zhaoxi
457d12834f
通过 StandaloneProxy 适配层让 .remote() 调用在单机模式下透明降级为 asyncio 协程调用，7 个 Actor 和 workflow task 均可在纯 asyncio 环境运行， 启动快、资源占用低。分布式模式行为完全不变。 Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
129 lines
4.0 KiB
Python
129 lines
4.0 KiB
Python
# Copyright 2026 zhaoxi826
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import time
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from functools import lru_cache
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from typing import Any, Dict
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_STANDALONE = os.environ.get("KILOSTAR_MODE", "distributed") == "standalone"
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if not _STANDALONE:
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import ray
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class ActorList:
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"""属性式访问的简易容器，用 ``a.actor_name`` 取代 ``d["actor_name"]``。"""
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def __init__(self):
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super().__setattr__("dict", {})
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def __setattr__(self, key, value):
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self.dict[key] = value
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def __getattr__(self, key):
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if key in self.dict:
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return self.dict[key]
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raise AttributeError(f"ActorList 对象没有属性 '{key}'")
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def __delattr__(self, key):
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if key in self.dict:
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del self.dict[key]
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else:
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raise AttributeError(f"ActorList对象没有属性 '{key}'")
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# ─── Standalone Registry ───
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_standalone_registry: Dict[str, Any] = {}
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def register_standalone(name: str, instance: Any) -> None:
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"""注册一个单机模式下的 Actor 单例（已包装为 StandaloneProxy）。"""
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from kilostar.utils.standalone_proxy import StandaloneProxy
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_standalone_registry[name] = StandaloneProxy(instance)
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# ─── Distributed Mode Helpers ───
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if not _STANDALONE:
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@lru_cache(maxsize=128)
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def _get_cached_actor_handle(actor_name: str):
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"""缓存接口"""
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return ray.get_actor(actor_name, namespace="kilostar")
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def clear_actor_cache():
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"""清理接口"""
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_get_cached_actor_handle.cache_clear()
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def wait_for_actor(
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actor_name: str, *, timeout: float = 10.0, interval: float = 0.5
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):
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"""阻塞等待某个 actor 就绪，返回其句柄。"""
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deadline = time.monotonic() + max(timeout, 0.0)
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last_err: Exception | None = None
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while True:
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try:
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return _get_cached_actor_handle(actor_name)
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except Exception as e:
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last_err = e
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if time.monotonic() >= deadline:
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raise TimeoutError(
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f"等待 actor {actor_name!r} 就绪超时（{timeout}s）：{last_err}"
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) from last_err
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time.sleep(interval)
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else:
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def _get_cached_actor_handle(actor_name: str):
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raise RuntimeError("单机模式下不应调用 _get_cached_actor_handle")
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def clear_actor_cache():
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pass
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def wait_for_actor(actor_name: str, **kwargs):
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raise RuntimeError("单机模式下不应调用 wait_for_actor")
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# ─── 统一入口 ───
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def ray_actor_hook(*actor_names: str, timeout: float = 0.0, interval: float = 0.5):
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"""按名字批量取出 Actor 句柄，组装成一个 ActorList 返回。
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单机模式从 _standalone_registry 取，分布式模式走 ray.get_actor。
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"""
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actor_list = ActorList()
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if _STANDALONE:
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for name in actor_names:
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if name not in _standalone_registry:
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raise ValueError(
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f"Standalone registry: actor {name!r} not registered"
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)
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setattr(actor_list, name, _standalone_registry[name])
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return actor_list
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for actor_name in actor_names:
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if timeout > 0:
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handle = wait_for_actor(
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actor_name, timeout=timeout, interval=interval
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)
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else:
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handle = _get_cached_actor_handle(actor_name)
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setattr(actor_list, actor_name, handle)
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return actor_list
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