Source code for torch.distributed.elastic.multiprocessing
#!/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()
[docs]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")
# create subdirs for each local rank in the logs_dir
# logs_dir
# |- 0
# |- error.json
# |- stdout.log
# |- stderr.log
# |- ...
# |- (nprocs-1)
redirs = to_map(redirects, nprocs)
ts = to_map(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
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
