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Source code for torch.distributed.elastic.multiprocessing.api

#!/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 time
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, Dict, Optional, Set, Tuple, Union
from abc import ABC, abstractmethod

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 SubprocessHandler, get_subprocess_handler
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 = sigval


def _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`. """ pass
@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.""" 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) 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()

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