wehub-resource-sync
719 lines
22 KiB
Python
719 lines
22 KiB
Python
import logging
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import os
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import signal
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import sys
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import threading
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import time
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import numpy as np
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import pytest
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import ray
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import ray._private.ray_constants as ray_constants
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import ray._private.utils
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from ray._common.test_utils import SignalActor, wait_for_condition
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from ray._private.test_utils import (
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get_error_message,
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init_error_pubsub,
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)
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from ray.exceptions import ActorDiedError, GetTimeoutError, RayActorError, RayTaskError
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def test_unhandled_errors(ray_start_regular):
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@ray.remote
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def f():
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raise ValueError()
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@ray.remote
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class Actor:
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def f(self):
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raise ValueError()
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a = Actor.remote()
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num_exceptions = 0
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def interceptor(e):
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nonlocal num_exceptions
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num_exceptions += 1
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# Test we report unhandled exceptions.
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ray._private.worker._unhandled_error_handler = interceptor
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x1 = f.remote()
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x2 = a.f.remote()
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del x1
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del x2
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wait_for_condition(lambda: num_exceptions == 2)
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# Test we don't report handled exceptions.
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x1 = f.remote()
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x2 = a.f.remote()
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with pytest.raises(ray.exceptions.RayError) as err: # noqa
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ray.get([x1, x2])
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del x1
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del x2
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time.sleep(1)
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assert num_exceptions == 2, num_exceptions
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# Test suppression with env var works.
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try:
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os.environ["RAY_IGNORE_UNHANDLED_ERRORS"] = "1"
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x1 = f.remote()
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del x1
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time.sleep(1)
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assert num_exceptions == 2, num_exceptions
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finally:
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del os.environ["RAY_IGNORE_UNHANDLED_ERRORS"]
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def test_publish_error_to_driver(ray_start_regular, error_pubsub):
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address_info = ray_start_regular
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error_message = "Test error message"
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ray._private.utils.publish_error_to_driver(
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ray_constants.DASHBOARD_AGENT_DIED_ERROR,
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error_message,
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gcs_client=ray._raylet.GcsClient(address=address_info["gcs_address"]),
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)
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errors = get_error_message(
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error_pubsub, 1, ray_constants.DASHBOARD_AGENT_DIED_ERROR
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)
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assert errors[0]["type"] == ray_constants.DASHBOARD_AGENT_DIED_ERROR
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assert errors[0]["error_message"] == error_message
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def test_get_throws_quickly_when_found_exception(ray_start_regular):
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# We use an actor instead of functions here. If we use functions, it's
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# very likely that two normal tasks are submitted before the first worker
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# is registered to Raylet. Since `maximum_startup_concurrency` is 1,
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# the worker pool will wait for the registration of the first worker
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# and skip starting new workers. The result is, the two tasks will be
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# executed sequentially, which breaks an assumption of this test case -
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# the two tasks run in parallel.
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@ray.remote
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class Actor(object):
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def bad_func1(self):
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raise Exception("Test function intentionally failed.")
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def bad_func2(self):
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os._exit(0)
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def slow_func(self, signal):
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ray.get(signal.wait.remote())
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def expect_exception(objects, exception):
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with pytest.raises(ray.exceptions.RayError) as err:
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ray.get(objects)
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assert issubclass(err.type, exception)
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signal1 = SignalActor.remote()
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actor = Actor.options(max_concurrency=2).remote()
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expect_exception(
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[actor.bad_func1.remote(), actor.slow_func.remote(signal1)],
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ray.exceptions.RayTaskError,
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)
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ray.get(signal1.send.remote())
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signal2 = SignalActor.remote()
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actor = Actor.options(max_concurrency=2).remote()
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expect_exception(
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[actor.bad_func2.remote(), actor.slow_func.remote(signal2)],
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ray.exceptions.RayActorError,
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)
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ray.get(signal2.send.remote())
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def test_failed_actor_init(ray_start_regular, error_pubsub):
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error_message1 = "actor constructor failed"
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error_message2 = "actor method failed"
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@ray.remote
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class FailedActor:
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def __init__(self):
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raise Exception(error_message1)
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def fail_method(self):
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raise Exception(error_message2)
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a = FailedActor.remote()
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# Incoming methods will get the exception in creation task
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with pytest.raises(ray.exceptions.RayActorError) as e:
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ray.get(a.fail_method.remote())
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assert error_message1 in str(e.value)
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def test_incorrect_method_calls(ray_start_regular):
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@ray.remote
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class Actor:
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def __init__(self, missing_variable_name):
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pass
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def get_val(self, x):
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pass
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# Make sure that we get errors if we call the constructor incorrectly.
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# Create an actor with too few arguments.
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with pytest.raises(Exception):
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a = Actor.remote()
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# Create an actor with too many arguments.
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with pytest.raises(Exception):
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a = Actor.remote(1, 2)
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# Create an actor the correct number of arguments.
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a = Actor.remote(1)
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# Call a method with too few arguments.
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with pytest.raises(Exception):
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a.get_val.remote()
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# Call a method with too many arguments.
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with pytest.raises(Exception):
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a.get_val.remote(1, 2)
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# Call a method that doesn't exist.
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with pytest.raises(AttributeError):
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a.nonexistent_method()
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with pytest.raises(AttributeError):
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a.nonexistent_method.remote()
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def test_worker_raising_exception(ray_start_regular, error_pubsub):
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p = error_pubsub
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@ray.remote(max_calls=2)
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def f():
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# This is the only reasonable variable we can set here that makes the
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# execute_task function fail after the task got executed.
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worker = ray._private.worker.global_worker
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worker.function_actor_manager.increase_task_counter = None
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# Running this task should cause the worker to raise an exception after
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# the task has successfully completed.
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f.remote()
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errors = get_error_message(p, 1, ray_constants.WORKER_CRASH_PUSH_ERROR)
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assert len(errors) == 1
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assert errors[0]["type"] == ray_constants.WORKER_CRASH_PUSH_ERROR
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def test_worker_dying(ray_start_regular, error_pubsub):
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p = error_pubsub
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# Define a remote function that will kill the worker that runs it.
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@ray.remote(max_retries=0)
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def f():
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eval("exit()")
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with pytest.raises(ray.exceptions.WorkerCrashedError):
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ray.get(f.remote())
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errors = get_error_message(p, 1, ray_constants.WORKER_DIED_PUSH_ERROR)
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assert len(errors) == 1
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assert errors[0]["type"] == ray_constants.WORKER_DIED_PUSH_ERROR
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assert "died or was killed while executing" in errors[0]["error_message"]
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def test_actor_worker_dying(ray_start_regular, error_pubsub):
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p = error_pubsub
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@ray.remote
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class Actor:
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def kill(self):
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eval("exit()")
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@ray.remote
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def consume(x):
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pass
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a = Actor.remote()
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[obj], _ = ray.wait([a.kill.remote()], timeout=5)
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with pytest.raises(ray.exceptions.RayActorError):
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ray.get(obj)
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with pytest.raises(ray.exceptions.RayTaskError):
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ray.get(consume.remote(obj))
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errors = get_error_message(p, 1, ray_constants.WORKER_DIED_PUSH_ERROR)
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assert len(errors) == 1
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assert errors[0]["type"] == ray_constants.WORKER_DIED_PUSH_ERROR
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def test_actor_worker_dying_future_tasks(ray_start_regular, error_pubsub):
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p = error_pubsub
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@ray.remote(max_restarts=0)
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class Actor:
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def getpid(self):
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return os.getpid()
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def sleep(self):
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time.sleep(1)
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a = Actor.remote()
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pid = ray.get(a.getpid.remote())
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tasks1 = [a.sleep.remote() for _ in range(10)]
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os.kill(pid, 9)
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time.sleep(0.1)
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tasks2 = [a.sleep.remote() for _ in range(10)]
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for obj in tasks1 + tasks2:
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with pytest.raises(Exception):
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ray.get(obj)
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errors = get_error_message(p, 1, ray_constants.WORKER_DIED_PUSH_ERROR)
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assert len(errors) == 1
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assert errors[0]["type"] == ray_constants.WORKER_DIED_PUSH_ERROR
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def test_actor_worker_dying_nothing_in_progress(ray_start_regular):
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@ray.remote(max_restarts=0)
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class Actor:
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def getpid(self):
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return os.getpid()
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a = Actor.remote()
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pid = ray.get(a.getpid.remote())
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os.kill(pid, 9)
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time.sleep(0.1)
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task2 = a.getpid.remote()
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with pytest.raises(Exception):
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ray.get(task2)
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@pytest.mark.skipif(sys.platform == "win32", reason="Too flaky on windows")
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def test_actor_scope_or_intentionally_killed_message(ray_start_regular, error_pubsub):
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p = error_pubsub
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@ray.remote
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class Actor:
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def __init__(self):
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# This log is added to debug a flaky test issue.
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print(os.getpid())
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def ping(self):
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pass
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a = Actor.remote()
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ray.get(a.ping.remote())
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del a
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a = Actor.remote()
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ray.get(a.ping.remote())
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with pytest.raises(ray.exceptions.ActorDiedError):
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ray.get(a.__ray_terminate__.remote())
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errors = get_error_message(p, 1, timeout=1)
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assert len(errors) == 0, "Should not have propogated an error - {}".format(errors)
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def test_mixed_hanging_and_exception_should_not_hang(ray_start_regular):
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@ray.remote
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class Actor:
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def __init__(self, _id):
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self._id = _id
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def execute(self, fn) -> None:
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return fn(self._id)
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def print_and_raise_error(i):
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print(i)
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raise ValueError
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def print_and_sleep_forever(i):
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print(i)
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while True:
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time.sleep(3600)
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actors = [Actor.remote(i) for i in range(10)]
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refs = [actor.execute.remote(print_and_raise_error) for actor in actors[:2]]
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with pytest.raises(ValueError):
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ray.get(refs)
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refs.extend([actor.execute.remote(print_and_sleep_forever) for actor in actors[2:]])
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with pytest.raises(ValueError):
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ray.get(refs)
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def test_mixed_hanging_and_died_actor_should_not_hang(ray_start_regular):
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@ray.remote
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class Actor:
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def __init__(self, _id):
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self._id = _id
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def execute(self, fn) -> None:
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return fn(self._id)
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def exit(self):
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ray.actor.exit_actor()
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def print_and_sleep_forever(i):
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print(i)
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while True:
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time.sleep(3600)
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actors = [Actor.remote(i) for i in range(10)]
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ray.get([actor.__ray_ready__.remote() for actor in actors])
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error_refs = [actor.exit.remote() for actor in actors[:2]]
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with pytest.raises(ActorDiedError):
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ray.get(error_refs)
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with pytest.raises(ActorDiedError):
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ray.get([actor.execute.remote(print_and_sleep_forever) for actor in actors])
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def test_exception_chain(ray_start_regular):
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@ray.remote
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def bar():
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return 1 / 0
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@ray.remote
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def foo():
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return ray.get(bar.remote())
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r = foo.remote()
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try:
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ray.get(r)
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except ZeroDivisionError as ex:
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assert isinstance(ex, RayTaskError)
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@pytest.mark.skip("This test does not work yet.")
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@pytest.mark.parametrize("ray_start_object_store_memory", [10**6], indirect=True)
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def test_put_error1(ray_start_object_store_memory, error_pubsub):
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p = error_pubsub
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num_objects = 3
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object_size = 4 * 10**5
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# Define a task with a single dependency, a numpy array, that returns
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# another array.
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@ray.remote
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def single_dependency(i, arg):
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arg = np.copy(arg)
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arg[0] = i
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return arg
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@ray.remote
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def put_arg_task():
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# Launch num_objects instances of the remote task, each dependent
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# on the one before it. The result of the first task should get
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# evicted.
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args = []
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arg = single_dependency.remote(0, np.zeros(object_size, dtype=np.uint8))
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for i in range(num_objects):
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arg = single_dependency.remote(i, arg)
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args.append(arg)
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# Get the last value to force all tasks to finish.
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value = ray.get(args[-1])
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assert value[0] == i
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# Get the first value (which should have been evicted) to force
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# reconstruction. Currently, since we're not able to reconstruct
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# `ray.put` objects that were evicted and whose originating tasks
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# are still running, this for-loop should hang and push an error to
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# the driver.
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ray.get(args[0])
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put_arg_task.remote()
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# Make sure we receive the correct error message.
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errors = get_error_message(p, 1, ray_constants.PUT_RECONSTRUCTION_PUSH_ERROR)
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assert len(errors) == 1
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assert errors[0]["type"] == ray_constants.PUT_RECONSTRUCTION_PUSH_ERROR
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@pytest.mark.skip("This test does not work yet.")
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@pytest.mark.parametrize("ray_start_object_store_memory", [10**6], indirect=True)
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def test_put_error2(ray_start_object_store_memory):
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# This is the same as the previous test, but it calls ray.put directly.
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num_objects = 3
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object_size = 4 * 10**5
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# Define a task with a single dependency, a numpy array, that returns
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# another array.
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@ray.remote
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def single_dependency(i, arg):
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arg = np.copy(arg)
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arg[0] = i
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return arg
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|
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@ray.remote
|
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def put_task():
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# Launch num_objects instances of the remote task, each dependent
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# on the one before it. The result of the first task should get
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# evicted.
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args = []
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arg = ray.put(np.zeros(object_size, dtype=np.uint8))
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for i in range(num_objects):
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arg = single_dependency.remote(i, arg)
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args.append(arg)
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# Get the last value to force all tasks to finish.
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value = ray.get(args[-1])
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assert value[0] == i
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# Get the first value (which should have been evicted) to force
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# reconstruction. Currently, since we're not able to reconstruct
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# `ray.put` objects that were evicted and whose originating tasks
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# are still running, this for-loop should hang and push an error to
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# the driver.
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ray.get(args[0])
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|
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put_task.remote()
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|
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# Make sure we receive the correct error message.
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# get_error_message(ray_constants.PUT_RECONSTRUCTION_PUSH_ERROR, 1)
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|
|
|
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def test_version_mismatch(ray_start_cluster):
|
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ray_version = ray.__version__
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try:
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cluster = ray_start_cluster
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cluster.add_node(num_cpus=1)
|
|
|
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# Test the driver.
|
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ray.__version__ = "fake ray version"
|
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with pytest.raises(RuntimeError):
|
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ray.init(address="auto")
|
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|
|
finally:
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# Reset the version.
|
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ray.__version__ = ray_version
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|
|
|
|
|
def test_export_large_objects(ray_start_regular, error_pubsub):
|
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p = error_pubsub
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large_object = np.zeros(
|
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2 * ray_constants.FUNCTION_SIZE_WARN_THRESHOLD, dtype=np.uint8
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)
|
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|
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@ray.remote
|
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def f():
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_ = large_object
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# Invoke the function so that the definition is exported.
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f.remote()
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|
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# Make sure that a warning is generated.
|
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errors = get_error_message(p, 1, ray_constants.PICKLING_LARGE_OBJECT_PUSH_ERROR)
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assert len(errors) == 1
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assert errors[0]["type"] == ray_constants.PICKLING_LARGE_OBJECT_PUSH_ERROR
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|
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@ray.remote
|
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class Foo:
|
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def __init__(self):
|
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_ = large_object
|
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|
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Foo.remote()
|
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|
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# Make sure that a warning is generated.
|
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errors = get_error_message(p, 1, ray_constants.PICKLING_LARGE_OBJECT_PUSH_ERROR)
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assert len(errors) == 1
|
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assert errors[0]["type"] == ray_constants.PICKLING_LARGE_OBJECT_PUSH_ERROR
|
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|
|
|
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def test_warning_many_actor_tasks_queued(shutdown_only):
|
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ray.init(num_cpus=1)
|
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p = init_error_pubsub()
|
|
|
|
@ray.remote(num_cpus=1)
|
|
class Foo:
|
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def f(self):
|
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time.sleep(1000)
|
|
|
|
a = Foo.remote()
|
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[a.f.remote() for _ in range(20000)]
|
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errors = get_error_message(p, 2, ray_constants.EXCESS_QUEUEING_WARNING)
|
|
msgs = [e["error_message"] for e in errors]
|
|
assert "Warning: More than 5000 tasks are pending submission to actor" in msgs[0]
|
|
assert "Warning: More than 10000 tasks are pending submission to actor" in msgs[1]
|
|
|
|
|
|
def test_no_warning_many_actor_tasks_queued_when_sequential(shutdown_only):
|
|
ray.init(num_cpus=1)
|
|
p = init_error_pubsub()
|
|
|
|
@ray.remote(num_cpus=1)
|
|
class Foo:
|
|
def f(self):
|
|
return 1
|
|
|
|
a = Foo.remote()
|
|
for _ in range(10000):
|
|
assert ray.get(a.f.remote()) == 1
|
|
errors = get_error_message(p, 1, ray_constants.EXCESS_QUEUEING_WARNING, timeout=1)
|
|
assert len(errors) == 0
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"ray_start_cluster_head",
|
|
[
|
|
{
|
|
"num_cpus": 0,
|
|
"_system_config": {
|
|
"raylet_death_check_interval_milliseconds": 10 * 1000,
|
|
"health_check_initial_delay_ms": 0,
|
|
"health_check_failure_threshold": 10,
|
|
"health_check_period_ms": 100,
|
|
"timeout_ms_task_wait_for_death_info": 100,
|
|
},
|
|
"include_dashboard": True, # for list_actors API
|
|
},
|
|
],
|
|
indirect=True,
|
|
)
|
|
def test_actor_failover_with_bad_network(ray_start_cluster_head):
|
|
# The test case is to cover the scenario that when an actor FO happens,
|
|
# the caller receives the actor ALIVE notification and connects to the new
|
|
# actor instance while there are still some tasks sent to the previous
|
|
# actor instance haven't returned.
|
|
#
|
|
# It's not easy to reproduce this scenario, so we set
|
|
# `raylet_death_check_interval_milliseconds` to a large value and add a
|
|
# never-return function for the actor to keep the RPC connection alive
|
|
# while killing the node to trigger actor failover. Later we send SIGKILL
|
|
# to kill the previous actor process to let the task fail.
|
|
#
|
|
# The expected behavior is that after the actor is alive again and the
|
|
# previous RPC connection is broken, tasks sent via the previous RPC
|
|
# connection should fail but tasks sent via the new RPC connection should
|
|
# succeed.
|
|
|
|
cluster = ray_start_cluster_head
|
|
node = cluster.add_node(num_cpus=1)
|
|
|
|
@ray.remote(max_restarts=1)
|
|
class Actor:
|
|
def getpid(self):
|
|
return os.getpid()
|
|
|
|
def never_return(self):
|
|
while True:
|
|
time.sleep(1)
|
|
return 0
|
|
|
|
# The actor should be placed on the non-head node.
|
|
actor = Actor.remote()
|
|
pid = ray.get(actor.getpid.remote())
|
|
|
|
# Submit a never-return task (task 1) to the actor. The return
|
|
# object should be unready.
|
|
obj1 = actor.never_return.remote()
|
|
with pytest.raises(GetTimeoutError):
|
|
ray.get(obj1, timeout=1)
|
|
|
|
# Kill the non-head node and start a new one. Now GCS should trigger actor
|
|
# FO. Since we changed the interval of worker checking death of Raylet,
|
|
# the actor process won't quit in a short time.
|
|
cluster.remove_node(node, allow_graceful=False)
|
|
cluster.add_node(num_cpus=1)
|
|
|
|
# The removed node will be marked as dead by GCS after 1 second and task 1
|
|
# will return with failure after that.
|
|
with pytest.raises(RayActorError):
|
|
ray.get(obj1, timeout=2)
|
|
|
|
# Wait for the actor to be alive again in a new worker process.
|
|
def check_actor_restart():
|
|
actors = ray.util.state.list_actors(
|
|
detail=True
|
|
) # detail is needed for num_restarts to populate
|
|
assert len(actors) == 1
|
|
return actors[0].state == "ALIVE" and actors[0].num_restarts == 1
|
|
|
|
wait_for_condition(check_actor_restart)
|
|
|
|
# Kill the previous actor process.
|
|
os.kill(pid, signal.SIGKILL)
|
|
|
|
# Submit another task (task 2) to the actor.
|
|
obj2 = actor.getpid.remote()
|
|
|
|
# We should be able to get the return value of task 2 without any issue
|
|
ray.get(obj2)
|
|
|
|
|
|
# Previously when threading.Lock is in the exception, it causes
|
|
# the serialization to fail. This test case is to cover that scenario.
|
|
def test_unserializable_exception(ray_start_regular, propagate_logs):
|
|
class UnserializableException(Exception):
|
|
def __init__(self):
|
|
self.lock = threading.Lock()
|
|
|
|
@ray.remote
|
|
def func():
|
|
raise UnserializableException
|
|
|
|
with pytest.raises(ray.exceptions.RayTaskError) as exc_info:
|
|
ray.get(func.remote())
|
|
|
|
assert isinstance(exc_info.value, ray.exceptions.RayTaskError)
|
|
assert isinstance(exc_info.value.cause, ray.exceptions.RayError)
|
|
assert "isn't serializable" in str(exc_info.value.cause)
|
|
|
|
|
|
def test_final_user_exception(ray_start_regular, propagate_logs, caplog):
|
|
class MyFinalException(Exception):
|
|
def __init_subclass__(cls, /, *args, **kwargs):
|
|
raise TypeError("Can't subclass special typing classes")
|
|
|
|
# This should error.
|
|
with pytest.raises(MyFinalException):
|
|
raise MyFinalException("MyFinalException from driver")
|
|
|
|
@ray.remote
|
|
def func():
|
|
# This should also error. Problem is, the user exception is final so we can't
|
|
# subclass it (raises exception if so). This means Ray cannot raise an exception
|
|
# that can be caught as both `RayTaskError` and the user exception. So we
|
|
# issue a warning and just raise it as `RayTaskError`. User needs to use
|
|
# `e.cause` to get the user exception.
|
|
raise MyFinalException("MyFinalException from task")
|
|
|
|
with caplog.at_level(logging.WARNING, logger="ray.exceptions"):
|
|
with pytest.raises(ray.exceptions.RayTaskError) as exc_info:
|
|
ray.get(func.remote())
|
|
|
|
assert (
|
|
"This exception is raised as RayTaskError only. You can use "
|
|
"`ray_task_error.cause` to access the user exception."
|
|
) in caplog.text
|
|
assert isinstance(exc_info.value, ray.exceptions.RayTaskError)
|
|
assert isinstance(exc_info.value.cause, MyFinalException)
|
|
assert str(exc_info.value.cause) == "MyFinalException from task"
|
|
|
|
caplog.clear()
|
|
|
|
|
|
def test_raytaskerror_serialization(ray_start_regular):
|
|
"""Test that RayTaskError with dual exception instances can be properly serialized."""
|
|
import ray.cloudpickle as pickle
|
|
|
|
class MyException(Exception):
|
|
def __init__(self, one, two):
|
|
self.one = one
|
|
self.two = two
|
|
|
|
def __reduce__(self):
|
|
return self.__class__, (self.one, self.two)
|
|
|
|
original_exception = MyException("test 1", "test 2")
|
|
ray_task_error = ray.exceptions.RayTaskError(
|
|
function_name="test_function",
|
|
traceback_str="test traceback",
|
|
cause=original_exception,
|
|
)
|
|
|
|
dual_exception = ray_task_error.make_dual_exception_instance()
|
|
pickled = pickle.dumps(dual_exception)
|
|
unpickled = pickle.loads(pickled)
|
|
|
|
assert isinstance(unpickled, ray.exceptions.RayTaskError)
|
|
assert isinstance(unpickled, MyException)
|
|
assert unpickled.one == "test 1"
|
|
assert unpickled.two == "test 2"
|
|
|
|
|
|
if __name__ == "__main__":
|
|
sys.exit(pytest.main(["-sv", __file__]))
|