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Refine multi-threading support (#3672)

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* [Python] refine multi-threading support

fix

* [java] refine multithreading code

fix java

* format
This commit is contained in:
Hao Chen
2019-01-11 05:58:11 +08:00
committed by Stephanie Wang
parent 71243203a4
commit 597abb24ea
9 changed files with 394 additions and 313 deletions
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python/ray/actor.py
+28 -35
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@@ -7,6 +7,7 @@ import hashlib
import inspect
import logging
import sys
import threading
import traceback
import ray.cloudpickle as pickle
@@ -225,8 +226,7 @@ class ActorMethod(object):
self._method_name,
args=args,
kwargs=kwargs,
num_return_vals=num_return_vals,
dependency=self._actor._ray_actor_cursor)
num_return_vals=num_return_vals)
class ActorClass(object):
@@ -525,13 +525,13 @@ class ActorHandle(object):
self._ray_actor_method_cpus = actor_method_cpus
self._ray_actor_driver_id = actor_driver_id
self._ray_new_actor_handles = []
self._ray_actor_lock = threading.Lock()
def _actor_method_call(self,
method_name,
args=None,
kwargs=None,
num_return_vals=None,
dependency=None):
num_return_vals=None):
"""Method execution stub for an actor handle.
This is the function that executes when
@@ -570,41 +570,34 @@ class ActorHandle(object):
return getattr(worker.actors[self._ray_actor_id],
method_name)(*copy.deepcopy(args))
# Add the execution dependency.
if dependency is None:
execution_dependencies = []
else:
execution_dependencies = [dependency]
is_actor_checkpoint_method = (method_name == "__ray_checkpoint__")
function_descriptor = FunctionDescriptor(
self._ray_module_name, method_name, self._ray_class_name)
object_ids = worker.submit_task(
function_descriptor,
args,
actor_id=self._ray_actor_id,
actor_handle_id=self._ray_actor_handle_id,
actor_counter=self._ray_actor_counter,
is_actor_checkpoint_method=is_actor_checkpoint_method,
actor_creation_dummy_object_id=(
self._ray_actor_creation_dummy_object_id),
execution_dependencies=execution_dependencies,
new_actor_handles=self._ray_new_actor_handles,
# We add one for the dummy return ID.
num_return_vals=num_return_vals + 1,
resources={"CPU": self._ray_actor_method_cpus},
placement_resources={},
driver_id=self._ray_actor_driver_id)
# Update the actor counter and cursor to reflect the most recent
# invocation.
self._ray_actor_counter += 1
# The last object returned is the dummy object that should be
# passed in to the next actor method. Do not return it to the user.
self._ray_actor_cursor = object_ids.pop()
# We have notified the backend of the new actor handles to expect since
# the last task was submitted, so clear the list.
self._ray_new_actor_handles = []
with self._ray_actor_lock:
object_ids = worker.submit_task(
function_descriptor,
args,
actor_id=self._ray_actor_id,
actor_handle_id=self._ray_actor_handle_id,
actor_counter=self._ray_actor_counter,
is_actor_checkpoint_method=is_actor_checkpoint_method,
actor_creation_dummy_object_id=(
self._ray_actor_creation_dummy_object_id),
execution_dependencies=[self._ray_actor_cursor],
new_actor_handles=self._ray_new_actor_handles,
# We add one for the dummy return ID.
num_return_vals=num_return_vals + 1,
resources={"CPU": self._ray_actor_method_cpus},
placement_resources={},
driver_id=self._ray_actor_driver_id,
)
# Update the actor counter and cursor to reflect the most recent
# invocation.
self._ray_actor_counter += 1
# The last object returned is the dummy object that should be
# passed in to the next actor method. Do not return it to the user.
self._ray_actor_cursor = object_ids.pop()
if len(object_ids) == 1:
object_ids = object_ids[0]