base.py

# Copyright 2024 Bytedance Ltd. and/or its affiliates
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import inspect
import logging
import time
from copy import deepcopy
from typing import Any, Optional

import ray
from ray.experimental.state.api import get_actor
from ray.util import list_named_actors
from ray.util.placement_group import PlacementGroup, placement_group
from ray.util.scheduling_strategies import NodeAffinitySchedulingStrategy, PlacementGroupSchedulingStrategy

from verl.protocol import DataProto, _padding_size_key
from verl.single_controller.base import ClassWithInitArgs, ResourcePool, Worker, WorkerGroup
from verl.single_controller.base.decorator import MAGIC_ATTR, Dispatch
from verl.utils.py_functional import temp_env_var

__all__ = ["Worker"]


def get_random_string(length: int) -> str:
    import random
    import string

    letters_digits = string.ascii_letters + string.digits
    return "".join(random.choice(letters_digits) for _ in range(length))


def func_generator(self, method_name, dispatch_fn, collect_fn, execute_fn, blocking):
    class Functor:
        def __call__(this, *args, **kwargs):
            args, kwargs = dispatch_fn(self, *args, **kwargs)
            padding_count = kwargs.pop(_padding_size_key, 0)
            output = execute_fn(method_name, *args, **kwargs)
            if blocking:
                output = ray.get(output)
            output = collect_fn(self, output)
            if padding_count > 0:
                if isinstance(output, DataProto):
                    indices = [i for i in range(len(output))][:-padding_count]
                    output = output.select_idxs(indices)
                elif isinstance(output, list):
                    output = output[:-padding_count]
            return output

    # use class type to pass the method_name to get a better observability
    return type(method_name, (Functor,), {})()


def sort_placement_group_by_node_ip(pgs: list[PlacementGroup]) -> list[PlacementGroup]:
    """
    Sort the placement groups by node ip, all bundles in a single placement group should be on the same node.

    FSDPCheckpointManager saves sharded model states and optimizer states in local storage, which requires RANK
    to be consistent across nodes when resume from checkpoint.

    With this function, if there's only one resource pool and there's no node change, RANK should be consistent
    across nodes in multiple ray jobs, even if the whole ray cluster is restarted.
    """
    node_ip = {node["NodeID"]: node["NodeManagerAddress"] for node in ray.nodes()}
    pg_ip = {}
    for pg in pgs:
        specs = ray._private.state.state.placement_group_table(pg.id)
        # all bunles should be on the same node
        node_id = specs["bundles_to_node_id"][0]
        pg_ip[pg.id] = node_ip[node_id]
    return sorted(pgs, key=lambda pg: pg_ip[pg.id])


class RayResourcePool(ResourcePool):
    def __init__(
        self,
        process_on_nodes: Optional[list[int]] = None,
        use_gpu: bool = True,
        name_prefix: str = None,
        max_colocate_count: int = 10,
        detached=False,
        accelerator_type: Optional[str] = None,
    ) -> None:
        super().__init__(process_on_nodes, max_colocate_count)
        self.use_gpu = use_gpu
        # print(f"in RayProcessDispatchConfiguration: name_prefix = {name_prefix}")
        self.name_prefix = get_random_string(length=6) if name_prefix is None else name_prefix
        self.pgs = None
        self.detached = detached
        self.accelerator_type = accelerator_type

    def get_placement_groups(self, strategy="STRICT_PACK", name=None, device_name="cuda"):
        if self.pgs is not None:
            return self.pgs

        pg_name_prefix = (
            name if name else f"{self.name_prefix}verl_group_{'_'.join([str(count) for count in self._store])}:"
        )
        # print(f"pg_name_prefix = {pg_name_prefix}")
        if device_name == "npu":
            device_name = "NPU"
        elif device_name == "cuda":
            device_name = "GPU"

        bundle = {"CPU": self.max_colocate_count}
        if self.use_gpu:
            bundle[device_name] = 1
            if self.accelerator_type is not None:
                bundle[self.accelerator_type] = 1e-4
        pg_scheme = [[bundle.copy() for _ in range(process_count)] for process_count in self._store]

        lifetime = "detached" if self.detached else None

        pgs = [
            placement_group(bundles=bundles, strategy=strategy, name=pg_name_prefix + str(idx), lifetime=lifetime)
            for idx, bundles in enumerate(pg_scheme)
        ]

        ray.get([pg.ready() for pg in pgs])

        self.pgs = pgs
        return pgs


def extract_pg_from_exist(
    resource_pools: dict[str, RayResourcePool], src_role_names: list[str], resource_pool: RayResourcePool
) -> list:
    src_pgs = [
        pg
        for role_name, resource_pool in resource_pools.items()
        for pg in resource_pool.get_placement_groups()
        if role_name in src_role_names
    ]

    sorted_src_pgs = sorted(src_pgs, key=lambda pg: pg.bundle_count, reverse=True)
    sorted_process_on_nodes = sorted([(val, idx) for idx, val in enumerate(resource_pool.store)], reverse=True)

    unsorted_pgs: list[tuple[int, PlacementGroup]] = []
    searching_idx = 0
    for request_process, original_idx in sorted_process_on_nodes:
        assert searching_idx < len(sorted_src_pgs), f"no enough nodes for request: searching {searching_idx} th node"
        assert request_process <= sorted_src_pgs[searching_idx].bundle_count, (
            f"requesting {request_process} processes, bundle count cannot satisfy"
        )
        unsorted_pgs.append((original_idx, sorted_src_pgs[searching_idx]))
        searching_idx += 1

    return [pg for _, pg in sorted(unsorted_pgs)]


def merge_resource_pool(rp1: RayResourcePool, rp2: RayResourcePool) -> RayResourcePool:
    assert rp1.use_gpu == rp2.use_gpu, "Both RayResourcePool must either use_gpu or not"
    assert rp1.max_colocate_count == rp2.max_colocate_count, "Both RayResourcePool must has the same max_colocate_count"
    assert rp1.n_gpus_per_node == rp2.n_gpus_per_node, "Both RayResourcePool must has the same n_gpus_per_node"
    assert rp1.detached == rp2.detached, "Detached ResourcePool cannot be merged with non-detached ResourcePool"

    new_store = rp1.store + rp2.store

    merged = type(rp1)(new_store, rp1.use_gpu, f"{rp1.name_prefix}_{rp2.name_prefix}")
    merged.pgs = rp1.get_placement_groups() + rp2.get_placement_groups()

    return merged


class RayClassWithInitArgs(ClassWithInitArgs):
    """A wrapper class for Ray actors with initialization arguments.

    This class extends ClassWithInitArgs to provide additional functionality for
    configuring and creating Ray actors with specific resource requirements and
    scheduling strategies.
    """

    def __init__(self, cls, *args, **kwargs) -> None:
        # self._options = kwargs.pop('options', dict())
        super().__init__(cls, *args, **kwargs)
        self._options = {}
        self._additional_resource = {}

    def set_additional_resource(self, additional_resource):
        """Set additional resource requirements for the actor.

        Args:
            additional_resource: Dictionary specifying additional resource requirements
        """
        self._additional_resource = additional_resource

    def update_options(self, options: dict):
        """Update the Ray actor creation options.

        Args:
            options: Dictionary of options to update
        """
        self._options.update(options)

    def __call__(
        self,
        placement_group,
        placement_group_bundle_idx,
        use_gpu: bool = True,
        num_gpus=1,
        sharing_with=None,
        device_name="cuda",
    ) -> Any:
        """Create and return a Ray actor with the configured options.

        Args:
            placement_group: Ray placement group for scheduling
            placement_group_bundle_idx: Index of the bundle in the placement group
            use_gpu: Whether to use GPU resources
            num_gpus: Number of GPUs to allocate
            sharing_with: Actor to share resources with
            device_name: Device for training

        Returns:
            A Ray actor handle with the configured options
        """
        if sharing_with is not None:
            target_node_id = ray.get(sharing_with.get_node_id.remote())
            visible_devices = ray.get(sharing_with.get_cuda_visible_devices.remote())
            options = {"scheduling_strategy": NodeAffinitySchedulingStrategy(node_id=target_node_id, soft=False)}
            return self.cls.options(**options).remote(*self.args, cuda_visible_devices=visible_devices, **self.kwargs)

        options = {
            "scheduling_strategy": PlacementGroupSchedulingStrategy(
                placement_group=placement_group, placement_group_bundle_index=placement_group_bundle_idx
            )
        }
        options.update(self._options)

        if use_gpu and device_name == "cuda":
            options["num_gpus"] = num_gpus
        if use_gpu and device_name == "npu":
            options["resources"] = {"NPU": num_gpus}

        if len(self._additional_resource) > 1:
            for k, v in self._additional_resource.items():
                options[k] = v

        # print("cls:", self.cls)
        # print("args: ", self.args)
        # print("kwargs: ", self.kwargs)
        return self.cls.options(**options).remote(*self.args, **self.kwargs)


class RayWorkerGroup(WorkerGroup):
    """A group of Ray workers that can be managed collectively.

    This class extends WorkerGroup to provide Ray-specific functionality for
    creating and managing groups of Ray actors with specific resource requirements
    and scheduling strategies.
    """

    def __init__(
        self,
        resource_pool: RayResourcePool = None,
        ray_cls_with_init: RayClassWithInitArgs = None,
        bin_pack: bool = True,
        name_prefix: str = None,
        detached=False,
        worker_names=None,
        worker_handles: list[ray.actor.ActorHandle] = None,
        ray_wait_register_center_timeout: int = 300,
        **kwargs,
    ) -> None:
        """Initialize a RayWorkerGroup.

        Args:
            resource_pool: Resource pool for worker allocation
            ray_cls_with_init: Class with initialization arguments for workers
            bin_pack: Whether to use strict bin packing for resource allocation
            name_prefix: Prefix for worker names
            detached: Whether workers should be detached
            worker_names: Names of existing workers to attach to
            ray_wait_register_center_timeout: Timeout for waiting on register center
            **kwargs: Additional keyword arguments
        """
        super().__init__(resource_pool=resource_pool, **kwargs)
        self.ray_cls_with_init = ray_cls_with_init
        self.name_prefix = get_random_string(length=6) if name_prefix is None else name_prefix
        self._ray_wait_register_center_timeout = ray_wait_register_center_timeout
        # Whether the WorkerGroup is a Colocate WorkerGroup created by FusedWorker.
        self.fused_worker_used = ray_cls_with_init.fused_worker_used
        # if a WorkerGroup is spawned from Colocate WorkerGroup, this indicates which sub-class is binded to
        # this WorkerGroup.
        self.sub_cls_name = ""
        self.device_name = kwargs.get("device_name", "cuda")
        self.profile_steps = kwargs.get("profile_steps", None)
        self.worker_nsight_options = kwargs.get("worker_nsight_options", None)
        self.customized_worker_env = kwargs.get("worker_env", {})
        if self.worker_nsight_options is not None and self.worker_nsight_options["capture-range-end"] is None:
            self.worker_nsight_options["capture-range-end"] = f"repeat-shutdown:{6 * len(self.profile_steps)}"

        if worker_names is not None and (not self.fused_worker_used):
            assert self._is_init_with_detached_workers
            self._worker_names = worker_names

        if self._is_init_with_detached_workers:
            self._init_with_detached_workers(worker_names=worker_names, worker_handles=worker_handles)
        else:
            self._init_with_resource_pool(
                resource_pool=resource_pool,
                ray_cls_with_init=ray_cls_with_init,
                bin_pack=bin_pack,
                detached=detached,
                worker_env=self.customized_worker_env,
            )

        if ray_cls_with_init is not None:
            self._bind_worker_method(self.ray_cls_with_init.cls, func_generator)

        self.wg_dict = None
        self.method_names = []

    def _is_worker_alive(self, worker: ray.actor.ActorHandle):
        """Check if a worker actor is still alive.

        Args:
            worker: Ray actor handle to check

        Returns:
            bool: True if the worker is alive, False otherwise
        """
        worker_state_dict = get_actor(worker._actor_id.hex())
        return worker_state_dict.get("state", "undefined") == "ALIVE" if worker_state_dict is not None else False

    def _init_with_detached_workers(self, worker_names, worker_handles):
        # ray.get_actor holds a weak reference to the actor, which causes actors garbage collected unexpectedly
        # if we only hold spawn RayWorkerGroup. By passing actor handle explicitly, spawn RayWorkerGroup have
        # strong reference to these actors.
        # https://github.com/ray-project/ray/pull/45699
        workers = worker_handles if worker_handles else [ray.get_actor(name=name) for name in worker_names]
        self._workers = workers
        self._world_size = len(worker_names)

    def _init_with_resource_pool(self, resource_pool, ray_cls_with_init, bin_pack, detached, worker_env=None):
        """Initialize the worker group by creating new workers from a resource pool.

        Args:
            resource_pool: Resource pool for worker allocation
            ray_cls_with_init: Class with initialization arguments for workers
            bin_pack: Whether to use strict bin packing for resource allocation
            detached: Whether workers should be detached
        """
        use_gpu = resource_pool.use_gpu

        strategy = "PACK"
        if bin_pack:
            strategy = "STRICT_PACK"
        pgs = resource_pool.get_placement_groups(strategy=strategy, device_name=self.device_name)
        world_size = resource_pool.world_size
        self._world_size = world_size
        # cia.add_kwarg("_world_size", world_size)
        num_gpus = 1 / resource_pool.max_colocate_count

        rank = -1
        local_world_size = resource_pool.store[0]
        for pg_idx, pg in enumerate(sort_placement_group_by_node_ip(pgs)):
            assert local_world_size <= pg.bundle_count, f"when generating for {self.name_prefix}, for the "
            for local_rank in range(local_world_size):
                rank += 1

                # we pass in environment variable at option so that Worker can use environment variable to set
                env_vars = {
                    "WORLD_SIZE": str(world_size),
                    "RANK": str(rank),
                    "WG_PREFIX": self.name_prefix,
                    "WG_BACKEND": "ray",
                    "RAY_LOCAL_WORLD_SIZE": str(local_world_size),
                    "RAY_LOCAL_RANK": str(local_rank),
                }
                if rank != 0:
                    env_vars["MASTER_ADDR"] = self._master_addr
                    env_vars["MASTER_PORT"] = self._master_port

                if worker_env is not None:
                    logging.debug(f"Appending ray class env, origin: {env_vars}, customized env: {worker_env}")
                    conflict_env_vars = set(env_vars.keys()) & set(worker_env.keys())
                    if len(conflict_env_vars) > 0:
                        logging.error(
                            f"User customized env vars conflict with system env: {conflict_env_vars} "
                            f"Overriding may cause unexpected behavior."
                        )
                        raise ValueError(f"Cannot override protected system env: {conflict_env_vars}")
                    env_vars.update(worker_env)
                import re

                cia_name = type(ray_cls_with_init.cls).__name__
                match = re.search(r"ActorClass\(([^)]+)\)", cia_name)  # ray.remote(Obj) -> "ActorClass(Obj)"
                cia_name = match.group(1) if match else cia_name  # "ActorClass(Obj)" -> "Obj"
                name = f"{self.name_prefix}{cia_name}_{pg_idx}:{local_rank}"  # e.g. Worker_2:5

                if self.profile_steps and self.device_name == "cuda":
                    ray_cls_with_init.update_options(
                        {
                            "runtime_env": {
                                "env_vars": env_vars,
                                "nsight": self.worker_nsight_options,
                            },
                            "name": name,
                        }
                    )
                else:
                    ray_cls_with_init.update_options({"runtime_env": {"env_vars": env_vars}, "name": name})

                if detached:
                    ray_cls_with_init.update_options({"lifetime": "detached"})

                # create a worker
                worker = ray_cls_with_init(
                    placement_group=pg,
                    placement_group_bundle_idx=local_rank,
                    use_gpu=use_gpu,
                    num_gpus=num_gpus,
                    device_name=self.device_name,
                )
                self._workers.append(worker)
                self._worker_names.append(name)

                if rank == 0:
                    register_center_actor = None
                    actor_name = f"{self.name_prefix}_register_center"
                    start_time = time.time()

                    while time.time() - start_time < self._ray_wait_register_center_timeout:
                        if actor_name in list_named_actors():
                            register_center_actor = ray.get_actor(actor_name)
                            break

                        elapsed = int(time.time() - start_time)
                        if elapsed % 30 == 0:
                            logging.warning(
                                "Waiting for register center actor %s to be ready. Elapsed time: %s seconds out of "
                                "%s seconds.",
                                actor_name,
                                elapsed,
                                self._ray_wait_register_center_timeout,
                            )
                        time.sleep(1)

                    if register_center_actor is None:
                        raise TimeoutError(
                            f"Failed to get register_center_actor {actor_name} "
                            f"in {list_named_actors(all_namespaces=True)} "
                            f"for {self._ray_wait_register_center_timeout} seconds. "
                            "Ensure that any lingering Ray resources from previous "
                            "runs are cleaned up (e.g., by restarting the Ray cluster), "
                            "or adjust the waiting time by modifying the config "
                            "`trainer.ray_wait_register_center_timeout`."
                        )

                    rank_zero_info = ray.get(register_center_actor.get_rank_zero_info.remote())
                    self._master_addr, self._master_port = rank_zero_info["MASTER_ADDR"], rank_zero_info["MASTER_PORT"]
                    # print(f"rank_zero_info: {rank_zero_info}")
                    # print(f"master_addr: {self._master_addr}, master_port: {self._master_port}")

    @property
    def worker_names(self):
        return self._worker_names

    @classmethod
    def from_detached(
        cls,
        name_prefix=None,
        worker_names=None,
        worker_handles=None,
        ray_cls_with_init=None,
        **kwargs,
    ):
        """Create a worker group from existing detached workers.

        Args:
            name_prefix: Prefix for worker names
            worker_names: Names of existing workers to attach to
            ray_cls_with_init: Class with initialization arguments for workers

        Returns:
            A new RayWorkerGroup instance
        """
        worker_group = cls(
            resource_pool=None,
            ray_cls_with_init=ray_cls_with_init,
            name_prefix=name_prefix,
            worker_names=worker_names,
            worker_handles=worker_handles,
            **kwargs,
        )
        return worker_group

    def spawn(self, prefix_set):
        """Spawn to a dictionary of worker groups, each with a subset of method with prefix.

        Args:
            prefix_set: Set of prefixes to create worker groups for

        Returns:
            Dictionary of worker groups keyed by prefix
        """
        if self.fused_worker_used:
            return self.spawn_fused(prefix_set)

        def _rebind_actor_methods(worker_group, actor_name):
            prefix: str = actor_name + "_"
            for method_name in dir(worker_group):
                if method_name.startswith(prefix):
                    original_method_name = method_name.removeprefix(prefix)
                    method = getattr(worker_group, method_name)
                    setattr(worker_group, original_method_name, method)

        new_worker_group_dict = {}
        for prefix in prefix_set:
            new_worker_group = self.from_detached(
                name_prefix=self.name_prefix,
                worker_names=self._worker_names,
                worker_handles=self._workers,
                ray_cls_with_init=self.ray_cls_with_init,
                profile_steps=self.profile_steps,
                worker_nsight_options=self.worker_nsight_options,
            )

            _rebind_actor_methods(new_worker_group, prefix)
            new_worker_group_dict[prefix] = new_worker_group
        return new_worker_group_dict

    def spawn_fused(self, prefix_set):
        """Create a dictionary of worker groups for fused workers.

        Args:
            prefix_set: Set of prefixes to create worker groups for

        Returns:
            Dictionary of worker groups keyed by prefix
        """
        wg_dict = dict()
        for key in prefix_set:
            new_wg = deepcopy(self)
            new_wg._bind_worker_method(self.ray_cls_with_init.cls.raw_cls_dict[key], func_generator)
            new_wg.sub_cls_name = key
            wg_dict[key] = new_wg
        return wg_dict

    def fuse(self, prefix_set):
        """Fuse multiple worker groups into the current worker group.

        Args:
            prefix_set: Set of prefixes to fuse into the worker group
        """
        if self.wg_dict is None:
            self.wg_dict = self.spawn(prefix_set)
        for role_name, role_wg in self.wg_dict.items():
            setattr(self, role_name, role_wg)
        self.method_names = self._bind_worker_method(self.ray_cls_with_init.cls, func_generator)

    def _execute_remote_single_worker(self, worker, method_name: str, *args, **kwargs):
        """Execute a method on a single worker remotely.

        Args:
            worker: The worker actor handle
            method_name: Name of the method to execute
            *args: Positional arguments for the method
            **kwargs: Keyword arguments for the method

        Returns:
            Remote object reference to the method execution
        """
        if self.fused_worker_used and method_name not in self.method_names:
            remote_call = getattr(worker, self.fused_worker_execute_fn_name)
            return remote_call.remote(f"{self.sub_cls_name}_fwmn_{method_name}", *args, **kwargs)
        # fused worker not used
        remote_call = getattr(worker, method_name)
        return remote_call.remote(*args, **kwargs)

    def execute_rank_zero_sync(self, method_name: str, *args, **kwargs):
        """Execute a method on rank zero worker synchronously.

        Args:
            method_name: Name of the method to execute
            *args: Positional arguments for the method
            **kwargs: Keyword arguments for the method

        Returns:
            Result of the method execution
        """
        return ray.get(self.execute_rank_zero_async(method_name, *args, **kwargs))

    def execute_rank_zero_async(self, method_name: str, *args, **kwargs):
        """Execute a method on rank zero worker asynchronously.

        Args:
            method_name: Name of the method to execute
            *args: Positional arguments for the method
            **kwargs: Keyword arguments for the method

        Returns:
            Remote object reference to the method execution
        """
        return self._execute_remote_single_worker(self._workers[0], method_name, *args, **kwargs)

    def execute_rank_zero(self, method_name: str, *args, **kwargs):
        """Alias for execute_rank_zero_async.

        Args:
            method_name: Name of the method to execute
            *args: Positional arguments for the method
            **kwargs: Keyword arguments for the method

        Returns:
            Remote object reference to the method execution
        """
        return self.execute_rank_zero_async(method_name, *args, **kwargs)

    def execute_all(self, method_name: str, *args, **kwargs):
        """Alias for execute_all_async.

        Args:
            method_name: Name of the method to execute
            *args: Positional arguments for the method
            **kwargs: Keyword arguments for the method

        Returns:
            List of remote object references to the method executions
        """
        return self.execute_all_async(method_name, *args, **kwargs)

    def execute_all_sync(self, method_name: str, *args, **kwargs):
        """Execute a method on all workers synchronously.

        Args:
            method_name: Name of the method to execute
            *args: Positional arguments for the method
            **kwargs: Keyword arguments for the method

        Returns:
            List of results from all workers
        """
        return ray.get(self.execute_all_async(method_name, *args, **kwargs))

    def execute_all_async(self, method_name: str, *args, **kwargs):
        """Execute a method on all workers asynchronously.

        Args:
            method_name: Name of the method to execute
            *args: Positional arguments for the method
            **kwargs: Keyword arguments for the method

        Returns:
            List of remote object references to the method executions
        """
        # Here, we assume that if all arguments in args and kwargs are lists,
        # and their lengths match len(self._workers), we'll distribute each
        # element in these lists to the corresponding worker
        # print(f"execute_all_async: method {method_name}({args}, {kwargs})")
        length = len(self._workers)
        if all(isinstance(arg, list) for arg in args) and all(isinstance(kwarg, list) for kwarg in kwargs.values()):
            if all(len(arg) == length for arg in args) and all(len(kwarg) == length for kwarg in kwargs.values()):
                # print(f"splitting args and kwargs into {length} shards")
                result = []
                for i in range(length):
                    sliced_args = tuple(arg[i] for arg in args)
                    sliced_kwargs = {k: v[i] for k, v in kwargs.items()}
                    result.append(
                        self._execute_remote_single_worker(self._workers[i], method_name, *sliced_args, **sliced_kwargs)
                    )
                return result

        return [self._execute_remote_single_worker(worker, method_name, *args, **kwargs) for worker in self._workers]

    @property
    def master_address(self):
        return self._master_addr

    @property
    def master_port(self):
        return self._master_port

    @property
    def workers(self):
        return self._workers

    @property
    def world_size(self):
        return self._world_size


"""
Utilities that enables creating workers inside the same ray.Actor,
with code written in separate ray.Actors.
"""


# deprecated, switching to FusedWorker
def _bind_workers_method_to_parent(cls, key, user_defined_cls):
    """
    Binds the methods of each worker to the WorkerDict.
    Note that we only bind public methods that are decorated by register
    """

    for method_name in dir(user_defined_cls):
        try:
            method = getattr(user_defined_cls, method_name)
            assert callable(method), f"{method_name} in {user_defined_cls} is not callable"
        except Exception:
            # if it is a property, it will fail because Class doesn't have instance property
            continue

        if hasattr(method, MAGIC_ATTR):

            def generate_function(name, key=key):
                def func(self, *args, **kwargs):
                    # dispatch to the actual worker
                    return getattr(self.worker_dict[key], name)(*args, **kwargs)

                async def async_func(self, *args, **kwargs):
                    # dispatch to the actual worker
                    return await getattr(self.worker_dict[key], name)(*args, **kwargs)

                wrapper = async_func if inspect.iscoroutinefunction(method) else func  # noqa: B023

                return wrapper

            func = generate_function(method_name)
            # pass MAGIC_ATTR for outer worker group
            attrs = getattr(method, MAGIC_ATTR)
            setattr(func, MAGIC_ATTR, attrs)
            try:
                # bind direct rollout method to class without prefix
                if attrs["dispatch_mode"] == Dispatch.DIRECT_ROLLOUT_METHOD and "rollout" in key:
                    assert not hasattr(cls, method_name), (
                        f"conflict direct rollout method {method_name} with role {key}"
                    )
                    setattr(cls, method_name, func)
                    print(f"bind role {key} method {method_name} to class {cls}")
                else:
                    method_name_with_prefix = key + "_" + method_name
                    setattr(cls, method_name_with_prefix, func)
            except Exception as e:
                raise ValueError(f"Fail to set method_name {method_name}") from e


def _unwrap_ray_remote(cls):
    if hasattr(cls, "__ray_actor_class__"):
        cls = cls.__ray_actor_class__
    return cls


def _determine_fsdp_megatron_base_class(mros: list):
    """
    - megatron: base class should be MegatronWorker
    - fsdp: base class should be Worker
    """
    for cls in mros[0]:
        if cls.__name__ == "MegatronWorker":
            return cls
        if cls.__name__ == "Worker":
            return cls
    raise ValueError(f"Cannot determine base class for {mros}")


# deprecated, switching to FusedWorker
def create_colocated_worker_cls(class_dict: dict[str, RayClassWithInitArgs]):
    """
    This function should return a class instance that delegates the calls to every
    cls in cls_dict
    """
    cls_dict = {}
    init_args_dict = {}
    worker_cls = _determine_fsdp_megatron_base_class(
        [cls.cls.__ray_actor_class__.__mro__ for cls in class_dict.values()]
    )
    assert issubclass(worker_cls, Worker), f"worker_cls {worker_cls} should be a subclass of Worker"
    print(f"colocated worker base class {worker_cls}")

    for key, cls in class_dict.items():
        cls_dict[key] = cls.cls
        init_args_dict[key] = {"args": cls.args, "kwargs": cls.kwargs}

    assert cls_dict.keys() == init_args_dict.keys()

    # TODO: create a class with customizable name
    class WorkerDict(worker_cls):
        def __init__(self):
            super().__init__()
            self.worker_dict = {}
            for key, user_defined_cls in cls_dict.items():
                user_defined_cls = _unwrap_ray_remote(user_defined_cls)
                # directly instantiate the class without remote
                # in worker class, e.g. <verl.single_controller.base.worker.Worker>
                # when DISABLE_WORKER_INIT == 1 it will return immediately
                with temp_env_var("DISABLE_WORKER_INIT", "1"):
                    self.worker_dict[key] = user_defined_cls(
                        *init_args_dict[key].get("args", ()), **init_args_dict[key].get("kwargs", {})
                    )

    # now monkey-patch the methods from inner class to WorkerDict
    for key, user_defined_cls in cls_dict.items():
        user_defined_cls = _unwrap_ray_remote(user_defined_cls)
        _bind_workers_method_to_parent(WorkerDict, key, user_defined_cls)

    remote_cls = ray.remote(WorkerDict)
    remote_cls = RayClassWithInitArgs(cls=remote_cls)
    return remote_cls


FusedWorkerCLSName = "FusedWorker"


def create_colocated_worker_raw_cls(class_dict: dict[str, RayClassWithInitArgs]):
    """
    This function returns a FusedWorker class.

    `FusedWorker.{class_name}` -> FusedClass
        Use `class_name` as a param to directly access the underlying class.

    `FusedWorker._fuw_execute("{class_name}_fwmn_{method_name}", *args, **kwargs)`
        First param must be "{class_name}_fwmn_{method_name}" in order to access `method_name`
        of underlying class `{class_name}`.

    `FusedWorker.fused_worker_dict` -> {"class_name": FusedClass}
        Stores all underlying classes.

    `FusedClass.fused_worker_dict` -> {"class_name": FusedClass}
        The same as `FusedWorker.fused_worker_dict`, enables underlying class to access other
        underlying classes.
    """
    raw_cls_dict = {cls_name: _unwrap_ray_remote(cia.cls) for cls_name, cia in class_dict.items()}
    init_args_dict = {cls_name: cia.args for cls_name, cia in class_dict.items()}
    init_kwargs_dict = {cls_name: cia.kwargs for cls_name, cia in class_dict.items()}
    cls_names = list(class_dict.keys())

    # FusedWorker_Actor_Critic
    class_name_renamed = "_".join([FusedWorkerCLSName] + cls_names)

    class FusedWorker(Worker):
        def __init__(self, *args, **kwargs):
            super().__init__(*args, **kwargs)
            self.cls_names = cls_names
            self.raw_cls_dict = raw_cls_dict
            self.init_args_dict = init_args_dict
            self.init_kwargs_dict = init_kwargs_dict

            for cls_name, udc, ud_args, ud_kwargs in zip(
                self.cls_names,
                self.raw_cls_dict.values(),
                self.init_args_dict.values(),
                self.init_kwargs_dict.values(),
                strict=True,
            ):
                with temp_env_var("DISABLE_WORKER_INIT", "1"):
                    udc._get_ray_actor_cls_name = lambda x, name_renamed=class_name_renamed: name_renamed
                    udc._get_ray_method_prefix = lambda x, name_prefixed=cls_name: f"{name_prefixed}_"
                    # cls_name = "actor", "critic", udc = ActorWorker, CriticWorker
                    self.fused_worker_dict[cls_name] = udc(*ud_args, **ud_kwargs)
                    setattr(self, cls_name, self.fused_worker_dict[cls_name])

            # injecting fused_worker to each sub worker so they can be aware of existence of each other
            for _, worker in self.fused_worker_dict.items():
                setattr(worker, Worker.fused_worker_attr_name, self.fused_worker_dict)

        def _fuw_execute(self, method_name: str, *args, **kwargs):
            # for fused_worker, method_name is in a form of "{cls_name}_fwmn_{method_name}"
            # where fwmn stands "fused worker method name"
            names = method_name.split("_fwmn_")
            cls_name = names[0]
            method_name = names[1]

            assert cls_name in self.fused_worker_dict, (
                f"calling {cls_name}'s {method_name}, but {cls_name} not in fused_worker_dict"
            )
            udc_method = getattr(self.fused_worker_dict[cls_name], method_name)
            return udc_method(*args, **kwargs)

    renamed_fused_worker_cls = type(class_name_renamed, (FusedWorker,), {})
    renamed_fused_worker_cls.is_fused_worker = True
    renamed_fused_worker_cls.raw_cls_dict = raw_cls_dict

    return renamed_fused_worker_cls


def create_colocated_worker_cls_fused(class_dict: dict[str, RayClassWithInitArgs]):
    """
    This function returns a RayClassWithInitArgs instance of FusedWorker, which is an replacement
    of `create_colocated_worker_cls`. WorkerGroup constructed using this class will be a colocated
    WorkerGroup, which will be referenced as `ColocateWorkerGroup` below.

    `ColocateWorkerGroup.spawn(prefix_set)`
        returns a dict of WorkerGroup {"class_name": WorkerGroup}, WorkerGroup in this dict will
        have methods of underlying class `class_name` attached.

    `ColocateWorkerGroup.fuse(prefix_set)`
        After executing this function, `ColocateWorkerGroup.{class_name}` will return WorkerGroup
        with methods of underlying class `class_name` attached.
    """
    raw_colocated_worker_cls = create_colocated_worker_raw_cls(class_dict)

    remote_cls = ray.remote(raw_colocated_worker_cls)
    cia = RayClassWithInitArgs(cls=remote_cls)
    cia.fused_worker_used = True

    return cia