
,
args
.
entrypoint_script
,
args
.
entrypoint_args
,
args
.
log_dir
)
def
launch
():
args
=
_parse_args
()
if
args
.
print_config
:
_print_arguments
(
args
)
start_procs
(
args
)
if
__name__
==
"__main__"
:
main
()
launch
()
python/paddle/fluid/tests/unittests/CMakeLists.txt
浏览文件 @
6a1df469
...
...
@@ -19,6 +19,8 @@ if(NOT WITH_DISTRIBUTE)
LIST
(
REMOVE_ITEM TEST_OPS test_hsigmoid_remote_table_op
)
endif
(
NOT WITH_DISTRIBUTE
)
LIST
(
REMOVE_ITEM TEST_OPS test_launch
)
if
(
NOT
${
WITH_GPU
}
)
LIST
(
REMOVE_ITEM TEST_OPS test_conv2d_fusion_op
)
LIST
(
REMOVE_ITEM TEST_OPS test_parallel_dygraph_mnist
)
# TODO(Yancey1989): parallel dygraph support CPU device in future
...
...
@@ -66,6 +68,29 @@ function(py_test_modules TARGET_NAME)
set_tests_properties
(
${
TARGET_NAME
}
PROPERTIES TIMEOUT 350
)
endif
()
endfunction
()
function
(
bash_test_modules TARGET_NAME
)
if
(
NOT WITH_TESTING
)
return
()
endif
()
set
(
options SERIAL
)
set
(
oneValueArgs
""
)
set
(
multiValueArgs MODULES DEPS ENVS
)
cmake_parse_arguments
(
bash_test_modules
"
${
options
}
"
"
${
oneValueArgs
}
"
"
${
multiValueArgs
}
"
${
ARGN
}
)
message
(
STATUS
"CMAKE_CURRENT_BINARY_DIR:"
${
CMAKE_CURRENT_BINARY_DIR
}
)
add_test
(
NAME
${
TARGET_NAME
}
COMMAND
${
CMAKE_COMMAND
}
-E env PYTHONPATH=
${
PADDLE_BINARY_DIR
}
/python
${
bash_test_modules_ENVS
}
bash
${
CMAKE_CURRENT_BINARY_DIR
}
/
${
bash_test_modules_MODULES
}
WORKING_DIRECTORY
${
CMAKE_CURRENT_BINARY_DIR
}
)
if
(
bash_test_modules_SERIAL
)
set_property
(
TEST
${
TARGET_NAME
}
PROPERTY RUN_SERIAL 1
)
endif
()
set_tests_properties
(
${
TARGET_NAME
}
PROPERTIES TIMEOUT 600
)
endfunction
()
list
(
REMOVE_ITEM TEST_OPS test_warpctc_op
)
list
(
REMOVE_ITEM TEST_OPS test_dist_train
)
list
(
REMOVE_ITEM TEST_OPS test_dist_transpiler
)
...
...
@@ -154,6 +179,7 @@ if(WITH_DISTRIBUTE)
set_tests_properties
(
test_dist_word2vec PROPERTIES TIMEOUT 200
)
py_test_modules
(
test_dist_se_resnext MODULES test_dist_se_resnext
)
py_test_modules
(
test_dist_se_resnext_nccl MODULES test_dist_se_resnext_nccl
)
bash_test_modules
(
test_launch MODULES test_launch.sh
)
# FIXME(typhoonzero): add these tests back
# py_test_modules(test_dist_transformer MODULES test_dist_transformer)
# set_tests_properties(test_dist_transformer PROPERTIES TIMEOUT 1000)
...
...
python/paddle/fluid/tests/unittests/multi_process.py
0 → 100644
浏览文件 @
6a1df469
# Copyright (c) 2019 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
os
def
train
():
selected_gpus
=
os
.
getenv
(
"FLAGS_selected_gpus"
)
trainer_id
=
int
(
os
.
getenv
(
"PADDLE_TRAINER_ID"
))
worker_endpoints_env
=
os
.
getenv
(
"PADDLE_TRAINER_ENDPOINTS"
)
current_endpoint
=
os
.
getenv
(
"PADDLE_CURRENT_ENDPOINT"
)
worker_endpoints
=
worker_endpoints_env
.
split
(
","
)
trainers_num
=
len
(
worker_endpoints
)
name
=
"selected_gpus:{} worker_endpoints:{} trainers_num:{} current_endpoint:{} trainer_id:{}"
\
.
format
(
selected_gpus
,
worker_endpoints
,
trainers_num
,
current_endpoint
,
trainer_id
)
print
(
name
)
with
open
(
"multi_process.check.log"
,
"w"
)
as
f
:
f
.
write
(
name
)
if
__name__
==
'__main__'
:
train
()
python/paddle/fluid/tests/unittests/test_launch.sh
0 → 100644
浏览文件 @
6a1df469
#!/bin/bash
set
-e
# use default values
python
-m
paddle.distributed.launch multi_process.py
# use specified values
cluster_node_ips
=
"127.0.0.1"
node_ip
=
"127.0.0.1"
distributed_args
=
"--cluster_node_ips
${
cluster_node_ips
}
--node_ip
${
node_ip
}
--selected_gpus=0,1"
python
-m
paddle.distributed.launch
${
distributed_args
}
multi_process.py
str1
=
"selected_gpus:0 worker_endpoints:['127.0.0.1:6170', '127.0.0.1:6171'] trainers_num:2 current_endpoint:127.0.0.1:6170 trainer_id:0"
str2
=
"selected_gpus:1 worker_endpoints:['127.0.0.1:6170', '127.0.0.1:6171'] trainers_num:2 current_endpoint:127.0.0.1:6171 trainer_id:1"
file
=
"multi_process.check.log"
if
!
grep
-q
"
$str1
"
"
$file
"
;
then
echo
"find trainer 0"
else
echo
"not find trainer 0"
exit
-1
fi
if
!
grep
-q
"
$str2
"
"
$file
"
;
then
echo
"find trainer 1"
else
echo
"not find trainer 0"
exit
-1
fi
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