FM / multiprocessing

/usr/local/lib64/python3.6/site-packages/torch/distributed/elastic/multiprocessing/

/usr/local/lib64/python3.6/site-packages/torch/distributed/elastic/multiprocessing

Name	Size	Mode	Actions
errors/	-	0755	rm
__pycache__/	-	0755	rm
api.py	25932	0644	edit dl rm
redirects.py	2702	0644	edit dl rm
tail_log.py	4401	0644	edit dl rm
__init__.py	9180	0644	edit dl rm

Edit: /usr/local/lib64/python3.6/site-packages/torch/distributed/elastic/multiprocessing/__init__.py (9180B)

#!/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.

"""
Library that launches and manages ``n`` copies of worker subprocesses
either specified by a function or a binary.

For functions, it uses ``torch.multiprocessing`` (and therefore python
``multiprocessing``) to spawn/fork worker processes. For binaries it uses python
``subprocessing.Popen`` to create worker processes.

Usage 1: Launching two trainers as a function

from torch.distributed.elastic.multiprocessing import Std, start_processes

def trainer(a, b, c):
     pass # train

# runs two trainers
 # LOCAL_RANK=0 trainer(1,2,3)
 # LOCAL_RANK=1 trainer(4,5,6)
 ctx = start_processes(
         name="trainer",
         entrypoint=trainer,
         args={0: (1,2,3), 1: (4,5,6)},
         envs={0: {"LOCAL_RANK": 0}, 1: {"LOCAL_RANK": 1}},
         log_dir="/tmp/foobar",
         redirects=Std.ALL, # write all worker stdout/stderr to a log file
         tee={0: Std.ERR}, # tee only local rank 0's stderr to console
       )

# waits for all copies of trainer to finish
 ctx.wait()

Usage 2: Launching 2 echo workers as a binary

# same as invoking
 # echo hello
 # echo world > stdout.log
 ctx = start_processes(
         name="echo"
         entrypoint="echo",
         log_dir="/tmp/foobar",
         args={0: "hello", 1: "world"},
         redirects={1: Std:OUT},
        )

Just like ``torch.multiprocessing``, the return value of the function
:func:`start_processes` is a process context (:class:`api.PContext`). If a function
was launched, a :class:`api.MultiprocessContext` is returned and if a binary
was launched a :class:`api.SubprocessContext` is returned. Both are specific
implementations of the parent :class:`api.PContext` class.
"""

import os
from typing import Callable, Dict, Tuple, Union

from torch.distributed.elastic.multiprocessing.api import (  # noqa: F401
    MultiprocessContext,
    PContext,
    ProcessFailure,
    RunProcsResult,
    Std,
    SignalException,
    SubprocessContext,
    _validate_full_rank,
    to_map,
)
from torch.distributed.elastic.utils.logging import get_logger

log = get_logger()

def start_processes(
    name: str,
    entrypoint: Union[Callable, str],
    args: Dict[int, Tuple],
    envs: Dict[int, Dict[str, str]],
    log_dir: str,
    start_method: str = "spawn",
    redirects: Union[Std, Dict[int, Std]] = Std.NONE,
    tee: Union[Std, Dict[int, Std]] = Std.NONE,
) -> PContext:
    """
    Starts ``n`` copies of ``entrypoint`` processes with the provided options.
    ``entrypoint`` is either a ``Callable`` (function) or a ``str`` (binary).
    The number of copies is determined by the number of entries for ``args`` and
    ``envs`` arguments, which need to have the same key set.

``args`` and ``env`` parameters are the arguments and environment variables
    to pass down to the entrypoint mapped by the replica index (local rank).
    All local ranks must be accounted for.
    That is, the keyset should be ``{0,1,...,(nprocs-1)}``.

.. note:: When the ``entrypoint`` is a binary (``str``), ``args`` can only be strings.
              If any other type is given, then it is casted to a string representation
              (e.g. ``str(arg1)``). Furthermore, a binary failure will only write
              an ``error.json`` error file if the main function is annotated with
              ``torch.distributed.elastic.multiprocessing.errors.record``. For function launches,
              this is done by default and there is no need to manually annotate
              with the ``@record`` annotation.

``redirects`` and ``tees`` are bitmasks specifying which std stream(s) to redirect
    to a log file in the ``log_dir``. Valid mask values are defined in ``Std``.
    To redirect/tee only certain local ranks, pass ``redirects`` as a map with the key as
    the local rank to specify the redirect behavior for.
    Any missing local ranks will default to ``Std.NONE``.

``tee`` acts like the unix "tee" command in that it redirects + prints to console.
    To avoid worker stdout/stderr from printing to console, use the ``redirects`` parameter.

For each process, the ``log_dir`` will contain:

#. ``{local_rank}/error.json``: if the process failed, a file with the error info
    #. ``{local_rank}/stdout.json``: if ``redirect & STDOUT == STDOUT``
    #. ``{local_rank}/stderr.json``: if ``redirect & STDERR == STDERR``

.. note:: It is expected that the ``log_dir`` exists, is empty, and is a directory.

Example:

log_dir = "/tmp/test"

# ok; two copies of foo: foo("bar0"), foo("bar1")
     start_processes(
        name="trainer",
        entrypoint=foo,
        args:{0:("bar0",), 1:("bar1",),
        envs:{0:{}, 1:{}},
        log_dir=log_dir
     )

# invalid; envs missing for local rank 1
     start_processes(
        name="trainer",
        entrypoint=foo,
        args:{0:("bar0",), 1:("bar1",),
        envs:{0:{}},
        log_dir=log_dir
     )

# ok; two copies of /usr/bin/touch: touch file1, touch file2
     start_processes(
        name="trainer",
        entrypoint="/usr/bin/touch",
        args:{0:("file1",), 1:("file2",),
        envs:{0:{}, 1:{}},
        log_dir=log_dir
      )

# caution; arguments casted to string, runs:
     # echo "1" "2" "3" and echo "[1, 2, 3]"
     start_processes(
        name="trainer",
        entrypoint="/usr/bin/echo",
        args:{0:(1,2,3), 1:([1,2,3],),
        envs:{0:{}, 1:{}},
        log_dir=log_dir
      )

Args:
        name: a human readable short name that describes what the processes are
              (used as header when tee'ing stdout/stderr outputs)
        entrypoint: either a ``Callable`` (function) or ``cmd`` (binary)
        args: arguments to each replica
        envs: env vars to each replica
        log_dir: directory used to write log files
        nprocs: number of copies to create (one on each process)
        start_method: multiprocessing start method (spawn, fork, forkserver)
                      ignored for binaries
        redirects: which std streams to redirect to a log file
        tees: which std streams to redirect + print to console

"""

# listdir raises FileNotFound or NotADirectoryError so no need to check manually
    if os.listdir(log_dir):
        raise RuntimeError(
            f"log_dir: {log_dir} is not empty, please provide an empty log_dir"
        )

nprocs = len(args)
    _validate_full_rank(args, nprocs, "args")
    _validate_full_rank(envs, nprocs, "envs")

# 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

stdouts = {local_rank: "" for local_rank in range(nprocs)}
    stderrs = {local_rank: "" for local_rank in range(nprocs)}
    tee_stdouts: Dict[int, str] = {}
    tee_stderrs: Dict[int, str] = {}
    error_files = {}

for local_rank in range(nprocs):
        clogdir = os.path.join(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]

error_file = os.path.join(clogdir, "error.json")
        error_files[local_rank] = error_file
        log.info(f"Setting worker{local_rank} reply file to: {error_file}")
        envs[local_rank]["TORCHELASTIC_ERROR_FILE"] = error_file

context: PContext
    if isinstance(entrypoint, str):
        context = SubprocessContext(
            name=name,
            entrypoint=entrypoint,
            args=args,
            envs=envs,
            stdouts=stdouts,
            stderrs=stderrs,
            tee_stdouts=tee_stdouts,
            tee_stderrs=tee_stderrs,
            error_files=error_files,
        )
    else:
        context = MultiprocessContext(
            name=name,
            entrypoint=entrypoint,
            args=args,
            envs=envs,
            stdouts=stdouts,
            stderrs=stderrs,
            tee_stdouts=tee_stdouts,
            tee_stderrs=tee_stderrs,
            error_files=error_files,
            start_method=start_method,
        )

try:
        context.start()
        return context
    except Exception:
        context.close()
        raise

cmd: