Source code for torch.multiprocessing.spawn
# mypy: allow-untyped-defs
import logging
import multiprocessing
import multiprocessing.connection
import os
import pickle
import signal
import sys
import tempfile
import time
import warnings
from concurrent.futures import as_completed, ThreadPoolExecutor
from typing import Optional
from . import _prctl_pr_set_pdeathsig # type: ignore[attr-defined]
ENV_VAR_PARALLEL_START = "TORCH_MP_PARALLEL_START"
log = logging.getLogger(__name__)
__all__ = [
"ProcessContext",
"ProcessException",
"ProcessExitedException",
"ProcessRaisedException",
"spawn",
"SpawnContext",
"start_processes",
]
class ProcessException(Exception):
__slots__ = ["error_index", "error_pid"]
def __init__(self, msg: str, error_index: int, pid: int):
super().__init__(msg)
self.msg = msg
self.error_index = error_index
self.pid = pid
def __reduce__(self):
return type(self), (self.msg, self.error_index, self.pid)
class ProcessRaisedException(ProcessException):
"""Exception raised when a process failed due to an exception raised by the code."""
def __init__(
self,
msg: str,
error_index: int,
error_pid: int,
):
super().__init__(msg, error_index, error_pid)
class ProcessExitedException(ProcessException):
"""Exception raised when a process failed due to signal or exited with a specific code."""
__slots__ = ["exit_code"]
def __init__(
self,
msg: str,
error_index: int,
error_pid: int,
exit_code: int,
signal_name: Optional[str] = None,
):
super().__init__(msg, error_index, error_pid)
self.exit_code = exit_code
self.signal_name = signal_name
def __reduce__(self):
return (
type(self),
(self.msg, self.error_index, self.pid, self.exit_code, self.signal_name),
)
def _wrap(fn, i, args, error_file):
# prctl(2) is a Linux specific system call.
# On other systems the following function call has no effect.
# This is set to ensure that non-daemonic child processes can
# terminate if their parent terminates before they do.
_prctl_pr_set_pdeathsig(signal.SIGINT)
try:
fn(i, *args)
except KeyboardInterrupt:
pass # SIGINT; Killed by parent, do nothing
except Exception:
# Propagate exception to parent process, keeping original traceback
import traceback
with open(error_file, "wb") as fh:
pickle.dump(traceback.format_exc(), fh)
sys.exit(1)
class ProcessContext:
def __init__(self, processes, error_files):
self.error_files = error_files
self.processes = processes
self.sentinels = {
process.sentinel: index for index, process in enumerate(processes)
}
def pids(self):
return [int(process.pid) for process in self.processes]
def join(self, timeout=None):
r"""Join one or more processes within spawn context.
Attempt to join one or more processes in this spawn context.
If one of them exited with a non-zero exit status, this function
kills the remaining processes and raises an exception with the cause
of the first process exiting.
Returns ``True`` if all processes have been joined successfully,
``False`` if there are more processes that need to be joined.
Args:
timeout (float): Wait this long before giving up on waiting.
"""
# Ensure this function can be called even when we're done.
if len(self.sentinels) == 0:
return True
# Wait for any process to fail or all of them to succeed.
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
# Return if there was no error.
if error_index is None:
# Return whether or not all processes have been joined.
return len(self.sentinels) == 0
# Assume failure. Terminate processes that are still alive.
# Try SIGTERM then SIGKILL if the process isn't going down.
# The reason is related to python signal handling is limited
# to main thread and if that is in c/c++ land and stuck it won't
# to handle it. We have seen processes getting stuck not handling
# SIGTERM for the above reason.
timeout: int = 30
for process in self.processes:
if process.is_alive():
log.warning("Terminating process %s via signal SIGTERM", process.pid)
process.terminate()
end = time.monotonic() + timeout
for process in self.processes:
time_to_wait = max(0, end - time.monotonic())
process.join(time_to_wait)
for process in self.processes:
if process.is_alive():
log.warning(
"Unable to shutdown process %s via SIGTERM , forcefully exiting via SIGKILL",
process.pid,
)
process.kill()
process.join()
# The file will only be created if the process crashed.
failed_process = self.processes[error_index]
if not os.access(self.error_files[error_index], os.R_OK):
exitcode = self.processes[error_index].exitcode
if exitcode < 0:
try:
name = signal.Signals(-exitcode).name
except ValueError:
name = f"<Unknown signal {-exitcode}>"
raise ProcessExitedException(
"process %d terminated with signal %s" % (error_index, name),
error_index=error_index,
error_pid=failed_process.pid,
exit_code=exitcode,
signal_name=name,
)
else:
raise ProcessExitedException(
"process %d terminated with exit code %d" % (error_index, exitcode),
error_index=error_index,
error_pid=failed_process.pid,
exit_code=exitcode,
)
with open(self.error_files[error_index], "rb") as fh:
original_trace = pickle.load(fh)
msg = "\n\n-- Process %d terminated with the following error:\n" % error_index
msg += original_trace
raise ProcessRaisedException(msg, error_index, failed_process.pid)
[docs]class SpawnContext(ProcessContext):
def __init__(self, processes, error_files):
warnings.warn("SpawnContext is renamed to ProcessContext since 1.4 release.")
super().__init__(processes, error_files)
# Note: [start_processes]
# mp.start_processes handles both start_method='spawn' and 'fork'. It's supposed to be a
# more generalized API than mp.spawn. Currently we only document mp.spawn as it's the
# CUDA compatible start_method. However, in environments like Ipython notebooks, 'fork'
# works better than 'spawn'. Every helper function we created for mp.spawn is indeed
# general enough, and backends like XLA can reuse them in Colab notebooks as well.
# Currently we only add this API first, we can consider adding it to documentation as
# needed in the future.
def start_processes(
fn,
args=(),
nprocs=1,
join=True,
daemon=False,
start_method="spawn",
):
# To speed up performance in certain cases (see https://github.com/pytorch/pytorch/issues/133010),
# this func will start processes in parallel if start_method is 'forkserver'.
# Please opt in to this perf optimization by setting env var (TORCH_MP_PARALLEL_START) to 1.
# todo: investigate why spawn does not work with threadpool and raises SIGINT
if (
start_method == "forkserver"
and os.environ.get(ENV_VAR_PARALLEL_START, "0") == "1"
):
log.info("Starting processes in parallel.")
start_parallel = True
else:
# Set env var TORCH_MP_PARALLEL_START to 0 to disable parallel start
start_parallel = False
mp = multiprocessing.get_context(start_method)
error_files = [None] * nprocs
processes = [None] * nprocs
def start_process(i):
# Each process is assigned a file to write tracebacks to. We
# use the file being non-empty to indicate an exception
# occurred (vs an expected shutdown). Note: this previously
# used a multiprocessing.Queue but that can be prone to
# deadlocks, so we went with a simpler solution for a one-shot
# message between processes.
tf = tempfile.NamedTemporaryFile(
prefix="pytorch-errorfile-", suffix=".pickle", delete=False
)
tf.close()
os.unlink(tf.name)
process = mp.Process(
target=_wrap,
args=(fn, i, args, tf.name),
daemon=daemon,
)
process.start()
return i, process, tf.name
if not start_parallel:
for i in range(nprocs):
idx, process, tf_name = start_process(i)
error_files[idx] = tf_name
processes[idx] = process
else:
with ThreadPoolExecutor(max_workers=nprocs) as executor:
futures = [executor.submit(start_process, i) for i in range(nprocs)]
for fut in as_completed(futures):
idx, process, tf_name = fut.result()
# idx and process rank needs to be the same.
error_files[idx] = tf_name
processes[idx] = process
context = ProcessContext(processes, error_files)
if not join:
return context
# Loop on join until it returns True or raises an exception.
while not context.join():
pass
[docs]def spawn(fn, args=(), nprocs=1, join=True, daemon=False, start_method="spawn"):
r"""Spawns ``nprocs`` processes that run ``fn`` with ``args``.
If one of the processes exits with a non-zero exit status, the
remaining processes are killed and an exception is raised with the
cause of termination. In the case an exception was caught in the
child process, it is forwarded and its traceback is included in
the exception raised in the parent process.
Args:
fn (function): Function is called as the entrypoint of the
spawned process. This function must be defined at the top
level of a module so it can be pickled and spawned. This
is a requirement imposed by multiprocessing.
The function is called as ``fn(i, *args)``, where ``i`` is
the process index and ``args`` is the passed through tuple
of arguments.
args (tuple): Arguments passed to ``fn``.
nprocs (int): Number of processes to spawn.
join (bool): Perform a blocking join on all processes.
daemon (bool): The spawned processes' daemon flag. If set to True,
daemonic processes will be created.
start_method (str): (deprecated) this method will always use ``spawn``
as the start method. To use a different start method
use ``start_processes()``.
Returns:
None if ``join`` is ``True``,
:class:`~ProcessContext` if ``join`` is ``False``
"""
if start_method != "spawn":
msg = (
f"This method only supports start_method=spawn (got: {start_method}).\n"
"To use a different start_method use:\n\t\t"
" torch.multiprocessing.start_processes(...)"
)
warnings.warn(msg, FutureWarning, stacklevel=2)
return start_processes(fn, args, nprocs, join, daemon, start_method="spawn")
