#!/data/data/com.termux/files/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.
"""

from collections.abc import Callable
from typing import Optional, Union

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


__all__ = [
    "start_processes",
    "MultiprocessContext",
    "PContext",
    "ProcessFailure",
    "RunProcsResult",
    "SignalException",
    "Std",
    "LogsDest",
    "LogsSpecs",
    "DefaultLogsSpecs",
    "SubprocessContext",
    "to_map",
]


def start_processes(
    name: str,
    entrypoint: Callable | str,
    args: dict[int, tuple],
    envs: dict[int, dict[str, str]],
    logs_specs: LogsSpecs,
    log_line_prefixes: dict[int, str] | None = None,
    start_method: str = "spawn",
    numa_options: NumaOptions | None = None,
    duplicate_stdout_filters: list[str] | None = None,
    duplicate_stderr_filters: list[str] | None = None,
) -> PContext:
    """
    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.

    Inside ``logs_specs``, ``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``.

    ``duplicate_stdout_filters`` and ``duplicate_stderr_filters``, if non-empty,
    duplicate stdouts and stderrs respectively specified in ``logs_specs``'s ``tee``
    to a file containing only lines that match _any_ of the filter strings. The log
    file is aggregated across all ranks selected by ``tee``.

    ``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.log``: if ``redirect & STDOUT == STDOUT``
    #. ``{local_rank}/stderr.log``: if ``redirect & STDERR == STDERR``
    #. ``filtered_stdout.log``: if ``duplicate_stdout_filters`` is non-empty
    #. ``filtered_stderr.log``: if ``duplicate_stderr_filters`` is non-empty

    .. 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
        logs_specs: defines ``log_dir``, ``redirects``, and ``tee``.
                    inside ``logs_specs``:
                    - 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
        duplicate_stdout_filters: filters for the duplicated stdout logs
        duplicate_stderr_filters: filters for the duplicated stderr logs

    """

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

    context: PContext
    if isinstance(entrypoint, str):
        context = SubprocessContext(
            name=name,
            entrypoint=entrypoint,
            args=args,
            envs=envs,
            duplicate_stdout_filters=duplicate_stdout_filters,
            duplicate_stderr_filters=duplicate_stderr_filters,
            logs_specs=logs_specs,
            log_line_prefixes=log_line_prefixes,
            numa_options=numa_options,
        )
    else:
        context = MultiprocessContext(
            name=name,
            entrypoint=entrypoint,
            args=args,
            envs=envs,
            duplicate_stdout_filters=duplicate_stdout_filters,
            duplicate_stderr_filters=duplicate_stderr_filters,
            log_line_prefixes=log_line_prefixes,
            start_method=start_method,
            logs_specs=logs_specs,
            numa_options=numa_options,
        )

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