#!/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 functools import json import os import signal import socket import time import traceback import warnings from contextlib import closing from dataclasses import dataclass, field from enum import Enum from typing import Any, Callable, Dict, List, Optional, Tuple, Union import torch.distributed.elastic.rendezvous as rdzv import torch.distributed.elastic.utils.store as store_util from torch.distributed import Store from torch.distributed.elastic.events import Event, EventSource, record from torch.distributed.elastic.metrics import prof, put_metric from torch.distributed.elastic.multiprocessing import ( ProcessFailure, SignalException, Std, ) from torch.distributed.elastic.utils.logging import get_logger _TERMINAL_STATE_SYNC_ID = "torchelastic/agent/terminal_state" DEFAULT_ROLE = "default" log = get_logger() @dataclass class WorkerSpec: """ Contains blueprint information about a particular type of worker. For a given role, there must only exist a single worker spec. Worker spec is expected to be homogenous across all nodes (machine), that is each node runs the same number of workers for a particular spec. Args: role: user-defined role for the workers with this spec local_world_size: number local workers to run fn: (deprecated use entrypoint instead) entrypoint: worker function or command args: arguments to pass to ``entrypoint`` rdzv_handler: handles rdzv for this set of workers max_restarts: number of max retries for the workers monitor_interval: monitor status of workers every ``n`` seconds master_port: fixed port to run the c10d store on rank 0 if not specified then will chose a random free port master_addr: fixed master_addr to run the c10d store on rank 0 if not specified then will chose hostname on agent rank 0 redirects: redirect std streams to a file, selectively redirect for a particular local rank by passing a map tee: tees the specified std stream(s) to console + file, selectively tee for a particular local rank by passing a map, takes precedence over ``redirects`` settings. """ role: str local_world_size: int rdzv_handler: rdzv.RendezvousHandler fn: Optional[Callable] = None # TODO @kiuk - make entrypoint a required field entrypoint: Union[Callable, str, None] = None args: Tuple = () max_restarts: int = 3 monitor_interval: float = 30.0 master_port: Optional[int] = None master_addr: Optional[str] = None redirects: Union[Std, Dict[int, Std]] = Std.NONE tee: Union[Std, Dict[int, Std]] = Std.NONE def __post_init__(self): assert self.local_world_size > 0 assert self.monitor_interval > 0 if self.fn: warnings.warn( "WorkerSpec.fn will be deprecated," " please use WorkerSpec.entrypoint instead", category=DeprecationWarning, ) self.entrypoint = self.fn assert self.entrypoint def get_entrypoint_name(self): """ If the entrypoint is a function (e.g. ``Callable``) returns its ``__qualname__``, else if the entrypoint is a binary (e.g. ``str``), returns the binary name. """ if isinstance(self.entrypoint, str): return os.path.basename(self.entrypoint) else: assert self.entrypoint is not None return self.entrypoint.__qualname__ class Worker: """ Represents a worker instance. Contrast this with ``WorkerSpec`` that represents the specifications of a worker. A ``Worker`` is created from a ``WorkerSpec``. A ``Worker`` is to a ``WorkerSpec`` as an object is to a class. The ``id`` of the worker is interpreted by the specific implementation of ``ElasticAgent``. For a local agent, it could be the ``pid (int)`` of the worker, for a remote agent it could be encoded as ``host:port (string)``. Args: id (Any): uniquely identifies a worker (interpreted by the agent) local_rank (int): local rank of the worker global_rank (int): global rank of the worker role_rank (int): rank of the worker across all workers that have the same role world_size (int): number of workers (globally) role_world_size (int): number of workers that have the same role """ __slots__ = [ "id", "local_rank", "global_rank", "role_rank", "world_size", "role_world_size", ] def __init__( self, local_rank: int, global_rank: int = -1, role_rank: int = -1, world_size: int = -1, role_world_size: int = -1, ): # unique identifier for this worker self.id: Any = None # rank of the worker among workers with the same role being monitored # by the same ``agent`` instance. self.local_rank: int = local_rank # rank of the worker among all the workers across all roles # across all ``agent`` instances. # Global rank is not stable between re-rendezvous. self.global_rank: int = global_rank # rank of the worker among all the workers with the same role # across all ``agent`` instances. # Role rank is not stable between re-rendezvous. self.role_rank: int = role_rank # total number of workers (globally). Due to elasticity # the world size may change between re-rendezvous. self.world_size: int = world_size # total number of workers that share the same role. Due to elasticity # the role world size may change between re-rendezvous. self.role_world_size: int = role_world_size def __str__(self): return ( f"local_rank={self.local_rank},global_rank={self.global_rank}" f",role_rank={self.role_rank},world_size={self.world_size}" f",role_world_size={self.role_world_size}" ) def __repr__(self): return str(self) class WorkerState(str, Enum): """ State of the ``WorkerGroup``. Workers in a worker group change state as a unit. If a single worker in a worker group fails the entire set is considered failed:: UNKNOWN - agent lost track of worker group state, unrecoverable INIT - worker group object created not yet started HEALTHY - workers running and healthy UNHEALTHY - workers running and unhealthy STOPPED - workers stopped (interruped) by the agent SUCCEEDED - workers finished running (exit 0) FAILED - workers failed to successfully finish (exit !0) A worker group starts from an initial ``INIT`` state, then progresses to ``HEALTHY`` or ``UNHEALTHY`` states, and finally reaches a terminal ``SUCCEEDED`` or ``FAILED`` state. Worker groups can be interrupted and temporarily put into ``STOPPED`` state by the agent. Workers in ``STOPPED`` state are scheduled to be restarted in the near future by the agent. Some examples of workers being put into ``STOPPED`` state are: 1. Worker group failure|unhealthy observed 2. Membership change detected When actions (start, stop, rdzv, retry, etc) on worker group fails and results in the action being partially applied to the worker group the state will be ``UNKNOWN``. Typically this happens on uncaught/unhandled exceptions during state change events on the agent. The agent is not expected to recover worker groups in ``UNKNOWN`` state and is better off self terminating and allowing the job manager to retry the node. """ UNKNOWN = "UNKNOWN" INIT = "INIT" HEALTHY = "HEALTHY" UNHEALTHY = "UNHEALTHY" STOPPED = "STOPPED" SUCCEEDED = "SUCCEEDED" FAILED = "FAILED" @staticmethod def is_running(state: "WorkerState") -> bool: """ Returns: True if the worker state represents workers still running (e.g. that the process exists but not necessarily healthy). """ return state in {WorkerState.HEALTHY, WorkerState.UNHEALTHY} class WorkerGroup: """ Represents the set of ``Worker`` instances for the given ``WorkerSpec`` managed by ``ElasticAgent``. Whether the worker group contains cross instance workers or not depends on the implementation of the agent. """ __slots__ = ["spec", "workers", "store", "group_rank", "group_world_size", "state"] def __init__(self, spec: WorkerSpec): self.spec = spec self.workers = [Worker(local_rank=i) for i in range(self.spec.local_world_size)] # assigned after rdzv self.store = None self.group_rank = None self.group_world_size = None self.state = WorkerState.INIT class _RoleInstanceInfo: """ The class is used by the agent to exchange the information with other agents. The information is used to determine the rank of the workers that agent manages in heterogeneous environments, where different agents can have different number of workers. """ __slots__ = ["role", "rank", "local_world_size"] def __init__(self, role: str, rank: int, local_world_size: int): r""" Args: role (str): user-defined role for the workers with this spec rank (int): the rank of the agent local_world_size (int): number of local workers to run """ self.role = role self.rank = rank self.local_world_size = local_world_size def serialize(self) -> bytes: dict_data = { "role": self.role, "rank": self.rank, "local_world_size": self.local_world_size, } return json.dumps(dict_data).encode(encoding="UTF-8") @staticmethod def deserialize(data: bytes): dict_data = json.loads(data.decode(encoding="UTF-8")) return _RoleInstanceInfo( dict_data["role"], dict_data["rank"], dict_data["local_world_size"] ) @staticmethod def compare(obj1, obj2) -> int: if obj1.role == obj2.role: return obj1.rank - obj2.rank elif obj1.role > obj2.role: return 1 else: return -1 @staticmethod def find_role_boundaries(roles_infos: List, role: str) -> Tuple[int, int]: start_idx, end_idx = -1, -1 for idx, role_info in enumerate(roles_infos): if role_info.role == role: if start_idx == -1: start_idx = idx end_idx = idx return (start_idx, end_idx) @dataclass class RunResult: """ Results returned by the worker executions. Run results follow an "all-or-nothing" policy where the run is successful if and only if ALL local workers managed by this agent complete successfully. If the result is successful (e.g. ``is_failed() = False``) then the ``return_values`` field contains the outputs (return values) of the workers managed by THIS agent mapped by their GLOBAL ranks. That is ``result.return_values[0]`` is the return value of global rank 0. .. note:: ``return_values`` are only meaningful for when the worker entrypoint is a function. Workers specified as a binary entrypoint do not canonically have a return value and the ``return_values`` field is meaningless and may be empty. If ``is_failed()`` returns ``True`` then the ``failures`` field contains the failure information, again, mapped by the GLOBAL rank of the worker that failed. The keys in ``return_values`` and ``failures`` are mutually exclusive, that is, a worker's final state can only be one of: succeeded, failed. Workers intentionally terminated by the agent according to the agent's restart policy, are not represented in either ``return_values`` nor ``failures``. """ state: WorkerState return_values: Dict[int, Any] = field(default_factory=dict) failures: Dict[int, ProcessFailure] = field(default_factory=dict) def is_failed(self) -> bool: return self.state == WorkerState.FAILED def _get_socket_with_port() -> socket.socket: """ Returns a free port on localhost that is "reserved" by binding a temporary socket on it. Close the socket before passing the port to the entity that requires it. Usage example :: sock = _get_socket_with_port() with closing(sock): port = sock.getsockname()[1] sock.close() # there is still a race-condition that some other process # may grab this port before func() runs func(port) """ addrs = socket.getaddrinfo( host="localhost", port=None, family=socket.AF_UNSPEC, type=socket.SOCK_STREAM ) for addr in addrs: family, type, proto, _, _ = addr s = socket.socket(family, type, proto) try: s.bind(("localhost", 0)) s.listen(0) return s except OSError as e: s.close() log.info("Socket creation attempt failed.", exc_info=e) raise RuntimeError("Failed to create a socket") def _get_fq_hostname() -> str: return socket.getfqdn(socket.gethostname()) class ElasticAgent(abc.ABC): """ Agent process responsible for managing one or more worker processes. The worker processes are assumed to be regular distributed PyTorch scripts. When the worker process is created by the agent, the agent provides the necessary information for the worker processes to properly initialize a torch process group. The exact deployment topology and ratio of agent-to-worker is dependent on the specific implementation of the agent and the user's job placement preferences. For instance, to run a distributed training job on GPU with 8 trainers (one per GPU) one can: 1. Use 8 x single GPU instances, place an agent per instance, managing 1 worker per agent. 2. Use 4 x double GPU instances, place an agent per instance, managing 2 workers per agent. 3. Use 2 x quad GPU instances, place an agent per instance, managing 4 workers per agent. 4. Use 1 x 8 GPU instance, place an agent per instance, managing 8 workers per agent. Usage :: group_result = agent.run() if group_result.is_failed(): # workers failed failure = group_result.failures[0] log.exception(f"worker 0 failed with exit code : {failure.exit_code}") else: return group_result.return_values[0] # return rank 0's results """ @abc.abstractmethod def run(self, role: str = DEFAULT_ROLE) -> RunResult: """ Runs the agent, retrying the worker group on failures up to ``max_restarts``. Returns: The result of the execution, containing the return values or failure details for each worker mapped by the worker's global rank. Raises: Exception - any other failures NOT related to worker process """ raise NotImplementedError() @abc.abstractmethod def get_worker_group(self, role: str = DEFAULT_ROLE) -> WorkerGroup: """ Returns: The ``WorkerGroup`` for the given ``role``. Note that the worker group is a mutable object and hence in a multi-threaded/process environment it may change state. Implementors are encouraged (but not required) to return a defensive read-only copy. """ raise NotImplementedError() class SimpleElasticAgent(ElasticAgent): """ An ``ElasticAgent`` that manages workers (``WorkerGroup``) for a single ``WorkerSpec`` (e.g. one particular type of worker role). """ def __init__(self, spec: WorkerSpec, exit_barrier_timeout: float = 300): self._worker_group = WorkerGroup(spec) self._remaining_restarts = self._worker_group.spec.max_restarts self._store = None self._exit_barrier_timeout = exit_barrier_timeout self._total_execution_time = 0 def get_worker_group(self, role: str = DEFAULT_ROLE) -> WorkerGroup: return self._worker_group @abc.abstractmethod def _start_workers(self, worker_group: WorkerGroup) -> Dict[int, Any]: r""" Starts ``worker_group.spec.local_world_size`` number of workers according to worker spec for the worker group . Returns a map of ``local_rank`` to worker ``id``. """ raise NotImplementedError() @abc.abstractmethod def _stop_workers(self, worker_group: WorkerGroup) -> None: r""" Stops all workers in the given worker group. Implementors must deal with workers in all states defined by ``WorkerState``. That is, it must gracefully handle stopping non-existent workers, unhealthy (stuck) workers, etc. """ raise NotImplementedError() @abc.abstractmethod def _monitor_workers(self, worker_group: WorkerGroup) -> RunResult: r""" Checks on the workers for the ``worker_group`` and returns the new state of the worker group. """ raise NotImplementedError() @abc.abstractmethod def _shutdown(self, death_sig: signal.Signals = signal.SIGTERM) -> None: """ Cleans up any resources that were allocated during the agent's work. Args: death_sig: Signal to send to the child process, SIGTERM is default """ raise NotImplementedError() @staticmethod def _set_master_addr_port( store: Store, master_addr: Optional[str], master_port: Optional[int] ): if master_port is None: sock = _get_socket_with_port() with closing(sock): master_port = sock.getsockname()[1] if master_addr is None: master_addr = _get_fq_hostname() store.set("MASTER_ADDR", master_addr.encode(encoding="UTF-8")) store.set("MASTER_PORT", str(master_port).encode(encoding="UTF-8")) @staticmethod def _get_master_addr_port(store: Store) -> Tuple[str, int]: master_addr = store.get("MASTER_ADDR").decode(encoding="UTF-8") master_port = int(store.get("MASTER_PORT").decode(encoding="UTF-8")) return (master_addr, master_port) # pyre-fixme[56]: Pyre was not able to infer the type of the decorator # `torch.distributed.elastic.metrics.prof`. @prof def _rendezvous(self, worker_group: WorkerGroup) -> None: r""" Runs rendezvous for the workers specified by worker spec. Assigns workers a new global rank and world size. Updates the rendezvous store for the worker group. """ spec = worker_group.spec store, group_rank, group_world_size = spec.rdzv_handler.next_rendezvous() self._store = store workers = self._assign_worker_ranks(store, group_rank, group_world_size, spec) worker_group.workers = workers worker_group.store = store worker_group.group_rank = group_rank worker_group.group_world_size = group_world_size if group_rank == 0: self._set_master_addr_port(store, spec.master_addr, spec.master_port) master_addr, master_port = self._get_master_addr_port(store) restart_count = spec.max_restarts - self._remaining_restarts log.info( f"[{spec.role}] Rendezvous complete for workers. Result:\n" f" restart_count={restart_count}\n" f" master_addr={master_addr}\n" f" master_port={master_port}\n" f" group_rank={group_rank}\n" f" group_world_size={group_world_size}\n" f" local_ranks={[worker.local_rank for worker in workers]}\n" f" role_ranks={[worker.role_rank for worker in workers]}\n" f" global_ranks={[worker.global_rank for worker in workers]}\n" f" role_world_sizes={[worker.role_world_size for worker in workers]}\n" f" global_world_sizes={[worker.world_size for worker in workers]}\n" ) def _get_ranks( self, role_infos: List[_RoleInstanceInfo], role_idx: int, start_idx: int = 0, end_idx: int = -1, ) -> Tuple[int, List[int]]: if end_idx == -1: end_idx = len(role_infos) prefix_sum = 0 total_sum = 0 for idx in range(start_idx, end_idx): if role_idx > idx: prefix_sum += role_infos[idx].local_world_size total_sum += role_infos[idx].local_world_size return ( total_sum, list(range(prefix_sum, prefix_sum + role_infos[role_idx].local_world_size)), ) # pyre-fixme[56]: Pyre was not able to infer the type of the decorator # `torch.distributed.elastic.metrics.prof`. @prof def _assign_worker_ranks( self, store, group_rank: int, group_world_size: int, spec: WorkerSpec ) -> List[Worker]: """ Determines proper ranks for worker processes. The rank assignment is done according to the following algorithm: 1. Each agent writes its configuration(group_rank, group_world_size , num_workers) to the common store. 2. Each agent retrieves configuration for all agents and performs two level sort using role and rank. 3. Determine the global rank: the global rank of the workers for the current agent is the offset of the infos array up to group_rank of the agent. The offset is computed as a sum of local_world_size of all agents that have rank less than the group_rank. The workers would have the ranks: [offset, offset+local_world_size) 4. Determine the role rank: The role rank is determined using the algorithms in the point 3 with the exception that the offset is done from the first agent that has the same role as current one and has the minimum group rank. """ role_infos = self._share_and_gather(store, group_rank, group_world_size, spec) my_role_info = role_infos[group_rank] worker_world_size, worker_global_ranks = self._get_ranks(role_infos, group_rank) role_infos = sorted( role_infos, key=functools.cmp_to_key(_RoleInstanceInfo.compare) ) role_start_idx, role_end_idx = _RoleInstanceInfo.find_role_boundaries( role_infos, my_role_info.role ) role_pos = next( idx for idx, role_info in enumerate(role_infos) if _RoleInstanceInfo.compare(role_info, my_role_info) == 0 ) role_world_size, role_ranks = self._get_ranks( role_infos, role_pos, role_start_idx, role_end_idx + 1 ) workers = [] for ind in range(spec.local_world_size): worker = Worker( local_rank=ind, global_rank=worker_global_ranks[ind], role_rank=role_ranks[ind], world_size=worker_world_size, role_world_size=role_world_size, ) workers.append(worker) return workers def _share_and_gather( self, store, group_rank: int, group_world_size: int, spec: WorkerSpec ) -> List: agent_role_info = _RoleInstanceInfo( spec.role, group_rank, spec.local_world_size ) key_prefix = "torchelastic/role_info" agent_config_enc = agent_role_info.serialize() role_infos_bytes = store_util.synchronize( store, agent_config_enc, group_rank, group_world_size, key_prefix ) role_infos = [ _RoleInstanceInfo.deserialize(role_info_bytes) for role_info_bytes in role_infos_bytes ] return role_infos # pyre-fixme[56]: Pyre was not able to infer the type of the decorator # `torch.distributed.elastic.metrics.prof`. @prof def _initialize_workers(self, worker_group: WorkerGroup) -> None: r""" Starts a fresh set of workers for the worker_group. Essentially a rendezvous followed by a start_workers. The caller should first call ``_stop_workers()`` to stop running workers prior to calling this method. Optimistically sets the state of the worker group that just started as ``HEALTHY`` and delegates the actual monitoring of state to ``_monitor_workers()`` method """ role = worker_group.spec.role log.info(f"[{role}] Rendezvous'ing worker group") # TODO after stopping workers, wait at least monitor_interval*2 for # workers on different nodes to fail on a collective op before waiting # on the rdzv barrier, this way we ensure that nodes enter rdzv # at around the same time and reduce false positive rdzv timeout errors self._rendezvous(worker_group) log.info(f"[{role}] Starting worker group") worker_ids = self._start_workers(worker_group) for local_rank, w_id in worker_ids.items(): worker = worker_group.workers[local_rank] worker.id = w_id worker_group.state = WorkerState.HEALTHY # pyre-fixme[56]: Pyre was not able to infer the type of the decorator # `torch.distributed.elastic.metrics.prof`. @prof def _restart_workers(self, worker_group: WorkerGroup) -> None: """ Restarts (stops, rendezvous, starts) all local workers in the group. """ role = worker_group.spec.role log.info(f"[{role}] Stopping worker group") self._stop_workers(worker_group) worker_group.state = WorkerState.STOPPED self._initialize_workers(worker_group) # pyre-fixme[56]: Pyre was not able to infer the type of the decorator # `torch.distributed.elastic.metrics.prof`. @prof def run(self, role: str = DEFAULT_ROLE) -> RunResult: start_time = time.monotonic() shutdown_called: bool = False try: result = self._invoke_run(role) self._total_execution_time = int(time.monotonic() - start_time) self._record_metrics(result) self._record_worker_events(result) return result except SignalException as e: log.warning(f"Received {e.sigval} death signal, shutting down workers") self._shutdown(e.sigval) shutdown_called = True raise finally: if not shutdown_called: self._shutdown() # record the execution time in case there were any exceptions during run. self._total_execution_time = int(time.monotonic() - start_time) def get_event_failed(self) -> Event: return self._construct_event( state="FAILED", source=EventSource.AGENT, raw_error=traceback.format_exc(), ) def get_event_succeeded(self) -> Event: return self._construct_event( state="SUCCEEDED", source=EventSource.AGENT, ) def _record_worker_events(self, result: RunResult) -> None: for worker in self._worker_group.workers: failure = result.failures.get(worker.global_rank) state: str = self._get_worker_state(worker, result) raw_error = json.dumps(failure.error_file_data) if failure else None record(self._construct_event(state, EventSource.WORKER, worker, raw_error)) def _get_worker_state(self, worker: Worker, result: RunResult) -> str: failure = result.failures.get(worker.global_rank) if result.state in {WorkerState.UNHEALTHY, WorkerState.FAILED} and not failure: # The worker got terminated by the torchelastic agent via SIGTERM signal return "TERMINATED" elif failure or worker.global_rank in result.return_values: return result.state.value else: raise ValueError(f"Unknow worker: {worker.global_rank}") def _construct_event( self, state: str, source: EventSource, worker: Optional[Worker] = None, raw_error: Optional[str] = None, ) -> Event: wg = self._worker_group spec = wg.spec md = { "group_world_size": wg.group_world_size, "entry_point": spec.get_entrypoint_name(), } if worker: md["local_rank"] = (worker.local_rank,) md["role_rank"] = (worker.role_rank,) md["role_world_size"] = (worker.role_world_size,) global_rank = worker.global_rank worker_id = str(worker.id) else: global_rank = None worker_id = None md_str = json.dumps(md) metadata = { "run_id": spec.rdzv_handler.get_run_id(), "global_rank": global_rank, "group_rank": wg.group_rank, "worker_id": worker_id, "role": spec.role, "hostname": _get_fq_hostname(), "state": state, "total_run_time": self._total_execution_time, "rdzv_backend": spec.rdzv_handler.get_backend(), "raw_error": raw_error, "metadata": md_str, "agent_restarts": spec.max_restarts - self._remaining_restarts, } return Event( f"torchelastic.worker.status.{state}", source=source, metadata=metadata ) def _record_metrics(self, group_results: RunResult): is_failed = group_results.is_failed() self._record_flakiness_metric(is_failed) spec = self._worker_group.spec restarts_happened = self._remaining_restarts != spec.max_restarts put_metric(f"workers.{spec.role}.run_total", 1) self._record_metric_with_condition( "run_success_with_retries", not is_failed and restarts_happened ) self._record_metric_with_condition( "run_success_no_retries", not is_failed and not restarts_happened ) self._record_metric_with_condition( "run_failed_with_retries", is_failed and restarts_happened ) self._record_metric_with_condition( "run_failed_no_retries", is_failed and not restarts_happened ) def _record_metric_with_condition(self, metric_name, condition): spec = self._worker_group.spec if condition: put_metric(f"workers.{spec.role}.{metric_name}", 1) else: put_metric(f"workers.{spec.role}.{metric_name}", 0) def _record_flakiness_metric(self, is_failed: bool = False): if is_failed: flakiness = 100.0 else: spec = self._worker_group.spec flakiness = 100.0 - 100.0 * (self._remaining_restarts + 1) / ( spec.max_restarts + 1 ) spec = self._worker_group.spec put_metric(f"workers.{spec.role}.flakiness", int(flakiness)) def _invoke_run(self, role: str = DEFAULT_ROLE) -> RunResult: # NOTE: currently only works for a single role spec = self._worker_group.spec role = spec.role log.info( f"[{role}] starting workers for entrypoint: {spec.get_entrypoint_name()}" ) self._initialize_workers(self._worker_group) monitor_interval = spec.monitor_interval rdzv_handler = spec.rdzv_handler while True: assert self._worker_group.state != WorkerState.INIT time.sleep(monitor_interval) run_result = self._monitor_workers(self._worker_group) state = run_result.state self._worker_group.state = state put_metric(f"workers.{role}.remaining_restarts", self._remaining_restarts) put_metric(f"workers.{role}.{state.name.lower()}", 1) if state == WorkerState.SUCCEEDED: log.info( f"[{role}] worker group successfully finished." f" Waiting {self._exit_barrier_timeout} seconds for other agents to finish." ) self._exit_barrier() return run_result elif state in {WorkerState.UNHEALTHY, WorkerState.FAILED}: if self._remaining_restarts > 0: log.info( f"[{role}] Worker group {state.name}. " f"{self._remaining_restarts}/{spec.max_restarts} attempts left;" f" will restart worker group" ) self._remaining_restarts -= 1 self._restart_workers(self._worker_group) else: self._stop_workers(self._worker_group) self._worker_group.state = WorkerState.FAILED self._exit_barrier() return run_result elif state == WorkerState.HEALTHY: # membership changes do not count as retries num_nodes_waiting = rdzv_handler.num_nodes_waiting() group_rank = self._worker_group.group_rank if num_nodes_waiting > 0: log.info( f"[{role}] Detected {num_nodes_waiting} " f"new nodes from group_rank={group_rank}; " f"will restart worker group" ) self._restart_workers(self._worker_group) else: raise Exception(f"[{role}] Worker group in {state.name} state") def _exit_barrier(self): """ Wait for ``exit_barrier_timeout`` seconds for all agents to finish executing their local workers (either successfully or not). This acts as a safety guard against user scripts that terminate at different times. This barrier keeps the agent process alive until all workers finish. """ log.info( f"Local worker group finished ({self._worker_group.state}). " f"Waiting {self._exit_barrier_timeout} seconds for other agents to finish" ) start = time.time() try: store_util.barrier( self._store, self._worker_group.group_rank, self._worker_group.group_world_size, key_prefix=_TERMINAL_STATE_SYNC_ID, barrier_timeout=self._exit_barrier_timeout, ) log.info( f"Done waiting for other agents. Elapsed: {time.time() - start} seconds" ) except SignalException as e: log.warn(f"Got termination signal: {e.sigval}") raise except Exception: log.exception( f"Error waiting on exit barrier. Elapsed: {time.time() - start} seconds" )