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ckpts/universal/global_step120/zero/20.input_layernorm.weight/exp_avg.pt

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ckpts/universal/global_step120/zero/26.attention.dense.weight/exp_avg.pt

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ckpts/universal/global_step120/zero/6.attention.dense.weight/exp_avg.pt

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ckpts/universal/global_step120/zero/6.attention.dense.weight/exp_avg_sq.pt

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ckpts/universal/global_step120/zero/6.attention.dense.weight/fp32.pt

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ckpts/universal/global_step120/zero/7.mlp.dense_h_to_4h_swiglu.weight/exp_avg.pt

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ckpts/universal/global_step120/zero/7.mlp.dense_h_to_4h_swiglu.weight/fp32.pt

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venv/lib/python3.10/site-packages/torch/distributed/elastic/events/__init__.py

@@ -0,0 +1,134 @@
+#!/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.
+
+"""
+Module contains events processing mechanisms that are integrated with the standard python logging.
+
+Example of usage:
+
+::
+
+  from torch.distributed.elastic import events
+  event = events.Event(name="test_event", source=events.EventSource.WORKER, metadata={...})
+  events.get_logging_handler(destination="console").info(event)
+
+"""
+
+import inspect
+import logging
+import os
+import socket
+import traceback
+from enum import Enum
+from typing import Dict, Optional
+
+from torch.distributed.elastic.events.handlers import get_logging_handler
+
+from .api import (  # noqa: F401
+    Event,
+    EventMetadataValue,
+    EventSource,
+    NodeState,
+    RdzvEvent,
+)
+
+_events_loggers: Dict[str, logging.Logger] = {}
+
+def _get_or_create_logger(destination: str = "null") -> logging.Logger:
+    """
+    Construct python logger based on the destination type or extends if provided.
+
+    Available destination could be found in ``handlers.py`` file.
+    The constructed logger does not propagate messages to the upper level loggers,
+    e.g. root logger. This makes sure that a single event can be processed once.
+
+    Args:
+        destination: The string representation of the event handler.
+            Available handlers found in ``handlers`` module
+    """
+    global _events_loggers
+
+    if destination not in _events_loggers:
+        _events_logger = logging.getLogger(f"torchelastic-events-{destination}")
+        _events_logger.setLevel(os.environ.get("LOGLEVEL", "INFO"))
+        # Do not propagate message to the root logger
+        _events_logger.propagate = False
+
+        logging_handler = get_logging_handler(destination)
+        _events_logger.addHandler(logging_handler)
+
+        # Add the logger to the global dictionary
+        _events_loggers[destination] = _events_logger
+
+    return _events_loggers[destination]
+
+
+def record(event: Event, destination: str = "null") -> None:
+    _get_or_create_logger(destination).info(event.serialize())
+
+def record_rdzv_event(event: RdzvEvent) -> None:
+    _get_or_create_logger("dynamic_rendezvous").info(event.serialize())
+
+
+def construct_and_record_rdzv_event(
+    run_id: str,
+    message: str,
+    node_state: NodeState,
+    name: str = "",
+    hostname: str = "",
+    pid: Optional[int] = None,
+    master_endpoint: str = "",
+    local_id: Optional[int] = None,
+    rank: Optional[int] = None,
+) -> None:
+    # We don't want to perform an extra computation if not needed.
+    if isinstance(get_logging_handler("dynamic_rendezvous"), logging.NullHandler):
+        return
+
+    # Set up parameters.
+    if not hostname:
+        hostname = socket.getfqdn()
+    if not pid:
+        pid = os.getpid()
+
+    # Determines which file called this function.
+    callstack = inspect.stack()
+    filename = "no_file"
+    if len(callstack) > 1:
+        stack_depth_1 = callstack[1]
+        filename = os.path.basename(stack_depth_1.filename)
+        if not name:
+            name = stack_depth_1.function
+
+    # Delete the callstack variable. If kept, this can mess with python's
+    # garbage collector as we are holding on to stack frame information in
+    # the inspect module.
+    del callstack
+
+    # Set up error trace if this is an exception
+    if node_state == NodeState.FAILED:
+        error_trace = traceback.format_exc()
+    else:
+        error_trace = ""
+
+    # Initialize event object
+    event = RdzvEvent(
+        name=f"{filename}:{name}",
+        run_id=run_id,
+        message=message,
+        hostname=hostname,
+        pid=pid,
+        node_state=node_state,
+        master_endpoint=master_endpoint,
+        rank=rank,
+        local_id=local_id,
+        error_trace=error_trace,
+    )
+
+    # Finally, record the event.
+    record_rdzv_event(event)

venv/lib/python3.10/site-packages/torch/distributed/elastic/events/api.py

@@ -0,0 +1,112 @@
+#!/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 json
+from dataclasses import asdict, dataclass, field
+from enum import Enum
+from typing import Dict, Union, Optional
+
+__all__ = ['EventSource', 'Event', 'NodeState', 'RdzvEvent']
+
+EventMetadataValue = Union[str, int, float, bool, None]
+
+
+class EventSource(str, Enum):
+    """Known identifiers of the event producers."""
+
+    AGENT = "AGENT"
+    WORKER = "WORKER"
+
+
+@dataclass
+class Event:
+    """
+    The class represents the generic event that occurs during the torchelastic job execution.
+
+    The event can be any kind of meaningful action.
+
+    Args:
+        name: event name.
+        source: the event producer, e.g. agent or worker
+        timestamp: timestamp in milliseconds when event occurred.
+        metadata: additional data that is associated with the event.
+    """
+
+    name: str
+    source: EventSource
+    timestamp: int = 0
+    metadata: Dict[str, EventMetadataValue] = field(default_factory=dict)
+
+    def __str__(self):
+        return self.serialize()
+
+    @staticmethod
+    def deserialize(data: Union[str, "Event"]) -> "Event":
+        if isinstance(data, Event):
+            return data
+        if isinstance(data, str):
+            data_dict = json.loads(data)
+        data_dict["source"] = EventSource[data_dict["source"]]  # type: ignore[possibly-undefined]
+        return Event(**data_dict)
+
+    def serialize(self) -> str:
+        return json.dumps(asdict(self))
+
+
+class NodeState(str, Enum):
+    """The states that a node can be in rendezvous."""
+
+    INIT = "INIT"
+    RUNNING = "RUNNING"
+    SUCCEEDED = "SUCCEEDED"
+    FAILED = "FAILED"
+
+
+@dataclass
+class RdzvEvent:
+    """
+    Dataclass to represent any rendezvous event.
+
+    Args:
+        name: Event name. (E.g. Current action being performed)
+        run_id: The run id of the rendezvous
+        message: The message describing the event
+        hostname: Hostname of the node
+        pid: The process id of the node
+        node_state: The state of the node (INIT, RUNNING, SUCCEEDED, FAILED)
+        master_endpoint: The master endpoint for the rendezvous store, if known
+        rank: The rank of the node, if known
+        local_id: The local_id of the node, if defined in dynamic_rendezvous.py
+        error_trace: Error stack trace, if this is an error event.
+    """
+
+    name: str
+    run_id: str
+    message: str
+    hostname: str
+    pid: int
+    node_state: NodeState
+    master_endpoint: str = ""
+    rank: Optional[int] = None
+    local_id: Optional[int] = None
+    error_trace: str = ""
+
+    def __str__(self):
+        return self.serialize()
+
+    @staticmethod
+    def deserialize(data: Union[str, "RdzvEvent"]) -> "RdzvEvent":
+        if isinstance(data, RdzvEvent):
+            return data
+        if isinstance(data, str):
+            data_dict = json.loads(data)
+        data_dict["node_state"] = NodeState[data_dict["node_state"]]  # type: ignore[possibly-undefined]
+        return RdzvEvent(**data_dict)
+
+    def serialize(self) -> str:
+        return json.dumps(asdict(self))

venv/lib/python3.10/site-packages/torch/distributed/elastic/events/handlers.py

@@ -0,0 +1,22 @@
+#!/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 logging
+from typing import Dict
+
+
+_log_handlers: Dict[str, logging.Handler] = {
+    "console": logging.StreamHandler(),
+    "dynamic_rendezvous": logging.NullHandler(),
+    "null": logging.NullHandler(),
+}
+
+
+def get_logging_handler(destination: str = "null") -> logging.Handler:
+    global _log_handlers
+    return _log_handlers[destination]

venv/lib/python3.10/site-packages/torch/distributed/elastic/metrics/__init__.py

@@ -0,0 +1,163 @@
+#!/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.
+
+"""Metrics API.
+
+**Overview**:
+
+The metrics API in torchelastic is used to publish telemetry metrics.
+It is designed to be used by torchelastic's internal modules to
+publish metrics for the end user with the goal of increasing visibility
+and helping with debugging. However you may use the same API in your
+jobs to publish metrics to the same metrics ``sink``.
+
+A ``metric`` can be thought of as timeseries data
+and is uniquely identified by the string-valued tuple
+``(metric_group, metric_name)``.
+
+torchelastic makes no assumptions about what a ``metric_group`` is
+and what relationship it has with ``metric_name``. It is totally up
+to the user to use these two fields to uniquely identify a metric.
+
+.. note:: The metric group ``torchelastic`` is reserved by torchelastic for
+          platform level metrics that it produces.
+          For instance torchelastic may output the latency (in milliseconds)
+          of a re-rendezvous operation from the agent as
+          ``(torchelastic, agent.rendezvous.duration.ms)``
+
+A sensible way to use metric groups is to map them to a stage or module
+in your job. You may also encode certain high level properties
+the job such as the region or stage (dev vs prod).
+
+**Publish Metrics**:
+
+Using torchelastic's metrics API is similar to using python's logging
+framework. You first have to configure a metrics handler before
+trying to add metric data.
+
+The example below measures the latency for the ``calculate()`` function.
+
+::
+
+  import time
+  import torch.distributed.elastic.metrics as metrics
+
+  # makes all metrics other than the one from "my_module" to go /dev/null
+  metrics.configure(metrics.NullMetricsHandler())
+  metrics.configure(metrics.ConsoleMetricsHandler(), "my_module")
+
+  def my_method():
+    start = time.time()
+    calculate()
+    end = time.time()
+    metrics.put_metric("calculate_latency", int(end-start), "my_module")
+
+You may also use the torch.distributed.elastic.metrics.prof` decorator
+to conveniently and succinctly profile functions
+
+::
+
+  # -- in module examples.foobar --
+
+  import torch.distributed.elastic.metrics as metrics
+
+  metrics.configure(metrics.ConsoleMetricsHandler(), "foobar")
+  metrics.configure(metrics.ConsoleMetricsHandler(), "Bar")
+
+  @metrics.prof
+  def foo():
+    pass
+
+  class Bar():
+
+    @metrics.prof
+    def baz():
+        pass
+
+``@metrics.prof`` will publish the following metrics
+::
+
+  <leaf_module or classname>.success - 1 if the function finished successfully
+  <leaf_module or classname>.failure - 1 if the function threw an exception
+  <leaf_module or classname>.duration.ms - function duration in milliseconds
+
+**Configuring Metrics Handler**:
+
+`torch.distributed.elastic.metrics.MetricHandler` is responsible for emitting
+the added metric values to a particular destination. Metric groups can be
+configured with different metric handlers.
+
+By default torchelastic emits all metrics to ``/dev/null``.
+By adding the following configuration metrics,
+``torchelastic`` and ``my_app`` metric groups will be printed out to
+console.
+
+::
+
+  import torch.distributed.elastic.metrics as metrics
+
+  metrics.configure(metrics.ConsoleMetricHandler(), group = "torchelastic")
+  metrics.configure(metrics.ConsoleMetricHandler(), group = "my_app")
+
+**Writing a Custom Metric Handler**:
+
+If you want your metrics to be emitted to a custom location, implement
+the `torch.distributed.elastic.metrics.MetricHandler` interface
+and configure your job to use your custom metric handler.
+
+Below is a toy example that prints the metrics to ``stdout``
+
+::
+
+  import torch.distributed.elastic.metrics as metrics
+
+  class StdoutMetricHandler(metrics.MetricHandler):
+     def emit(self, metric_data):
+         ts = metric_data.timestamp
+         group = metric_data.group_name
+         name = metric_data.name
+         value = metric_data.value
+         print(f"[{ts}][{group}]: {name}={value}")
+
+  metrics.configure(StdoutMetricHandler(), group="my_app")
+
+Now all metrics in the group ``my_app`` will be printed to stdout as:
+
+::
+
+  [1574213883.4182858][my_app]: my_metric=<value>
+  [1574213940.5237644][my_app]: my_metric=<value>
+
+"""
+
+from typing import Optional
+
+from .api import (  # noqa: F401
+    ConsoleMetricHandler,
+    MetricData,
+    MetricHandler,
+    MetricsConfig,
+    NullMetricHandler,
+    configure,
+    get_elapsed_time_ms,
+    getStream,
+    prof,
+    profile,
+    publish_metric,
+    put_metric,
+)
+
+
+def initialize_metrics(cfg: Optional[MetricsConfig] = None):
+    pass
+
+
+try:
+    from torch.distributed.elastic.metrics.static_init import *  # type: ignore[import] # noqa: F401 F403
+except ModuleNotFoundError:
+    pass

venv/lib/python3.10/site-packages/torch/distributed/elastic/metrics/api.py

@@ -0,0 +1,201 @@
+#!/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 time
+import warnings
+from collections import namedtuple
+from functools import wraps
+from typing import Dict, Optional
+
+__all__ = ['MetricsConfig', 'MetricHandler', 'ConsoleMetricHandler', 'NullMetricHandler', 'MetricStream',
+           'configure', 'getStream', 'prof', 'profile', 'put_metric', 'publish_metric', 'get_elapsed_time_ms',
+           'MetricData']
+
+MetricData = namedtuple("MetricData", ["timestamp", "group_name", "name", "value"])
+
+
+class MetricsConfig:
+    __slots__ = ["params"]
+
+    def __init__(self, params: Optional[Dict[str, str]] = None):
+        self.params = params
+        if self.params is None:
+            self.params = {}
+
+
+class MetricHandler(abc.ABC):
+    @abc.abstractmethod
+    def emit(self, metric_data: MetricData):
+        pass
+
+
+class ConsoleMetricHandler(MetricHandler):
+    def emit(self, metric_data: MetricData):
+        print(
+            f"[{metric_data.timestamp}][{metric_data.group_name}]: {metric_data.name}={metric_data.value}"
+        )
+
+
+class NullMetricHandler(MetricHandler):
+    def emit(self, metric_data: MetricData):
+        pass
+
+
+class MetricStream:
+    def __init__(self, group_name: str, handler: MetricHandler):
+        self.group_name = group_name
+        self.handler = handler
+
+    def add_value(self, metric_name: str, metric_value: int):
+        self.handler.emit(
+            MetricData(time.time(), self.group_name, metric_name, metric_value)
+        )
+
+
+_metrics_map: Dict[str, MetricHandler] = {}
+_default_metrics_handler: MetricHandler = NullMetricHandler()
+
+
+# pyre-fixme[9]: group has type `str`; used as `None`.
+def configure(handler: MetricHandler, group: Optional[str] = None):
+    if group is None:
+        global _default_metrics_handler
+        # pyre-fixme[9]: _default_metrics_handler has type `NullMetricHandler`; used
+        #  as `MetricHandler`.
+        _default_metrics_handler = handler
+    else:
+        _metrics_map[group] = handler
+
+
+def getStream(group: str):
+    if group in _metrics_map:
+        handler = _metrics_map[group]
+    else:
+        handler = _default_metrics_handler
+    return MetricStream(group, handler)
+
+
+def _get_metric_name(fn):
+    qualname = fn.__qualname__
+    split = qualname.split(".")
+    if len(split) == 1:
+        module = fn.__module__
+        if module:
+            return module.split(".")[-1] + "." + split[0]
+        else:
+            return split[0]
+    else:
+        return qualname
+
+
+def prof(fn=None, group: str = "torchelastic"):
+    r"""
+    @profile decorator publishes duration.ms, count, success, failure metrics for the function that it decorates.
+
+    The metric name defaults to the qualified name (``class_name.def_name``) of the function.
+    If the function does not belong to a class, it uses the leaf module name instead.
+
+    Usage
+
+    ::
+
+     @metrics.prof
+     def x():
+         pass
+
+     @metrics.prof(group="agent")
+     def y():
+         pass
+    """
+
+    def wrap(f):
+        @wraps(f)
+        def wrapper(*args, **kwargs):
+            key = _get_metric_name(f)
+            try:
+                start = time.time()
+                result = f(*args, **kwargs)
+                put_metric(f"{key}.success", 1, group)
+            except Exception:
+                put_metric(f"{key}.failure", 1, group)
+                raise
+            finally:
+                put_metric(f"{key}.duration.ms", get_elapsed_time_ms(start), group)  # type: ignore[possibly-undefined]
+            return result
+
+        return wrapper
+
+    if fn:
+        return wrap(fn)
+    else:
+        return wrap
+
+
+def profile(group=None):
+    """
+    @profile decorator adds latency and success/failure metrics to any given function.
+
+    Usage
+
+    ::
+
+     @metrics.profile("my_metric_group")
+     def some_function(<arguments>):
+    """
+    warnings.warn("Deprecated, use @prof instead", DeprecationWarning)
+
+    def wrap(func):
+        @wraps(func)
+        def wrapper(*args, **kwargs):
+            try:
+                start_time = time.time()
+                result = func(*args, **kwargs)
+                publish_metric(group, f"{func.__name__}.success", 1)
+            except Exception:
+                publish_metric(group, f"{func.__name__}.failure", 1)
+                raise
+            finally:
+                publish_metric(
+                    group,
+                    f"{func.__name__}.duration.ms",
+                    get_elapsed_time_ms(start_time),  # type: ignore[possibly-undefined]
+                )
+            return result
+
+        return wrapper
+
+    return wrap
+
+
+def put_metric(metric_name: str, metric_value: int, metric_group: str = "torchelastic"):
+    """
+    Publish a metric data point.
+
+    Usage
+
+    ::
+
+     put_metric("metric_name", 1)
+     put_metric("metric_name", 1, "metric_group_name")
+    """
+    getStream(metric_group).add_value(metric_name, metric_value)
+
+
+def publish_metric(metric_group: str, metric_name: str, metric_value: int):
+    warnings.warn(
+        "Deprecated, use put_metric(metric_group)(metric_name, metric_value) instead"
+    )
+    metric_stream = getStream(metric_group)
+    metric_stream.add_value(metric_name, metric_value)
+
+
+def get_elapsed_time_ms(start_time_in_seconds: float):
+    """Return the elapsed time in millis from the given start time."""
+    end_time = time.time()
+    return int((end_time - start_time_in_seconds) * 1000)

venv/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/__init__.py

@@ -0,0 +1,235 @@
+#!/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, Optional, Tuple, Union, Set
+
+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
+
+__all__ = [
+    "start_processes",
+    "MultiprocessContext",
+    "PContext",
+    "ProcessFailure",
+    "RunProcsResult",
+    "SignalException",
+    "Std",
+    "LogsDest",
+    "LogsSpecs",
+    "DefaultLogsSpecs",
+    "SubprocessContext",
+    "to_map",
+]
+
+log = get_logger(__name__)
+
+
+def start_processes(
+    name: str,
+    entrypoint: Union[Callable, str],
+    args: Dict[int, Tuple],
+    envs: Dict[int, Dict[str, str]],
+    logs_specs: LogsSpecs,
+    log_line_prefixes: Optional[Dict[int, str]] = None,
+    start_method: str = "spawn",
+) -> 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.
+
+    ``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
+
+    """
+
+    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,
+            logs_specs=logs_specs,
+            log_line_prefixes=log_line_prefixes,
+        )
+    else:
+        context = MultiprocessContext(
+            name=name,
+            entrypoint=entrypoint,
+            args=args,
+            envs=envs,
+            log_line_prefixes=log_line_prefixes,
+            start_method=start_method,
+            logs_specs=logs_specs,
+        )
+
+    try:
+        context.start()
+        return context
+    except Exception:
+        context.close()
+        raise

venv/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/api.py

@@ -0,0 +1,873 @@
+#!/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 shutil
+import signal
+import subprocess
+import sys
+import tempfile
+import time
+from contextlib import nullcontext
+from dataclasses import dataclass, field
+from enum import IntFlag
+from multiprocessing import synchronize
+from types import FrameType
+from typing import Any, Callable, Dict, Optional, Set, Tuple, Union
+from abc import ABC, abstractmethod
+
+import torch.multiprocessing as mp
+from torch.distributed.elastic.multiprocessing.errors import ProcessFailure, record
+from torch.distributed.elastic.multiprocessing.redirects import (
+    redirect_stderr,
+    redirect_stdout,
+)
+
+from torch.distributed.elastic.multiprocessing.subprocess_handler import SubprocessHandler, get_subprocess_handler
+from torch.distributed.elastic.multiprocessing.tail_log import TailLog
+
+IS_WINDOWS = sys.platform == "win32"
+IS_MACOS = sys.platform == "darwin"
+
+
+log = logging.getLogger(__name__)
+
+__all__ = [
+    "DefaultLogsSpecs",
+    "SignalException",
+    "Std",
+    "to_map",
+    "RunProcsResult",
+    "PContext",
+    "get_std_cm",
+    "MultiprocessContext",
+    "SubprocessContext",
+]
+
+class SignalException(Exception):
+    """
+    Exception is raised inside the torchelastic agent process by the termination handler
+    if the death signal got received by the process.
+    """
+
+    def __init__(self, msg: str, sigval: signal.Signals) -> None:
+        super().__init__(msg)
+        self.sigval = sigval
+
+
+def _terminate_process_handler(signum: int, frame: Optional[FrameType]) -> None:
+    """Termination handler that raises exceptions on the main process.
+
+    When the process receives death signal(SIGTERM, SIGINT), this termination handler will
+    be invoked. It raises the ``SignalException`` exception that should be processed by the
+    user code. Python does not terminate process after the termination handler is finished,
+    so the exception should not be silently ignored, otherwise the process will never
+    be terminated.
+    """
+    sigval = signal.Signals(signum)
+    raise SignalException(f"Process {os.getpid()} got signal: {sigval}", sigval=sigval)
+
+
+def _get_kill_signal() -> signal.Signals:
+    """Get the kill signal. SIGKILL for unix, CTRL_C_EVENT for windows."""
+    if IS_WINDOWS:
+        return signal.CTRL_C_EVENT  # type: ignore[attr-defined] # noqa: F821
+    else:
+        return signal.SIGKILL
+
+
+def _get_default_signal() -> signal.Signals:
+    """Get the default termination signal. SIGTERM for unix, CTRL_C_EVENT for windows."""
+    if IS_WINDOWS:
+        return signal.CTRL_C_EVENT  # type: ignore[attr-defined] # noqa: F821
+    else:
+        return signal.SIGTERM
+
+
+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
+
+    @classmethod
+    def from_str(cls, vm: str) -> Union["Std", Dict[int, "Std"]]:
+        """
+        Example:
+        ::
+
+         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: str) -> Std:  # type: ignore[return]
+            s = Std(int(v))
+            if s in Std:
+                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
+        else:
+            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.
+
+    Example:
+    ::
+
+     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 dict.fromkeys(range(local_world_size), val_or_map)
+    else:
+        map = {}
+        for i in range(local_world_size):
+            map[i] = val_or_map.get(i, Std.NONE)
+        return map
+
+
+@dataclass
+class LogsDest:
+    """
+    For each log type, holds mapping of local rank ids to file paths.
+    """
+    stdouts: Dict[int, str] = field(default_factory=dict)
+    stderrs: Dict[int, str] = field(default_factory=dict)
+    tee_stdouts: Dict[int, str] = field(default_factory=dict)
+    tee_stderrs: Dict[int, str] = field(default_factory=dict)
+    error_files: Dict[int, str] = field(default_factory=dict)
+
+
+class LogsSpecs(ABC):
+    """
+    Defines logs processing and redirection for each worker process.
+
+    Args:
+        log_dir:
+            Base directory where logs will be written.
+        redirects:
+            Streams to redirect to files. Pass a single ``Std``
+            enum to redirect for all workers, or a mapping keyed
+            by local_rank to selectively redirect.
+        tee:
+            Streams to duplicate to stdout/stderr.
+            Pass a single ``Std`` enum to duplicate streams for all workers,
+            or a mapping keyed by local_rank to selectively duplicate.
+    """
+
+    def __init__(
+        self,
+        log_dir: Optional[str] = None,
+        redirects: Union[Std, Dict[int, Std]] = Std.NONE,
+        tee: Union[Std, Dict[int, Std]] = Std.NONE,
+        local_ranks_filter: Optional[Set[int]] = None,
+    ) -> None:
+        self._root_log_dir = log_dir
+        self._redirects = redirects
+        self._tee = tee
+        self._local_ranks_filter = local_ranks_filter
+
+    @abstractmethod
+    def reify(self, envs: Dict[int, Dict[str, str]],) -> LogsDest:
+        """
+        Given the environment variables, builds destination of log files for each of the local ranks.
+
+        Envs parameter contains env variables dict for each of the local ranks, where entries are defined in:
+        :func:`~torchelastic.distributed.elastic.agent.server.local_elastic_agent.LocalElasticAgent._start_workers`.
+        """
+        pass
+
+    @property
+    @abstractmethod
+    def root_log_dir(self) -> str:
+        pass
+
+class DefaultLogsSpecs(LogsSpecs):
+    """
+    Default LogsSpecs implementation:
+
+    - `log_dir` will be created if it doesn't exist
+    - Generates nested folders for each attempt and rank.
+    """
+    def __init__(
+        self,
+        log_dir: Optional[str] = None,
+        redirects: Union[Std, Dict[int, Std]] = Std.NONE,
+        tee: Union[Std, Dict[int, Std]] = Std.NONE,
+        local_ranks_filter: Optional[Set[int]] = None,
+    ) -> None:
+        if log_dir != os.devnull:
+            if not log_dir:
+                log_dir = tempfile.mkdtemp(prefix="torchelastic_")
+            elif not os.path.exists(log_dir):
+                os.makedirs(log_dir)
+            else:
+                if os.path.isfile(log_dir):
+                    raise NotADirectoryError(f"log_dir: {log_dir} is a file")
+        super().__init__(log_dir, redirects, tee, local_ranks_filter)
+        # initialized only once
+        self._run_log_dir = None
+
+    @property
+    def root_log_dir(self) -> str:
+        return str(self._root_log_dir)
+
+    def _make_log_dir(self, log_dir: Optional[str], rdzv_run_id: str):
+        base_log_dir = log_dir or tempfile.mkdtemp(prefix="torchelastic_")
+        os.makedirs(base_log_dir, exist_ok=True)
+        dir = tempfile.mkdtemp(prefix=f"{rdzv_run_id}_", dir=base_log_dir)
+        log.info("log directory set to: %s", dir)
+        return dir
+
+    def reify(self, envs: Dict[int, Dict[str, str]],) -> LogsDest:
+        """
+        Uses following scheme to build log destination paths:
+
+        - `<log_dir>/<rdzv_run_id>/attempt_<attempt>/<rank>/stdout.log`
+        - `<log_dir>/<rdzv_run_id>/attempt_<attempt>/<rank>/stderr.log`
+        - `<log_dir>/<rdzv_run_id>/attempt_<attempt>/<rank>/error.json`
+        """
+        nprocs = len(envs)
+        global_env = {}  # use only to query properies that are not dependent on a rank
+        if nprocs > 0:
+            global_env = envs[0]
+        else:
+            log.warning("Empty envs map provided when defining logging destinations.")
+        # Keys are always defined, but values can be missing in unit tests
+        run_id = global_env.get("TORCHELASTIC_RUN_ID", "test_run_id")
+        restart_count = global_env.get("TORCHELASTIC_RESTART_COUNT", "0")
+
+        attempt_log_dir: str = ""
+        if self._root_log_dir != os.devnull:
+            if not self._run_log_dir:
+                self._run_log_dir = self._make_log_dir(self._root_log_dir, run_id)
+
+            attempt_log_dir = os.path.join(self._run_log_dir, f"attempt_{restart_count}")  # type: ignore[call-overload]
+            shutil.rmtree(attempt_log_dir, ignore_errors=True)
+            os.makedirs(attempt_log_dir)
+
+        if self._root_log_dir == os.devnull:
+            attempt_log_dir = os.devnull
+
+        # create subdirs for each local rank in the logs_dir
+        # logs_dir
+        #       |- 0
+        #          |- error.json
+        #          |- stdout.log
+        #          |- stderr.log
+        #       |- ...
+        #       |- (nprocs-1)
+        redirs = to_map(self._redirects, nprocs)
+        ts = to_map(self._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
+
+        SYS_STREAM = ""  # special case to indicate to output to console
+        stdouts = dict.fromkeys(range(nprocs), SYS_STREAM)
+        stderrs = dict.fromkeys(range(nprocs), SYS_STREAM)
+        tee_stdouts: Dict[int, str] = {}
+        tee_stderrs: Dict[int, str] = {}
+        error_files = {}
+
+        for local_rank in range(nprocs):
+
+            if attempt_log_dir == os.devnull:
+                tee_stdouts[local_rank] = os.devnull
+                tee_stderrs[local_rank] = os.devnull
+                error_files[local_rank] = os.devnull
+                envs[local_rank]["TORCHELASTIC_ERROR_FILE"] = ""
+            else:
+                clogdir = os.path.join(attempt_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]
+
+                if self._local_ranks_filter and local_rank not in self._local_ranks_filter:
+                    # If stream is tee'd, only write to file, but don't tail
+                    if local_rank in tee_stdouts:
+                        tee_stdouts.pop(local_rank, None)
+                    if local_rank in tee_stderrs:
+                        tee_stderrs.pop(local_rank, None)
+
+                    # If stream is not redirected, don't print
+                    if stdouts[local_rank] == SYS_STREAM:
+                        stdouts[local_rank] = os.devnull
+                    if stderrs[local_rank] == SYS_STREAM:
+                        stderrs[local_rank] = os.devnull
+
+                error_file = os.path.join(clogdir, "error.json")
+                error_files[local_rank] = error_file
+                log.info("Setting worker%s reply file to: %s", local_rank, error_file)
+                envs[local_rank]["TORCHELASTIC_ERROR_FILE"] = error_file
+
+        return LogsDest(stdouts, stderrs, tee_stdouts, tee_stderrs, error_files)
+
+    def __repr__(self) -> str:
+        return (
+            f"DefaultLogsSpecs(root_log_dir={self._root_log_dir}, redirects={self._redirects}, "
+            f"tee={self._tee}, local_ranks_filter={self._local_ranks_filter})"
+        )
+
+    def __eq__(self, other: object) -> bool:
+        if not isinstance(other, DefaultLogsSpecs):
+            return False
+
+        return (
+            self._root_log_dir == other._root_log_dir
+            and self._redirects == other._redirects
+            and self._tee == other._tee
+            and self._local_ranks_filter == other._local_ranks_filter
+        )
+
+
+@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
+
+
+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]],
+        logs_specs: LogsSpecs,
+        log_line_prefixes: Optional[Dict[int, str]] = None,
+
+    ):
+        self.name = name
+        # validate that all mappings have the same number of keys and
+        # all local ranks are accounted for
+        nprocs = len(args)
+
+        # TODO log_line_prefixes can be exanded too
+        logs_dest = logs_specs.reify(envs)
+
+        _validate_full_rank(logs_dest.stdouts, nprocs, "stdouts")
+        _validate_full_rank(logs_dest.stderrs, nprocs, "stderrs")
+
+        self.entrypoint = entrypoint
+        self.args = args
+        self.envs = envs
+        self.stdouts = logs_dest.stdouts
+        self.stderrs = logs_dest.stderrs
+        self.error_files = logs_dest.error_files
+        self.nprocs = nprocs
+
+        self._stdout_tail = TailLog(name, logs_dest.tee_stdouts, sys.stdout, log_line_prefixes)
+        self._stderr_tail = TailLog(name, logs_dest.tee_stderrs, sys.stderr, log_line_prefixes)
+
+    def start(self) -> None:
+        """Start processes using parameters defined in the constructor."""
+        signal.signal(signal.SIGTERM, _terminate_process_handler)
+        signal.signal(signal.SIGINT, _terminate_process_handler)
+        if not IS_WINDOWS:
+            signal.signal(signal.SIGHUP, _terminate_process_handler)
+            signal.signal(signal.SIGQUIT, _terminate_process_handler)
+        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]:
+        """
+        Poll 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]:
+        """
+        Wait 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).
+
+        ..note: Multiprocessing library registers SIGTERM and SIGINT signal handlers that raise
+                ``SignalException`` when the signals received. It is up to the consumer of the code
+                to properly handle the exception. It is important not to swallow the exception otherwise
+                the process would not terminate. Example of the typical workflow can be:
+
+        .. code-block:: python
+            pc = start_processes(...)
+            try:
+                pc.wait(1)
+                .. do some other work
+            except SignalException as e:
+                pc.shutdown(e.sigval, timeout=30)
+
+        If SIGTERM or SIGINT occurs, the code above will try to shutdown child processes by propagating
+        received signal. If child processes will not terminate in the timeout time, the process will send
+        the SIGKILL.
+        """
+        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]:
+        """Return pids of processes mapped by their respective local_ranks."""
+        raise NotImplementedError()
+
+    @abc.abstractmethod
+    def _close(self, death_sig: signal.Signals, timeout: int = 30) -> 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, death_sig: Optional[signal.Signals] = None, timeout: int = 30
+    ) -> None:
+        r"""
+        Terminates all processes managed by this context and cleans up any
+        meta resources (e.g. redirect, error_file files).
+
+        Args:
+            death_sig: Death signal to terminate processes.
+            timeout: Time to wait for processes to finish, if process is
+                still alive after this time, it will be terminated via SIGKILL.
+        """
+        if not death_sig:
+            death_sig = _get_default_signal()
+        self._close(death_sig=death_sig, timeout=timeout)
+        if self._stdout_tail:
+            self._stdout_tail.stop()
+        if self._stderr_tail:
+            self._stderr_tail.stop()
+
+
+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()
+
+
+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]],
+        start_method: str,
+        logs_specs: LogsSpecs,
+        log_line_prefixes: Optional[Dict[int, str]] = None,
+    ):
+        super().__init__(
+            name,
+            entrypoint,
+            args,
+            envs,
+            logs_specs,
+            log_line_prefixes,
+        )
+
+        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:
+            # torch.mp.ProcessContext 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: https://docs.python.org/2/library/multiprocessing.html#all-platforms
+            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.exception(
+                "failed (exitcode: %s)"
+                " local_rank: %s (pid: %s)"
+                " of fn: %s (start_method: %s)",
+                failed_proc.exitcode,
+                failed_local_rank, e.pid,
+                fn_name, self.start_method,
+            )
+
+            self.close()
+            return RunProcsResult(
+                failures={
+                    failed_local_rank: ProcessFailure(
+                        local_rank=failed_local_rank,
+                        pid=e.pid,
+                        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 dict(enumerate(self._pc.pids()))
+
+    def _close(self, death_sig: signal.Signals, timeout: int = 30) -> None:
+        if not self._pc:
+            return
+        for proc in self._pc.processes:
+            if proc.is_alive():
+                log.warning("Closing process %s via signal %s", proc.pid, death_sig.name)
+                try:
+                    os.kill(proc.pid, death_sig)
+                except ProcessLookupError:
+                    # If the process exited because of some reason,
+                    # `ProcessLookupError` will be raised, it is safe to ignore it.
+                    pass
+        end = time.monotonic() + timeout
+        for proc in self._pc.processes:
+            time_to_wait = end - time.monotonic()
+            if time_to_wait <= 0:
+                break
+            proc.join(time_to_wait)
+        for proc in self._pc.processes:
+            if proc.is_alive():
+                log.warning(
+                    "Unable to shutdown process %s via %s, forcefully exiting via %s",
+                    proc.pid, death_sig, _get_kill_signal()
+                )
+                try:
+                    os.kill(proc.pid, _get_kill_signal())
+                except ProcessLookupError:
+                    # If the process exited because of some reason,
+                    # `ProcessLookupError` will be raised, it is safe to ignore it.
+                    pass
+            proc.join()
+
+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]],
+        logs_specs: LogsSpecs,
+        log_line_prefixes: Optional[Dict[int, str]] = None,
+
+    ):
+        super().__init__(
+            name,
+            entrypoint,
+            args,
+            envs,
+            logs_specs,
+            log_line_prefixes,
+        )
+
+        # 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: get_subprocess_handler(
+                entrypoint=self.entrypoint,  # type: ignore[arg-type] # entrypoint is always a str
+                args=self.args[local_rank],
+                env=self.envs[local_rank],
+                stdout=self.stdouts[local_rank],
+                stderr=self.stderrs[local_rank],
+                local_rank_id=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,
+                        pid=handler.proc.pid,
+                        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(
+                    "failed (exitcode: %s)"
+                    " local_rank: %s (pid: %s)"
+                    " of binary: %s",
+                    first_failure.exitcode, first_failure.local_rank, first_failure.pid, self.entrypoint
+                )
+            else:
+                # Populate return with dummy values. This provides consistency with MultiprocessingHandler
+                result.return_values = dict.fromkeys(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: sh.proc.pid
+            for local_rank, sh in self.subprocess_handlers.items()
+        }
+
+    def _close(self, death_sig: signal.Signals, timeout: int = 30) -> None:
+        if not self.subprocess_handlers:
+            return
+        for handler in self.subprocess_handlers.values():
+            if handler.proc.poll() is None:
+                log.warning(
+                    "Sending process %s closing signal %s", handler.proc.pid, death_sig.name
+                )
+                handler.close(death_sig=death_sig)
+        end = time.monotonic() + timeout
+        for handler in self.subprocess_handlers.values():
+            time_to_wait = end - time.monotonic()
+            if time_to_wait <= 0:
+                break
+            try:
+                handler.proc.wait(time_to_wait)
+            except subprocess.TimeoutExpired:
+                # Ignore the timeout expired exception, since
+                # the child process will be forcefully terminated via SIGKILL
+                pass
+        for handler in self.subprocess_handlers.values():
+            if handler.proc.poll() is None:
+                log.warning(
+                    "Unable to shutdown process %s via %s, forcefully exiting via %s",
+                    handler.proc.pid, death_sig, _get_kill_signal()
+                )
+                handler.close(death_sig=_get_kill_signal())
+                handler.proc.wait()

venv/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/errors/__init__.py

@@ -0,0 +1,375 @@
+#!/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.
+
+"""
+Each host in a distributed PyTorch job runs with a single TorchElastic agent,
+and multiple workers (as children processes of the TorchElastic agent).
+Since the workers are user-provided (your PyTorch script/job), TorchElastic
+has a way to propagate errors on the trainers through the agent and up to the
+scheduler, which ultimately informs the end-user about the state of the job
+and applies any retry policies.
+
+TorchElastic categorizes errors into 3 categories:
+
++----------------+----------------+--------------------------------------------------------------+
+| Category       | Sub-Category   |  Description                                                 |
++================+================+==============================================================+
+| User Error     | Input Error    | invalid inputs to TorchElastic APIs (e.g. min > max nodes)   |
+|                +----------------+--------------------------------------------------------------+
+|                | Worker Failure | any failures on the worker child process                     |
++----------------+----------------+--------------------------------------------------------------+
+| Platform Error |      n/a       | failures caused by the agent                                 |
++----------------+----------------+--------------------------------------------------------------+
+| Infra Error    |      n/a       | failures outside the domain of the agent and workers         |
+|                |                | (e.g. host failures)                                         |
++----------------+----------------+--------------------------------------------------------------+
+
+All errors other than "Worker Failure" are either raised canonically from the
+agent process or implicitly or explicitly crash the agent process. So the
+standard language (python) provided exception handling strategies apply.
+
+Worker Failures are special because the exception/failure originates on a different
+process from the agent so the error needs to be propagated inter-process
+(e.g. the agent cannot simply ``try-catch`` an exception raised on the worker process).
+
+TorchElastic agents use :func:`torch.distributed.elastic.multiprocessing.start_processes`
+to launch the workers which has a simple file based inter-process error propagation
+built-in.
+
+Any function or binary entrypoint decorated with :func:`record`
+will write uncaught exceptions (with the trace information) to a file specified by the
+environment variable ``TORCHELASTIC_ERROR_FILE``. The parent process (e.g. agent)
+sets this env var on each child it launches, then aggregates the error files for all
+children, and propagates the one with the **smallest** timestamp (e.g. the **first** error).
+"""
+
+import json
+import os
+import signal
+import socket
+import time
+import warnings
+from dataclasses import dataclass, field
+from datetime import datetime
+from functools import wraps
+from string import Template
+from typing import Any, Callable, Dict, List, Optional, Tuple, TypeVar
+
+from torch.distributed.elastic.utils.logging import get_logger
+
+from .error_handler import ErrorHandler  # noqa: F401
+from .handlers import get_error_handler  # noqa: F401
+
+__all__ = ["ProcessFailure", "ChildFailedError", "record", "ErrorHandler", "get_error_handler"]
+
+log = get_logger(__name__)
+
+
+JSON = Dict
+
+_EMPTY_ERROR_DATA = {"message": "<NONE>"}
+_NOT_AVAILABLE = "<N/A>"
+
+T = TypeVar("T")
+
+
+@dataclass
+class ProcessFailure:
+    """
+    Represent the failed process result. When the worker process fails, it may record failure root cause into the file.
+
+    Tries to read the failure timestamp from the provided ``error_file``,
+    if the ``error_file`` does not exist, the timestamp is the current
+    timestamp (seconds since epoch).
+
+    The ``message`` field is a concise explanation of the failure. If
+    the error file exists then the message is obtained from the error file.
+    Otherwise one is generated based on the failure signature.
+
+    .. note:: It is assumed that the ``error_file`` is written by
+              ``torch.distributed.elastic.multiprocessing.errors.error_handler.ErrorHandler``.
+              Otherwise the behavior is undefined.
+
+    """
+
+    local_rank: int
+    pid: int
+    exitcode: int
+    error_file: str
+    error_file_data: JSON = field(init=False)
+    message: str = field(init=False)
+    timestamp: int = field(init=False)
+
+    def __post_init__(self):
+        self.error_file_data = _EMPTY_ERROR_DATA
+        if os.path.isfile(self.error_file):
+            try:
+                with open(self.error_file) as fp:
+                    self.error_file_data = json.load(fp)
+                    log.debug(
+                        "User process failed with error data: %s", json.dumps(self.error_file_data, indent=2)
+                    )
+                    self.message, self.timestamp = self._get_error_data(
+                        self.error_file_data
+                    )
+            except Exception:
+                log.exception("Failed to parse reply file: %s", self.error_file)
+                raise
+        else:
+            self._set_no_reply_file()
+
+        # make up an informative message if not already present
+        if not self.message:
+            # signals typically do not generate an error file message
+            if self.exitcode < 0:
+                self.message = (
+                    f"Signal {-self.exitcode} ({self.signal_name()})"
+                    f" received by PID {self.pid}"
+                )
+            else:
+                self.message = "To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html"
+
+    def _get_error_data(self, error_file_data: Dict[str, Any]) -> Tuple[str, int]:
+        message = error_file_data["message"]
+        if isinstance(message, str):
+            timestamp = int(error_file_data.get("timestamp", 0))
+        else:
+            timestamp = int(message["extraInfo"]["timestamp"])
+        return (message, timestamp)
+
+    def _set_no_reply_file(self):
+        self.error_file = _NOT_AVAILABLE
+        self.error_file_data = _EMPTY_ERROR_DATA
+        self.message = ""
+        self.timestamp = int(time.time())
+
+    def signal_name(self) -> str:
+        if self.exitcode < 0:
+            # We don't want to kill the parent process trying to find the signal name.
+            # if the signal doesn't map to a known name, use not available.
+            try:
+                return signal.Signals(-self.exitcode).name
+            except Exception:
+                return _NOT_AVAILABLE
+        else:
+            return _NOT_AVAILABLE
+
+    def timestamp_isoformat(self):
+        """Return timestamp in ISO format (YYYY-MM-DD_HH:MM:SS)."""
+        return datetime.fromtimestamp(self.timestamp).isoformat(sep="_")
+
+
+GlobalRank = int
+
+_FAILURE_FORMAT_TEMPLATE = """[${idx}]:
+  time      : ${time}
+  host      : ${hostname}
+  rank      : ${rank} (local_rank: ${local_rank})
+  exitcode  : ${exitcode} (pid: ${pid})
+  error_file: ${error_file}
+  traceback : ${message}"""
+
+# extra new lines before and after are intentional
+_MSG_FORMAT_TEMPLATE = """
+${boarder}
+${title}
+${section}
+Failures:
+${other_failures}
+${section}
+Root Cause (first observed failure):
+${root_failure}
+${boarder}"""
+
+
+class ChildFailedError(Exception):
+    """
+    Special exception type that can be raised from a function annotated with the
+    ``@record`` decorator to have the child process' (root exception) propagate
+    up the stack as-is (e.g. without being wrapped in the parent's traceback).
+
+    Useful in cases where the parent is a simple nanny process
+    and the child (worker) processes are actually doing meaningful compute.
+    In this case, errors typically occur on the child process as the parent
+    is not doing anything non-trivial, and child errors should be propagated
+    to the scheduler for accurate root cause diagnostics.
+
+    .. note:: The propagation relies on error files rather than exception handling to
+              support both function and binary launches.
+
+    Example:
+    ::
+
+     # process tree on a host (container)
+     0: scheduler-init-process:
+                |- 1: torchelastic_agent:
+                         |- 2: trainer_0 (ok)
+                         |- 3: trainer_1 (fail) -> error.json
+                         |- ...
+                         |- n+2: trainer_n (ok)
+                |- n+3: other processes
+                |- ...
+
+    In the example above, trainer 1's failure (written into error.json) is
+    the root cause and should be reported to the scheduler's init process.
+    The torchelastic agent raises a ``ChildFailedError("trainer", {1: "trainer_1/error.json"})``
+    upon detecting trainer 1's failure which would propagate the contents
+    of trainer 1's error file to the scheduler's init process.
+    """
+
+    def __init__(self, name: str, failures: Dict[GlobalRank, ProcessFailure]):
+        self.name = name
+        self.failures = failures
+        assert (
+            self.failures
+        )  # does not make sense to create a ChildFaileError with no failures
+        super().__init__(self.format_msg())
+
+    def get_first_failure(self) -> Tuple[GlobalRank, ProcessFailure]:
+        rank = min(self.failures.keys(), key=lambda r: self.failures[r].timestamp)
+        return rank, self.failures[rank]
+
+    def format_msg(self, boarder_delim="=", section_delim="-"):
+        title = f"{self.name} FAILED"
+        root_rank, root_failure = self.get_first_failure()
+
+        root_failure_fmt: str = ""
+        other_failures_fmt: List[str] = []
+        width = len(title)
+        for idx, (rank, failure) in enumerate(self.failures.items()):
+            fmt, w = self._format_failure(idx, rank, failure)
+            width = max(width, w)
+            if rank == root_rank:
+                root_failure_fmt = fmt
+            else:
+                other_failures_fmt.append(fmt)
+
+        # upper boundary on width
+        width = min(width, 60)
+
+        return Template(_MSG_FORMAT_TEMPLATE).substitute(
+            boarder=boarder_delim * width,
+            title=title,
+            section=section_delim * width,
+            root_failure=root_failure_fmt,
+            other_failures="\n".join(other_failures_fmt or ["  <NO_OTHER_FAILURES>"]),
+        )
+
+    def _format_failure(
+        self, idx: int, rank: int, failure: ProcessFailure
+    ) -> Tuple[str, int]:
+
+        # failure.message is either a str (when the failure does not generate a traceback - e.g. signals)
+        # or a dict (json) of the form
+        # {"message": $ERROR_MSG, "extraInfo": {"py_callstack": $TRACEBACK, timestamp: $TS}}
+        # so the display logic is:
+        # 1. if failure.message is not a dict (it is a str) just show it as is
+        # 2. else try to get the traceback (py_callstack)
+        # 3.      if the traceback is not there, use the message
+        # 4.      if the message  is not there show <N/A>
+        msg = failure.message
+        if isinstance(failure.message, dict):
+            msg = (
+                failure.message.get("extraInfo", {})
+                .get("py_callstack", failure.message.get("message", "<N/A>"))
+                .replace("\n", "\n  ")  # to properly indent the traceback
+            )
+
+        fmt = Template(_FAILURE_FORMAT_TEMPLATE).substitute(
+            idx=idx,
+            time=failure.timestamp_isoformat(),
+            hostname=socket.getfqdn(),
+            rank=rank,
+            local_rank=failure.local_rank,
+            exitcode=failure.exitcode,
+            pid=failure.pid,
+            error_file=failure.error_file,
+            message=msg,
+        )
+        width = 0
+        for line in fmt.split("\n"):
+            width = max(width, len(line))
+        return fmt, width
+
+
+def record(
+    fn: Callable[..., T], error_handler: Optional[ErrorHandler] = None
+) -> Callable[..., T]:
+    """
+    Syntactic sugar to record errors/exceptions that happened in the decorated
+    function using the provided ``error_handler``.
+
+    Using this decorator is equivalent to:
+
+    ::
+
+     error_handler = get_error_handler()
+     error_handler.initialize()
+     try:
+        foobar()
+     except ChildFailedError as e:
+        _, failure = e.get_first_failure()
+        error_handler.dump_error_file(failure.error_file, failure.exitcode)
+        raise
+     except Exception as e:
+        error_handler.record(e)
+        raise
+
+    .. important:: use this decorator once per process at the top level method,
+                   typically this is the main method.
+
+    Example
+
+    ::
+
+     @record
+     def main():
+         pass
+
+     if __name__=="__main__":
+        main()
+
+    """
+    if not error_handler:
+        error_handler = get_error_handler()
+
+    def wrap(f):
+        @wraps(f)
+        def wrapper(*args, **kwargs):
+            assert error_handler is not None  # assertion for mypy type checker
+            error_handler.initialize()
+            try:
+                return f(*args, **kwargs)
+            except SystemExit as se:
+                # For run_path based entrypoints, SystemExit with code = 0 will never exit.
+                # Handling it here by returning a value:
+                if se.code == 0:
+                    return None
+                else:
+                    raise
+            except ChildFailedError as e:
+                rank, failure = e.get_first_failure()
+                if failure.error_file != _NOT_AVAILABLE:
+                    error_handler.dump_error_file(failure.error_file, failure.exitcode)
+                else:
+                    log.info(
+                        (
+                            "local_rank %s FAILED with no error file."
+                            " Decorate your entrypoint fn with @record for traceback info."
+                            " See: https://pytorch.org/docs/stable/elastic/errors.html",
+                            rank
+                        )
+                    )
+                raise
+            except Exception as e:
+                error_handler.record_exception(e)
+                raise
+
+        return wrapper
+
+    return wrap(fn)

venv/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/errors/error_handler.py

@@ -0,0 +1,159 @@
+#!/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 faulthandler
+import json
+import logging
+import os
+import time
+import traceback
+import warnings
+from typing import Any, Dict, Optional
+
+__all__ = ['ErrorHandler']
+
+log = logging.getLogger(__name__)
+
+
+class ErrorHandler:
+    """
+    Write the provided exception object along with some other metadata about
+    the error in a structured way in JSON format to an error file specified by the
+    environment variable: ``TORCHELASTIC_ERROR_FILE``. If this environment
+    variable is not set, then simply logs the contents of what would have been
+    written to the error file.
+
+    This handler may be subclassed to customize the handling of the error.
+    Subclasses should override ``initialize()`` and ``record_exception()``.
+    """
+
+    def _get_error_file_path(self) -> Optional[str]:
+        """
+        Return the error file path.
+
+        May return ``None`` to have the structured error be logged only.
+        """
+        return os.environ.get("TORCHELASTIC_ERROR_FILE", None)
+
+    def initialize(self) -> None:
+        """
+        Call prior to running code that we wish to capture errors/exceptions.
+
+        Typically registers signal/fault handlers. Users can override this
+        function to add custom initialization/registrations that aid in
+        propagation/information of errors/signals/exceptions/faults.
+        """
+        try:
+            faulthandler.enable(all_threads=True)
+        except Exception as e:
+            warnings.warn(f"Unable to enable fault handler. {type(e).__name__}: {e}")
+
+    def _write_error_file(self, file_path: str, error_msg: str) -> None:
+        """Write error message to the file."""
+        try:
+            with open(file_path, "w") as fp:
+                fp.write(error_msg)
+        except Exception as e:
+            warnings.warn(f"Unable to write error to file. {type(e).__name__}: {e}")
+
+    def record_exception(self, e: BaseException) -> None:
+        """
+        Write a structured information about the exception into an error file in JSON format.
+
+        If the error file cannot be determined, then logs the content
+        that would have been written to the error file.
+        """
+        file = self._get_error_file_path()
+        if file:
+            data = {
+                "message": {
+                    "message": f"{type(e).__name__}: {e}",
+                    "extraInfo": {
+                        "py_callstack": traceback.format_exc(),
+                        "timestamp": str(int(time.time())),
+                    },
+                }
+            }
+            with open(file, "w") as fp:
+                json.dump(data, fp)
+
+    def override_error_code_in_rootcause_data(
+        self,
+        rootcause_error_file: str,
+        rootcause_error: Dict[str, Any],
+        error_code: int = 0,
+    ):
+        """Modify the rootcause_error read from the file, to correctly set the exit code."""
+        if "message" not in rootcause_error:
+            log.warning(
+                "child error file (%s) does not have field `message`. \n"
+                "cannot override error code: %s",
+                rootcause_error_file, error_code
+            )
+        elif isinstance(rootcause_error["message"], str):
+            log.warning(
+                "child error file (%s) has a new message format. \n"
+                "skipping error code override",
+                rootcause_error_file
+            )
+        else:
+            rootcause_error["message"]["errorCode"] = error_code
+
+    def dump_error_file(self, rootcause_error_file: str, error_code: int = 0):
+        """Dump parent error file from child process's root cause error and error code."""
+        with open(rootcause_error_file) as fp:
+            rootcause_error = json.load(fp)
+            # Override error code since the child process cannot capture the error code if it
+            # is terminated by signals like SIGSEGV.
+            if error_code:
+                self.override_error_code_in_rootcause_data(rootcause_error_file, rootcause_error, error_code)
+            log.debug(
+                "child error file (%s) contents:\n"
+                "%s",
+                rootcause_error_file, json.dumps(rootcause_error, indent=2)
+            )
+
+        my_error_file = self._get_error_file_path()
+        if my_error_file:
+            # Guard against existing error files
+            # This can happen when the child is created using multiprocessing
+            # and the same env var (TORCHELASTIC_ERROR_FILE) is used on the
+            # parent and child to specify the error files (respectively)
+            # because the env vars on the child is set in the wrapper function
+            # and by default the child inherits the parent's env vars, if the child
+            # process receives a signal before the wrapper function kicks in
+            # and the signal handler writes to the error file, then the child
+            # will write to the parent's error file. In this case just log the
+            # original error file contents and overwrite the error file.
+            self._rm(my_error_file)
+            self._write_error_file(my_error_file, json.dumps(rootcause_error))
+            log.info("dumped error file to parent's %s", my_error_file)
+        else:
+            log.error(
+                "no error file defined for parent, to copy child error file (%s)", rootcause_error_file
+            )
+
+    def _rm(self, my_error_file):
+        if os.path.isfile(my_error_file):
+            # Log the contents of the original file.
+            with open(my_error_file) as fp:
+                try:
+                    original = json.dumps(json.load(fp), indent=2)
+                    log.warning(
+                        "%s already exists"
+                        " and will be overwritten."
+                        " Original contents:\n%s",
+                        my_error_file, original
+                    )
+                except json.decoder.JSONDecodeError as err:
+                    log.warning(
+                        "%s already exists"
+                        " and will be overwritten."
+                        " Unable to load original contents:\n",
+                        my_error_file
+                    )
+            os.remove(my_error_file)

venv/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/errors/handlers.py

@@ -0,0 +1,16 @@
+#!/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.
+# Multiprocessing error-reporting module
+
+
+from torch.distributed.elastic.multiprocessing.errors.error_handler import ErrorHandler
+
+__all__ = ['get_error_handler']
+
+def get_error_handler():
+    return ErrorHandler()

venv/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/redirects.py

@@ -0,0 +1,102 @@
+# !/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.
+
+# Taken and modified from original source:
+# https://eli.thegreenplace.net/2015/redirecting-all-kinds-of-stdout-in-python/
+import ctypes
+import logging
+import os
+import sys
+from contextlib import contextmanager
+from functools import partial
+
+IS_WINDOWS = sys.platform == "win32"
+IS_MACOS = sys.platform == "darwin"
+
+
+logger = logging.getLogger(__name__)
+
+
+def get_libc():
+    if IS_WINDOWS or IS_MACOS:
+        logger.warning(
+            "NOTE: Redirects are currently not supported in Windows or MacOs."
+        )
+        return None
+    else:
+        return ctypes.CDLL("libc.so.6")
+
+
+libc = get_libc()
+
+
+def _c_std(stream: str):
+    return ctypes.c_void_p.in_dll(libc, stream)
+
+
+def _python_std(stream: str):
+    return {"stdout": sys.stdout, "stderr": sys.stderr}[stream]
+
+
+_VALID_STD = {"stdout", "stderr"}
+
+
+@contextmanager
+def redirect(std: str, to_file: str):
+    """
+    Redirect ``std`` (one of ``"stdout"`` or ``"stderr"``) to a file in the path specified by ``to_file``.
+
+    This method redirects the underlying std file descriptor (not just python's ``sys.stdout|stderr``).
+    See usage for details.
+
+    Directory of ``dst_filename`` is assumed to exist and the destination file
+    is overwritten if it already exists.
+
+    .. note:: Due to buffering cross source writes are not guaranteed to
+              appear in wall-clock order. For instance in the example below
+              it is possible for the C-outputs to appear before the python
+              outputs in the log file.
+
+    Usage:
+
+    ::
+
+     # syntactic-sugar for redirect("stdout", "tmp/stdout.log")
+     with redirect_stdout("/tmp/stdout.log"):
+        print("python stdouts are redirected")
+        libc = ctypes.CDLL("libc.so.6")
+        libc.printf(b"c stdouts are also redirected"
+        os.system("echo system stdouts are also redirected")
+
+     print("stdout restored")
+
+    """
+    if std not in _VALID_STD:
+        raise ValueError(
+            f"unknown standard stream <{std}>, must be one of {_VALID_STD}"
+        )
+
+    c_std = _c_std(std)
+    python_std = _python_std(std)
+    std_fd = python_std.fileno()
+
+    def _redirect(dst):
+        libc.fflush(c_std)
+        python_std.flush()
+        os.dup2(dst.fileno(), std_fd)
+
+    with os.fdopen(os.dup(std_fd)) as orig_std, open(to_file, mode="w+b") as dst:
+        _redirect(dst)
+        try:
+            yield
+        finally:
+            _redirect(orig_std)
+
+
+redirect_stdout = partial(redirect, "stdout")
+redirect_stderr = partial(redirect, "stderr")

venv/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/subprocess_handler/__init__.py

@@ -0,0 +1,15 @@
+#!/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.
+from torch.distributed.elastic.multiprocessing.subprocess_handler.handlers import (
+    get_subprocess_handler,
+)
+from torch.distributed.elastic.multiprocessing.subprocess_handler.subprocess_handler import (
+    SubprocessHandler,
+)
+
+__all__ = ["SubprocessHandler", "get_subprocess_handler"]

venv/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/subprocess_handler/handlers.py

@@ -0,0 +1,32 @@
+#!/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.
+from typing import Dict, Tuple
+
+from torch.distributed.elastic.multiprocessing.subprocess_handler.subprocess_handler import (
+    SubprocessHandler,
+)
+
+__all__ = ["get_subprocess_handler"]
+
+
+def get_subprocess_handler(
+    entrypoint: str,
+    args: Tuple,
+    env: Dict[str, str],
+    stdout: str,
+    stderr: str,
+    local_rank_id: int,
+):
+    return SubprocessHandler(
+        entrypoint=entrypoint,
+        args=args,
+        env=env,
+        stdout=stdout,
+        stderr=stderr,
+        local_rank_id=local_rank_id,
+    )

venv/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/subprocess_handler/subprocess_handler.py

@@ -0,0 +1,78 @@
+#!/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 os
+import signal
+import subprocess
+import sys
+
+from typing import Any, Dict, Optional, Tuple
+
+__all__ = ["SubprocessHandler"]
+
+IS_WINDOWS = sys.platform == "win32"
+
+
+def _get_default_signal() -> signal.Signals:
+    """Get the default termination signal. SIGTERM for unix, CTRL_C_EVENT for windows."""
+    if IS_WINDOWS:
+        return signal.CTRL_C_EVENT  # type: ignore[attr-defined] # noqa: F821
+    else:
+        return signal.SIGTERM
+
+
+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],
+        stdout: str,
+        stderr: str,
+        local_rank_id: int,
+    ):
+        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.local_rank_id = local_rank_id
+        self.proc: subprocess.Popen = self._popen(args_str, env_vars)
+
+    def _popen(self, args: Tuple, env: Dict[str, str]) -> subprocess.Popen:
+        kwargs: Dict[str, Any] = {}
+        if not IS_WINDOWS:
+            kwargs["start_new_session"] = True
+        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,
+            stdout=self._stdout,
+            stderr=self._stderr,
+            **kwargs,
+        )
+
+    def close(self, death_sig: Optional[signal.Signals] = None) -> None:
+        if not death_sig:
+            death_sig = _get_default_signal()
+        if IS_WINDOWS:
+            self.proc.send_signal(death_sig)
+        else:
+            os.killpg(self.proc.pid, death_sig)
+        if self._stdout:
+            self._stdout.close()
+        if self._stderr:
+            self._stderr.close()

venv/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/tail_log.py

@@ -0,0 +1,153 @@
+#!/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 logging
+import os
+import time
+from concurrent.futures._base import Future
+from concurrent.futures.thread import ThreadPoolExecutor
+from threading import Event
+from typing import Dict, List, Optional, TextIO
+
+__all__ = ["tail_logfile", "TailLog"]
+
+log = logging.getLogger(__name__)
+
+
+def tail_logfile(
+    header: str, file: str, dst: TextIO, finished: Event, interval_sec: float
+):
+
+    while not os.path.exists(file):
+        if finished.is_set():
+            return
+        time.sleep(interval_sec)
+
+    with open(file, errors="replace") as fp:
+        while True:
+            line = fp.readline()
+
+            if line:
+                dst.write(f"{header}{line}")
+            else:  # reached EOF
+                if finished.is_set():
+                    # log line producer is finished
+                    break
+                else:
+                    # log line producer is still going
+                    # wait for a bit before looping again
+                    time.sleep(interval_sec)
+
+
+class TailLog:
+    """
+    Tail the given log files.
+
+    The log files do not have to exist when the ``start()`` method is called. The tail-er will gracefully wait until
+    the log files are created by the producer and will tail the contents of the
+    log files until the ``stop()`` method is called.
+
+    .. warning:: ``TailLog`` will wait indefinitely for the log file to be created!
+
+    Each log file's line will be suffixed with a header of the form: ``[{name}{idx}]:``,
+    where the ``name`` is user-provided and ``idx`` is the index of the log file
+    in the ``log_files`` mapping. ``log_line_prefixes`` can be used to override the
+    header for each log file.
+
+    Usage:
+
+    ::
+
+     log_files = {0: "/tmp/0_stdout.log", 1: "/tmp/1_stdout.log"}
+     tailer = TailLog("trainer", log_files, sys.stdout).start()
+     # actually run the trainers to produce 0_stdout.log and 1_stdout.log
+     run_trainers()
+     tailer.stop()
+
+     # once run_trainers() start writing the ##_stdout.log files
+     # the tailer will print to sys.stdout:
+     # >>> [trainer0]:log_line1
+     # >>> [trainer1]:log_line1
+     # >>> [trainer0]:log_line2
+     # >>> [trainer0]:log_line3
+     # >>> [trainer1]:log_line2
+
+    .. note:: Due to buffering log lines between files may not necessarily
+              be printed out in order. You should configure your application's
+              logger to suffix each log line with a proper timestamp.
+
+    """
+
+    def __init__(
+        self,
+        name: str,
+        log_files: Dict[int, str],
+        dst: TextIO,
+        log_line_prefixes: Optional[Dict[int, str]] = None,
+        interval_sec: float = 0.1,
+    ):
+        n = len(log_files)
+        self._threadpool = None
+        if n > 0:
+            self._threadpool = ThreadPoolExecutor(
+                max_workers=n,
+                thread_name_prefix=f"{self.__class__.__qualname__}_{name}",
+            )
+
+        self._name = name
+        self._dst = dst
+        self._log_files = log_files
+        self._log_line_prefixes = log_line_prefixes
+        self._finished_events: Dict[int, Event] = {
+            local_rank: Event() for local_rank in log_files.keys()
+        }
+        self._futs: List[Future] = []
+        self._interval_sec = interval_sec
+        self._stopped = False
+
+    def start(self) -> "TailLog":
+        if not self._threadpool:
+            return self
+
+        for local_rank, file in self._log_files.items():
+            header = f"[{self._name}{local_rank}]:"
+            if self._log_line_prefixes and local_rank in self._log_line_prefixes:
+                header = self._log_line_prefixes[local_rank]
+            self._futs.append(
+                self._threadpool.submit(
+                    tail_logfile,
+                    header=header,
+                    file=file,
+                    dst=self._dst,
+                    finished=self._finished_events[local_rank],
+                    interval_sec=self._interval_sec,
+                )
+            )
+        return self
+
+    def stop(self) -> None:
+        for finished in self._finished_events.values():
+            finished.set()
+
+        for local_rank, f in enumerate(self._futs):
+            try:
+                f.result()
+            except Exception as e:
+                log.error(
+                    "error in log tailor for %s%s. %s: %s",
+                    self._name, local_rank,
+                    e.__class__.__qualname__, e,
+                )
+
+        if self._threadpool:
+            self._threadpool.shutdown(wait=True)
+
+        self._stopped = True
+
+    def stopped(self) -> bool:
+        return self._stopped

venv/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/__init__.py

@@ -0,0 +1,150 @@
+# 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.
+
+"""
+In the context of Torch Distributed Elastic we use the term *rendezvous* to
+refer to a particular functionality that combines a **distributed
+synchronization** primitive with **peer discovery**.
+
+It is used by Torch Distributed Elastic to gather participants of a training
+job (i.e. nodes) such that they all agree on the same list of participants and
+everyone's roles, as well as make a consistent collective decision on when
+training can begin/resume.
+
+Torch Distributed Elastic rendezvous provides the following critical
+functionalities:
+
+**Barrier**:
+
+Nodes performing rendezvous will all block until the rendezvous is considered
+complete - this happens when at least ``min`` total number of nodes have joined
+the rendezvous barrier (for the same job). This also implies the barrier is not
+necessarily of fixed size.
+
+There's an additional small waiting time after reaching ``min`` number of
+nodes - this is used to ensure the rendezvous is not completed "too quickly"
+(which could potentially exclude additional nodes attempting to join at
+approximately the same time).
+
+If ``max`` number of nodes is gathered at the barrier, the rendezvous is
+completed immediately.
+
+There's also an overall timeout which causes the rendezvous to fail if ``min``
+number of nodes is never reached - this is meant to be a simple fail-safe to
+help release partially allocated job resources, in case there's a problem with
+the resource manager, and is meant to be interpreted as non-retryable.
+
+**Exclusivity**:
+
+A simple distributed barrier would not be sufficient, as we also need to ensure
+that only one group of nodes exists at any given time (for a given job). In
+other words, new nodes (i.e. joining late) should not be able to form a parallel
+independent group of workers for the same job.
+
+Torch Distributed Elastic rendezvous ensures that if a group of nodes has
+already completed a rendezvous (and hence might already be training), then
+additional "late" nodes attempting to rendezvous will only announce themselves
+as waiting, and will have to wait until the (previously completed) existing
+rendezvous is destroyed first.
+
+**Consistency**:
+
+When a rendezvous is completed, all its members will agree on the job membership
+and everyone's role in it. This role is represented using an integer, called
+rank, that is between between 0 and world size.
+
+Note that ranks are *not stable*, in the sense that the same node can be
+assigned a different rank in the next (re-)rendezvous.
+
+**Fault-tolerance**:
+
+Torch Distributed Elastic rendezvous is designed to tolerate node failures
+during the rendezvous process. Should a process crash (or lose network
+connectivity, etc), between joining the rendezvous and it being completed, then
+a re-rendezvous with remaining healthy nodes will happen automatically.
+
+A node can also fail *after* it has completed (or *has been observered* by other
+nodes to have completed) the rendezvous - this scenario will be handled by the
+Torch Distributed Elastic ``train_loop`` instead (where it will also trigger a
+re-rendezvous).
+
+**Shared key-value store**:
+
+When the rendezvous is completed, a shared key-value store is created and
+returned. This store implements a ``torch.distributed.Store`` API (see
+`distributed communication docs
+<https://pytorch.org/docs/stable/distributed.html>`__).
+
+This store is only shared by the members of the completed rendezvous. It
+is intended to be used by Torch Distributed Elastic to exchange information
+necessary to initialize job control and data-planes.
+
+**Waiting workers and rendezvous closing**:
+
+Torch Distributed Elastic rendezvous handler object provides additional
+functionalities, which are technically not part of the rendezvous process:
+
+1. Querying how many workers arrived late at the barrier, who can participate in
+   *next* rendezvous.
+
+2. Setting the rendezvous *closed* to signal all nodes not to participate in
+   next rendezvous.
+
+**DynamicRendezvousHandler**:
+
+Torch Distributed Elastic comes with the :py:class:`.DynamicRendezvousHandler`
+class that implements the rendezvous mechanism described above. It is a backend-
+agnostic type that expects a particular :py:class:`.RendezvousBackend` instance
+to be specified during construction.
+
+Torch distributed users can either implement their own backend type or use one
+of the following implementations that come with PyTorch:
+
+- :py:class:`.C10dRendezvousBackend`: Uses a C10d store (by default
+  ``TCPStore``) as the rendezvous backend. The main advantage of using a C10d
+  store is that it requires no 3rd-party dependency (such as etcd) to establish
+  a rendezvous.
+- :py:class:`.EtcdRendezvousBackend`: Supersedes the legacy
+  :py:class:`.EtcdRendezvousHandler` class. Passing an
+  :py:class:`.EtcdRendezvousBackend` instance to
+  :py:class:`.DynamicRendezvousHandler` is functionally equivalent to
+  instantiating an :py:class:`.EtcdRendezvousHandler`.
+
+  ::
+
+     store = TCPStore("localhost")
+
+     backend = C10dRendezvousBackend(store, "my_run_id")
+
+     rdzv_handler = DynamicRendezvousHandler.from_backend(
+         run_id="my_run_id",
+         store=store,
+         backend=backend,
+         min_nodes=2,
+         max_nodes=4
+     )
+"""
+
+from .api import *  # noqa: F403
+from .registry import _register_default_handlers
+
+
+_register_default_handlers()
+
+
+__all__ = [
+    "RendezvousClosedError",
+    "RendezvousConnectionError",
+    "RendezvousError",
+    "RendezvousGracefulExitError",
+    "RendezvousHandler",
+    "RendezvousHandlerCreator",
+    "RendezvousHandlerRegistry",
+    "RendezvousParameters",
+    "RendezvousStateError",
+    "RendezvousTimeoutError",
+    "rendezvous_handler_registry",
+]