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spawn.py
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spawn.py
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# Copyright (c) 2020 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 print_function, division
import multiprocessing
import os
import signal
import six
import sys
import warnings
from paddle.distributed.utils import _print_arguments, _prepare_trainer_env, get_host_name_ip
from paddle.distributed.cloud_utils import get_cluster_and_pod
from paddle.distributed.fleet.cloud_utils import use_paddlecloud
from paddle.device import get_device
# deprecated module import
from paddle.fluid import core
from paddle.fluid.framework import _cpu_num, set_flags
class ParallelEnvArgs(object):
def __init__(self):
# Paddle cluster nodes ips, such as 192.168.0.16,192.168.0.17..
self.cluster_node_ips = None
# The current node ip.
self.node_ip = None
# whether to use paddlecloud platform to run your multi-process job.
# If false, no need to set this argument.
self.use_paddlecloud = None
# The trainer's started port on a single node
self.started_port = None
# Print the config or not
self.print_config = True
# It's for gpu training and the training process will run
# on the selected_gpus, each process is bound to a single GPU.
# And if it's not set, this module will use all the gpu cards
# for training.
self.selected_gpus = None
def _py_supported_check():
if not sys.version_info >= (3, 4):
raise RuntimeError(
"Use `paddle.distributed.spawn` to start parallel training "
"requires python version greater than 3.4, if your python "
"is lower than this version, please use "
"`paddle.distributed.launch` instead.")
def _options_valid_check(options):
# `print_config` keeped as a debug options, not show to users
supported_options = ['start_method', 'ips', 'gpus', 'print_config']
deprecated_options = [
'selected_gpus', 'started_port', 'cluster_node_ips', 'node_ip',
'use_paddlecloud'
]
for key in options:
if key not in supported_options:
if key in deprecated_options:
warnings.warn(
"The config option (%s) of `paddle.distributed.spawn` is deprecated. "
"Please use the latest config options stated in the `spawn` API documentation."
% key, DeprecationWarning)
else:
raise ValueError(
"The config option (%s) of `paddle.distributed.spawn` is not supported."
% key)
def _get_node_ip(ips):
node_ip = None
node_ips = [x.strip() for x in ips.split(',')]
if len(node_ips) == 1:
node_ip = node_ips[0]
else:
_, node_ip = get_host_name_ip()
return node_ip
def _get_subprocess_env_list(nprocs, options):
# contruct processes env list
processes_env_list = []
# get args from kwargs
args = ParallelEnvArgs()
# deal with `ips`
args.cluster_node_ips = options.get('ips', None)
if args.cluster_node_ips is None:
args.cluster_node_ips = options.get('cluster_node_ips', None)
if args.cluster_node_ips is None:
args.cluster_node_ips = "127.0.0.1"
# deal with `gpus`
# set default selected gpus
# e.g. if the nprocs is 4, the selected gpus is "0,1,2,3"
# NOTE(chenweihang): [ why not use FLAGS_selected_gpus directly? ]
# because the FLAGS_selected_gpus may be used in other place,
# if we set FLAGS_selected_gpus to be `0,1,2,3`, it may cause error
# when using `ParallelEnv`
# NOTE(chenweihang): use absolute gpu card id
args.selected_gpus = options.get('gpus', None)
if args.selected_gpus is None:
args.selected_gpus = options.get('selected_gpus', None)
env_devices = os.getenv("CUDA_VISIBLE_DEVICES", None)
if env_devices is None or env_devices == "":
env_devices_list = [
str(x) for x in six.moves.range(core.get_cuda_device_count())
]
else:
env_devices_list = env_devices.split(',')
if args.selected_gpus is None:
if len(env_devices_list) < nprocs:
raise RuntimeError(
"the number of visible devices(%d) is less than the number "
"of spawn processes(%d), please ensure that the correct "
"`nprocs` argument is passed or the environment variable "
"`CUDA_VISIBLE_DEVICES` is correctly configured." %
(len(env_devices_list), nprocs))
args.selected_gpus = ",".join(
[str(env_devices_list[x]) for x in range(0, nprocs)])
else:
selected_gpu_list = args.selected_gpus.split(',')
if len(selected_gpu_list) != nprocs:
raise ValueError(
"The number of selected gpus(%s) is not equal to "
"the number of spawn processes(%d), please ensure that the "
"correct `nprocs` and `gpus` arguments are passed." %
(len(selected_gpu_list), nprocs))
for card_id in selected_gpu_list:
if card_id not in env_devices_list:
raise ValueError("The selected gpu card %s cannot found in "
"CUDA_VISIBLE_DEVICES (%s)." %
(card_id, ",".join(env_devices_list)))
# set other inner args
args.node_ip = options.get('node_ip', None)
if args.node_ip is None:
args.node_ip = _get_node_ip(args.cluster_node_ips)
args.started_port = options.get('started_port', None)
args.use_paddlecloud = options.get('use_paddlecloud', None)
if args.use_paddlecloud is None:
args.use_paddlecloud = use_paddlecloud()
# get cluster and pod config
cluster, pod = get_cluster_and_pod(args)
# prepare subprocess env list
for trainer in pod.trainers:
processes_env_list.append(_prepare_trainer_env(cluster, trainer))
# [Debug] print config
args.print_config = options.get('print_config', False)
if args.print_config:
_print_arguments(args)
return processes_env_list
def _remove_risky_env():
# remove useless env vars
# no copy, each process will hold env vars itself
os.environ.pop("http_proxy", None)
os.environ.pop("https_proxy", None)
def _set_trainer_env(env_dict):
# NOTE(chenweihang): [ Why need set FLAGS_selected_gpus here? ]
# When the child process starts, it will inherit the configuration of the
# main process and set the FLAGS once, but the environment variable has
# not been set at this time, which leads to the FLAGS_selected_gpus
# is keep same with mainprocess(usually empty), so manually update the flags here
set_flags({'FLAGS_selected_gpus': env_dict['FLAGS_selected_gpus']})
for var_name in env_dict:
os.environ[var_name] = env_dict[var_name]
def _func_wrapper(func, args, error_queue, return_queue, env_dict):
try:
# config subprocess environment variables
_remove_risky_env()
_set_trainer_env(env_dict)
# execute function
result = func(*args)
# record function return value
return_queue.put(result)
except KeyboardInterrupt:
pass
except Exception:
import traceback
error_queue.put(traceback.format_exc())
sys.exit(1)
class MultiprocessContext(object):
def __init__(self, processes, error_queues, return_queues):
_py_supported_check()
self.error_queues = error_queues
# NOTE(chenweihang): The `spawn` method is mainly used
# to wrap the outermost execution function of the program for
# parallel execution. Generally, the return value is not concerned,
# but if the user needs to obtain the return value, users can get
# the return result of each process from context.return_queues
self.return_queues = return_queues
self.processes = processes
self.sentinels = {
process.sentinel: index
for index, process in enumerate(processes)
}
def join(self, timeout=None):
if len(self.sentinels) == 0:
return True
ready = multiprocessing.connection.wait(
self.sentinels.keys(), timeout=timeout)
error_index = None
for sentinel in ready:
index = self.sentinels.pop(sentinel)
process = self.processes[index]
process.join()
if process.exitcode != 0:
error_index = index
break
if error_index is None:
return len(self.sentinels) == 0
for process in self.processes:
if process.is_alive():
process.terminate()
process.join()
self._throw_exception(error_index)
def _throw_exception(self, error_index):
if self.error_queues[error_index].empty():
exitcode = self.processes[error_index].exitcode
if exitcode < 0:
name = signal.Signals(-exitcode).name
raise Exception("Process %d terminated with signal %s." %
(error_index, name))
else:
raise Exception("Process %d terminated with exit code %d." & (
error_index, exitcode))
original_trace = self.error_queues[error_index].get()
msg = "\n\n----------------------------------------------\n" \
"Process %d terminated with the following error:\n" \
"----------------------------------------------\n\n" % error_index
msg += original_trace
raise Exception(msg)
def spawn(func, args=(), nprocs=-1, join=True, daemon=False, **options):
"""
Start multiple processes with ``spawn`` method for parallel training.
.. note::
``spawn`` now only supports GPU collective mode.
Args:
func (function): The target function is called by spawned process.
This function need to be able to pickled, so it must be defined
at the top level of a module.
args (tuple, optional): Arguments passed to ``func``.
nprocs (int, optional): Number of processed to start. Default: -1.
when nprocs is -1, the available device will be obtained from
the environment variable when the model is executed: If use GPU,
the currently available device ID is obtained from the environment
variable CUDA_VISIBLE_DEVICES; If use CPU, the currently available
CPU number is obtained from the environment variable CPU_NUM.
For example, export CPU_NUM=4, if the environment variable is not set,
the spawn method will add default value to the environment variable
and set its value to 1.
join (bool, optional): Perform a blocking join on all spawned processes.
Default: True.
daemon (bool, optional): The spawned processes' daemon flag. Default: False.
**options(dict, optional): Other initial parallel execution environment
configuration options. The following options are currently supported:
(1) start_method (string): the way to start a process.
The start method can be ``spawn`` , ``fork`` , ``forkserver`` .
Because the CUDA runtime does not support the ``fork`` start method,
when use CUDA in subprocesses, we should start process by ``spawn``
or ``forkserver`` method. Default: "spawn" ;
(2) gpus (string): The training process will run on the
selected gpus, such as "0,1,2,3". Default: None;
(3) ips (string): Paddle cluster nodes ips, such as
"192.168.0.16,192.168.0.17". Default: "127.0.0.1" .
Returns:
``MultiprocessContext`` object, it hold the spawned processes.
Examples:
.. code-block:: python
from __future__ import print_function
import paddle
import paddle.nn as nn
import paddle.optimizer as opt
import paddle.distributed as dist
class LinearNet(nn.Layer):
def __init__(self):
super(LinearNet, self).__init__()
self._linear1 = nn.Linear(10, 10)
self._linear2 = nn.Linear(10, 1)
def forward(self, x):
return self._linear2(self._linear1(x))
def train(print_result=False):
# 1. initialize parallel environment
dist.init_parallel_env()
# 2. create data parallel layer & optimizer
layer = LinearNet()
dp_layer = paddle.DataParallel(layer)
loss_fn = nn.MSELoss()
adam = opt.Adam(
learning_rate=0.001, parameters=dp_layer.parameters())
# 3. run layer
inputs = paddle.randn([10, 10], 'float32')
outputs = dp_layer(inputs)
labels = paddle.randn([10, 1], 'float32')
loss = loss_fn(outputs, labels)
if print_result is True:
print("loss:", loss.numpy())
loss.backward()
adam.step()
adam.clear_grad()
# Usage 1: only pass function.
# If your training method no need any argument, and
# use all visible devices for parallel training.
if __name__ == '__main__':
dist.spawn(train)
# Usage 2: pass function and arguments.
# If your training method need some arguments, and
# use all visible devices for parallel training.
if __name__ == '__main__':
dist.spawn(train, args=(True,))
# Usage 3: pass function, arguments and nprocs.
# If your training method need some arguments, and
# only use part of visible devices for parallel training.
# If your machine hold 8 cards {0,1,2,3,4,5,6,7},
# this case will use cards {0,1}; If you set
# CUDA_VISIBLE_DEVICES=4,5,6,7, this case will use
# cards {4,5}
if __name__ == '__main__':
dist.spawn(train, args=(True,), nprocs=2)
# Usage 4: pass function, arguments, nprocs and gpus.
# If your training method need some arguments, and
# only use part of visible devices for parallel training,
# but you can't set your machine's environment variable
# CUDA_VISIBLE_DEVICES, such as it is None or all cards
# {0,1,2,3,4,5,6,7}, you can pass `gpus` to
# select the GPU cards you want to use. For example,
# this case will use cards {4,5} if your machine hold 8 cards.
if __name__ == '__main__':
dist.spawn(train, args=(True,), nprocs=2, gpus='4,5')
"""
# NOTE(chenweihang): [ why only supports python3.4+ ? ]
# Python supported setting the child process startup method
# since 3.4. The previous version can only use the default startup
# method, while the default startup method of Unix is fork, which
# cannot support CUDA runtime multi-process
_py_supported_check()
# Give an error hint when the users enter a configuration option
# that does not exist
_options_valid_check(options)
# get default nprocs
if nprocs == -1:
device = get_device()
if device == 'cpu':
# TODO: not supports cpu parallel now
nprocs = _cpu_num()
else:
nprocs = core.get_cuda_device_count()
# NOTE(chenweihang): [ why need get cluster info before run? ]
# when using `paddle.distributed.spawn` start parallel training,
# we should get cluster info before starting subprocess, and pass
# correct info to each subprocess
procs_env_list = _get_subprocess_env_list(nprocs, options)
# start processes
# NOTE(chenweihang): [ why default start method is spawn? ]
# The CUDA runtime does not support the fork start method,
# either the spawn or forkserver start method are required
# to use CUDA in subprocesses.
start_method = options.get('start_method', None)
if start_method is None:
start_method = 'spawn'
mp = multiprocessing.get_context(start_method)
error_queues = []
return_queues = []
processes = []
for i in range(nprocs):
error_queue = mp.SimpleQueue()
return_queue = mp.SimpleQueue()
process = mp.Process(
target=_func_wrapper,
args=(func, args, error_queue, return_queue, procs_env_list[i]))
process.daemon = daemon
process.start()
error_queues.append(error_queue)
return_queues.append(return_queue)
processes.append(process)
context = MultiprocessContext(processes, error_queues, return_queues)
if not join:
return context
# loop until all process end
while not context.join():
pass
# finally return context
return context