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Code Issues Packages Projects Releases Wiki Activity

Moving Local Mode to C++ (#7670)

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This commit is contained in:
ijrsvt
2020-04-01 13:50:57 -07:00
committed by GitHub
parent 65054a2c7c
commit 9bfc2c4b54
17 changed files with 489 additions and 648 deletions
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python/ray/actor.py
+52 -66
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@@ -1,4 +1,3 @@
import copy
import inspect
import logging
import weakref
@@ -10,7 +9,7 @@ import ray.ray_constants as ray_constants
import ray._raylet
import ray.signature as signature
import ray.worker
from ray import ActorID, ActorClassID, Language
from ray import ActorClassID, Language
from ray._raylet import PythonFunctionDescriptor
from ray import cross_language
@@ -503,66 +502,57 @@ class ActorClass:
if meta.num_cpus is None else meta.num_cpus)
actor_method_cpu = ray_constants.DEFAULT_ACTOR_METHOD_CPU_SPECIFIED
# Do not export the actor class or the actor if run in LOCAL_MODE
# Instead, instantiate the actor locally and add it to the worker's
# dictionary
# LOCAL_MODE cannot handle cross_language
if worker.mode == ray.LOCAL_MODE:
assert not meta.is_cross_language, \
"Cross language ActorClass cannot be executed locally."
actor_id = ActorID.from_random()
worker.actors[actor_id] = meta.modified_class(
*copy.deepcopy(args), **copy.deepcopy(kwargs))
# Export the actor.
if not meta.is_cross_language and (meta.last_export_session_and_job !=
worker.current_session_and_job):
# If this actor class was not exported in this session and job,
# we need to export this function again, because current GCS
# doesn't have it.
meta.last_export_session_and_job = (worker.current_session_and_job)
# After serialize / deserialize modified class, the __module__
# of modified class will be ray.cloudpickle.cloudpickle.
# So, here pass actor_creation_function_descriptor to make
# sure export actor class correct.
worker.function_actor_manager.export_actor_class(
meta.modified_class, meta.actor_creation_function_descriptor,
meta.method_meta.methods.keys())
resources = ray.utils.resources_from_resource_arguments(
cpus_to_use, meta.num_gpus, meta.memory, meta.object_store_memory,
meta.resources, num_cpus, num_gpus, memory, object_store_memory,
resources)
# If the actor methods require CPU resources, then set the required
# placement resources. If actor_placement_resources is empty, then
# the required placement resources will be the same as resources.
actor_placement_resources = {}
assert actor_method_cpu in [0, 1]
if actor_method_cpu == 1:
actor_placement_resources = resources.copy()
actor_placement_resources["CPU"] += 1
if meta.is_cross_language:
creation_args = cross_language.format_args(worker, args, kwargs)
else:
# Export the actor.
if not meta.is_cross_language and (meta.last_export_session_and_job
!=
worker.current_session_and_job):
# If this actor class was not exported in this session and job,
# we need to export this function again, because current GCS
# doesn't have it.
meta.last_export_session_and_job = (
worker.current_session_and_job)
# After serialize / deserialize modified class, the __module__
# of modified class will be ray.cloudpickle.cloudpickle.
# So, here pass actor_creation_function_descriptor to make
# sure export actor class correct.
worker.function_actor_manager.export_actor_class(
meta.modified_class,
meta.actor_creation_function_descriptor,
meta.method_meta.methods.keys())
resources = ray.utils.resources_from_resource_arguments(
cpus_to_use, meta.num_gpus, meta.memory,
meta.object_store_memory, meta.resources, num_cpus, num_gpus,
memory, object_store_memory, resources)
# If the actor methods require CPU resources, then set the required
# placement resources. If actor_placement_resources is empty, then
# the required placement resources will be the same as resources.
actor_placement_resources = {}
assert actor_method_cpu in [0, 1]
if actor_method_cpu == 1:
actor_placement_resources = resources.copy()
actor_placement_resources["CPU"] += 1
if meta.is_cross_language:
creation_args = cross_language.format_args(
worker, args, kwargs)
else:
function_signature = meta.method_meta.signatures["__init__"]
creation_args = signature.flatten_args(function_signature,
args, kwargs)
actor_id = worker.core_worker.create_actor(
meta.language,
meta.actor_creation_function_descriptor,
creation_args,
meta.max_reconstructions,
resources,
actor_placement_resources,
max_concurrency,
detached,
is_asyncio,
# Store actor_method_cpu in actor handle's extension data.
extension_data=str(actor_method_cpu))
function_signature = meta.method_meta.signatures["__init__"]
creation_args = signature.flatten_args(function_signature, args,
kwargs)
actor_id = worker.core_worker.create_actor(
meta.language,
meta.actor_creation_function_descriptor,
creation_args,
meta.max_reconstructions,
resources,
actor_placement_resources,
max_concurrency,
detached,
is_asyncio,
# Store actor_method_cpu in actor handle's extension data.
extension_data=str(actor_method_cpu))
actor_handle = ActorHandle(
meta.language,
@@ -705,14 +695,10 @@ class ActorHandle:
assert not self._ray_is_cross_language,\
"Cross language remote actor method " \
"cannot be executed locally."
function = getattr(worker.actors[self._actor_id], method_name)
object_ids = worker.local_mode_manager.execute(
function, method_name, args, kwargs, num_return_vals)
else:
object_ids = worker.core_worker.submit_actor_task(
self._ray_actor_language, self._ray_actor_id,
function_descriptor, list_args, num_return_vals,
self._ray_actor_method_cpus)
object_ids = worker.core_worker.submit_actor_task(
self._ray_actor_language, self._ray_actor_id, function_descriptor,
list_args, num_return_vals, self._ray_actor_method_cpus)
if len(object_ids) == 1:
object_ids = object_ids[0]