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Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/34650 Resubmit of https://github.com/pytorch/pytorch/pull/33840, which was overly eager in the sense that it deleted a lot of code that we didn't want to get rid of yet (default timeout handling). This PR adds an optional argument into `rpc_sync` and `rpc_async` as well as `RpcAgent::send()` that allows the user to specify a timeout for an RPC to override the default set timeout. If the user does not specify this argument, then the currently set default RPC timeout given in the RPC constructor or by `rpc.set_rpc_timeout()` is used. Otherwise, we use the passed in timeout. This diff does not address: 1) timeout support when called rpc.rpc_async is called as a JIT operator. For this to work, we would need to change the logic in `register_distributed_ops` to pass in this timeout to `rpcTorchscript`. One more issue is that torchscript doesn't support the timedelta object. This will be done in a follow up PR as it requires a fair amount of changes to the argument parsing logic. 2) Per-RPC timeouts for internal messages or `rpc.remote()`. A follow-up diff will address the latter with the approach of raising the timeout error at the earliest next possible time to the user, such as when the next time the RRef is forked or `to_here` is called Added unit tests to confirm the current behavior ghstack-source-id: 102622601 Test Plan: Added unit tests in rpc_test Differential Revision: D20376953 fbshipit-source-id: 9fb3f147520588308ab50dd33286255658d76d47
216 lines
8.0 KiB
Python
216 lines
8.0 KiB
Python
from __future__ import absolute_import, division, print_function, unicode_literals
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import time
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from functools import partial, wraps
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import re
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import torch.distributed as dist
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import torch.distributed.rpc as rpc
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from torch.distributed.rpc import _rref_context_get_debug_info
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if not dist.is_available():
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print("c10d not available, skipping tests")
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sys.exit(0)
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class TestConfig:
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__slots__ = ["rpc_backend_name", "build_rpc_backend_options"]
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def __init__(self, *args, **kwargs):
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assert len(args) == 0, "TestConfig only takes kwargs."
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for k, v in kwargs.items():
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setattr(self, k, v)
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TEST_CONFIG = TestConfig()
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INIT_METHOD_TEMPLATE = "file://{file_name}"
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def dist_init(old_test_method=None, setup_rpc=True, clean_shutdown=True,
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faulty_messages=None):
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"""
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We use this decorator for setting up and tearing down state since
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MultiProcessTestCase runs each `test*` method in a separate process and
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each process just runs the `test*` method without actually calling
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'setUp' and 'tearDown' methods of unittest.
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Note: pass the string representation of MessageTypes that should be used
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with the faulty agent's send function. By default, all retriable messages
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("RREF_FORK_REQUEST", "RREF_CHILD_ACCEPT", "RREF_USER_DELETE",
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"CLEANUP_AUTOGRAD_CONTEXT_REQ") will use the faulty send (this default is
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set from faulty_rpc_agent_test_fixture.py).
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"""
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# If we use dist_init without arguments (ex: @dist_init), old_test_method is
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# appropriately set and we return the wrapper appropriately. On the other
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# hand if dist_init has arguments (ex: @dist_init(clean_shutdown=False)),
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# old_test_method is None and we return a functools.partial which is the real
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# decorator that is used and as a result we recursively call dist_init with
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# old_test_method and the rest of the arguments appropriately set.
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if old_test_method is None:
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return partial(
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dist_init,
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setup_rpc=setup_rpc,
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clean_shutdown=clean_shutdown,
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faulty_messages=faulty_messages,
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)
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@wraps(old_test_method)
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def new_test_method(self, *arg, **kwargs):
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# Setting _ignore_rref_leak to make sure OwnerRRefs are properly deleted
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# in tests.
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import torch.distributed.rpc.api as api
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api._ignore_rref_leak = False
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self.worker_id = self.rank
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if faulty_messages:
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_build_faulty_backend_options(faulty_messages)
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if setup_rpc:
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rpc.init_rpc(
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name="worker%d" % self.rank,
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backend=self.rpc_backend,
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rank=self.rank,
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world_size=self.world_size,
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rpc_backend_options=self.rpc_backend_options,
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)
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return_value = old_test_method(self, *arg, **kwargs)
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if setup_rpc:
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rpc.shutdown(graceful=clean_shutdown)
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return return_value
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return new_test_method
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# Set PROCESS_GROUP as the default RPC backend.
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TEST_CONFIG.rpc_backend_name = "PROCESS_GROUP"
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TEST_CONFIG.build_rpc_backend_options = lambda test_object: rpc.backend_registry.construct_rpc_backend_options(
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test_object.rpc_backend,
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init_method=test_object.init_method,
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# Some tests need additional threads (ex: test_trainer_ps)
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num_send_recv_threads=8,
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)
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def _build_faulty_backend_options(faulty_messages):
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'''
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Constructs the backend options object for the faulty process group agent
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based on the faulty_messages input to dist_init.
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'''
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TEST_CONFIG.build_rpc_backend_options = lambda test_object: rpc.backend_registry.construct_rpc_backend_options(
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test_object.rpc_backend,
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init_method=test_object.init_method,
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num_send_recv_threads=8,
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num_fail_sends=1,
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messages_to_fail=faulty_messages,
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)
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def noop():
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pass
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def wait_until_node_failure(rank, expected_error_regex=".*"):
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'''
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Loops until an RPC to the given rank fails. This is used to
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indicate that the node has failed in unit tests.
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Args:
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rank (int): Rank of the node expected to fail
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expected_error_regex (optional, str): Regex of exception message expected. Useful to ensure a specific failure
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occurs, not just any.
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'''
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while True:
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try:
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rpc.rpc_sync("worker{}".format(rank), noop, args=())
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time.sleep(0.1)
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except Exception as e:
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if re.search(pattern=expected_error_regex, string=str(e)):
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return str(e)
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# Shutdown sequence is not well defined, so we may see any of the following errors
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# When running tests that simulate errors via a shutdown on the remote end.
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def get_shutdown_error_regex(rpc_backend):
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"""
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Return various error message we may see from RPC agents while running tests that check for failures. This function
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is used to match against possible errors to ensure failures were raised properly.
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"""
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if rpc_backend == "PROCESS_GROUP":
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error_regexes = [
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"Encountered exception in ProcessGroupAgent::enqueueSend",
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"Encountered exception in ProcessGroupAgent::listenLoop()",
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"Exception in thread pool task",
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"Connection reset by peer",
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"Connection closed by peer"
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]
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else:
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error_regexes = [
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"Request aborted during client shutdown",
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"worker.: Error in reponse from worker.: server shutting down",
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"worker.: Error in response from worker.: Failed to write to remote endpoint",
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"worker.: Error in response from worker.: AsyncSocketException: recv() failed",
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"worker.: Error in response from worker.: Dropping unsent request"
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]
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error_regex = "".join(["({})|".format(error_str) for error_str in error_regexes])
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# Strip out the last | or else it will match anything
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error_regex = error_regex[:-1]
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return error_regex
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def get_timeout_error_regex(rpc_backend_name):
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"""
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Given an RPC backend name, returns a partial string indicating the error we
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should receive when an RPC has timed out. Useful for use with
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assertRaisesRegex() to ensure we have the right errors during timeout.
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"""
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if rpc_backend_name == "PROCESS_GROUP":
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return "RPC ran for more than"
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else:
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return "(Timed out)|(Task expired)"
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def wait_until_pending_users_flushed():
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'''
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The RRef protocol holds forkIds of rrefs in a map until those forks are
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confirmed by the owner. The message confirming the fork may arrive after
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our tests check whether this map is empty, which leads to failures and
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flaky tests. to_here also does not guarantee that we have finished
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processind the owner's confirmation message for the RRef. This function
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loops until the map is empty, which means the messages have been received
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as processed. Call this function before asserting the map returned by
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_get_debug_info is empty.
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'''
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num_pending_users = int(_rref_context_get_debug_info()["num_pending_users"])
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while num_pending_users != 0:
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time.sleep(0.1)
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num_pending_users = int(_rref_context_get_debug_info()["num_pending_users"])
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return
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def initialize_pg(init_method, rank, world_size):
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# This is for tests using `dist.barrier`.
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# For `RpcAgent` other than `ProcessGroupAgent`,
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# no `_default_pg` is initialized.
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if not dist.is_initialized():
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dist.init_process_group(
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backend="gloo",
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init_method=init_method,
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rank=rank,
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world_size=world_size,
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)
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def worker_name(rank):
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return "worker{}".format(rank)
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def get_function_event(function_events, partial_event_name):
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"""
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Returns the first event that matches partial_event_name in the provided
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function_events. These function_events should be the output of
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torch.autograd.profiler.function_events().
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Args:
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function_events: function_events returned by the profiler.
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event_name (str): partial key that the event was profiled with.
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"""
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event = [event for event in function_events if partial_event_name in event.name][0]
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return event
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