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

#!/usr/bin/env python3

# 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 signal
import subprocess
import sys
import time
from contextlib import AbstractContextManager
from dataclasses import dataclass, field
from enum import IntFlag
from multiprocessing import synchronize
from typing import Any, Callable, Dict, Optional, Set, Tuple, Union

import torch.multiprocessing as mp
from torch.distributed.elastic.multiprocessing.errors import ProcessFailure, record
from torch.distributed.elastic.multiprocessing.redirects import (
from torch.distributed.elastic.multiprocessing.tail_log import TailLog

IS_WINDOWS = sys.platform == "win32"
IS_MACOS = sys.platform == "darwin"

log = logging.getLogger(__name__)

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

    def from_str(cls, vm: str) -> Union["Std", Dict[int, "Std"]]:


         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):
            v = int(v)
            for s in Std:
                if s == v:
                    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
            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.



     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 {i: val_or_map for i in range(local_world_size)}
        map = {}
        for i in range(local_world_size):
            map[i] = val_or_map.get(i, Std.NONE)
        return map

[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]], stdouts: Dict[int, str], stderrs: Dict[int, str], tee_stdouts: Dict[int, str], tee_stderrs: Dict[int, str], error_files: Dict[int, str], ): = name # validate that all mappings have the same number of keys and # all local ranks are accounted for nprocs = len(args) _validate_full_rank(stdouts, nprocs, "stdouts") _validate_full_rank(stderrs, nprocs, "stderrs") self.entrypoint = entrypoint self.args = args self.envs = envs self.stdouts = stdouts self.stderrs = stderrs self.error_files = error_files self.nprocs = nprocs self._stdout_tail = TailLog(name, tee_stdouts, sys.stdout) self._stderr_tail = TailLog(name, tee_stderrs, sys.stderr) def start(self) -> None: """ Start processes using parameters defined in the constructor. """ 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]: """ Polls 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]: """ Waits 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). """ 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]: """ Returns pids of processes mapped by their respective local_ranks """ raise NotImplementedError() @abc.abstractmethod def _close(self) -> 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) -> None: self._close() if self._stdout_tail: self._stdout_tail.stop() if self._stderr_tail: self._stderr_tail.stop()
class _nullcontext(AbstractContextManager): # TODO remove and replace in favor of contextlib.nullcontext # when torch drops support for python3.6 def __init__(self, enter_result=None): self.enter_result = enter_result def __enter__(self): return self.enter_result def __exit__(self, *excinfo): pass 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]], stdouts: Dict[int, str], stderrs: Dict[int, str], tee_stdouts: Dict[int, str], tee_stderrs: Dict[int, str], error_files: Dict[int, str], start_method: str, ): super().__init__( name, entrypoint, args, envs, stdouts, stderrs, tee_stdouts, tee_stderrs, error_files, ) 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: # 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: 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() # Wait untill all processes are finished. At this point workers finished executing # user function self._pc.join() _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] log.error( f"failed (exitcode: {failed_proc.exitcode})" f" local_rank: {failed_local_rank} (pid: {})" f" of fn: {fn_name} (start_method: {self.start_method})", exc_info=True, ) self.close() return RunProcsResult( failures={ failed_local_rank: ProcessFailure( local_rank=failed_local_rank,, 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 {local_rank: pid for local_rank, pid in enumerate(self._pc.pids())} def _close(self) -> None: if self._pc: for proc in self._pc.processes: proc.terminate() proc.join()
class SubprocessHandler: """ Convenience wrapper around python's ``subprocess.Popen``. Keeps track of meta-objects associated to the process (e.g. stdout and stderr redirect fds). """ def __init__( self, entrypoint: str, args: Tuple, env: Dict[str, str], preexec_fn: Optional[Callable], stdout: str, stderr: str, ): self._stdout = open(stdout, "w") if stdout else None self._stderr = open(stderr, "w") if stderr else None # inherit parent environment vars env_vars = os.environ.copy() env_vars.update(env) args_str = (entrypoint, *[str(e) for e in args]) self.proc: subprocess.Popen = self._popen(args_str, env_vars, preexec_fn) def _popen( self, args: Tuple, env: Dict[str, str], preexec_fn: Optional[Callable] ) -> subprocess.Popen: if IS_WINDOWS: # Reset preexec_fn on windows, since windows does not support it preexec_fn = None return subprocess.Popen( # pyre-fixme[6]: Expected `Union[typing.Sequence[Union[_PathLike[bytes], # _PathLike[str], bytes, str]], bytes, str]` for 1st param but got # `Tuple[str, *Tuple[Any, ...]]`. args=args, env=env, preexec_fn=preexec_fn, stdout=self._stdout, stderr=self._stderr, ) def close(self): self.proc.terminate() self.proc.wait() if self._stdout: self._stdout.close() if self._stderr: self._stderr.close() def _pr_set_pdeathsig() -> None: """ Sets PR_SET_PDEATHSIG to ensure a child process is terminated appropriately. See for more information. For read """ mp._prctl_pr_set_pdeathsig(signal.SIGTERM) # type: ignore[attr-defined]
[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]], stdouts: Dict[int, str], stderrs: Dict[int, str], tee_stdouts: Dict[int, str], tee_stderrs: Dict[int, str], error_files: Dict[int, str], ): super().__init__( name, entrypoint, args, envs, stdouts, stderrs, tee_stdouts, tee_stderrs, error_files, ) # 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: SubprocessHandler( entrypoint=self.entrypoint, # type: ignore[arg-type] # entrypoint is always a str args=self.args[local_rank], env=self.envs[local_rank], preexec_fn=_pr_set_pdeathsig, stdout=self.stdouts[local_rank], stderr=self.stderrs[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,, 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) log.error( f"failed (exitcode: {first_failure.exitcode})" f" local_rank: {first_failure.local_rank} (pid: {})" f" of binary: {self.entrypoint}" ) else: # Populate return with dummy values. This provides consistency with MultiprocessingHandler result.return_values = { local_rank: None for local_rank in 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: for local_rank, sh in self.subprocess_handlers.items() } def _close(self) -> None: if self.subprocess_handlers: for handler in self.subprocess_handlers.values(): handler.close()


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