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d5a66fd7
编写于
8月 10, 2020
作者:
D
danleifeng
提交者:
GitHub
8月 10, 2020
浏览文件
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电子邮件补丁
差异文件
【paddle.fleet】support multi-node cpu training for fleetrun (#26011)
* support multi-ps training mode for fleetrun; test=develop
上级
0067a2e4
变更
3
隐藏空白更改
内联
并排
Showing
3 changed file
with
171 addition
and
46 deletion
+171
-46
python/paddle/fleet/launch.py
python/paddle/fleet/launch.py
+125
-43
python/paddle/fleet/launch_utils.py
python/paddle/fleet/launch_utils.py
+38
-3
python/paddle/fluid/tests/unittests/test_fleet_launch.sh
python/paddle/fluid/tests/unittests/test_fleet_launch.sh
+8
-0
未找到文件。
python/paddle/fleet/launch.py
浏览文件 @
d5a66fd7
...
...
@@ -12,10 +12,10 @@
# See the License for the specific language governing permissions and
# limitations under the License.
"""
paddle.distributed.launch is a module that spawns multiple distributed
paddle.distributed.launch is a module that spawns multiple distributed
process on each training node for gpu training and cpu training.
Usage:
In both of single node training or multiple node training, this module
In both of single node training or multiple node training, this module
launch a process on each of the given gpu card or cpu machine.
GPU training:
1. for single node training with all visible gpu cards:
...
...
@@ -24,11 +24,10 @@ launch a process on each of the given gpu card or cpu machine.
fleetrun --gpus="0,1,2,3" your_training_py (arg1 arg2 and all others)
3. for multiple node training such as two node:192.168.0.16, 192.168.0.17
on 192.168.0.16:
fleetrun --ips="192.168.0.16,192.168.0.17"
--node_ip=192.168.0.16
\
fleetrun --ips="192.168.0.16,192.168.0.17"
\
your_training_py (arg1 arg2 and all others)
on 192.168.0.17:
fleetrun --ips="192.168.0.16,192.168.0.17"
\
--node_ip=192.168.0.17
\
your_training_py (arg1 arg2 and all others)
CPU training:
1. for single node training with multi servers and workers:
...
...
@@ -96,15 +95,14 @@ see: http://www.paddlepaddle.org/documentation/docs/zh/1.6/user_guides/howto/tra
"--servers"
,
type
=
str
,
default
=
""
,
help
=
"User defined servers ip:port"
)
parser
.
add_argument
(
"--workers"
,
type
=
str
,
default
=
""
,
help
=
"User defined workers ip:port"
)
parser
.
add_argument
(
"--worker_num"
,
type
=
int
,
default
=
2
,
help
=
"number of workers"
)
parser
.
add_argument
(
"--worker_num"
,
type
=
int
,
help
=
"number of workers"
)
parser
.
add_argument
(
"--server_num"
,
type
=
int
,
default
=
2
,
help
=
"number of servers"
)
parser
.
add_argument
(
"--server_num"
,
type
=
int
,
help
=
"number of servers"
)
parser
.
add_argument
(
"--log_dir"
,
type
=
str
,
default
=
"log"
,
help
=
"The path for each process's log.If it's not set, the log will printed to default pipe."
)
#positional
...
...
@@ -129,11 +127,11 @@ def get_cluster_from_args(args, gpus):
_
,
node_ip
=
get_host_name_ip
()
# node_ip = args.node_ip
assert
node_ip
in
node_ips
,
"Can't find your local ip {%s} in node_ips:{%s}"
\
assert
node_ip
in
node_ips
,
"Can't find your local ip {%s} in node_ips:
{%s}"
\
%
(
node_ip
,
node_ips
)
node_rank
=
node_ips
.
index
(
node_ip
)
logger
.
debug
(
"parsed from args:node_ips:{} node_ip:{} node_rank:{}"
.
format
(
logger
.
debug
(
"parsed from args:
node_ips:{} node_ip:{} node_rank:{}"
.
format
(
node_ips
,
node_ip
,
node_rank
))
free_ports
=
None
...
...
@@ -187,8 +185,11 @@ def launch_collective(args):
cluster
=
None
pod
=
None
start_port
=
6170
if
os
.
environ
.
get
(
'FLAGS_START_PORT'
)
is
not
None
:
start_port
=
os
.
environ
.
get
(
'FLAGS_START_PORT'
)
if
cloud_utils
.
use_paddlecloud
()
and
trainers_num
!=
1
:
cluster
,
pod
=
cloud_utils
.
get_cloud_cluster
(
args
.
ips
,
gpus
)
cluster
,
pod
=
cloud_utils
.
get_cloud_cluster
(
args
.
ips
,
gpus
,
start_port
)
logger
.
info
(
"get cluster from cloud:{}"
.
format
(
cluster
))
else
:
# trainers_num = 1 or not use paddlecloud ips="a,b"
...
...
@@ -213,11 +214,78 @@ def launch_collective(args):
def
launch_ps
(
args
):
worker_num
=
args
.
worker_num
server_num
=
args
.
server_num
start_port
=
6170
if
os
.
environ
.
get
(
'FLAGS_START_PORT'
)
is
not
None
:
start_port
=
os
.
environ
.
get
(
'FLAGS_START_PORT'
)
ports
=
None
if
args
.
server_num
:
server_num
=
args
.
server_num
ports
=
get_ports
(
server_num
,
0
)
server_endpoints
=
","
.
join
([
"127.0.0.1:"
+
str
(
x
)
for
x
in
ports
])
else
:
assert
args
.
servers
!=
""
,
"The setting of CPU mode must be either server_num or servers."
server_endpoints
=
args
.
servers
server_endpoints_ips
=
[
x
.
strip
().
split
(
":"
)[
0
]
for
x
in
server_endpoints
.
split
(
","
)
]
server_endpoints_port
=
[
x
.
strip
().
split
(
":"
)[
1
]
for
x
in
server_endpoints
.
split
(
","
)
]
server_num
=
len
(
server_endpoints_ips
)
if
args
.
worker_num
:
worker_num
=
args
.
worker_num
ports
=
get_ports
(
worker_num
,
server_num
)
worker_endpoints
=
","
.
join
([
"127.0.0.1:"
+
str
(
x
)
for
x
in
ports
])
else
:
assert
args
.
workers
!=
""
,
"The setting of CPU mode must be either worker_num or workers."
worker_endpoints
=
args
.
workers
worker_endpoints_ips
=
[
x
.
strip
().
split
(
":"
)[
0
]
for
x
in
worker_endpoints
.
split
(
","
)
]
worker_endpoints_port
=
[
x
.
strip
().
split
(
":"
)[
1
]
for
x
in
worker_endpoints
.
split
(
","
)
]
worker_num
=
len
(
worker_endpoints_ips
)
node_ips
=
list
(
set
(
server_endpoints_ips
+
worker_endpoints_ips
))
# local train
if
len
(
set
(
node_ips
))
==
1
:
current_node_ip
=
node_ips
[
0
]
else
:
_
,
current_node_ip
=
get_host_name_ip
()
assert
current_node_ip
in
node_ips
,
"Can't find your local ip {%s} in args.servers and args.workers ips: {%s}"
\
%
(
current_node_ip
,
node_ips
)
node_rank
=
node_ips
.
index
(
current_node_ip
)
logger
.
debug
(
"parsed from args: node_ips:{} current_node_ip:{} node_rank:{}, server_ports:{}"
.
format
(
node_ips
,
current_node_ip
,
node_rank
,
server_endpoints_port
))
cluster
=
Cluster
(
hdfs
=
None
)
server_rank
=
0
worker_rank
=
0
for
node_rank
,
ip
in
enumerate
(
node_ips
):
pod
=
Pod
()
pod
.
rank
=
node_rank
pod
.
addr
=
ip
for
i
in
range
(
len
(
server_endpoints_ips
)):
if
ip
==
server_endpoints_ips
[
i
]:
server
=
Trainer
()
server
.
endpoint
=
"%s:%s"
%
(
ip
,
server_endpoints_port
[
i
])
server
.
rank
=
server_rank
server_rank
+=
1
pod
.
servers
.
append
(
server
)
for
j
in
range
(
len
(
worker_endpoints_ips
)):
if
ip
==
worker_endpoints_ips
[
j
]:
worker
=
Trainer
()
worker
.
endpoint
=
"%s:%s"
%
(
ip
,
worker_endpoints_port
[
i
])
worker
.
rank
=
worker_rank
worker_rank
+=
1
pod
.
workers
.
append
(
worker
)
cluster
.
pods
.
append
(
pod
)
pod_rank
=
node_ips
.
index
(
current_node_ip
)
pod
=
cluster
.
pods
[
pod_rank
]
default_env
=
os
.
environ
.
copy
()
current_env
=
copy
.
copy
(
default_env
)
current_env
.
pop
(
"http_proxy"
,
None
)
...
...
@@ -225,68 +293,78 @@ def launch_ps(args):
procs
=
[]
cmds
=
[]
log_fns
=
[]
ports
=
range
(
start_port
,
start_port
+
server_num
,
1
)
default_endpoints
=
","
.
join
([
"127.0.0.1:"
+
str
(
x
)
for
x
in
ports
])
user_endpoints
=
""
if
args
.
servers
==
""
:
user_endpoints
=
default_endpoints
else
:
user_endpoints
=
args
.
servers
user_endpoints_ips
=
[
x
.
split
(
":"
)[
0
]
for
x
in
user_endpoints
.
split
(
","
)]
user_endpoints_port
=
[
x
.
split
(
":"
)[
1
]
for
x
in
user_endpoints
.
split
(
","
)]
for
i
in
range
(
server_num
):
for
idx
,
cur_server
in
enumerate
(
pod
.
servers
):
current_env
.
update
({
"PADDLE_PSERVERS_IP_PORT_LIST"
:
us
er_endpoints
,
"PADDLE_PORT"
:
user_endpoints_port
[
i
],
"PADDLE_PSERVERS_IP_PORT_LIST"
:
serv
er_endpoints
,
"PADDLE_PORT"
:
cur_server
.
endpoint
.
split
(
":"
)[
1
],
"TRAINING_ROLE"
:
"PSERVER"
,
"PADDLE_TRAINERS_NUM"
:
str
(
worker_num
),
"POD_IP"
:
user_endpoints_ips
[
i
]
"POD_IP"
:
cur_server
.
endpoint
.
split
(
":"
)[
0
]
})
cmd
=
[
sys
.
executable
,
"-u"
,
args
.
training_script
]
+
args
.
training_script_args
cmds
.
append
(
cmd
)
if
args
.
log_dir
is
not
None
:
os
.
system
(
"mkdir -p {}"
.
format
(
args
.
log_dir
))
fn
=
open
(
"%s/serverlog.%d"
%
(
args
.
log_dir
,
i
),
"w"
)
fn
=
open
(
"%s/serverlog.%d"
%
(
args
.
log_dir
,
i
dx
),
"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
(
worker_num
):
tp
=
TrainerProc
()
tp
.
proc
=
proc
tp
.
rank
=
cur_server
.
rank
tp
.
local_rank
=
idx
tp
.
log_fn
=
fn
tp
.
log_offset
=
0
if
fn
else
None
tp
.
cmd
=
cmd
procs
.
append
(
tp
)
for
idx
,
cur_worker
in
enumerate
(
pod
.
workers
):
current_env
.
update
({
"PADDLE_PSERVERS_IP_PORT_LIST"
:
us
er_endpoints
,
"PADDLE_PSERVERS_IP_PORT_LIST"
:
serv
er_endpoints
,
"PADDLE_TRAINERS_NUM"
:
str
(
worker_num
),
"TRAINING_ROLE"
:
"TRAINER"
,
"PADDLE_TRAINER_ID"
:
str
(
i
)
"PADDLE_TRAINER_ID"
:
str
(
cur_worker
.
rank
)
})
cmd
=
[
sys
.
executable
,
"-u"
,
args
.
training_script
]
+
args
.
training_script_args
cmds
.
append
(
cmd
)
if
args
.
log_dir
is
not
None
:
os
.
system
(
"mkdir -p {}"
.
format
(
args
.
log_dir
))
fn
=
open
(
"%s/workerlog.%d"
%
(
args
.
log_dir
,
i
),
"w"
)
fn
=
open
(
"%s/workerlog.%d"
%
(
args
.
log_dir
,
i
dx
),
"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
)
tp
=
TrainerProc
()
tp
.
proc
=
proc
tp
.
rank
=
cur_worker
.
rank
tp
.
local_rank
=
idx
tp
.
log_fn
=
fn
tp
.
log_offset
=
0
if
fn
else
None
tp
.
cmd
=
cmd
procs
.
append
(
tp
)
# only wait worker to finish here
for
i
,
proc
in
enumerate
(
procs
):
if
i
<
server_num
:
if
i
<
len
(
pod
.
servers
)
:
continue
procs
[
i
].
wait
()
procs
[
i
].
proc
.
wait
()
if
len
(
log_fns
)
>
0
:
log_fns
[
i
].
close
()
print
(
"all workers exit, going to finish parameter server"
,
file
=
sys
.
stderr
)
for
i
in
range
(
server_num
):
for
i
in
range
(
len
(
pod
.
servers
)
):
if
len
(
log_fns
)
>
0
:
log_fns
[
i
].
close
()
procs
[
i
].
terminate
()
procs
[
i
].
proc
.
terminate
()
print
(
"all parameter server are killed"
,
file
=
sys
.
stderr
)
...
...
@@ -303,11 +381,15 @@ def launch():
co_arg
for
co_arg
in
collective_args
if
co_arg
in
" "
.
join
(
sys
.
argv
[
1
:
-
1
])
]
if
len
(
has_ps_args
)
>
0
or
fluid
.
core
.
get_cuda_device_count
()
==
0
:
logger
.
info
(
"Run cpu parameter-sever mode."
)
cuda_device_num
=
fluid
.
core
.
get_cuda_device_count
()
if
len
(
has_ps_args
)
>
0
or
cuda_device_num
==
0
:
logger
.
info
(
"Run parameter-sever cpu mode. pserver args:{}, cuda count:{}"
.
format
(
has_ps_args
,
cuda_device_num
))
launch_ps
(
args
)
elif
len
(
has_collective_args
)
>
0
:
logger
.
info
(
"Run gpu collective mode."
)
logger
.
info
(
"Run collective gpu mode. gpu args:{}, cuda count:{}"
.
format
(
has_collective_args
,
cuda_device_num
))
launch_collective
(
args
)
else
:
logger
.
warning
(
...
...
python/paddle/fleet/launch_utils.py
浏览文件 @
d5a66fd7
...
...
@@ -142,12 +142,16 @@ class Pod(object):
self
.
addr
=
None
self
.
port
=
None
self
.
trainers
=
[]
self
.
servers
=
[]
self
.
workers
=
[]
self
.
gpus
=
[]
def
__str__
(
self
):
return
"rank:{} id:{} addr:{} port:{} visible_gpu:{} trainers:{}"
.
format
(
self
.
rank
,
self
.
id
,
self
.
addr
,
self
.
port
,
self
.
gpus
,
[
str
(
t
)
for
t
in
self
.
trainers
])
return
"rank:{} id:{} addr:{} port:{} visible_gpu:{} trainers:{} servers:{}
\
workers:{}"
.
format
(
self
.
rank
,
self
.
id
,
self
.
addr
,
self
.
port
,
self
.
gpus
,
[
str
(
t
)
for
t
in
self
.
trainers
],
[
str
(
s
)
for
s
in
self
.
servers
],
[
str
(
w
)
for
w
in
self
.
workers
])
def
__eq__
(
self
,
pod
):
if
self
.
rank
!=
pod
.
rank
or
\
...
...
@@ -168,6 +172,26 @@ class Pod(object):
pod
.
trainers
[
i
]))
return
False
if
len
(
self
.
servers
)
!=
len
(
pod
.
servers
):
logger
.
debug
(
"servers {} != {}"
.
format
(
self
.
servers
,
pod
.
servers
))
return
False
for
i
in
range
(
len
(
self
.
servers
)):
if
self
.
servers
[
i
]
!=
pod
.
servers
[
i
]:
logger
.
debug
(
"servers {} != {}"
.
format
(
self
.
servers
[
i
],
pod
.
servers
[
i
]))
return
False
if
len
(
self
.
workers
)
!=
len
(
pod
.
workers
):
logger
.
debug
(
"workers {} != {}"
.
format
(
self
.
workers
,
pod
.
workers
))
return
False
for
i
in
range
(
len
(
self
.
workers
)):
if
self
.
workers
[
i
]
!=
pod
.
workers
[
i
]:
logger
.
debug
(
"workers {} != {}"
.
format
(
self
.
workers
[
i
],
pod
.
workers
[
i
]))
return
False
return
True
def
__ne__
(
self
,
pod
):
...
...
@@ -303,6 +327,17 @@ def find_free_ports(num):
return
None
def
get_ports
(
num
,
offset
):
if
os
.
environ
.
get
(
'FLAGS_START_PORT'
)
is
None
:
ports
=
find_free_ports
(
num
)
if
ports
is
not
None
:
ports
=
list
(
ports
)
else
:
start_port
=
os
.
environ
.
get
(
'FLAGS_START_PORT'
)
ports
=
range
(
start_port
+
offset
,
start_port
+
offset
+
num
,
1
)
return
ports
class
TrainerProc
(
object
):
def
__init__
(
self
):
self
.
proc
=
None
...
...
python/paddle/fluid/tests/unittests/test_fleet_launch.sh
浏览文件 @
d5a66fd7
...
...
@@ -10,6 +10,14 @@ function test_launch_ps(){
echo
"test pserver launch failed"
exit
-1
fi
fleetrun
--servers
=
"120.0.0.1:6780,120.0.0.1:6781"
--workers
=
"120.0.0.1:6782,120.0.0.1:6783"
fleet_ps_training.py 2> ut.elog
if
grep
-q
"server are killed"
ut.elog
;
then
echo
"test pserver launch succeed"
else
echo
"test pserver launch failed"
exit
-1
fi
}
if
[[
${
WITH_GPU
}
==
"OFF"
]]
;
then
...
...
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