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import contextlib
from dataclasses import dataclass
import logging
import os
import ray
from ray import cloudpickle
from ray.types import ObjectRef
from ray.workflow import common, workflow_storage
from typing import Any, Dict, Generator, List, Optional, Tuple, TYPE_CHECKING
from collections import ChainMap
import io
if TYPE_CHECKING:
from ray.actor import ActorHandle
logger = logging.getLogger(__name__)
def init_manager() -> None:
get_or_create_manager(warn_on_creation=False)
def get_or_create_manager(warn_on_creation: bool = True) -> "ActorHandle":
"""Get or create the storage manager."""
# TODO(suquark): We should not get the actor everytime. We also need to
# resume the actor if it failed. Using a global variable to cache the
# actor seems not enough to resume the actor, because there is no
# aliveness detection for an actor.
try:
return ray.get_actor(
common.STORAGE_ACTOR_NAME, namespace=common.MANAGEMENT_ACTOR_NAMESPACE
)
except ValueError:
if warn_on_creation:
logger.warning(
"Cannot access workflow serialization manager. It "
"could be because "
"the workflow manager exited unexpectedly. A new "
"workflow manager is being created. "
)
handle = Manager.options(
name=common.STORAGE_ACTOR_NAME,
namespace=common.MANAGEMENT_ACTOR_NAMESPACE,
lifetime="detached",
).remote()
ray.get(handle.ping.remote())
return handle
@dataclass
class Upload:
identifier_ref: ObjectRef[str]
upload_task: ObjectRef[None]
@ray.remote(num_cpus=0)
class Manager:
"""
Responsible for deduping the serialization/upload of object references.
"""
def __init__(self):
self._uploads: Dict[ray.ObjectRef, Upload] = {}
self._num_uploads = 0
def ping(self) -> None:
"""
Trivial function to ensure actor creation is successful.
"""
return None
async def save_objectref(
self, ref_tuple: Tuple[ray.ObjectRef], workflow_id: "str"
) -> Tuple[List[str], ray.ObjectRef]:
"""Serialize and upload an object reference exactly once.
Args:
ref_tuple: A 1-element tuple which wraps the reference.
Returns:
A pair. The first element is the paths the ref will be uploaded to.
The second is an object reference to the upload task.
"""
(ref,) = ref_tuple
# Use the hex as the key to avoid holding a reference to the object.
key = (ref.hex(), workflow_id)
if key not in self._uploads:
# TODO(Alex): We should probably eventually free these refs.
identifier_ref = common.calculate_identifier.remote(ref)
upload_task = _put_helper.remote(identifier_ref, ref, workflow_id)
self._uploads[key] = Upload(
identifier_ref=identifier_ref, upload_task=upload_task
)
self._num_uploads += 1
info = self._uploads[key]
identifer = await info.identifier_ref
key = _obj_id_to_key(identifer)
return key, info.upload_task
async def export_stats(self) -> Dict[str, Any]:
return {"num_uploads": self._num_uploads}
OBJECTS_DIR = "objects"
def _obj_id_to_key(object_id: str) -> str:
return os.path.join(OBJECTS_DIR, object_id)
@ray.remote(num_cpus=0)
def _put_helper(identifier: str, obj: Any, workflow_id: str) -> None:
# TODO (Alex): This check isn't sufficient, it only works for directly
# nested object refs.
if isinstance(obj, ray.ObjectRef):
raise NotImplementedError(
"Workflow does not support checkpointing nested object references yet."
)
key = _obj_id_to_key(identifier)
dump_to_storage(
key,
obj,
workflow_id,
workflow_storage.WorkflowStorage(workflow_id),
update_existing=False,
)
def _reduce_objectref(
workflow_id: str,
obj_ref: ObjectRef,
tasks: List[ObjectRef],
):
manager = get_or_create_manager()
key, task = ray.get(manager.save_objectref.remote((obj_ref,), workflow_id))
assert task
tasks.append(task)
return _load_object_ref, (key, workflow_id)
def dump_to_storage(
key: str,
obj: Any,
workflow_id: str,
storage: "workflow_storage.WorkflowStorage",
update_existing=True,
) -> None:
"""Serializes and puts arbitrary object, handling references. The object will
be uploaded at `paths`. Any object references will be uploaded to their
global, remote storage.
Args:
key: The key of the object.
obj: The object to serialize. If it contains object references, those
will be serialized too.
workflow_id: The workflow id.
storage: The storage to use. If obj contains object references,
`storage.put` will be called on them individually.
update_existing: If False, the object will not be uploaded if the path
exists.
"""
if not update_existing:
if storage._exists(key):
return
tasks = []
# NOTE: Cloudpickle doesn't support private dispatch tables, so we extend
# the cloudpickler instead to avoid changing cloudpickle's global dispatch
# table which is shared with `ray.put`. See
# https://github.com/cloudpipe/cloudpickle/issues/437
class ObjectRefPickler(cloudpickle.CloudPickler):
_object_ref_reducer = {
ray.ObjectRef: lambda ref: _reduce_objectref(workflow_id, ref, tasks)
}
dispatch_table = ChainMap(
_object_ref_reducer, cloudpickle.CloudPickler.dispatch_table
)
dispatch = dispatch_table
ray.get(tasks)
# TODO(Alex): We should be able to do this without the extra buffer.
with io.BytesIO() as f:
pickler = ObjectRefPickler(f)
pickler.dump(obj)
f.seek(0)
# use the underlying storage to avoid cyclic calls of "dump_to_storage"
storage._storage.put(key, f.read())
@ray.remote
def _load_ref_helper(key: str, workflow_id: str):
# TODO(Alex): We should stream the data directly into `cloudpickle.load`.
storage = workflow_storage.WorkflowStorage(workflow_id)
return storage._get(key)
# TODO (Alex): We should use weakrefs here instead requiring a context manager.
_object_cache: Optional[Dict[str, ray.ObjectRef]] = None
def _load_object_ref(key: str, workflow_id: str) -> ray.ObjectRef:
global _object_cache
if _object_cache is None:
return _load_ref_helper.remote(key, workflow_id)
if _object_cache is None:
return _load_ref_helper.remote(key, workflow_id)
if key not in _object_cache:
_object_cache[key] = _load_ref_helper.remote(key, workflow_id)
return _object_cache[key]
@contextlib.contextmanager
def objectref_cache() -> Generator:
"""A reentrant caching context for object refs."""
global _object_cache
clear_cache = _object_cache is None
if clear_cache:
_object_cache = {}
try:
yield
finally:
if clear_cache:
_object_cache = None
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