torch.distributed.elastic.multiprocessing.api — PyTorch 2.7 documentation (original) (raw)
#!/usr/bin/env python3
mypy: allow-untyped-defs
Copyright (c) Facebook, Inc. and its affiliates.
All rights reserved.
This source code is licensed under the BSD-style license found in the
LICENSE file in the root directory of this source tree.
import abc import logging import os import re import shutil import signal import subprocess import sys import tempfile import threading import time from abc import ABC, abstractmethod from contextlib import nullcontext from dataclasses import dataclass, field from enum import IntFlag from multiprocessing import synchronize from types import FrameType from typing import Any, Callable, Optional, Union
import torch.multiprocessing as mp from torch.distributed.elastic.multiprocessing.errors import ProcessFailure, record from torch.distributed.elastic.multiprocessing.redirects import ( redirect_stderr, redirect_stdout, ) from torch.distributed.elastic.multiprocessing.subprocess_handler import ( get_subprocess_handler, SubprocessHandler, ) from torch.distributed.elastic.multiprocessing.tail_log import TailLog
IS_WINDOWS = sys.platform == "win32" IS_MACOS = sys.platform == "darwin"
logger = logging.getLogger(name)
all = [ "DefaultLogsSpecs", "SignalException", "Std", "to_map", "RunProcsResult", "PContext", "get_std_cm", "MultiprocessContext", "SubprocessContext", "LogsDest", "LogsSpecs", ]
class SignalException(Exception): """ Exception is raised inside the torchelastic agent process by the termination handler if the death signal got received by the process. """
def __init__(self, msg: str, sigval: signal.Signals) -> None:
super().__init__(msg)
self.sigval = sigvaldef _terminate_process_handler(signum: int, frame: Optional[FrameType]) -> None: """Termination handler that raises exceptions on the main process.
When the process receives death signal(SIGTERM, SIGINT), this termination handler will
be invoked. It raises the ``SignalException`` exception that should be processed by the
user code. Python does not terminate process after the termination handler is finished,
so the exception should not be silently ignored, otherwise the process will never
be terminated.
"""
sigval = signal.Signals(signum)
raise SignalException(f"Process {os.getpid()} got signal: {sigval}", sigval=sigval)def _get_kill_signal() -> signal.Signals: """Get the kill signal. SIGKILL for unix, CTRL_C_EVENT for windows.""" if IS_WINDOWS: return signal.CTRL_C_EVENT # type: ignore[attr-defined] # noqa: F821 else: return signal.SIGKILL
def _get_default_signal() -> signal.Signals: """Get the default termination signal. SIGTERM for unix, CTRL_C_EVENT for windows.""" if IS_WINDOWS: return signal.CTRL_C_EVENT # type: ignore[attr-defined] # noqa: F821 else: return signal.SIGTERM
def _validate_full_rank(d: dict[int, Any], nprocs: int, what: str): actual_keys = set(d.keys()) expected_keys = set(range(nprocs))
if actual_keys != expected_keys:
raise RuntimeError(
f"{what}, local rank mapping mismatch,"
f" expected: {expected_keys}, actual: {actual_keys}"
)_MAPPING_REGEX = r"^(\d:[0123],)*(\d:[0123])$" _VALUE_REGEX = r"^[0123]$"
class Std(IntFlag): NONE = 0 OUT = 1 ERR = 2 ALL = OUT | ERR
@classmethod
def from_str(cls, vm: str) -> Union["Std", dict[int, "Std"]]:
"""
Example:
::
from_str("0") -> Std.NONE
from_str("1") -> Std.OUT
from_str("0:3,1:0,2:1,3:2") -> {0: Std.ALL, 1: Std.NONE, 2: Std.OUT, 3: Std.ERR}
Any other input raises an exception
"""
def to_std(v: str) -> Std: # type: ignore[return]
s = Std(int(v))
if s in Std:
return s
# return None -> should NEVER reach here since we regex check input
if re.match(_VALUE_REGEX, vm): # vm is a number (e.g. 0)
return to_std(vm)
elif re.match(_MAPPING_REGEX, vm): # vm is a mapping (e.g. 0:1,1:2)
d: dict[int, Std] = {}
for m in vm.split(","):
i, v = m.split(":")
d[int(i)] = to_std(v)
return d
else:
raise ValueError(
f"{vm} does not match: <{_VALUE_REGEX}> or <{_MAPPING_REGEX}>"
)def to_map( val_or_map: Union[Std, dict[int, Std]], local_world_size: int ) -> dict[int, Std]: """ Certain APIs take redirect settings either as a single value (e.g. apply to all local ranks) or as an explicit user-provided mapping. This method is a convenience method that converts a value or mapping into a mapping.
Example:
::
to_map(Std.OUT, local_world_size=2) # returns: {0: Std.OUT, 1: Std.OUT}
to_map({1: Std.OUT}, local_world_size=2) # returns: {0: Std.NONE, 1: Std.OUT}
to_map(
{0: Std.OUT, 1: Std.OUT}, local_world_size=2
) # returns: {0: Std.OUT, 1: Std.OUT}
"""
if isinstance(val_or_map, Std):
return dict.fromkeys(range(local_world_size), val_or_map)
else:
map = {}
for i in range(local_world_size):
map[i] = val_or_map.get(i, Std.NONE)
return map[docs]@dataclass class LogsDest: """ For each log type, holds mapping of local rank ids to file paths. """
stdouts: dict[int, str] = field(default_factory=dict)
stderrs: dict[int, str] = field(default_factory=dict)
tee_stdouts: dict[int, str] = field(default_factory=dict)
tee_stderrs: dict[int, str] = field(default_factory=dict)
error_files: dict[int, str] = field(default_factory=dict)[docs]class LogsSpecs(ABC): """ Defines logs processing and redirection for each worker process.
Args:
log_dir:
Base directory where logs will be written.
redirects:
Streams to redirect to files. Pass a single ``Std``
enum to redirect for all workers, or a mapping keyed
by local_rank to selectively redirect.
tee:
Streams to duplicate to stdout/stderr.
Pass a single ``Std`` enum to duplicate streams for all workers,
or a mapping keyed by local_rank to selectively duplicate.
"""
def __init__(
self,
log_dir: Optional[str] = None,
redirects: Union[Std, dict[int, Std]] = Std.NONE,
tee: Union[Std, dict[int, Std]] = Std.NONE,
local_ranks_filter: Optional[set[int]] = None,
) -> None:
self._root_log_dir = log_dir
self._redirects = redirects
self._tee = tee
self._local_ranks_filter = local_ranks_filter[docs] @abstractmethod def reify( self, envs: dict[int, dict[str, str]], ) -> LogsDest: """ Given the environment variables, builds destination of log files for each of the local ranks.
Envs parameter contains env variables dict for each of the local ranks, where entries are defined in:
:func:`~torchelastic.distributed.elastic.agent.server.local_elastic_agent.LocalElasticAgent._start_workers`.
"""
@property
@abstractmethod
def root_log_dir(self) -> str:
pass[docs]class DefaultLogsSpecs(LogsSpecs): """ Default LogsSpecs implementation:
- `log_dir` will be created if it doesn't exist
- Generates nested folders for each attempt and rank.
"""
def __init__(
self,
log_dir: Optional[str] = None,
redirects: Union[Std, dict[int, Std]] = Std.NONE,
tee: Union[Std, dict[int, Std]] = Std.NONE,
local_ranks_filter: Optional[set[int]] = None,
) -> None:
if log_dir != os.devnull:
if not log_dir:
log_dir = tempfile.mkdtemp(prefix="torchelastic_")
elif not os.path.exists(log_dir):
os.makedirs(log_dir, exist_ok=True)
else:
if os.path.isfile(log_dir):
raise NotADirectoryError(f"log_dir: {log_dir} is a file")
super().__init__(log_dir, redirects, tee, local_ranks_filter)
# initialized only once
self._run_log_dir = None
@property
def root_log_dir(self) -> str:
return str(self._root_log_dir)
def _make_log_dir(self, log_dir: Optional[str], rdzv_run_id: str):
base_log_dir = log_dir or tempfile.mkdtemp(prefix="torchelastic_")
os.makedirs(base_log_dir, exist_ok=True)
dir = tempfile.mkdtemp(prefix=f"{rdzv_run_id}_", dir=base_log_dir)
logger.info("log directory set to: %s", dir)
return dir[docs] def reify( self, envs: dict[int, dict[str, str]], ) -> LogsDest: """ Uses following scheme to build log destination paths:
- `<log_dir>/<rdzv_run_id>/attempt_<attempt>/<rank>/stdout.log`
- `<log_dir>/<rdzv_run_id>/attempt_<attempt>/<rank>/stderr.log`
- `<log_dir>/<rdzv_run_id>/attempt_<attempt>/<rank>/error.json`
"""
nprocs = len(envs)
global_env = {} # use only to query properies that are not dependent on a rank
if nprocs > 0:
global_env = envs[0]
else:
logger.warning(
"Empty envs map provided when defining logging destinations."
)
# Keys are always defined, but values can be missing in unit tests
run_id = global_env.get("TORCHELASTIC_RUN_ID", "test_run_id")
restart_count = global_env.get("TORCHELASTIC_RESTART_COUNT", "0")
attempt_log_dir: str = ""
if self._root_log_dir != os.devnull:
if not self._run_log_dir:
self._run_log_dir = self._make_log_dir(self._root_log_dir, run_id)
attempt_log_dir = os.path.join(
self._run_log_dir, f"attempt_{restart_count}"
) # type: ignore[call-overload]
shutil.rmtree(attempt_log_dir, ignore_errors=True)
os.makedirs(attempt_log_dir)
if self._root_log_dir == os.devnull:
attempt_log_dir = os.devnull
# create subdirs for each local rank in the logs_dir
# logs_dir
# |- 0
# |- error.json
# |- stdout.log
# |- stderr.log
# |- ...
# |- (nprocs-1)
redirs = to_map(self._redirects, nprocs)
ts = to_map(self._tee, nprocs)
# to tee stdout/stderr we first redirect into a file
# then tail -f stdout.log/stderr.log so add tee settings to redirects
for local_rank, tee_std in ts.items():
redirect_std = redirs[local_rank]
redirs[local_rank] = redirect_std | tee_std
SYS_STREAM = "" # special case to indicate to output to console
stdouts = dict.fromkeys(range(nprocs), SYS_STREAM)
stderrs = dict.fromkeys(range(nprocs), SYS_STREAM)
tee_stdouts: dict[int, str] = {}
tee_stderrs: dict[int, str] = {}
error_files = {}
for local_rank in range(nprocs):
if attempt_log_dir == os.devnull:
tee_stdouts[local_rank] = os.devnull
tee_stderrs[local_rank] = os.devnull
error_files[local_rank] = os.devnull
envs[local_rank]["TORCHELASTIC_ERROR_FILE"] = ""
else:
clogdir = os.path.join(attempt_log_dir, str(local_rank))
os.mkdir(clogdir)
rd = redirs[local_rank]
if (rd & Std.OUT) == Std.OUT:
stdouts[local_rank] = os.path.join(clogdir, "stdout.log")
if (rd & Std.ERR) == Std.ERR:
stderrs[local_rank] = os.path.join(clogdir, "stderr.log")
t = ts[local_rank]
if t & Std.OUT == Std.OUT:
tee_stdouts[local_rank] = stdouts[local_rank]
if t & Std.ERR == Std.ERR:
tee_stderrs[local_rank] = stderrs[local_rank]
if (
self._local_ranks_filter
and local_rank not in self._local_ranks_filter
):
# If stream is tee'd, only write to file, but don't tail
if local_rank in tee_stdouts:
tee_stdouts.pop(local_rank, None)
if local_rank in tee_stderrs:
tee_stderrs.pop(local_rank, None)
# If stream is not redirected, don't print
if stdouts[local_rank] == SYS_STREAM:
stdouts[local_rank] = os.devnull
if stderrs[local_rank] == SYS_STREAM:
stderrs[local_rank] = os.devnull
error_file = os.path.join(clogdir, "error.json")
error_files[local_rank] = error_file
logger.info(
"Setting worker%s reply file to: %s", local_rank, error_file
)
envs[local_rank]["TORCHELASTIC_ERROR_FILE"] = error_file
return LogsDest(stdouts, stderrs, tee_stdouts, tee_stderrs, error_files)
def __repr__(self) -> str:
return (
f"DefaultLogsSpecs(root_log_dir={self._root_log_dir}, redirects={self._redirects}, "
f"tee={self._tee}, local_ranks_filter={self._local_ranks_filter})"
)
def __eq__(self, other: object) -> bool:
if not isinstance(other, DefaultLogsSpecs):
return False
return (
self._root_log_dir == other._root_log_dir
and self._redirects == other._redirects
and self._tee == other._tee
and self._local_ranks_filter == other._local_ranks_filter
)[docs]@dataclass
class RunProcsResult:
"""
Results of a completed run of processes started with start_processes(). Returned by PContext.
Note the following:
1. All fields are mapped by local rank
2. ``return_values`` - only populated for functions (not the binaries).
3. ``stdouts`` - path to stdout.log (empty string if no redirect)
4. ``stderrs`` - path to stderr.log (empty string if no redirect)
"""
return_values: dict[int, Any] = field(default_factory=dict)
failures: dict[int, ProcessFailure] = field(default_factory=dict)
stdouts: dict[int, str] = field(default_factory=dict)
stderrs: dict[int, str] = field(default_factory=dict)
def is_failed(self) -> bool:
return len(self.failures) > 0[docs]class PContext(abc.ABC): """ The base class that standardizes operations over a set of processes that are launched via different mechanisms.
The name ``PContext`` is intentional to disambiguate with ``torch.multiprocessing.ProcessContext``.
.. warning:: stdouts and stderrs should ALWAYS be a superset of
tee_stdouts and tee_stderrs (respectively) this is b/c
tee is implemented as a redirect + tail -f <stdout/stderr.log>
"""
def __init__(
self,
name: str,
entrypoint: Union[Callable, str],
args: dict[int, tuple],
envs: dict[int, dict[str, str]],
logs_specs: LogsSpecs,
log_line_prefixes: Optional[dict[int, str]] = None,
):
self.name = name
# validate that all mappings have the same number of keys and
# all local ranks are accounted for
nprocs = len(args)
# TODO log_line_prefixes can be exanded too
logs_dest = logs_specs.reify(envs)
_validate_full_rank(logs_dest.stdouts, nprocs, "stdouts")
_validate_full_rank(logs_dest.stderrs, nprocs, "stderrs")
self.entrypoint = entrypoint
self.args = args
self.envs = envs
self.stdouts = logs_dest.stdouts
self.stderrs = logs_dest.stderrs
self.error_files = logs_dest.error_files
self.nprocs = nprocs
self._stdout_tail = TailLog(
name, logs_dest.tee_stdouts, sys.stdout, log_line_prefixes
)
self._stderr_tail = TailLog(
name, logs_dest.tee_stderrs, sys.stderr, log_line_prefixes
)
def start(self) -> None:
"""Start processes using parameters defined in the constructor."""
if threading.current_thread() is threading.main_thread():
signal.signal(signal.SIGTERM, _terminate_process_handler)
signal.signal(signal.SIGINT, _terminate_process_handler)
if not IS_WINDOWS:
signal.signal(signal.SIGHUP, _terminate_process_handler)
signal.signal(signal.SIGQUIT, _terminate_process_handler)
else:
logger.warning(
"Failed to register signal handlers since torchelastic is running on a child thread. "
"This could lead to orphaned worker processes if the torchrun is terminated."
)
self._start()
self._stdout_tail.start()
self._stderr_tail.start()
@abc.abstractmethod
def _start(self) -> None:
"""Start processes using strategy defined in a particular context."""
raise NotImplementedError
@abc.abstractmethod
def _poll(self) -> Optional[RunProcsResult]:
"""
Poll the run status of the processes running under this context.
This method follows an "all-or-nothing" policy and returns
a ``RunProcessResults`` object if either all processes complete
successfully or any process fails. Returns ``None`` if
all processes are still running.
"""
raise NotImplementedError
def wait(self, timeout: float = -1, period: float = 1) -> Optional[RunProcsResult]:
"""
Wait for the specified ``timeout`` seconds, polling every ``period`` seconds
for the processes to be done. Returns ``None`` if the processes are still running
on timeout expiry. Negative timeout values are interpreted as "wait-forever".
A timeout value of zero simply queries the status of the processes (e.g. equivalent
to a poll).
.. note::
Multiprocessing library registers SIGTERM and SIGINT signal handlers that raise
``SignalException`` when the signals received. It is up to the consumer of the code
to properly handle the exception. It is important not to swallow the exception otherwise
the process would not terminate. Example of the typical workflow can be:
.. code-block:: python
pc = start_processes(...)
try:
pc.wait(1)
.. do some other work
except SignalException as e:
pc.shutdown(e.sigval, timeout=30)
If SIGTERM or SIGINT occurs, the code above will try to shutdown child processes by propagating
received signal. If child processes will not terminate in the timeout time, the process will send
the SIGKILL.
"""
if timeout == 0:
return self._poll()
if timeout < 0:
timeout = sys.maxsize
expiry = time.time() + timeout
while time.time() < expiry:
pr = self._poll()
if pr:
return pr
time.sleep(period)
return None
@abc.abstractmethod
def pids(self) -> dict[int, int]:
"""Return pids of processes mapped by their respective local_ranks."""
raise NotImplementedError
@abc.abstractmethod
def _close(self, death_sig: signal.Signals, timeout: int = 30) -> None:
r"""
Terminates all processes managed by this context and cleans up any
meta resources (e.g. redirect, error_file files).
"""
raise NotImplementedError
def close(
self, death_sig: Optional[signal.Signals] = None, timeout: int = 30
) -> None:
r"""
Terminates all processes managed by this context and cleans up any
meta resources (e.g. redirect, error_file files).
Args:
death_sig: Death signal to terminate processes.
timeout: Time to wait for processes to finish, if process is
still alive after this time, it will be terminated via SIGKILL.
"""
if not death_sig:
death_sig = _get_default_signal()
self._close(death_sig=death_sig, timeout=timeout)
if self._stdout_tail:
self._stdout_tail.stop()
if self._stderr_tail:
self._stderr_tail.stop()def get_std_cm(std_rd: str, redirect_fn): if IS_WINDOWS or IS_MACOS or not std_rd: return nullcontext() else: return redirect_fn(std_rd)
def wrap( local_rank: int, fn: Callable, args: dict[int, tuple], envs: dict[int, dict[str, str]], stdout_redirects: dict[int, str], # redirect file for stdout (to console if None) stderr_redirects: dict[int, str], # redirect file for stderr (to console if None) ret_vals: dict[int, mp.SimpleQueue], queue_finished_reading_event: synchronize.Event, ) -> None: # get the per-rank params up front so we fail fast if no mapping is found args = args[local_rank] env_ = envs[local_rank] ret_val_ = ret_vals[local_rank]
stdout_rd = stdout_redirects[local_rank]
stderr_rd = stderr_redirects[local_rank]
stdout_cm = get_std_cm(stdout_rd, redirect_stdout)
stderr_cm = get_std_cm(stderr_rd, redirect_stderr)
for k, v in env_.items():
os.environ[k] = v
with stdout_cm, stderr_cm:
ret = record(fn)(*args_)
ret_val_.put(ret)
queue_finished_reading_event.wait()[docs]class MultiprocessContext(PContext):
"""PContext holding worker processes invoked as a function."""
def __init__(
self,
name: str,
entrypoint: Callable,
args: dict[int, tuple],
envs: dict[int, dict[str, str]],
start_method: str,
logs_specs: LogsSpecs,
log_line_prefixes: Optional[dict[int, str]] = None,
):
super().__init__(
name,
entrypoint,
args,
envs,
logs_specs,
log_line_prefixes,
)
self.start_method = start_method
# each ret_val queue will always contain a single element.
self._ret_vals = {
local_rank: mp.get_context(self.start_method).SimpleQueue()
for local_rank in range(self.nprocs)
}
# see comments in ``join()`` for what this is
self._return_values: dict[int, Any] = {}
self._pc: Optional[mp.ProcessContext] = None
# Note: set method should ONLY be invoked for the use case when all processes finished
# successfully. If any process died on event.wait() calling set() method will deadlock.
self._worker_finished_event = mp.get_context(self.start_method).Event()
def _start(self):
if self._pc:
raise ValueError(
"The process context already initialized."
" Most likely the start method got called twice."
)
self._pc = mp.start_processes(
fn=_wrap,
args=(
self.entrypoint,
self.args,
self.envs,
self.stdouts,
self.stderrs,
self._ret_vals,
self._worker_finished_event,
),
nprocs=self.nprocs,
join=False,
daemon=False,
start_method=self.start_method,
)
def _is_done(self) -> bool:
return len(self._return_values) == self.nprocs
def _poll(self) -> Optional[RunProcsResult]:
assert self._pc is not None # assertion for mypy type checker
try:
# torch.mp.ProcessContext Throws an Exception if some/all of
# worker processes failed
# timeout < 0 checks worker status and return immediately
# Join will never return success since we use synchronize.Event to wait
# for all processes to finish.
self._pc.join(-1)
# IMPORTANT: we use multiprocessing.Queue to carry worker return values
# back to the parent, the worker process will wait before terminating
# until all the buffered items are fed by the feeder thread to the underlying
# pipe. Hence to prevent deadlocks on large return values,
# we opportunistically try queue.get on each join call
# See: https://docs.python.org/2/library/multiprocessing.html#all-platforms
for local_rank in range(0, self.nprocs):
return_queue = self._ret_vals[local_rank]
if not return_queue.empty():
# save the return values temporarily into a member var
self._return_values[local_rank] = return_queue.get()
if self._is_done():
# we should ALWAYS have ALL the return values when all the processes are done
self._worker_finished_event.set()
# At this point workers finished running the user function
# But the child process might still have not exited. Wait for them.
# pc.join() blocks [forever] until "a" proc exits. Loop until all of them exits.
while not self._pc.join():
logger.debug(
"entrypoint fn finished, waiting for all child procs to exit..."
)
_validate_full_rank(
self._return_values, self.nprocs, "return_value queue"
)
self.close()
return RunProcsResult(
return_values=self._return_values,
stdouts=self.stdouts,
stderrs=self.stderrs,
)
else:
return None
except (mp.ProcessRaisedException, mp.ProcessExitedException) as e:
failed_local_rank = e.error_index
# entrypoint for MultiprocessContext will always be a Callable
fn_name = self.entrypoint.__qualname__ # type: ignore[union-attr]
failed_proc = self._pc.processes[failed_local_rank]
error_filepath = self.error_files[failed_local_rank]
logger.exception(
"failed (exitcode: %s)"
" local_rank: %s (pid: %s)"
" of fn: %s (start_method: %s)",
failed_proc.exitcode,
failed_local_rank,
e.pid,
fn_name,
self.start_method,
)
self.close()
return RunProcsResult(
failures={
failed_local_rank: ProcessFailure(
local_rank=failed_local_rank,
pid=e.pid,
exitcode=failed_proc.exitcode,
error_file=error_filepath,
)
},
stdouts=self.stdouts,
stderrs=self.stderrs,
)
def pids(self) -> dict[int, int]:
assert self._pc is not None # assertion for mypy type checking
return dict(enumerate(self._pc.pids()))
def _close(self, death_sig: signal.Signals, timeout: int = 30) -> None:
if not self._pc:
return
for proc in self._pc.processes:
if proc.is_alive():
logger.warning(
"Closing process %s via signal %s", proc.pid, death_sig.name
)
try:
os.kill(proc.pid, death_sig)
except ProcessLookupError:
# If the process exited because of some reason,
# `ProcessLookupError` will be raised, it is safe to ignore it.
pass
end = time.monotonic() + timeout
for proc in self._pc.processes:
time_to_wait = end - time.monotonic()
if time_to_wait <= 0:
break
proc.join(time_to_wait)
for proc in self._pc.processes:
if proc.is_alive():
logger.warning(
"Unable to shutdown process %s via %s, forcefully exiting via %s",
proc.pid,
death_sig,
_get_kill_signal(),
)
try:
os.kill(proc.pid, _get_kill_signal())
except ProcessLookupError:
# If the process exited because of some reason,
# `ProcessLookupError` will be raised, it is safe to ignore it.
pass
proc.join()[docs]class SubprocessContext(PContext):
"""PContext holding worker processes invoked as a binary."""
def __init__(
self,
name: str,
entrypoint: str,
args: dict[int, tuple],
envs: dict[int, dict[str, str]],
logs_specs: LogsSpecs,
log_line_prefixes: Optional[dict[int, str]] = None,
):
super().__init__(
name,
entrypoint,
args,
envs,
logs_specs,
log_line_prefixes,
)
# state vector; _vdone[local_rank] -> is local_rank finished or not
self._running_local_ranks: set[int] = set(range(self.nprocs))
self._failures: dict[int, ProcessFailure] = {}
self.subprocess_handlers: dict[int, SubprocessHandler] = {}
def _start(self):
if self.subprocess_handlers:
raise ValueError(
"The subprocess handlers already initialized. Most likely the start method got called twice."
)
self.subprocess_handlers = {
local_rank: get_subprocess_handler(
entrypoint=self.entrypoint, # type: ignore[arg-type] # entrypoint is always a str
args=self.args[local_rank],
env=self.envs[local_rank],
stdout=self.stdouts[local_rank],
stderr=self.stderrs[local_rank],
local_rank_id=local_rank,
)
for local_rank in range(self.nprocs)
}
def _poll(self) -> Optional[RunProcsResult]:
done_local_ranks = set()
for local_rank in self._running_local_ranks:
handler = self.subprocess_handlers[local_rank]
exitcode = handler.proc.poll()
if exitcode is not None:
done_local_ranks.add(local_rank)
if exitcode != 0: # failed or signaled
self._failures[local_rank] = ProcessFailure(
local_rank=local_rank,
pid=handler.proc.pid,
exitcode=exitcode,
error_file=self.error_files[local_rank],
)
# else: --> succeeded; nothing to do
self._running_local_ranks.difference_update(done_local_ranks)
# if ALL procs are finished or ANY have failed
if not self._running_local_ranks or self._failures:
self.close() # terminate all running procs
result = RunProcsResult(
failures=self._failures,
stdouts=self.stdouts,
stderrs=self.stderrs,
)
if result.is_failed():
first_failure = min(result.failures.values(), key=lambda f: f.timestamp)
logger.error(
"failed (exitcode: %s) local_rank: %s (pid: %s) of binary: %s",
first_failure.exitcode,
first_failure.local_rank,
first_failure.pid,
self.entrypoint,
)
else:
# Populate return with dummy values. This provides consistency with MultiprocessingHandler
result.return_values = dict.fromkeys(range(self.nprocs))
return result
else: # there are no failures and procs still running
return None
def pids(self) -> dict[int, int]:
return {
local_rank: sh.proc.pid
for local_rank, sh in self.subprocess_handlers.items()
}
def _close(self, death_sig: signal.Signals, timeout: int = 30) -> None:
if not self.subprocess_handlers:
return
for handler in self.subprocess_handlers.values():
if handler.proc.poll() is None:
logger.warning(
"Sending process %s closing signal %s",
handler.proc.pid,
death_sig.name,
)
handler.close(death_sig=death_sig)
end = time.monotonic() + timeout
for handler in self.subprocess_handlers.values():
time_to_wait = end - time.monotonic()
if time_to_wait <= 0:
break
try:
handler.proc.wait(time_to_wait)
except subprocess.TimeoutExpired:
# Ignore the timeout expired exception, since
# the child process will be forcefully terminated via SIGKILL
pass
for handler in self.subprocess_handlers.values():
if handler.proc.poll() is None:
logger.warning(
"Unable to shutdown process %s via %s, forcefully exiting via %s",
handler.proc.pid,
death_sig,
_get_kill_signal(),
)
handler.close(death_sig=_get_kill_signal())
handler.proc.wait()