mirror of
https://github.com/zebrajr/pytorch.git
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14f7e95c1a
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/40066 Builds on top of the previous PR to ensure that all remotely profiled events are prefixed with the key for the RPC that generated them. The key is generated by the result of `_build_rpc_profiling_key` in `rpc/internal.py` and prefixed onto the event name. In order to do this, we set the current-key when creating the RPC in Python, retrieve the currently-set key in C++ and save a GloballyUniqueId -> key mapping to an in-memory map. When we receive an RPC with profiling information, we expect to receive this ID back, and look up the corresponding profiling key in the map. The key is then added to all the remote events. Tested by adding tests to ensure the key is added to all the remote events. Also added a UT which tests in under the multi-threading scenario, to ensure that the mapping's correctness is maintained when several RPCs are in the process of being created at once. ghstack-source-id: 106316106 Test Plan: Unit test Differential Revision: D22040035 fbshipit-source-id: 9215feb06084b294edbfa6e03385e13c1d730c43
803 lines
31 KiB
Python
803 lines
31 KiB
Python
import collections
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import contextlib
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import functools
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import logging
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import numbers
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import threading
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from typing import Generic, TypeVar
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import torch
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import torch.distributed as dist
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from . import (
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PyRRef,
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RemoteProfilerManager,
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RpcBackendOptions,
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WorkerInfo,
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_cleanup_python_rpc_handler,
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_delete_all_user_and_unforked_owner_rrefs,
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_destroy_rref_context,
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_get_current_rpc_agent,
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_invoke_remote_builtin,
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_invoke_remote_python_udf,
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_invoke_remote_torchscript,
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_invoke_rpc_builtin,
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_invoke_rpc_python_udf,
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_invoke_rpc_torchscript,
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_is_current_rpc_agent_set,
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_reset_current_rpc_agent,
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_set_and_start_rpc_agent,
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backend_registry,
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)
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from .internal import (
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PythonUDF,
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RPCExecMode,
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_internal_rpc_pickler,
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_build_rpc_profiling_key,
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)
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from .constants import UNSET_RPC_TIMEOUT
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logger = logging.getLogger(__name__)
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# NB: Ignoring RRef leaks during shutdown. Without this, applications have to
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# make sure there is no references to any RRef in the application code and
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# Python GC has done its job to delete those RRefs. This is could result in bad
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# debugging experiences especially when for large applications. Therefore, by
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# default, we are going to ignore RRef leaks during shutdown. This is usually
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# fine as shutdown means applications have done training and no longer care
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# about states.
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#
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# To enable RRef leak checking, set this _ignore_rref_leak to False
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_ignore_rref_leak = True
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_default_pickler = _internal_rpc_pickler
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@contextlib.contextmanager
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def _use_rpc_pickler(rpc_pickler):
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r"""
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rpc_pickler: (.internal._InternalRPCPickler) Overrides the default RPC pickler
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"""
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global _default_pickler
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_default_pickler = rpc_pickler
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try:
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yield
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finally:
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_default_pickler = _internal_rpc_pickler
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def _require_initialized(func):
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@functools.wraps(func)
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def wrapper(*args, **kwargs):
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if not _is_current_rpc_agent_set():
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raise RuntimeError(
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"RPC has not been initialized. Call "
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"torch.distributed.rpc.init_rpc first."
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)
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return func(*args, **kwargs)
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return wrapper
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class WaitAllWorkersStates(object):
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def __init__(self):
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# Each `intent_worker_names` is an empty set at beginning.
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# It's only used by leader worker. Leader worker is user-specified or
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# elected as the first worker in a sorted worker name list.
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# Whenever there is a worker showing shutdown intention to the leader, by
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# calling `_wait_all_workers()`, the leader adds this worker's name to the set.
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# The leader also adds itself's name to the set on calling
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# `_wait_all_workers()`. We need this because, we confine `_wait_all_workers()`
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# to be called only once, by examing if leader's name has been added to the set.
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self.intent_worker_names = set()
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# Once `intent_worker_names == _ALL_WORKER_NAMES`,
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# we flip `_SHUTDOWN_PROCEED_SIGNAL` on the leader, and leader will send RPCs
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# to follower workers to flip their `_SHUTDOWN_PROCEED_SIGNAL`s.
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self.proceed_signal = threading.Event()
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# States used by `def _wait_all_workers()`.
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# `_ALL_WORKER_NAMES` is initialized on initiaizing RPC layer.
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_ALL_WORKER_NAMES = None
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_wait_all_workers_dict_lock = threading.RLock()
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_wait_all_workers_sequence_id = 0
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_wait_all_workers_sequence_id_to_states = collections.defaultdict(WaitAllWorkersStates)
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def _on_leader_follower_report_shutdown_intent(sequence_id, worker_name):
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with _wait_all_workers_dict_lock:
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assert (
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worker_name in _ALL_WORKER_NAMES
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), "{worker_name} is not expected by leader.".format(worker_name=worker_name)
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intent_worker_names = _wait_all_workers_sequence_id_to_states[
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sequence_id
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].intent_worker_names
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assert (
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worker_name not in intent_worker_names
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), "{worker_name} reported intent sequence id {sequence_id} twice. ".format(
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worker_name=worker_name, sequence_id=sequence_id
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)
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intent_worker_names.add(worker_name)
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if _ALL_WORKER_NAMES == intent_worker_names:
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_set_proceed_shutdown_signal(sequence_id)
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def _set_proceed_shutdown_signal(sequence_id):
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with _wait_all_workers_dict_lock:
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proceed_signal = _wait_all_workers_sequence_id_to_states[
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sequence_id
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].proceed_signal
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assert (
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not proceed_signal.is_set()
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), "Termination signal sequence id {} got set twice.".format(sequence_id)
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proceed_signal.set()
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@_require_initialized
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def _wait_all_workers():
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r"""
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Block until all local and remote RPC processes reach this method and wait
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for all outstanding work to complete. Every RPC process must call this
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method before exit to perform a graceful shutdown. This should be used to
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terminate the RPC framework, and there is no guarantee that the RPC
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framework will work after this method returns.
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"""
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assert (
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_ALL_WORKER_NAMES is not None
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), "`_ALL_WORKER_NAMES` is not initialized for `def _wait_all_workers`."
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leader_worker_name = sorted(_ALL_WORKER_NAMES)[0]
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self_worker_name = _get_current_rpc_agent().get_worker_info().name
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global _wait_all_workers_sequence_id
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with _wait_all_workers_dict_lock:
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sequence_id = _wait_all_workers_sequence_id
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_wait_all_workers_sequence_id += 1
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is_leader_worker = leader_worker_name == self_worker_name
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# Set a long enough timeout for all shutdown messages to be processed.
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timeout = 5 # seconds
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# Phase 1: Followers send intents.
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# All followers report intents to the leader.
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if is_leader_worker:
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_on_leader_follower_report_shutdown_intent(sequence_id, self_worker_name)
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else:
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rpc_sync(
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leader_worker_name,
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_on_leader_follower_report_shutdown_intent,
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args=(sequence_id, self_worker_name,),
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timeout=timeout,
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)
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with _wait_all_workers_dict_lock:
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proceed_signal = _wait_all_workers_sequence_id_to_states[
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sequence_id
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].proceed_signal
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proceed_signal.wait()
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# Phase 2: Leader asks followers to proceed.
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# Leader's signal is the first to be unblocked,
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# after receiving all followers' intents.
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if is_leader_worker:
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# The leader sends out proceeed signals to all followers.
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worker_name_to_response_future_dict = dict()
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for follower_worker_name in _ALL_WORKER_NAMES - {leader_worker_name}:
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fut = rpc_async(follower_worker_name, _set_proceed_shutdown_signal,
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args=(sequence_id,), timeout=timeout)
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worker_name_to_response_future_dict[follower_worker_name] = fut
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for follower_worker_name, fut in worker_name_to_response_future_dict.items():
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try:
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fut.wait()
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except RuntimeError as ex:
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logger.error(
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"{worker_name} failed to respond to 'Shutdown Proceed.' request in {timeout}".format(
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worker_name=follower_worker_name, timeout=timeout
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)
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)
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@_require_initialized
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def shutdown(graceful=True):
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r"""
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Perform a shutdown of the RPC agent, and then destroy the RPC agent. This
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stops the local agent from accepting outstanding requests, and shuts
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down the RPC framework by terminating all RPC threads. If ``graceful=True``,
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this will block until all local and remote RPC processes reach this method
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and wait for all outstanding work to complete. Otherwise, if
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``graceful=False``, this is a local shutdown, and it does not wait for other
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RPC processes to reach this method.
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.. warning::
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For :class:`~torch.futures.Future` objects returned by
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:meth:`~torch.distributed.rpc.rpc_async`, ``future.wait()`` should not
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be called after ``shutdown()``.
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Arguments:
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graceful (bool): Whether to do a graceful shutdown or not. If True,
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this will 1) wait until there is no pending system
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messages for ``UserRRefs`` and delete them; 2) block
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until all local and remote RPC processes have reached
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this method and wait for all outstanding work to
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complete.
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Example::
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Make sure that ``MASTER_ADDR`` and ``MASTER_PORT`` are set properly
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on both workers. Refer to :meth:`~torch.distributed.init_process_group`
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API for more details. For example,
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>>> export MASTER_ADDR=localhost
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>>> export MASTER_PORT=5678
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Then run the following code in two different processes:
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>>> # On worker 0:
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>>> import torch
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>>> import torch.distributed.rpc as rpc
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>>> rpc.init_rpc("worker0", rank=0, world_size=2)
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>>> # do some work
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>>> result = rpc.rpc_sync("worker1", torch.add, args=(torch.ones(1), 1))
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>>> # ready to shutdown
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>>> rpc.shutdown()
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>>> # On worker 1:
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>>> import torch.distributed.rpc as rpc
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>>> rpc.init_rpc("worker1", rank=1, world_size=2)
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>>> # wait for worker 0 to finish work, and then shutdown.
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>>> rpc.shutdown()
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"""
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if graceful:
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_wait_all_workers()
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_delete_all_user_and_unforked_owner_rrefs()
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_get_current_rpc_agent().join()
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try:
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# This raises a `TORCH_CHECK()` exception on RRef leak detected.
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_destroy_rref_context(_ignore_rref_leak)
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finally:
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_get_current_rpc_agent().shutdown()
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# clean up python rpc handler in shutdown(), see comments in
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# PythonRpcHandler::cleanup(), call it in python API because the
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# cleanup() function has python dependency, it assumes python
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# interpreter exists.
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# No matter if RRef leak exception is raised, this clean-up code
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# must run to avoid destruction segfault in Python 3.5.
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#
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# future.wait() should not be called after shutdown().
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# pythonRpcHandler is cleaned up in shutdown(), after
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# shutdown(), python objects returned from rpc python call can not be
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# resolved.
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_cleanup_python_rpc_handler()
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_reset_current_rpc_agent()
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# TODO: add a context manager to wrap _init_rpc_backend and shutdown
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def _init_rpc_backend(
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backend=backend_registry.BackendType.PROCESS_GROUP,
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store=None,
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name=None,
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rank=-1,
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world_size=-1,
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rpc_backend_options=None,
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):
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_validate_rpc_args(backend, store, name, rank, world_size, rpc_backend_options)
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if _is_current_rpc_agent_set():
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raise RuntimeError("RPC is already initialized")
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# Initialize RPC.
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rpc_agent = backend_registry.init_backend(
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backend,
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store=store,
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name=name,
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rank=rank,
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world_size=world_size,
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rpc_backend_options=rpc_backend_options,
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)
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worker_infos = rpc_agent.get_worker_infos()
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global _ALL_WORKER_NAMES
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_ALL_WORKER_NAMES = {worker_info.name for worker_info in worker_infos}
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_set_and_start_rpc_agent(rpc_agent)
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@_require_initialized
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def get_worker_info(worker_name=None):
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r"""
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Get :class:`~torch.distributed.rpc.WorkerInfo` of a given worker name.
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Use this :class:`~torch.distributed.rpc.WorkerInfo` to avoid passing an
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expensive string on every invocation.
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Arguments:
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worker_name (str): the string name of a worker. If ``None``, return the
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the id of the current worker. (default ``None``)
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Returns:
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:class:`~torch.distributed.rpc.WorkerInfo` instance for the given
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``worker_name`` or :class:`~torch.distributed.rpc.WorkerInfo` of the
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current worker if ``worker_name`` is ``None``.
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"""
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if worker_name:
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return _get_current_rpc_agent().get_worker_info(worker_name)
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else:
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return _get_current_rpc_agent().get_worker_info()
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def _to_worker_info(name_or_info):
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if isinstance(name_or_info, WorkerInfo):
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return name_or_info
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elif isinstance(name_or_info, str):
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return get_worker_info(name_or_info)
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else:
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raise ValueError("Cannot get WorkerInfo from name {}".format(name_or_info))
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def _validate_rpc_args(backend, store, name, rank, world_size, rpc_backend_options):
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type_mapping = {
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backend: backend_registry.BackendType,
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store: dist.Store,
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name: str,
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rank: numbers.Integral,
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world_size: numbers.Integral,
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rpc_backend_options: RpcBackendOptions,
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}
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for arg, arg_type in type_mapping.items():
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if not isinstance(arg, arg_type):
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raise RuntimeError(
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"Argument {} must be of type {} but got type {}".format(
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arg, arg_type, type(arg)
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)
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)
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T = TypeVar("T")
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GenericWithOneTypeVar = Generic[T]
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try:
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class RRef(PyRRef, GenericWithOneTypeVar):
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# Combine the implementation class and the type class.
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pass
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except TypeError as exc:
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# TypeError: metaclass conflict: the metaclass of a derived class
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# must be a (non-strict) subclass of the metaclasses of all its bases
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class RRefMeta(PyRRef.__class__, GenericWithOneTypeVar.__class__):
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pass
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class RRef(PyRRef, GenericWithOneTypeVar, metaclass=RRefMeta):
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# Combine the implementation class and the type class.
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pass
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@_require_initialized
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def remote(to, func, args=None, kwargs=None, timeout=UNSET_RPC_TIMEOUT):
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r"""
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Make a remote call to run ``func`` on worker ``to`` and return an
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:class:`~torch.distributed.rpc.RRef` to the result value immediately.
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Worker ``to`` will be the owner of the returned
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:class:`~torch.distributed.rpc.RRef`, and the worker calling ``remote`` is
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a user. The owner manages the global reference count of its
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:class:`~torch.distributed.rpc.RRef`, and the owner
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:class:`~torch.distributed.rpc.RRef` is only destructed when globally there
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are no living references to it.
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Arguments:
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to (str or WorkerInfo): id or name of the destination worker.
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func (callable): a callable function, such as Python callables, builtin
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operators (e.g. :meth:`~torch.add`) and annotated
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TorchScript functions.
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args (tuple): the argument tuple for the ``func`` invocation.
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kwargs (dict): is a dictionary of keyword arguments for the ``func``
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invocation.
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timeout (float, optional): timeout in seconds for this remote call. If the
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creation of this
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:class:`~torch.distributed.rpc.RRef` on worker
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``to`` is not successfully processed on this
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worker within this timeout, then the next time
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there is an attempt to use the RRef (such as
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``to_here()``), a timeout will be raised
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indicating this failure. A value of 0 indicates
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an infinite timeout, i.e. a timeout error will
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never be raised. If not provided, the default
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value set during initialization or with
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``_set_rpc_timeout`` is used.
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Returns:
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A user :class:`~torch.distributed.rpc.RRef` instance to the result
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value. Use the blocking API :meth:`torch.distributed.rpc.RRef.to_here`
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to retrieve the result value locally.
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.. warning ::
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Using GPU tensors as arguments or return values of ``func`` is not
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supported since we don't support sending GPU tensors over the wire. You
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need to explicitly copy GPU tensors to CPU before using them as
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arguments or return values of ``func``.
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.. warning ::
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The ``remote`` API does not copy storages of argument tensors until
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sending them over the wire, which could be done by a different thread
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depending on the RPC backend type. The caller should make sure that the
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contents of those tensors stay intact until the returned RRef is
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confirmed by the owner, which can be checked using the
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:meth:`torch.distributed.rpc.RRef.confirmed_by_owner` API.
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.. warning ::
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Errors such as timeouts for the ``remote`` API are handled on a
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best-effort basis. This means that when remote calls initiated by
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``remote`` fail, such as with a timeout error, we take a best-effort
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approach to error handling. This means that errors are handled and set
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on the resulting RRef on an asynchronous basis. If the RRef has not been
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used by the application before this handling (such as ``to_here`` or
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fork call), then future uses of the ``RRef`` will appropriately raise
|
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errors. However, it is possible that the user application will use the
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``RRef`` before the errors are handled. In this case, errors may not be
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raised as they have not yet been handled.
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|
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Example::
|
|
Make sure that ``MASTER_ADDR`` and ``MASTER_PORT`` are set properly
|
|
on both workers. Refer to :meth:`~torch.distributed.init_process_group`
|
|
API for more details. For example,
|
|
|
|
>>> export MASTER_ADDR=localhost
|
|
>>> export MASTER_PORT=5678
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|
|
Then run the following code in two different processes:
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|
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>>> # On worker 0:
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>>> import torch
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>>> import torch.distributed.rpc as rpc
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>>> rpc.init_rpc("worker0", rank=0, world_size=2)
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>>> rref1 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 3))
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>>> rref2 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 1))
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>>> x = rref1.to_here() + rref2.to_here()
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>>> rpc.shutdown()
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>>> # On worker 1:
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>>> import torch.distributed.rpc as rpc
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>>> rpc.init_rpc("worker1", rank=1, world_size=2)
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>>> rpc.shutdown()
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Below is an example of running a TorchScript function using RPC.
|
|
|
|
>>> # On both workers:
|
|
>>> @torch.jit.script
|
|
>>> def my_script_add(t1, t2):
|
|
>>> return torch.add(t1, t2)
|
|
|
|
>>> # On worker 0:
|
|
>>> import torch.distributed.rpc as rpc
|
|
>>> rpc.init_rpc("worker0", rank=0, world_size=2)
|
|
>>> rref = rpc.remote("worker1", my_script_add, args=(torch.ones(2), 3))
|
|
>>> rref.to_here()
|
|
>>> rpc.shutdown()
|
|
|
|
>>> # On worker 1:
|
|
>>> import torch.distributed.rpc as rpc
|
|
>>> rpc.init_rpc("worker1", rank=1, world_size=2)
|
|
>>> rpc.shutdown()
|
|
"""
|
|
qualified_name = torch.jit._find_builtin(func)
|
|
dst_worker_info = _to_worker_info(to)
|
|
should_profile = torch.autograd._profiler_enabled()
|
|
|
|
ctx_manager = contextlib.suppress()
|
|
if should_profile:
|
|
# Create appropriate string representation based on type of func
|
|
# (builtin, script, python)
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if qualified_name is None:
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|
func_name = (
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|
torch.jit._qualified_name(func)
|
|
if isinstance(func, torch.jit.ScriptFunction)
|
|
else func.__qualname__
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|
)
|
|
else:
|
|
func_name = qualified_name
|
|
# Build RPC profiling key.
|
|
rpc_profiling_key = _build_rpc_profiling_key(
|
|
RPCExecMode.REMOTE,
|
|
func_name,
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|
get_worker_info().name,
|
|
dst_worker_info.name,
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|
)
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|
RemoteProfilerManager.set_current_profiling_key(rpc_profiling_key)
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|
ctx_manager = torch.autograd.profiler.record_function(rpc_profiling_key)
|
|
|
|
with ctx_manager as rf:
|
|
args = args if args else ()
|
|
kwargs = kwargs if kwargs else {}
|
|
|
|
is_async_exec = hasattr(func, "_wrapped_async_rpc_function")
|
|
|
|
if is_async_exec:
|
|
wrapped = func._wrapped_async_rpc_function
|
|
if isinstance(wrapped, torch.jit.ScriptFunction):
|
|
func = wrapped
|
|
|
|
if qualified_name is not None:
|
|
rref = _invoke_remote_builtin(dst_worker_info, qualified_name, timeout, *args, **kwargs)
|
|
elif isinstance(func, torch.jit.ScriptFunction):
|
|
rref = _invoke_remote_torchscript(
|
|
dst_worker_info.name,
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|
torch._jit_internal._qualified_name(func),
|
|
timeout,
|
|
is_async_exec,
|
|
*args,
|
|
**kwargs,
|
|
)
|
|
else:
|
|
(pickled_python_udf, tensors) = _default_pickler.serialize(
|
|
PythonUDF(func, args, kwargs)
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|
)
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|
rref = _invoke_remote_python_udf(
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|
dst_worker_info,
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pickled_python_udf,
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|
tensors,
|
|
timeout,
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|
is_async_exec
|
|
)
|
|
# attach profiling information
|
|
if should_profile:
|
|
assert torch.autograd._profiler_enabled()
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|
assert rf is not None
|
|
fut = rf._call_end_callbacks_on_future(rref._get_future())
|
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rref._set_profiling_future(fut)
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|
|
|
return rref
|
|
|
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def _invoke_rpc(to, func, rpc_type, args=None, kwargs=None, rpc_timeout=UNSET_RPC_TIMEOUT):
|
|
if not callable(func):
|
|
raise TypeError("function should be callable.")
|
|
|
|
qualified_name = torch.jit._find_builtin(func)
|
|
dst_worker_info = _to_worker_info(to)
|
|
|
|
# TODO: profiling logic does not really belong in invoke_rpc, it should be
|
|
# added as part of a context manager or helper (https://github.com/pytorch/pytorch/issues/36360)
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|
should_profile = torch.autograd._profiler_enabled()
|
|
|
|
ctx_manager = contextlib.suppress()
|
|
if should_profile:
|
|
# Create appropriate string representation based on type of func
|
|
# (builtin, script, python)
|
|
if qualified_name is None:
|
|
func_name = (
|
|
torch.jit._qualified_name(func)
|
|
if isinstance(func, torch.jit.ScriptFunction)
|
|
else func.__qualname__
|
|
)
|
|
else:
|
|
func_name = qualified_name
|
|
# Build RPC profiling key.
|
|
rpc_profiling_key = _build_rpc_profiling_key(
|
|
rpc_type,
|
|
func_name,
|
|
get_worker_info().name,
|
|
dst_worker_info.name,
|
|
)
|
|
RemoteProfilerManager.set_current_profiling_key(rpc_profiling_key)
|
|
ctx_manager = torch.autograd.profiler.record_function(rpc_profiling_key)
|
|
|
|
with ctx_manager as rf:
|
|
args = args if args else ()
|
|
kwargs = kwargs if kwargs else {}
|
|
|
|
is_async_exec = hasattr(func, "_wrapped_async_rpc_function")
|
|
|
|
if is_async_exec:
|
|
wrapped = func._wrapped_async_rpc_function
|
|
if isinstance(wrapped, torch.jit.ScriptFunction):
|
|
func = wrapped
|
|
|
|
if qualified_name is not None:
|
|
fut = _invoke_rpc_builtin(
|
|
dst_worker_info,
|
|
qualified_name,
|
|
rpc_timeout,
|
|
*args,
|
|
**kwargs
|
|
)
|
|
elif isinstance(func, torch.jit.ScriptFunction):
|
|
fut = _invoke_rpc_torchscript(
|
|
dst_worker_info.name,
|
|
torch.jit._qualified_name(func),
|
|
args,
|
|
kwargs,
|
|
rpc_timeout,
|
|
is_async_exec
|
|
)
|
|
else:
|
|
(pickled_python_udf, tensors) = _default_pickler.serialize(
|
|
PythonUDF(func, args, kwargs)
|
|
)
|
|
fut = _invoke_rpc_python_udf(
|
|
dst_worker_info,
|
|
pickled_python_udf,
|
|
tensors,
|
|
rpc_timeout,
|
|
is_async_exec
|
|
)
|
|
if should_profile:
|
|
assert torch.autograd._profiler_enabled()
|
|
assert rf is not None
|
|
# Schedule profiling callbacks to run when the future completes.
|
|
# This returns a future that is completed when the original future
|
|
# completes and the profiling callbacks have been completed as well,
|
|
# to guarantee that fut.wait() completes the profiling. This new
|
|
# future will contain the same value as the original future.
|
|
fut = rf._call_end_callbacks_on_future(fut)
|
|
return fut
|
|
|
|
|
|
@_require_initialized
|
|
def rpc_sync(to, func, args=None, kwargs=None, timeout=UNSET_RPC_TIMEOUT):
|
|
r"""
|
|
Make a blocking RPC call to run function ``func`` on worker ``to``. RPC
|
|
messages are sent and received in parallel to execution of Python code. This
|
|
method is thread-safe.
|
|
|
|
Arguments:
|
|
to (str or WorkerInfo): id or name of the destination worker.
|
|
func (callable): a callable function, such as Python callables, builtin
|
|
operators (e.g. :meth:`~torch.add`) and annotated
|
|
TorchScript functions.
|
|
args (tuple): the argument tuple for the ``func`` invocation.
|
|
kwargs (dict): is a dictionary of keyword arguments for the ``func``
|
|
invocation.
|
|
timeout (float, optional): timeout in seconds to use for this RPC. If
|
|
the RPC does not complete in this amount of
|
|
time, an exception indicating it has
|
|
timed out will be raised. A value of 0
|
|
indicates an infinite timeout, i.e. a timeout
|
|
error will never be raised. If not provided,
|
|
the default value set during initialization
|
|
or with ``_set_rpc_timeout`` is used.
|
|
|
|
Returns:
|
|
Returns the result of running ``func`` with ``args`` and ``kwargs``.
|
|
|
|
.. warning ::
|
|
Using GPU tensors as arguments or return values of ``func`` is not
|
|
supported since we don't support sending GPU tensors over the wire. You
|
|
need to explicitly copy GPU tensors to CPU before using them as
|
|
arguments or return values of ``func``.
|
|
|
|
Example::
|
|
Make sure that ``MASTER_ADDR`` and ``MASTER_PORT`` are set properly
|
|
on both workers. Refer to :meth:`~torch.distributed.init_process_group`
|
|
API for more details. For example,
|
|
|
|
>>> export MASTER_ADDR=localhost
|
|
>>> export MASTER_PORT=5678
|
|
|
|
Then run the following code in two different processes:
|
|
|
|
>>> # On worker 0:
|
|
>>> import torch
|
|
>>> import torch.distributed.rpc as rpc
|
|
>>> rpc.init_rpc("worker0", rank=0, world_size=2)
|
|
>>> ret = rpc.rpc_sync("worker1", torch.add, args=(torch.ones(2), 3))
|
|
>>> rpc.shutdown()
|
|
|
|
>>> # On worker 1:
|
|
>>> import torch.distributed.rpc as rpc
|
|
>>> rpc.init_rpc("worker1", rank=1, world_size=2)
|
|
>>> rpc.shutdown()
|
|
|
|
Below is an example of running a TorchScript function using RPC.
|
|
|
|
>>> # On both workers:
|
|
>>> @torch.jit.script
|
|
>>> def my_script_add(t1, t2):
|
|
>>> return torch.add(t1, t2)
|
|
|
|
>>> # On worker 0:
|
|
>>> import torch.distributed.rpc as rpc
|
|
>>> rpc.init_rpc("worker0", rank=0, world_size=2)
|
|
>>> ret = rpc.rpc_sync("worker1", my_script_add, args=(torch.ones(2), 3))
|
|
>>> rpc.shutdown()
|
|
|
|
>>> # On worker 1:
|
|
>>> import torch.distributed.rpc as rpc
|
|
>>> rpc.init_rpc("worker1", rank=1, world_size=2)
|
|
>>> rpc.shutdown()
|
|
|
|
"""
|
|
fut = _invoke_rpc(to, func, RPCExecMode.SYNC, args, kwargs, timeout)
|
|
return fut.wait()
|
|
|
|
|
|
@_require_initialized
|
|
def rpc_async(to, func, args=None, kwargs=None, timeout=UNSET_RPC_TIMEOUT):
|
|
r"""
|
|
Make a non-blocking RPC call to run function ``func`` on worker ``to``. RPC
|
|
messages are sent and received in parallel to execution of Python code. This
|
|
method is thread-safe. This method will immediately return a
|
|
:class:`~torch.futures.Future` that can be awaited on.
|
|
|
|
Arguments:
|
|
to (str or WorkerInfo): id or name of the destination worker.
|
|
func (callable): a callable function, such as Python callables, builtin
|
|
operators (e.g. :meth:`~torch.add`) and annotated
|
|
TorchScript functions.
|
|
args (tuple): the argument tuple for the ``func`` invocation.
|
|
kwargs (dict): is a dictionary of keyword arguments for the ``func``
|
|
invocation.
|
|
timeout (float, optional): timeout in seconds to use for this RPC. If
|
|
the RPC does not complete in this amount of
|
|
time, an exception indicating it has
|
|
timed out will be raised. A value of 0
|
|
indicates an infinite timeout, i.e. a timeout
|
|
error will never be raised. If not provided,
|
|
the default value set during initialization
|
|
or with ``_set_rpc_timeout`` is used.
|
|
|
|
|
|
Returns:
|
|
Returns a :class:`~torch.futures.Future` object that can be waited
|
|
on. When completed, the return value of ``func`` on ``args`` and
|
|
``kwargs`` can be retrieved from the :class:`~torch.futures.Future`
|
|
object.
|
|
|
|
.. warning ::
|
|
Using GPU tensors as arguments or return values of ``func`` is not
|
|
supported since we don't support sending GPU tensors over the wire. You
|
|
need to explicitly copy GPU tensors to CPU before using them as
|
|
arguments or return values of ``func``.
|
|
|
|
.. warning ::
|
|
The ``rpc_async`` API does not copy storages of argument tensors until
|
|
sending them over the wire, which could be done by a different thread
|
|
depending on the RPC backend type. The caller should make sure that the
|
|
contents of those tensors stay intact until the returned
|
|
:class:`~torch.futures.Future` completes.
|
|
|
|
Example::
|
|
Make sure that ``MASTER_ADDR`` and ``MASTER_PORT`` are set properly
|
|
on both workers. Refer to :meth:`~torch.distributed.init_process_group`
|
|
API for more details. For example,
|
|
|
|
>>> export MASTER_ADDR=localhost
|
|
>>> export MASTER_PORT=5678
|
|
|
|
Then run the following code in two different processes:
|
|
|
|
>>> # On worker 0:
|
|
>>> import torch
|
|
>>> import torch.distributed.rpc as rpc
|
|
>>> rpc.init_rpc("worker0", rank=0, world_size=2)
|
|
>>> fut1 = rpc.rpc_async("worker1", torch.add, args=(torch.ones(2), 3))
|
|
>>> fut2 = rpc.rpc_async("worker1", min, args=(1, 2))
|
|
>>> result = fut1.wait() + fut2.wait()
|
|
>>> rpc.shutdown()
|
|
|
|
>>> # On worker 1:
|
|
>>> import torch.distributed.rpc as rpc
|
|
>>> rpc.init_rpc("worker1", rank=1, world_size=2)
|
|
>>> rpc.shutdown()
|
|
|
|
Below is an example of running a TorchScript function using RPC.
|
|
|
|
>>> # On both workers:
|
|
>>> @torch.jit.script
|
|
>>> def my_script_add(t1, t2):
|
|
>>> return torch.add(t1, t2)
|
|
|
|
>>> # On worker 0:
|
|
>>> import torch.distributed.rpc as rpc
|
|
>>> rpc.init_rpc("worker0", rank=0, world_size=2)
|
|
>>> fut = rpc.rpc_async("worker1", my_script_add, args=(torch.ones(2), 3))
|
|
>>> ret = fut.wait()
|
|
>>> rpc.shutdown()
|
|
|
|
>>> # On worker 1:
|
|
>>> import torch.distributed.rpc as rpc
|
|
>>> rpc.init_rpc("worker1", rank=1, world_size=2)
|
|
>>> rpc.shutdown()
|
|
"""
|
|
return _invoke_rpc(to, func, RPCExecMode.ASYNC, args, kwargs, timeout)
|