Source code for torch.distributed.elastic.agent.server.local_elastic_agent
#!/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
import os
import shutil
import signal
import socket
import tempfile
import uuid
from typing import Any, Dict, Optional, Tuple
import torch.distributed.elastic.timer as timer
from torch.distributed.elastic import events
from torch.distributed.elastic.agent.server.api import (
RunResult,
SimpleElasticAgent,
WorkerGroup,
WorkerSpec,
WorkerState,
)
from torch.distributed.elastic.events.api import EventMetadataValue
from torch.distributed.elastic.metrics.api import prof
from torch.distributed.elastic.multiprocessing import PContext, start_processes
from torch.distributed.elastic.utils import macros
from torch.distributed.elastic.utils.logging import get_logger
log = get_logger()
__all__ = [
"LocalElasticAgent",
"TORCHELASTIC_ENABLE_FILE_TIMER",
"TORCHELASTIC_TIMER_FILE",
]
TORCHELASTIC_ENABLE_FILE_TIMER = "TORCHELASTIC_ENABLE_FILE_TIMER"
TORCHELASTIC_TIMER_FILE = "TORCHELASTIC_TIMER_FILE"
[docs]class LocalElasticAgent(SimpleElasticAgent):
"""
An implementation of :py:class:`torchelastic.agent.server.ElasticAgent`
that handles host-local workers.
This agent is deployed per host and is configured to spawn ``n`` workers.
When using GPUs, ``n`` maps to the number of GPUs available on the host.
The local agent does not communicate to other local agents deployed on
other hosts, even if the workers may communicate inter-host. The worker id
is interpreted to be a local process. The agent starts and stops all worker
processes as a single unit.
The worker function and argument passed to the worker function must be
python multiprocessing compatible. To pass multiprocessing data structures
to the workers you may create the data structure in the same multiprocessing
context as the specified ``start_method`` and pass it as a function argument.
The ``exit_barrier_timeout`` specifies the amount of time (in seconds) to wait
for other agents to finish. This acts as a safety net to handle cases where
workers finish at different times, to prevent agents from viewing workers
that finished early as a scale-down event. It is strongly advised that the
user code deal with ensuring that workers are terminated in a synchronous
manner rather than relying on the exit_barrier_timeout.
A named pipe based watchdog can be enabled in ```LocalElasticAgent``` if an
environment variable ``TORCHELASTIC_ENABLE_FILE_TIMER`` with value 1 has
been defined in the ```LocalElasticAgent``` process.
Optionally, another environment variable ```TORCHELASTIC_TIMER_FILE```
can be set with a unique file name for the named pipe. If the environment
variable ```TORCHELASTIC_TIMER_FILE``` is not set, ```LocalElasticAgent```
will internally create a unique file name and set it to the environment
variable ```TORCHELASTIC_TIMER_FILE```, and this environment variable will
be propagated to the worker processes to allow them to connect to the same
named pipe that ```LocalElasticAgent``` uses.
Example launching function
::
def trainer(args) -> str:
return "do train"
def main():
start_method="spawn"
shared_queue= multiprocessing.get_context(start_method).Queue()
spec = WorkerSpec(
role="trainer",
local_world_size=nproc_per_process,
entrypoint=trainer,
args=("foobar",),
...<OTHER_PARAMS...>)
agent = LocalElasticAgent(spec, start_method)
results = agent.run()
if results.is_failed():
print("trainer failed")
else:
print(f"rank 0 return value: {results.return_values[0]}")
# prints -> rank 0 return value: do train
Example launching binary
::
def main():
spec = WorkerSpec(
role="trainer",
local_world_size=nproc_per_process,
entrypoint="/usr/local/bin/trainer",
args=("--trainer_args", "foobar"),
...<OTHER_PARAMS...>)
agent = LocalElasticAgent(spec)
results = agent.run()
if not results.is_failed():
print("binary launches do not have return values")
"""
def __init__(
self,
spec: WorkerSpec,
start_method="spawn",
exit_barrier_timeout: float = 300,
log_dir: Optional[str] = None,
):
super().__init__(spec, exit_barrier_timeout)
self._start_method = start_method
self._pcontext: Optional[PContext] = None
rdzv_run_id = spec.rdzv_handler.get_run_id()
self._log_dir = self._make_log_dir(log_dir, rdzv_run_id)
self._worker_watchdog: Optional[timer.FileTimerServer] = None
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(f"log directory set to: {dir}")
return dir
def _setup_local_watchdog(self, envs: Dict[int, Dict[str, str]]) -> None:
enable_watchdog_env_name = TORCHELASTIC_ENABLE_FILE_TIMER
watchdog_enabled = os.getenv(enable_watchdog_env_name)
watchdog_file_env_name = TORCHELASTIC_TIMER_FILE
watchdog_file_path = os.getenv(watchdog_file_env_name)
if watchdog_enabled is not None and str(watchdog_enabled) == "1":
if watchdog_file_path is None:
watchdog_file_path = "/tmp/watchdog_timer_" + str(uuid.uuid4())
log.info(f"Starting a FileTimerServer with {watchdog_file_path} ...")
self._worker_watchdog = timer.FileTimerServer(
file_path=watchdog_file_path,
max_interval=0.1,
daemon=True,
log_event=self._log_watchdog_event)
self._worker_watchdog.start()
log.info("FileTimerServer started")
else:
log.info(f"Environment variable '{enable_watchdog_env_name}' not found. Do not start FileTimerServer.")
# Propagate the watchdog file env to worker processes
if watchdog_file_path is not None:
for _, worker_env in envs.items():
worker_env[watchdog_file_env_name] = watchdog_file_path
def _get_fq_hostname(self) -> str:
return socket.getfqdn(socket.gethostname())
def _log_watchdog_event(
self,
name: str,
request: Optional[timer.FileTimerRequest],
) -> None:
wg = self._worker_group
spec = wg.spec
md = {
"watchdog_event": name
}
if request is not None:
md["worker_pid"] = str(request.worker_pid)
md["scope_id"] = request.scope_id
md["expiration_time"] = str(request.expiration_time)
md["signal"] = str(request.signal)
md_str = json.dumps(md)
state = "RUNNING"
metadata: Dict[str, EventMetadataValue] = {
"run_id": spec.rdzv_handler.get_run_id(),
"global_rank": None,
"group_rank": wg.group_rank,
"worker_id": None,
"role": spec.role,
"hostname": self._get_fq_hostname(),
"state": state,
"total_run_time": self._total_execution_time,
"rdzv_backend": spec.rdzv_handler.get_backend(),
"raw_error": None,
"metadata": md_str,
"agent_restarts": spec.max_restarts - self._remaining_restarts,
}
# Note: The 'metadata' field of the Event is converted to a TorchelasticStatusLogEntry later.
# The 'name' field of the Event is NOT used in the TorchelasticStatusLogEntry.
event = events.Event(
name=name, source=events.EventSource.AGENT, metadata=metadata
)
events.record(event)
# pyre-fixme[56]: Pyre was not able to infer the type of the decorator
# `torch.distributed.elastic.metrics.prof`.
@prof
def _stop_workers(self, worker_group: WorkerGroup) -> None:
self._shutdown()
# pyre-fixme[56]: Pyre was not able to infer the type of the decorator
# `torch.distributed.elastic.metrics.prof`.
@prof
def _start_workers(self, worker_group: WorkerGroup) -> Dict[int, Any]:
spec = worker_group.spec
store = worker_group.store
assert store is not None
master_addr, master_port = super()._get_master_addr_port(store)
restart_count = spec.max_restarts - self._remaining_restarts
use_agent_store = spec.rdzv_handler.get_backend() == "static"
args: Dict[int, Tuple] = {}
envs: Dict[int, Dict[str, str]] = {}
for worker in worker_group.workers:
local_rank = worker.local_rank
worker_env = {
"LOCAL_RANK": str(local_rank),
"RANK": str(worker.global_rank),
"GROUP_RANK": str(worker_group.group_rank),
"ROLE_RANK": str(worker.role_rank),
"ROLE_NAME": spec.role,
"LOCAL_WORLD_SIZE": str(spec.local_world_size),
"WORLD_SIZE": str(worker.world_size),
"GROUP_WORLD_SIZE": str(worker_group.group_world_size),
"ROLE_WORLD_SIZE": str(worker.role_world_size),
"MASTER_ADDR": master_addr,
"MASTER_PORT": str(master_port),
"TORCHELASTIC_RESTART_COUNT": str(restart_count),
"TORCHELASTIC_MAX_RESTARTS": str(spec.max_restarts),
"TORCHELASTIC_RUN_ID": spec.rdzv_handler.get_run_id(),
"TORCHELASTIC_USE_AGENT_STORE": str(use_agent_store),
"NCCL_ASYNC_ERROR_HANDLING": os.getenv(
"NCCL_ASYNC_ERROR_HANDLING", str(1)
),
}
if "OMP_NUM_THREADS" in os.environ:
worker_env["OMP_NUM_THREADS"] = os.environ["OMP_NUM_THREADS"]
envs[local_rank] = worker_env
worker_args = list(spec.args)
worker_args = macros.substitute(worker_args, str(local_rank))
args[local_rank] = tuple(worker_args)
# scaling events do not count towards restarts (gets same attempt #)
# remove existing log dir if this restart is due to a scaling event
attempt_log_dir = os.path.join(self._log_dir, f"attempt_{restart_count}")
shutil.rmtree(attempt_log_dir, ignore_errors=True)
os.makedirs(attempt_log_dir)
self._setup_local_watchdog(envs=envs)
assert spec.entrypoint is not None
self._pcontext = start_processes(
name=spec.role,
entrypoint=spec.entrypoint,
args=args,
envs=envs,
log_dir=attempt_log_dir,
start_method=self._start_method,
redirects=spec.redirects,
tee=spec.tee,
)
return self._pcontext.pids()
def _shutdown(self, death_sig: signal.Signals = signal.SIGTERM) -> None:
if self._worker_watchdog is not None:
self._worker_watchdog.stop()
self._worker_watchdog = None
if self._pcontext:
self._pcontext.close(death_sig)
# pyre-fixme[56]: Pyre was not able to infer the type of the decorator
# `torch.distributed.elastic.metrics.prof`.
@prof
def _monitor_workers(self, worker_group: WorkerGroup) -> RunResult:
role = worker_group.spec.role
worker_pids = {w.id for w in worker_group.workers}
assert self._pcontext is not None
pc_pids = set(self._pcontext.pids().values())
if worker_pids != pc_pids:
log.error(
f"[{role}] worker pids do not match process_context pids."
f" Expected: {worker_pids}, actual: {pc_pids}"
)
return RunResult(state=WorkerState.UNKNOWN)
result = self._pcontext.wait(0)
if result:
if result.is_failed():
# map local rank failure to global rank
worker_failures = {}
for local_rank, failure in result.failures.items():
worker = worker_group.workers[local_rank]
worker_failures[worker.global_rank] = failure
return RunResult(
state=WorkerState.FAILED,
failures=worker_failures,
)
else:
# copy ret_val_queue into a map with a global ranks
workers_ret_vals = {}
for local_rank, ret_val in result.return_values.items():
worker = worker_group.workers[local_rank]
workers_ret_vals[worker.global_rank] = ret_val
return RunResult(
state=WorkerState.SUCCEEDED,
return_values=workers_ret_vals,
)
else:
return RunResult(state=WorkerState.HEALTHY)
