feat(standalone): 新增单机模式，KILOSTAR_MODE=standalone 时去掉 Ray 依赖
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>
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@@ -11,9 +11,15 @@
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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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import ray
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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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@@ -37,62 +43,80 @@ class ActorList:
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raise AttributeError(f"ActorList对象没有属性 '{key}'")
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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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# ─── Standalone Registry ───
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_standalone_registry: Dict[str, Any] = {}
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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 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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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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# ─── Distributed Mode Helpers ───
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用于"启动期 / ray task 入口刚拉起"这类场景——被依赖的 actor 可能还没注册。
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在 ``timeout`` 内按 ``interval`` 轮询 ``ray.get_actor``；拿到就立即返回，
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超时则抛带清晰上下文的 ``TimeoutError``（而不是裸 ``ValueError``）。
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Args:
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actor_name: actor 注册名
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timeout: 最长等待秒数；``<=0`` 表示只试一次（等价于直接取句柄）
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interval: 轮询间隔秒数
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if not _STANDALONE:
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Raises:
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TimeoutError: 超时仍未就绪。原始异常通过 ``raise ... from`` 链保留。
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"""
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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: # ray.get_actor 失败一般是 ValueError
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last_err = e
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# 失败不能让 lru_cache 留下脏数据（异常本身不会被缓存，
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# 但若底层换实现，这里清一次更稳妥）
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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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@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 Ray Actor 句柄，组装成一个 ``ActorList`` 返回。
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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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例：``actors = ray_actor_hook("postgres_database", "global_state_machine")``，
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随后即可用 ``actors.postgres_database`` 拿到对应句柄。
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Args:
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timeout: ``>0`` 时对每个 actor 走 ``wait_for_actor`` 等待就绪（启动期用）；
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缺省 ``0`` 保持原"快速失败"语义——actor 不在立即抛异常。
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interval: 等待轮询间隔，仅在 ``timeout>0`` 时生效。
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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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@@ -0,0 +1,86 @@
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"""KiloStar 单机模式适配层：用 asyncio 协程模拟 Ray Actor 接口。
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单机模式下，所有 Actor 退化为普通 Python 异步单例，通过 StandaloneProxy
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包装后暴露与 Ray Actor Handle 相同的 `.method.remote(args)` 调用接口，
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使上层代码在两种模式间无感切换。
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"""
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from __future__ import annotations
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import asyncio
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import os
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from typing import Any
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_STANDALONE = os.environ.get("KILOSTAR_MODE", "distributed") == "standalone"
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class _MethodProxy:
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"""包装单个方法，使 .remote(*args, **kwargs) 返回一个可 await 的 Task。"""
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__slots__ = ("_method",)
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def __init__(self, method: Any):
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self._method = method
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def remote(self, *args: Any, **kwargs: Any) -> asyncio.Task:
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async def _invoke():
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result = self._method(*args, **kwargs)
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if asyncio.iscoroutine(result):
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return await result
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return result
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return asyncio.ensure_future(_invoke())
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class StandaloneProxy:
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"""包装一个普通 Python 实例，模拟 Ray Actor Handle 的属性访问接口。
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用法：proxy.some_method.remote(x, y) → 等效于 await instance.some_method(x, y)
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"""
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__slots__ = ("_instance",)
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def __init__(self, instance: Any):
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object.__setattr__(self, "_instance", instance)
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def __getattr__(self, name: str) -> _MethodProxy:
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attr = getattr(object.__getattribute__(self, "_instance"), name)
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if callable(attr):
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return _MethodProxy(attr)
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return attr
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# ─── 条件装饰器 ───
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def actor_class(cls):
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"""条件装饰器：分布式模式 → @ray.remote，单机模式 → 原样返回类。"""
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if _STANDALONE:
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return cls
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import ray
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return ray.remote(cls)
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def remote_task(func):
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"""条件装饰器：分布式 → @ray.remote(func)，单机 → .remote() 转为 asyncio task。
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单机模式下返回一个 stub 对象，其 .remote() 方法把函数以协程方式调度到
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当前事件循环（workflow task 需要用 await 版本的 _entry，由调用方处理）。
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"""
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if _STANDALONE:
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class _TaskProxy:
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@staticmethod
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def remote(*args, **kwargs):
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async def _run():
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result = func(*args, **kwargs)
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if asyncio.iscoroutine(result):
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return await result
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return result
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return asyncio.ensure_future(_run())
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return _TaskProxy()
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import ray
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return ray.remote(func)
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