VhdZddlmZmZmZddlmZmZmZm Z m Z m Z m Z m Z mZmZmZmZddlmZgdZ ddedeeefd eeefd eeeeeffd e d eeeefd ede fdZy)aj 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. )CallableOptionalUnion) _validate_full_rankDefaultLogsSpecsLogsDest LogsSpecsMultiprocessContextPContextProcessFailureRunProcsResultSignalExceptionStdSubprocessContextto_map) get_logger) start_processesr r r r rrrr rrrNname entrypointargsenvs logs_specslog_line_prefixes start_methodreturnc t|}t||dt||dt|trt ||||||}nt |||||||} |j |S#t$r|jwxYw)a Start ``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 ``tee`` 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 start_method: multiprocessing start method (spawn, fork, forkserver) ignored for binaries redirects: which std streams to redirect to a log file tee: which std streams to redirect + print to console local_ranks_filter: which ranks' logs to print to console rr)rrrrrr)rrrrrrr) lenr isinstancestrrr start Exceptionclose) rrrrrrrnprocscontexts b/home/dcms/DCMS/lib/python3.12/site-packages/torch/distributed/elastic/multiprocessing/__init__.pyrresNYFff-ff-*c"#!!/  &!/%!     s A--B)Nspawn)__doc__typingrrr-torch.distributed.elastic.multiprocessing.apirrrr r r r r rrrr'torch.distributed.elastic.utils.loggingr__all__rdictinttuplerr%r1s7r-,    ? ,37E Ehm$E sEz E sDcN" # E  E  S#X/ EEEr0