mirror of
https://github.com/wassname/ray.git
synced 2026-06-28 02:01:24 +08:00
Robert Nishihara
496 lines
21 KiB
Python
496 lines
21 KiB
Python
from __future__ import absolute_import
|
|
from __future__ import division
|
|
from __future__ import print_function
|
|
|
|
import hashlib
|
|
import inspect
|
|
import json
|
|
import sys
|
|
import time
|
|
import traceback
|
|
from collections import (
|
|
namedtuple,
|
|
defaultdict,
|
|
)
|
|
|
|
import ray
|
|
from ray import profiling
|
|
from ray import ray_constants
|
|
from ray import cloudpickle as pickle
|
|
from ray.utils import (
|
|
is_cython,
|
|
is_function_or_method,
|
|
is_class_method,
|
|
check_oversized_pickle,
|
|
decode,
|
|
format_error_message,
|
|
push_error_to_driver,
|
|
)
|
|
|
|
FunctionExecutionInfo = namedtuple("FunctionExecutionInfo",
|
|
["function", "function_name", "max_calls"])
|
|
"""FunctionExecutionInfo: A named tuple storing remote function information."""
|
|
|
|
|
|
class FunctionActorManager(object):
|
|
"""A class used to export/load remote functions and actors.
|
|
|
|
Attributes:
|
|
_worker: The associated worker that this manager related.
|
|
_functions_to_export: The remote functions to export when
|
|
the worker gets connected.
|
|
_actors_to_export: The actors to export when the worker gets
|
|
connected.
|
|
_function_execution_info: The map from driver_id to finction_id
|
|
and execution_info.
|
|
_num_task_executions: The map from driver_id to function
|
|
execution times.
|
|
"""
|
|
|
|
def __init__(self, worker):
|
|
self._worker = worker
|
|
self._functions_to_export = []
|
|
self._actors_to_export = []
|
|
# This field is a dictionary that maps a driver ID to a dictionary of
|
|
# functions (and information about those functions) that have been
|
|
# registered for that driver (this inner dictionary maps function IDs
|
|
# to a FunctionExecutionInfo object. This should only be used on
|
|
# workers that execute remote functions.
|
|
self._function_execution_info = defaultdict(lambda: {})
|
|
self._num_task_executions = defaultdict(lambda: {})
|
|
|
|
def increase_task_counter(self, driver_id, function_id):
|
|
self._num_task_executions[driver_id][function_id] += 1
|
|
|
|
def get_task_counter(self, driver_id, function_id):
|
|
return self._num_task_executions[driver_id][function_id]
|
|
|
|
def export_cached(self):
|
|
"""Export cached remote functions
|
|
|
|
Note: this should be called only once when worker is connected.
|
|
"""
|
|
for remote_function in self._functions_to_export:
|
|
self._do_export(remote_function)
|
|
self._functions_to_export = None
|
|
for info in self._actors_to_export:
|
|
(key, actor_class_info) = info
|
|
self._publish_actor_class_to_key(key, actor_class_info)
|
|
|
|
def reset_cache(self):
|
|
self._functions_to_export = []
|
|
self._actors_to_export = []
|
|
|
|
def export(self, remote_function):
|
|
"""Export a remote function.
|
|
|
|
Args:
|
|
remote_function: the RemoteFunction object.
|
|
"""
|
|
if self._worker.mode is None:
|
|
# If the worker isn't connected, cache the function
|
|
# and export it later.
|
|
self._functions_to_export.append(remote_function)
|
|
return
|
|
if self._worker.mode != ray.worker.SCRIPT_MODE:
|
|
# Don't need to export if the worker is not a driver.
|
|
return
|
|
self._do_export(remote_function)
|
|
|
|
def _do_export(self, remote_function):
|
|
"""Pickle a remote function and export it to redis.
|
|
|
|
Args:
|
|
remote_function: the RemoteFunction object.
|
|
"""
|
|
# Work around limitations of Python pickling.
|
|
function = remote_function._function
|
|
function_name_global_valid = function.__name__ in function.__globals__
|
|
function_name_global_value = function.__globals__.get(
|
|
function.__name__)
|
|
# Allow the function to reference itself as a global variable
|
|
if not is_cython(function):
|
|
function.__globals__[function.__name__] = remote_function
|
|
try:
|
|
pickled_function = pickle.dumps(function)
|
|
finally:
|
|
# Undo our changes
|
|
if function_name_global_valid:
|
|
function.__globals__[function.__name__] = (
|
|
function_name_global_value)
|
|
else:
|
|
del function.__globals__[function.__name__]
|
|
|
|
check_oversized_pickle(pickled_function,
|
|
remote_function._function_name,
|
|
"remote function", self._worker)
|
|
|
|
key = (b"RemoteFunction:" + self._worker.task_driver_id.id() + b":" +
|
|
remote_function._function_id)
|
|
self._worker.redis_client.hmset(
|
|
key, {
|
|
"driver_id": self._worker.task_driver_id.id(),
|
|
"function_id": remote_function._function_id,
|
|
"name": remote_function._function_name,
|
|
"module": function.__module__,
|
|
"function": pickled_function,
|
|
"max_calls": remote_function._max_calls
|
|
})
|
|
self._worker.redis_client.rpush("Exports", key)
|
|
|
|
def fetch_and_register_remote_function(self, key):
|
|
"""Import a remote function."""
|
|
(driver_id, function_id_str, function_name, serialized_function,
|
|
num_return_vals, module, resources,
|
|
max_calls) = self._worker.redis_client.hmget(key, [
|
|
"driver_id", "function_id", "name", "function", "num_return_vals",
|
|
"module", "resources", "max_calls"
|
|
])
|
|
function_id = ray.ObjectID(function_id_str)
|
|
function_name = decode(function_name)
|
|
max_calls = int(max_calls)
|
|
module = decode(module)
|
|
|
|
# This is a placeholder in case the function can't be unpickled. This
|
|
# will be overwritten if the function is successfully registered.
|
|
def f():
|
|
raise Exception("This function was not imported properly.")
|
|
|
|
self._function_execution_info[driver_id][function_id.id()] = (
|
|
FunctionExecutionInfo(
|
|
function=f, function_name=function_name, max_calls=max_calls))
|
|
self._num_task_executions[driver_id][function_id.id()] = 0
|
|
|
|
try:
|
|
function = pickle.loads(serialized_function)
|
|
except Exception:
|
|
# If an exception was thrown when the remote function was imported,
|
|
# we record the traceback and notify the scheduler of the failure.
|
|
traceback_str = format_error_message(traceback.format_exc())
|
|
# Log the error message.
|
|
push_error_to_driver(
|
|
self._worker,
|
|
ray_constants.REGISTER_REMOTE_FUNCTION_PUSH_ERROR,
|
|
traceback_str,
|
|
driver_id=driver_id,
|
|
data={
|
|
"function_id": function_id.id(),
|
|
"function_name": function_name
|
|
})
|
|
else:
|
|
# The below line is necessary. Because in the driver process,
|
|
# if the function is defined in the file where the python script
|
|
# was started from, its module is `__main__`.
|
|
# However in the worker process, the `__main__` module is a
|
|
# different module, which is `default_worker.py`
|
|
function.__module__ = module
|
|
self._function_execution_info[driver_id][function_id.id()] = (
|
|
FunctionExecutionInfo(
|
|
function=function,
|
|
function_name=function_name,
|
|
max_calls=max_calls))
|
|
# Add the function to the function table.
|
|
self._worker.redis_client.rpush(
|
|
b"FunctionTable:" + function_id.id(), self._worker.worker_id)
|
|
|
|
def get_execution_info(self, driver_id, function_id):
|
|
"""Get the FunctionExecutionInfo of a remote function.
|
|
|
|
Args:
|
|
driver_id: ID of the driver that the function belongs to.
|
|
function_id: ID of the function to get.
|
|
|
|
Returns:
|
|
A FunctionExecutionInfo object.
|
|
"""
|
|
# Wait until the function to be executed has actually been registered
|
|
# on this worker. We will push warnings to the user if we spend too
|
|
# long in this loop.
|
|
with profiling.profile("wait_for_function", worker=self._worker):
|
|
self._wait_for_function(function_id, driver_id)
|
|
return self._function_execution_info[driver_id][function_id.id()]
|
|
|
|
def _wait_for_function(self, function_id, driver_id, timeout=10):
|
|
"""Wait until the function to be executed is present on this worker.
|
|
|
|
This method will simply loop until the import thread has imported the
|
|
relevant function. If we spend too long in this loop, that may indicate
|
|
a problem somewhere and we will push an error message to the user.
|
|
|
|
If this worker is an actor, then this will wait until the actor has
|
|
been defined.
|
|
|
|
Args:
|
|
function_id (str): The ID of the function that we want to execute.
|
|
driver_id (str): The ID of the driver to push the error message to
|
|
if this times out.
|
|
"""
|
|
start_time = time.time()
|
|
# Only send the warning once.
|
|
warning_sent = False
|
|
while True:
|
|
with self._worker.lock:
|
|
if (self._worker.actor_id == ray.worker.NIL_ACTOR_ID
|
|
and (function_id.id() in
|
|
self._function_execution_info[driver_id])):
|
|
break
|
|
elif self._worker.actor_id != ray.worker.NIL_ACTOR_ID and (
|
|
self._worker.actor_id in self._worker.actors):
|
|
break
|
|
if time.time() - start_time > timeout:
|
|
warning_message = ("This worker was asked to execute a "
|
|
"function that it does not have "
|
|
"registered. You may have to restart "
|
|
"Ray.")
|
|
if not warning_sent:
|
|
ray.utils.push_error_to_driver(
|
|
self._worker,
|
|
ray_constants.WAIT_FOR_FUNCTION_PUSH_ERROR,
|
|
warning_message,
|
|
driver_id=driver_id)
|
|
warning_sent = True
|
|
time.sleep(0.001)
|
|
|
|
@classmethod
|
|
def compute_actor_method_function_id(cls, class_name, attr):
|
|
"""Get the function ID corresponding to an actor method.
|
|
|
|
Args:
|
|
class_name (str): The class name of the actor.
|
|
attr (str): The attribute name of the method.
|
|
|
|
Returns:
|
|
Function ID corresponding to the method.
|
|
"""
|
|
function_id_hash = hashlib.sha1()
|
|
function_id_hash.update(class_name.encode("ascii"))
|
|
function_id_hash.update(attr.encode("ascii"))
|
|
function_id = function_id_hash.digest()
|
|
assert len(function_id) == ray_constants.ID_SIZE
|
|
return ray.ObjectID(function_id)
|
|
|
|
def _publish_actor_class_to_key(self, key, actor_class_info):
|
|
"""Push an actor class definition to Redis.
|
|
|
|
The is factored out as a separate function because it is also called
|
|
on cached actor class definitions when a worker connects for the first
|
|
time.
|
|
|
|
Args:
|
|
key: The key to store the actor class info at.
|
|
actor_class_info: Information about the actor class.
|
|
worker: The worker to use to connect to Redis.
|
|
"""
|
|
# We set the driver ID here because it may not have been available when
|
|
# the actor class was defined.
|
|
actor_class_info["driver_id"] = self._worker.task_driver_id.id()
|
|
self._worker.redis_client.hmset(key, actor_class_info)
|
|
self._worker.redis_client.rpush("Exports", key)
|
|
|
|
def export_actor_class(self, class_id, Class, actor_method_names,
|
|
checkpoint_interval):
|
|
key = b"ActorClass:" + class_id
|
|
actor_class_info = {
|
|
"class_name": Class.__name__,
|
|
"module": Class.__module__,
|
|
"class": pickle.dumps(Class),
|
|
"checkpoint_interval": checkpoint_interval,
|
|
"actor_method_names": json.dumps(list(actor_method_names))
|
|
}
|
|
|
|
check_oversized_pickle(actor_class_info["class"],
|
|
actor_class_info["class_name"], "actor",
|
|
self._worker)
|
|
|
|
if self._worker.mode is None:
|
|
# This means that 'ray.init()' has not been called yet and so we
|
|
# must cache the actor class definition and export it when
|
|
# 'ray.init()' is called.
|
|
assert self._actors_to_export is not None
|
|
self._actors_to_export.append((key, actor_class_info))
|
|
# This caching code path is currently not used because we only
|
|
# export actor class definitions lazily when we instantiate the
|
|
# actor for the first time.
|
|
assert False, "This should be unreachable."
|
|
else:
|
|
self._publish_actor_class_to_key(key, actor_class_info)
|
|
# TODO(rkn): Currently we allow actor classes to be defined
|
|
# within tasks. I tried to disable this, but it may be necessary
|
|
# because of https://github.com/ray-project/ray/issues/1146.
|
|
|
|
def fetch_and_register_actor(self, actor_class_key):
|
|
"""Import an actor.
|
|
|
|
This will be called by the worker's import thread when the worker
|
|
receives the actor_class export, assuming that the worker is an actor
|
|
for that class.
|
|
|
|
Args:
|
|
actor_class_key: The key in Redis to use to fetch the actor.
|
|
worker: The worker to use.
|
|
"""
|
|
actor_id_str = self._worker.actor_id
|
|
(driver_id, class_id, class_name, module, pickled_class,
|
|
checkpoint_interval,
|
|
actor_method_names) = self._worker.redis_client.hmget(
|
|
actor_class_key, [
|
|
"driver_id", "class_id", "class_name", "module", "class",
|
|
"checkpoint_interval", "actor_method_names"
|
|
])
|
|
|
|
class_name = decode(class_name)
|
|
module = decode(module)
|
|
checkpoint_interval = int(checkpoint_interval)
|
|
actor_method_names = json.loads(decode(actor_method_names))
|
|
|
|
# In Python 2, json loads strings as unicode, so convert them back to
|
|
# strings.
|
|
if sys.version_info < (3, 0):
|
|
actor_method_names = [
|
|
method_name.encode("ascii")
|
|
for method_name in actor_method_names
|
|
]
|
|
|
|
# Create a temporary actor with some temporary methods so that if
|
|
# the actor fails to be unpickled, the temporary actor can be used
|
|
# (just to produce error messages and to prevent the driver from
|
|
# hanging).
|
|
class TemporaryActor(object):
|
|
pass
|
|
|
|
self._worker.actors[actor_id_str] = TemporaryActor()
|
|
self._worker.actor_checkpoint_interval = checkpoint_interval
|
|
|
|
def temporary_actor_method(*xs):
|
|
raise Exception(
|
|
"The actor with name {} failed to be imported, "
|
|
"and so cannot execute this method".format(class_name))
|
|
|
|
# Register the actor method executors.
|
|
for actor_method_name in actor_method_names:
|
|
function_id = (
|
|
FunctionActorManager.compute_actor_method_function_id(
|
|
class_name, actor_method_name).id())
|
|
temporary_executor = self._make_actor_method_executor(
|
|
actor_method_name,
|
|
temporary_actor_method,
|
|
actor_imported=False)
|
|
self._function_execution_info[driver_id][function_id] = (
|
|
FunctionExecutionInfo(
|
|
function=temporary_executor,
|
|
function_name=actor_method_name,
|
|
max_calls=0))
|
|
self._num_task_executions[driver_id][function_id] = 0
|
|
|
|
try:
|
|
unpickled_class = pickle.loads(pickled_class)
|
|
self._worker.actor_class = unpickled_class
|
|
except Exception:
|
|
# If an exception was thrown when the actor was imported, we record
|
|
# the traceback and notify the scheduler of the failure.
|
|
traceback_str = ray.utils.format_error_message(
|
|
traceback.format_exc())
|
|
# Log the error message.
|
|
push_error_to_driver(
|
|
self._worker,
|
|
ray_constants.REGISTER_ACTOR_PUSH_ERROR,
|
|
traceback_str,
|
|
driver_id,
|
|
data={"actor_id": actor_id_str})
|
|
# TODO(rkn): In the future, it might make sense to have the worker
|
|
# exit here. However, currently that would lead to hanging if
|
|
# someone calls ray.get on a method invoked on the actor.
|
|
else:
|
|
# TODO(pcm): Why is the below line necessary?
|
|
unpickled_class.__module__ = module
|
|
self._worker.actors[actor_id_str] = unpickled_class.__new__(
|
|
unpickled_class)
|
|
|
|
actor_methods = inspect.getmembers(
|
|
unpickled_class, predicate=is_function_or_method)
|
|
for actor_method_name, actor_method in actor_methods:
|
|
function_id = (
|
|
FunctionActorManager.compute_actor_method_function_id(
|
|
class_name, actor_method_name).id())
|
|
executor = self._make_actor_method_executor(
|
|
actor_method_name, actor_method, actor_imported=True)
|
|
self._function_execution_info[driver_id][function_id] = (
|
|
FunctionExecutionInfo(
|
|
function=executor,
|
|
function_name=actor_method_name,
|
|
max_calls=0))
|
|
# We do not set function_properties[driver_id][function_id]
|
|
# because we currently do need the actor worker to submit new
|
|
# tasks for the actor.
|
|
|
|
def _make_actor_method_executor(self, method_name, method, actor_imported):
|
|
"""Make an executor that wraps a user-defined actor method.
|
|
|
|
The wrapped method updates the worker's internal state and performs any
|
|
necessary checkpointing operations.
|
|
|
|
Args:
|
|
worker (Worker): The worker that is executing the actor.
|
|
method_name (str): The name of the actor method.
|
|
method (instancemethod): The actor method to wrap. This should be a
|
|
method defined on the actor class and should therefore take an
|
|
instance of the actor as the first argument.
|
|
actor_imported (bool): Whether the actor has been imported.
|
|
Checkpointing operations will not be run if this is set to
|
|
False.
|
|
|
|
Returns:
|
|
A function that executes the given actor method on the worker's
|
|
stored instance of the actor. The function also updates the
|
|
worker's internal state to record the executed method.
|
|
"""
|
|
|
|
def actor_method_executor(dummy_return_id, actor, *args):
|
|
# Update the actor's task counter to reflect the task we're about
|
|
# to execute.
|
|
self._worker.actor_task_counter += 1
|
|
|
|
# If this is the first task to execute on the actor, try to resume
|
|
# from a checkpoint.
|
|
if actor_imported and self._worker.actor_task_counter == 1:
|
|
checkpoint_resumed = ray.actor.restore_and_log_checkpoint(
|
|
self._worker, actor)
|
|
if checkpoint_resumed:
|
|
# NOTE(swang): Since we did not actually execute the
|
|
# __init__ method, this will put None as the return value.
|
|
# If the __init__ method is supposed to return multiple
|
|
# values, an exception will be logged.
|
|
return
|
|
|
|
# Determine whether we should checkpoint the actor.
|
|
checkpointing_on = (actor_imported
|
|
and self._worker.actor_checkpoint_interval > 0)
|
|
# We should checkpoint the actor if user checkpointing is on, we've
|
|
# executed checkpoint_interval tasks since the last checkpoint, and
|
|
# the method we're about to execute is not a checkpoint.
|
|
save_checkpoint = (checkpointing_on
|
|
and (self._worker.actor_task_counter %
|
|
self._worker.actor_checkpoint_interval == 0
|
|
and method_name != "__ray_checkpoint__"))
|
|
|
|
# Execute the assigned method and save a checkpoint if necessary.
|
|
try:
|
|
if is_class_method(method):
|
|
method_returns = method(*args)
|
|
else:
|
|
method_returns = method(actor, *args)
|
|
except Exception:
|
|
# Save the checkpoint before allowing the method exception
|
|
# to be thrown.
|
|
if save_checkpoint:
|
|
ray.actor.save_and_log_checkpoint(self._worker, actor)
|
|
raise
|
|
else:
|
|
# Save the checkpoint before returning the method's return
|
|
# values.
|
|
if save_checkpoint:
|
|
ray.actor.save_and_log_checkpoint(self._worker, actor)
|
|
return method_returns
|
|
|
|
return actor_method_executor
|