ray/python/ray/remote_function.py at master · recogni/ray · GitHub

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import inspect

import logging

import os

import uuid

from functools import wraps

import ray._private.signature

from ray import Language, cross_language

from ray._private import ray_option_utils

from ray._private.auto_init_hook import wrap_auto_init

from ray._private.client_mode_hook import (

client_mode_convert_function,

client_mode_should_convert,

)

from ray._private.ray_option_utils import _warn_if_using_deprecated_placement_group

from ray._private.serialization import pickle_dumps

from ray._private.utils import get_runtime_env_info, parse_runtime_env

from ray._raylet import (

STREAMING_GENERATOR_RETURN,

PythonFunctionDescriptor,

StreamingObjectRefGenerator,

)

from ray.util.annotations import DeveloperAPI, PublicAPI

from ray.util.placement_group import _configure_placement_group_based_on_context

from ray.util.scheduling_strategies import PlacementGroupSchedulingStrategy

from ray.util.tracing.tracing_helper import (

_inject_tracing_into_function,

_tracing_task_invocation,

)

logger = logging.getLogger(__name__)

# Hook to call with (fn, resources, strategy) on each local task submission.

_task_launch_hook = None

@PublicAPI

class RemoteFunction:

"""A remote function.

This is a decorated function. It can be used to spawn tasks.

Attributes:

_language: The target language.

_function: The original function.

_function_descriptor: The function descriptor. This is not defined

until the remote function is first invoked because that is when the

function is pickled, and the pickled function is used to compute

the function descriptor.

_function_name: The module and function name.

_num_cpus: The default number of CPUs to use for invocations of this

remote function.

_num_gpus: The default number of GPUs to use for invocations of this

remote function.

_memory: The heap memory request in bytes for this task/actor,

rounded down to the nearest integer.

_resources: The default custom resource requirements for invocations of

this remote function.

_num_returns: The default number of return values for invocations

of this remote function.

_max_calls: The number of times a worker can execute this function

before exiting.

_max_retries: The number of times this task may be retried

on worker failure.

_retry_exceptions: Whether application-level errors should be retried.

This can be a boolean or a list/tuple of exceptions that should be retried.

_runtime_env: The runtime environment for this task.

_decorator: An optional decorator that should be applied to the remote

function invocation (as opposed to the function execution) before

invoking the function. The decorator must return a function that

takes in two arguments ("args" and "kwargs"). In most cases, it

should call the function that was passed into the decorator and

return the resulting ObjectRefs. For an example, see

"test_decorated_function" in "python/ray/tests/test_basic.py".

_function_signature: The function signature.

_last_export_session_and_job: A pair of the last exported session

and job to help us to know whether this function was exported.

This is an imperfect mechanism used to determine if we need to

export the remote function again. It is imperfect in the sense that

the actor class definition could be exported multiple times by

different workers.

_scheduling_strategy: Strategy about how to schedule

this remote function.

"""

def __init__(

self,

language,

function,

function_descriptor,

task_options,

if inspect.iscoroutinefunction(function):

raise ValueError(

"'async def' should not be used for remote tasks. You can wrap the "

"async function with `asyncio.run(f())`. See more at:"

"https://docs.ray.io/en/latest/ray-core/actors/async_api.html "

)

self._default_options = task_options

# When gpu is used, set the task non-recyclable by default.

# https://github.com/ray-project/ray/issues/29624 for more context.

num_gpus = self._default_options.get("num_gpus") or 0

if num_gpus > 0 and self._default_options.get("max_calls", None) is None:

self._default_options["max_calls"] = 1

# TODO(suquark): This is a workaround for class attributes of options.

# They are being used in some other places, mostly tests. Need cleanup later.

# E.g., actors uses "__ray_metadata__" to collect options, we can so something

# similar for remote functions.

for k, v in ray_option_utils.task_options.items():

setattr(self, "_" + k, task_options.get(k, v.default_value))

self._runtime_env = parse_runtime_env(self._runtime_env)

if "runtime_env" in self._default_options:

self._default_options["runtime_env"] = self._runtime_env

self._language = language

self._function = _inject_tracing_into_function(function)

self._function_name = function.__module__ + "." + function.__name__

self._function_descriptor = function_descriptor

self._is_cross_language = language != Language.PYTHON

self._decorator = getattr(function, "__ray_invocation_decorator__", None)

self._function_signature = ray._private.signature.extract_signature(

self._function

)

self._last_export_session_and_job = None

self._uuid = uuid.uuid4()

# Override task.remote's signature and docstring

@wraps(function)

def _remote_proxy(*args, **kwargs):

return self._remote(args=args, kwargs=kwargs, **self._default_options)

self.remote = _remote_proxy

def __call__(self, *args, **kwargs):

raise TypeError(

"Remote functions cannot be called directly. Instead "

f"of running '{self._function_name}()', "

f"try '{self._function_name}.remote()'."

)

def options(self, **task_options):

"""Configures and overrides the task invocation parameters.

The arguments are the same as those that can be passed to :obj:`ray.remote`.

Overriding `max_calls` is not supported.

Args:

num_returns: It specifies the number of object refs returned by

the remote function invocation.

num_cpus: The quantity of CPU cores to reserve

for this task or for the lifetime of the actor.

num_gpus: The quantity of GPUs to reserve

for this task or for the lifetime of the actor.

resources (Dict[str, float]): The quantity of various custom resources

to reserve for this task or for the lifetime of the actor.

This is a dictionary mapping strings (resource names) to floats.

accelerator_type: If specified, requires that the task or actor run

on a node with the specified type of accelerator.

See `ray.util.accelerators` for accelerator types.

memory: The heap memory request in bytes for this task/actor,

rounded down to the nearest integer.

object_store_memory: The object store memory request for actors only.

max_calls: This specifies the

maximum number of times that a given worker can execute

the given remote function before it must exit

(this can be used to address memory leaks in third-party

libraries or to reclaim resources that cannot easily be

released, e.g., GPU memory that was acquired by TensorFlow).

By default this is infinite for CPU tasks and 1 for GPU tasks

(to force GPU tasks to release resources after finishing).

max_retries: This specifies the maximum number of times that the remote

function should be rerun when the worker process executing it

crashes unexpectedly. The minimum valid value is 0,

the default is 3 (default), and a value of -1 indicates

infinite retries.

runtime_env (Dict[str, Any]): Specifies the runtime environment for

this actor or task and its children. See

:ref:`runtime-environments` for detailed documentation.

retry_exceptions: This specifies whether application-level errors

should be retried up to max_retries times.

scheduling_strategy: Strategy about how to

schedule a remote function or actor. Possible values are

None: ray will figure out the scheduling strategy to use, it

will either be the PlacementGroupSchedulingStrategy using parent's

placement group if parent has one and has

placement_group_capture_child_tasks set to true,

or "DEFAULT";

"DEFAULT": default hybrid scheduling;

"SPREAD": best effort spread scheduling;

`PlacementGroupSchedulingStrategy`:

placement group based scheduling;

`NodeAffinitySchedulingStrategy`:

node id based affinity scheduling.

_metadata: Extended options for Ray libraries. For example,

_metadata={"workflows.io/options": <workflow options>} for

Ray workflows.

Examples:

.. code-block:: python

@ray.remote(num_gpus=1, max_calls=1, num_returns=2)

def f():

return 1, 2

# Task g will require 2 gpus instead of 1.

g = f.options(num_gpus=2)

"""

func_cls = self

# override original options

default_options = self._default_options.copy()

# max_calls could not be used in ".options()", we should remove it before

# merging options from '@ray.remote'.

default_options.pop("max_calls", None)

updated_options = ray_option_utils.update_options(default_options, task_options)

ray_option_utils.validate_task_options(updated_options, in_options=True)

# only update runtime_env when ".options()" specifies new runtime_env

if "runtime_env" in task_options:

updated_options["runtime_env"] = parse_runtime_env(

updated_options["runtime_env"]

)

class FuncWrapper:

def remote(self, *args, **kwargs):

return func_cls._remote(args=args, kwargs=kwargs, **updated_options)

@DeveloperAPI

def bind(self, *args, **kwargs):

"""

For Ray DAG building that creates static graph from decorated

class or functions.

"""

from ray.dag.function_node import FunctionNode

return FunctionNode(func_cls._function, args, kwargs, updated_options)

return FuncWrapper()

@wrap_auto_init

@_tracing_task_invocation

def _remote(self, args=None, kwargs=None, **task_options):

"""Submit the remote function for execution."""

# We pop the "max_calls" coming from "@ray.remote" here. We no longer need

# it in "_remote()".

task_options.pop("max_calls", None)

if client_mode_should_convert():

return client_mode_convert_function(self, args, kwargs, **task_options)

worker = ray._private.worker.global_worker

worker.check_connected()

# If this function was not exported in this session and job, we need to

# export this function again, because the current GCS doesn't have it.

if (

not self._is_cross_language

and self._last_export_session_and_job != worker.current_session_and_job

self._function_descriptor = PythonFunctionDescriptor.from_function(

self._function, self._uuid

)

# There is an interesting question here. If the remote function is

# used by a subsequent driver (in the same script), should the

# second driver pickle the function again? If yes, then the remote

# function definition can differ in the second driver (e.g., if

# variables in its closure have changed). We probably want the

# behavior of the remote function in the second driver to be

# independent of whether or not the function was invoked by the

# first driver. This is an argument for repickling the function,

# which we do here.

self._pickled_function = pickle_dumps(

self._function,

f"Could not serialize the function {self._function_descriptor.repr}",

)

self._last_export_session_and_job = worker.current_session_and_job

worker.function_actor_manager.export(self)

kwargs = {} if kwargs is None else kwargs

args = [] if args is None else args

# fill task required options

for k, v in ray_option_utils.task_options.items():

if k == "max_retries":

# TODO(swang): We need to override max_retries here because the default

# value gets set at Ray import time. Ideally, we should allow setting

# default values from env vars for other options too.

v.default_value = os.environ.get(

"RAY_TASK_MAX_RETRIES", v.default_value

)

v.default_value = int(v.default_value)

task_options[k] = task_options.get(k, v.default_value)

# "max_calls" already takes effects and should not apply again.

# Remove the default value here.

task_options.pop("max_calls", None)

# TODO(suquark): cleanup these fields

name = task_options["name"]

runtime_env = parse_runtime_env(task_options["runtime_env"])

placement_group = task_options["placement_group"]

placement_group_bundle_index = task_options["placement_group_bundle_index"]

placement_group_capture_child_tasks = task_options[

"placement_group_capture_child_tasks"

]

scheduling_strategy = task_options["scheduling_strategy"]

num_returns = task_options["num_returns"]

if num_returns == "dynamic":

num_returns = -1

elif num_returns == "streaming":

# TODO(sang): This is a temporary private API.

# Remove it when we migrate to the streaming generator.

num_returns = ray._raylet.STREAMING_GENERATOR_RETURN

max_retries = task_options["max_retries"]

retry_exceptions = task_options["retry_exceptions"]

if isinstance(retry_exceptions, (list, tuple)):

retry_exception_allowlist = tuple(retry_exceptions)

retry_exceptions = True

else:

retry_exception_allowlist = None

if scheduling_strategy is None or not isinstance(

scheduling_strategy, PlacementGroupSchedulingStrategy

_warn_if_using_deprecated_placement_group(task_options, 4)

resources = ray._private.utils.resources_from_ray_options(task_options)

if scheduling_strategy is None or isinstance(

scheduling_strategy, PlacementGroupSchedulingStrategy

if isinstance(scheduling_strategy, PlacementGroupSchedulingStrategy):

placement_group = scheduling_strategy.placement_group

placement_group_bundle_index = (

scheduling_strategy.placement_group_bundle_index

)

placement_group_capture_child_tasks = (

scheduling_strategy.placement_group_capture_child_tasks

)

if placement_group_capture_child_tasks is None:

placement_group_capture_child_tasks = (

worker.should_capture_child_tasks_in_placement_group

)

placement_group = _configure_placement_group_based_on_context(

placement_group_capture_child_tasks,

placement_group_bundle_index,

resources,

{}, # no placement_resources for tasks

self._function_descriptor.function_name,

placement_group=placement_group,

)

if not placement_group.is_empty:

scheduling_strategy = PlacementGroupSchedulingStrategy(

placement_group,

placement_group_bundle_index,

placement_group_capture_child_tasks,

)

else:

scheduling_strategy = "DEFAULT"

serialized_runtime_env_info = None

if runtime_env is not None:

serialized_runtime_env_info = get_runtime_env_info(

runtime_env,

is_job_runtime_env=False,

serialize=True,

)

if _task_launch_hook:

_task_launch_hook(self._function_descriptor, resources, scheduling_strategy)

def invocation(args, kwargs):

if self._is_cross_language:

list_args = cross_language._format_args(worker, args, kwargs)

elif not args and not kwargs and not self._function_signature:

list_args = []

else:

list_args = ray._private.signature.flatten_args(

self._function_signature, args, kwargs

)

if worker.mode == ray._private.worker.LOCAL_MODE:

assert (

not self._is_cross_language

), "Cross language remote function cannot be executed locally."

object_refs = worker.core_worker.submit_task(

self._language,

self._function_descriptor,

list_args,

name if name is not None else "",

num_returns,

resources,

max_retries,

retry_exceptions,

retry_exception_allowlist,

scheduling_strategy,

worker.debugger_breakpoint,

serialized_runtime_env_info or "{}",

)

# Reset worker's debug context from the last "remote" command

# (which applies only to this .remote call).

worker.debugger_breakpoint = b""

if num_returns == STREAMING_GENERATOR_RETURN:

# Streaming generator will return a single ref

# that is for the generator task.

assert len(object_refs) == 1

generator_ref = object_refs[0]

return StreamingObjectRefGenerator(generator_ref, worker)

if len(object_refs) == 1:

return object_refs[0]

elif len(object_refs) > 1:

return object_refs

if self._decorator is not None:

invocation = self._decorator(invocation)

return invocation(args, kwargs)

@DeveloperAPI

def bind(self, *args, **kwargs):

"""

For Ray DAG building that creates static graph from decorated

class or functions.

"""

from ray.dag.function_node import FunctionNode

return FunctionNode(self._function, args, kwargs, self._default_options)

ray/python/ray/remote_function.py at master · recogni/ray · GitHub

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