torch/distributed/launcher/api.py

#!/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 sys
import uuid
from dataclasses import dataclass, field
from typing import Any, Callable, Dict, List, Optional, Union, cast, Tuple

import torch.distributed.elastic.rendezvous.registry as rdzv_registry
from torch.distributed.elastic import events, metrics
from torch.distributed.elastic.agent.server.api import WorkerSpec, WorkerState
from torch.distributed.elastic.agent.server.local_elastic_agent import LocalElasticAgent
from torch.distributed.elastic.multiprocessing import Std
from torch.distributed.elastic.multiprocessing.errors import ChildFailedError
from torch.distributed.elastic.rendezvous import RendezvousParameters
from torch.distributed.elastic.rendezvous.utils import parse_rendezvous_endpoint
from torch.distributed.elastic.utils.logging import get_logger


logger = get_logger()


@dataclass
class LaunchConfig:
    """
    min_nodes: Minimum amount of nodes that the user function will
                     be launched on. Elastic agent ensures that the user
                     function start only when the min_nodes amount enters
                     the rendezvous.
    max_nodes: Maximum amount of nodes that the user function
                     will be launched on.
    nproc_per_node: On each node the elastic agent will launch
                          this amount of workers that will execute user
                          defined function.
    rdzv_backend: rdzv_backend to use in the rendezvous (zeus-adapter, etcd).
    rdzv_endpoint: The endpoint of the rdzv sync. storage.
    rdzv_id: The unique run id of the job (if not passed a unique one will be
             deduced from run environment - flow workflow id in flow - or auto generated).
    role: User defined role of the worker (defaults to "trainer").
    max_restarts: The maximum amount of restarts that elastic agent will conduct
                  on workers before failure.
    monitor_interval: The interval in seconds that is used by the elastic_agent
                      as a period of monitoring workers.
    start_method: The method is used by the elastic agent to start the
                  workers (spawn, fork, forkserver).
    log_dir: base log directory where log files are written. If not set,
             one is created in a tmp dir but NOT removed on exit.
    redirects: configuration to redirect stdout/stderr to log files.
               Pass a single ``Std`` enum to redirect all workers,
               or a mapping keyed by local_rank to selectively redirect.
    tee: configuration to "tee" stdout/stderr to console + log file.
    metrics_cfg: configuration to initialize metrics.
    """

    min_nodes: int
    max_nodes: int
    nproc_per_node: int
    run_id: str = ""
    role: str = "default_role"
    rdzv_endpoint: str = ""
    rdzv_backend: str = "etcd"
    rdzv_configs: Dict[str, Any] = field(default_factory=dict)
    rdzv_timeout: int = 900
    max_restarts: int = 3
    monitor_interval: float = 30
    start_method: str = "spawn"
    log_dir: Optional[str] = None
    redirects: Union[Std, Dict[int, Std]] = Std.NONE
    tee: Union[Std, Dict[int, Std]] = Std.NONE
    metrics_cfg: Dict[str, str] = field(default_factory=dict)

    def __post_init__(self):
        self.rdzv_configs["timeout"] = self.rdzv_timeout


class elastic_launch:
    """
    Launches an torchelastic agent on the container that invoked the entrypoint.

        1. Pass the ``entrypoint`` arguments as non ``kwargs`` (e.g. no named parameters)/
           ``entrypoint`` can be a function or a command.
        2. The return value is a map of each worker's output mapped
           by their respective global rank.

    Usage

    ::

    def worker_fn(foo):
        # ...

    def main():
        # entrypoint is a function.
        outputs = elastic_launch(LaunchConfig, worker_fn)(foo)
        # return rank 0's output
        return outputs[0]

        # entrypoint is a command and ``script.py`` is the python module.
        ouptuts = elestic_launch(LaunchConfig, "script.py")(args)
        ouptuts = elestic_launch(LaunchConfig, "python")("script.py")
    """

    def __init__(
        self,
        config: LaunchConfig,
        entrypoint: Union[Callable, str, None],
    ):
        self._config = config
        self._entrypoint = entrypoint

    def __call__(self, *args):
        return launch_agent(self._config, self._entrypoint, list(args))


def _construct_event(config: LaunchConfig) -> events.Event:
    metadata = {
        "rdzv_backend": config.rdzv_backend,
        "run_id": config.run_id,
        "role": config.role,
    }
    return events.Event(
        name="torch.distributed.elastic.launch_agent",
        source=events.EventSource.AGENT,
        metadata=cast(Dict[str, events.EventMetadataValue], metadata),
    )


def _get_entrypoint_name(
    entrypoint: Union[Callable, str, None], args: List[Any]
) -> str:
    """Retrive entrypoint name with the rule:
    1. If entrypoint is a function, use ``entrypont.__qualname__``.
    2. If entrypoint is a string, check its value:
        2.1 if entrypoint equals to ``sys.executable`` (like "python"), use the first element from ``args``
            which does not start with hifen letter (for example, "-u" will be skipped).
        2.2 otherwise, use ``entrypoint`` value.
    3. Otherwise, return empty string.
    """
    if isinstance(entrypoint, Callable):  # type: ignore[arg-type]
        return entrypoint.__name__  # type: ignore[union-attr]
    elif isinstance(entrypoint, str):
        if entrypoint == sys.executable:
            return next((arg for arg in args if arg[0] != "-"), "")
        else:
            return entrypoint
    else:
        return ""


def _get_addr_and_port(
    rdzv_parameters: RendezvousParameters,
) -> Tuple[Optional[str], Optional[int]]:
    if rdzv_parameters.backend != "static":
        return (None, None)
    endpoint = rdzv_parameters.endpoint
    endpoint = endpoint.strip()
    if not endpoint:
        raise ValueError(
            "Endpoint is missing in endpoint. Try to add --master_addr and --master_port"
        )
    master_addr, master_port = parse_rendezvous_endpoint(endpoint, default_port=-1)
    if master_port == -1:
        raise ValueError(
            f"port is missing in endpoint: {endpoint}. Try to specify --master_port"
        )
    return (master_addr, master_port)


# pyre-fixme[56]: Pyre was not able to infer the type of the decorator
# torch.distributed.elastic.multiprocessing.errors.record.
def launch_agent(
    config: LaunchConfig,
    entrypoint: Union[Callable, str, None],
    args: List[Any],
) -> Dict[int, Any]:
    if not config.run_id:
        run_id = str(uuid.uuid4().int)
        logger.warning(f"config has no run_id, generate a new one: {run_id}")
        config.run_id = run_id

    entrypoint_name = _get_entrypoint_name(entrypoint, args)

    logger.info(
        f"Starting elastic_operator with launch configs:\n"
        f"  entrypoint       : {entrypoint_name}\n"
        f"  min_nodes        : {config.min_nodes}\n"
        f"  max_nodes        : {config.max_nodes}\n"
        f"  nproc_per_node   : {config.nproc_per_node}\n"
        f"  run_id           : {config.run_id}\n"
        f"  rdzv_backend     : {config.rdzv_backend}\n"
        f"  rdzv_endpoint    : {config.rdzv_endpoint}\n"
        f"  rdzv_configs     : {config.rdzv_configs}\n"
        f"  max_restarts     : {config.max_restarts}\n"
        f"  monitor_interval : {config.monitor_interval}\n"
        f"  log_dir          : {config.log_dir}\n"
        f"  metrics_cfg      : {config.metrics_cfg}\n"
    )

    rdzv_parameters = RendezvousParameters(
        backend=config.rdzv_backend,
        endpoint=config.rdzv_endpoint,
        run_id=config.run_id,
        min_nodes=config.min_nodes,
        max_nodes=config.max_nodes,
        **config.rdzv_configs,
    )

    agent = None
    rdzv_handler = rdzv_registry.get_rendezvous_handler(rdzv_parameters)
    master_addr, master_port = _get_addr_and_port(rdzv_parameters)
    try:
        spec = WorkerSpec(
            role=config.role,
            local_world_size=config.nproc_per_node,
            entrypoint=entrypoint,
            args=tuple(args),
            rdzv_handler=rdzv_handler,
            max_restarts=config.max_restarts,
            monitor_interval=config.monitor_interval,
            redirects=config.redirects,
            tee=config.tee,
            master_addr=master_addr,
            master_port=master_port,
        )

        cfg = metrics.MetricsConfig(config.metrics_cfg) if config.metrics_cfg else None
        metrics.initialize_metrics(cfg)

        agent = LocalElasticAgent(
            spec=spec, start_method=config.start_method, log_dir=config.log_dir
        )

        result = agent.run()
        events.record(agent.get_agent_status_event(WorkerState.SUCCEEDED))
        if result.is_failed():
            # ChildFailedError is treated specially by @record
            # if the error files for the failed children exist
            # @record will copy the first error (root cause)
            # to the error file of the launcher process.
            raise ChildFailedError(
                name=entrypoint_name,
                failures=result.failures,
            )
        else:
            return result.return_values
    except ChildFailedError:
        raise
    except Exception:
        if agent:
            events.record(agent.get_agent_status_event(WorkerState.FAILED))
        else:
            events.record(_construct_event(config))
        raise
    finally:
        rdzv_handler.shutdown()