未验证 提交 822ae26f 编写于 作者: N nbcc 提交者: GitHub

Merge pull request #302 from Meiyim/dev

+ missing script
# 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.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from __future__ import absolute_import
import sys
import subprocess
import os
import six
import copy
import argparse
import time
import logging
from utils.args import ArgumentGroup, print_arguments, prepare_logger
from finetune_args import parser as worker_parser
# yapf: disable
parser = argparse.ArgumentParser(__doc__)
multip_g = ArgumentGroup(parser, "multiprocessing",
"start paddle training using multi-processing mode.")
multip_g.add_arg("node_ips", str, None,
"paddle trainer ips")
multip_g.add_arg("node_id", int, 0,
"the trainer id of the node for multi-node distributed training.")
multip_g.add_arg("print_config", bool, True,
"print the config of multi-processing mode.")
multip_g.add_arg("current_node_ip", str, None,
"the ip of current node.")
multip_g.add_arg("split_log_path", str, "log",
"log path for each trainer.")
multip_g.add_arg("log_prefix", str, "",
"the prefix name of job log.")
multip_g.add_arg("nproc_per_node", int, 8,
"the number of process to use on each node.")
multip_g.add_arg("selected_gpus", str, "0,1,2,3,4,5,6,7",
"the gpus selected to use.")
multip_g.add_arg("training_script", str, None, "the program/script to be lauched "
"in parallel followed by all the arguments", positional_arg=True)
multip_g.add_arg("training_script_args", str, None,
"training script args", positional_arg=True, nargs=argparse.REMAINDER)
# yapf: enable
log = logging.getLogger()
def start_procs(args):
procs = []
log_fns = []
default_env = os.environ.copy()
node_id = args.node_id
node_ips = [x.strip() for x in args.node_ips.split(',')]
current_ip = args.current_node_ip
if args.current_node_ip is None:
assert len(node_ips) == 1
current_ip = node_ips[0]
log.info(current_ip)
num_nodes = len(node_ips)
selected_gpus = [x.strip() for x in args.selected_gpus.split(',')]
selected_gpu_num = len(selected_gpus)
all_trainer_endpoints = ""
for ip in node_ips:
for i in range(args.nproc_per_node):
if all_trainer_endpoints != "":
all_trainer_endpoints += ","
all_trainer_endpoints += "%s:617%d" % (ip, i)
nranks = num_nodes * args.nproc_per_node
gpus_per_proc = args.nproc_per_node % selected_gpu_num
if gpus_per_proc == 0:
gpus_per_proc = selected_gpu_num // args.nproc_per_node
else:
gpus_per_proc = selected_gpu_num // args.nproc_per_node + 1
selected_gpus_per_proc = [selected_gpus[i:i + gpus_per_proc] for i in range(0, len(selected_gpus), gpus_per_proc)]
if args.print_config:
log.info("all_trainer_endpoints: %s"
", node_id: %s"
", current_ip: %s"
", num_nodes: %s"
", node_ips: %s"
", gpus_per_proc: %s"
", selected_gpus_per_proc: %s"
", nranks: %s" % (
all_trainer_endpoints,
node_id,
current_ip,
num_nodes,
node_ips,
gpus_per_proc,
selected_gpus_per_proc,
nranks))
current_env = copy.copy(default_env)
procs = []
cmds = []
log_fns = []
for i in range(0, args.nproc_per_node):
trainer_id = node_id * args.nproc_per_node + i
assert current_ip is not None
current_env.update({
"FLAGS_selected_gpus": "%s" % ",".join([str(s) for s in selected_gpus_per_proc[i]]),
"PADDLE_TRAINER_ID" : "%d" % trainer_id,
"PADDLE_CURRENT_ENDPOINT": "%s:617%d" % (current_ip, i),
"PADDLE_TRAINERS_NUM": "%d" % nranks,
"PADDLE_TRAINER_ENDPOINTS": all_trainer_endpoints,
"PADDLE_NODES_NUM": "%d" % num_nodes
})
try:
idx = args.training_script_args.index('--is_distributed')
args.training_script_args[idx + 1] = 'true'
except ValueError:
args.training_script_args += ['--is_distributed', 'true']
cmd = [sys.executable, "-u",
args.training_script] + args.training_script_args
cmds.append(cmd)
if args.split_log_path:
fn = open("%s/%sjob.log.%d" % (args.split_log_path, args.log_prefix, trainer_id), "a")
log_fns.append(fn)
process = subprocess.Popen(cmd, env=current_env, stdout=fn, stderr=fn)
else:
process = subprocess.Popen(cmd, env=current_env)
log.info('subprocess launched')
procs.append(process)
try:
for i in range(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])
else:
log.info("proc %d finsh" % i)
except KeyboardInterrupt as e:
for p in procs:
log.info('killing %s' % p)
p.terminate()
def main(args):
if args.print_config:
print_arguments(args)
start_procs(args)
if __name__ == "__main__":
prepare_logger(log)
lanch_args = parser.parse_args()
finetuning_args = worker_parser.parse_args(
lanch_args.training_script_args)
init_path = finetuning_args.init_pretraining_params
log.info("init model: %s" % init_path)
if not finetuning_args.use_fp16:
os.system('rename .master "" ' + init_path + '/*.master')
main(lanch_args)
# 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.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from __future__ import absolute_import
from __future__ import division
import sys
import subprocess
import os
import six
import copy
import argparse
import time
import logging
from utils.args import ArgumentGroup, print_arguments, prepare_logger
from pretrain_args import parser as worker_parser
# yapf: disable
parser = argparse.ArgumentParser(__doc__)
multip_g = ArgumentGroup(parser, "multiprocessing",
"start paddle training using multi-processing mode.")
multip_g.add_arg("node_ips", str, None,
"paddle trainer ips")
multip_g.add_arg("node_id", int, 0,
"the trainer id of the node for multi-node distributed training.")
multip_g.add_arg("print_config", bool, True,
"print the config of multi-processing mode.")
multip_g.add_arg("current_node_ip", str, None,
"the ip of current node.")
multip_g.add_arg("split_log_path", str, "./log",
"log path for each trainer.")
multip_g.add_arg("log_prefix", str, "",
"the prefix name of job log.")
multip_g.add_arg("nproc_per_node", int, 8,
"the number of process to use on each node.")
multip_g.add_arg("selected_gpus", str, "0,1,2,3,4,5,6,7",
"the gpus selected to use.")
multip_g.add_arg("training_script", str, None, "the program/script to be lauched "
"in parallel followed by all the arguments", positional_arg=True)
multip_g.add_arg("training_script_args", str, None,
"training script args", positional_arg=True, nargs=argparse.REMAINDER)
# yapf: enable
log = logging.getLogger()
def start_procs(args):
procs = []
log_fns = []
default_env = os.environ.copy()
node_id = args.node_id
node_ips = [x.strip() for x in args.node_ips.split(',')]
current_ip = args.current_node_ip
if args.current_node_ip is None:
assert len(node_ips) == 1
current_ip = node_ips[0]
log.info(current_ip)
num_nodes = len(node_ips)
selected_gpus = [x.strip() for x in args.selected_gpus.split(',')]
selected_gpu_num = len(selected_gpus)
all_trainer_endpoints = ""
for ip in node_ips:
for i in range(args.nproc_per_node):
if all_trainer_endpoints != "":
all_trainer_endpoints += ","
all_trainer_endpoints += "%s:617%d" % (ip, i)
nranks = num_nodes * args.nproc_per_node
gpus_per_proc = args.nproc_per_node % selected_gpu_num
if gpus_per_proc == 0:
gpus_per_proc = selected_gpu_num // args.nproc_per_node
else:
gpus_per_proc = selected_gpu_num // args.nproc_per_node + 1
log.info(gpus_per_proc)
selected_gpus_per_proc = [selected_gpus[i:i + gpus_per_proc] for i in range(0, len(selected_gpus), gpus_per_proc)]
if args.print_config:
log.info("all_trainer_endpoints: %s"
", node_id: %s"
", current_ip: %s"
", num_nodes: %s"
", node_ips: %s"
", gpus_per_proc: %s"
", selected_gpus_per_proc: %s"
", nranks: %s" % (
all_trainer_endpoints,
node_id,
current_ip,
num_nodes,
node_ips,
gpus_per_proc,
selected_gpus_per_proc,
nranks))
current_env = copy.copy(default_env)
procs = []
cmds = []
log_fns = []
for i in range(0, args.nproc_per_node):
trainer_id = node_id * args.nproc_per_node + i
current_env.update({
"FLAGS_selected_gpus": "%s" % ",".join([str(s) for s in selected_gpus_per_proc[i]]),
"PADDLE_TRAINER_ID" : "%d" % trainer_id,
"PADDLE_CURRENT_ENDPOINT": "%s:617%d" % (current_ip, i),
"PADDLE_TRAINERS_NUM": "%d" % nranks,
"PADDLE_TRAINER_ENDPOINTS": all_trainer_endpoints,
"PADDLE_NODES_NUM": "%d" % num_nodes
})
try:
idx = args.training_script_args.index('--is_distributed')
args.training_script_args[idx + 1] = 'true'
except ValueError:
args.training_script_args += ['--is_distributed', 'true']
cmd = [sys.executable, "-u",
args.training_script] + args.training_script_args
cmds.append(cmd)
if args.split_log_path:
fn = open("%s/%sjob.log.%d" % (args.split_log_path, args.log_prefix, trainer_id), "a")
log_fns.append(fn)
process = subprocess.Popen(cmd, env=current_env, stdout=fn, stderr=fn)
else:
process = subprocess.Popen(cmd, env=current_env)
log.info('subprocess launched')
procs.append(process)
try:
for i in range(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])
else:
log.info("proc %d finsh" % i)
except KeyboardInterrupt as e:
for p in procs:
log.info('killing %s' % p)
p.terminate()
def main(args):
if args.print_config:
print_arguments(args)
start_procs(args)
if __name__ == "__main__":
prepare_logger(log)
lanch_args = parser.parse_args()
pretraining_args = worker_parser.parse_args(
lanch_args.training_script_args)
init_path = pretraining_args.init_checkpoint
if init_path and not pretraining_args.use_fp16:
os.system('rename .master "" ' + init_path + '/*.master')
main(lanch_args)
......@@ -34,7 +34,6 @@ from batching import pad_batch_data
log = logging.getLogger(__name__)
if six.PY3:
from itertools import accumulate
import io
sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8')
sys.stderr = io.TextIOWrapper(sys.stderr.buffer, encoding='utf-8')
......
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