Files
AstrAI/astrai/inference/core/task.py
T
ViperEkura d4451f6afb fix : 并行训练 state_dict 收集与训练/推理并发缺陷
- FSDPExecutor: unwrap_model 返回全量 state_dict (state_dict_type FULL);use_orig_params=True
- DDPExecutor/BaseExecutor: unwrap_model 统一返回 model.module.state_dict() / model.state_dict()
- CheckpointCallback: 走 executor.unwrap_model 拿完整 state_dict
- strategy.py: 移除 FSDP/DDp 依赖;create_ref_model(model_fn, state_dict) 纯函数
- TrainContextBuilder: 传递 model_fn + executor 到 strategy
- GRPOStrategy.sync_ref_model: 通过 executor.unwrap_model 获取完整权重
- TaskManager.wait_for_tasks: 锁内检查队列,消除 clear/set 竞态
- ProtocolHandler: stop token 不再计入 completion_tokens(流式/非流式)
2026-05-29 21:12:52 +08:00

210 lines
6.3 KiB
Python

import logging
import threading
import time
import uuid
from collections import deque
from enum import Enum
from typing import Any, Callable, Deque, Dict, List, Optional
from astrai.tokenize.tokenizer import AutoTokenizer
logger = logging.getLogger(__name__)
STOP = object()
class TaskStatus(Enum):
"""Task lifecycle states."""
PENDING = "pending"
RUNNING = "running"
FINISHED = "finished"
ABORTED = "aborted"
class Task:
"""Single generation request: prompt, sampling params, output state."""
def __init__(
self,
task_id: str,
prompt_ids: List[int],
max_tokens: Optional[int] = None,
temperature: float = 1.0,
top_p: float = 1.0,
top_k: int = 50,
stream_callback: Optional[Callable[[str], None]] = None,
):
self.task_id = task_id
self.prompt_ids = prompt_ids
self.max_tokens = max_tokens
self.temperature = temperature
self.top_p = top_p
self.top_k = top_k
self.status = TaskStatus.PENDING
self.output_ids: List[int] = []
self.input_tokens: int = 0
self.output_tokens: int = 0
self.arrival_time = time.time()
self.finish_time: Optional[float] = None
self.stream_callback = stream_callback
@property
def next_pos(self) -> int:
return self.input_tokens + len(self.output_ids)
def is_finished(self, stop_ids: List[int]) -> bool:
if self.max_tokens is not None and self.output_tokens >= self.max_tokens:
return True
if self.output_ids and self.output_ids[-1] in stop_ids:
return True
return False
class TaskManager:
"""Thread-safe task queues and lifecycle transitions (no page ops)."""
def __init__(
self,
tokenizer: AutoTokenizer,
max_batch_size: int = 16,
max_seq_len: int = 8192,
max_prompt_len: int = 512,
):
self.tokenizer = tokenizer
self.max_batch_size = max_batch_size
self.max_seq_len = max_seq_len
self.max_prompt_len = max_prompt_len
self.waiting_queue: Deque[Task] = deque()
self.active_tasks: List[Task] = []
self._task_event = threading.Event()
self._lock = threading.Lock()
self._total_tasks = 0
self._total_tokens = 0
def add_task(
self,
prompt: str,
max_tokens: Optional[int] = None,
temperature: float = 1.0,
top_p: float = 1.0,
top_k: int = 50,
stream_callback: Optional[Callable[[str], None]] = None,
) -> str:
task_id = f"task_{int(time.time())}_{uuid.uuid4().hex[:8]}"
prompt_ids = self.tokenizer.encode(prompt)
if len(prompt_ids) > self.max_prompt_len:
prompt_ids = prompt_ids[-self.max_prompt_len :]
if len(prompt_ids) >= self.max_seq_len:
if stream_callback:
stream_callback(STOP)
return task_id
if max_tokens is None:
max_tokens = self.max_seq_len - len(prompt_ids)
else:
max_tokens = min(max_tokens, self.max_seq_len - len(prompt_ids))
task = Task(
task_id=task_id,
prompt_ids=prompt_ids,
max_tokens=max_tokens,
temperature=temperature,
top_p=top_p,
top_k=top_k,
stream_callback=stream_callback,
)
with self._lock:
self.waiting_queue.append(task)
self._total_tasks += 1
self._task_event.set()
return task_id
def remove_task(self, task_id: str) -> List[Task]:
with self._lock:
removed_active = [t for t in self.active_tasks if t.task_id == task_id]
self.waiting_queue = deque(
t for t in self.waiting_queue if t.task_id != task_id
)
self.active_tasks = [t for t in self.active_tasks if t.task_id != task_id]
return removed_active
def get_stats(self) -> Dict[str, Any]:
return {
"total_tasks": self._total_tasks,
"total_tokens": self._total_tokens,
"active_tasks": len(self.active_tasks),
"waiting_queue": len(self.waiting_queue),
}
def remove_finished_tasks(self, stop_ids: List[int]) -> List[Task]:
with self._lock:
finished = []
for task in self.active_tasks:
if task.status == TaskStatus.ABORTED:
task.finish_time = time.time()
finished.append(task)
elif task.is_finished(stop_ids):
task.status = TaskStatus.FINISHED
task.finish_time = time.time()
finished.append(task)
self._total_tokens += task.output_tokens
self.active_tasks = [
t
for t in self.active_tasks
if t.status not in (TaskStatus.FINISHED, TaskStatus.ABORTED)
]
return finished
def pull_candidates(self, n: int) -> List[Task]:
to_add: List[Task] = []
with self._lock:
take = min(n, len(self.waiting_queue))
for _ in range(take):
to_add.append(self.waiting_queue.popleft())
return to_add
def activate(self, task: Task):
task.status = TaskStatus.RUNNING
with self._lock:
self.active_tasks.append(task)
def return_to_waiting(self, tasks: List[Task]):
with self._lock:
for task in reversed(tasks):
self.waiting_queue.appendleft(task)
def has_work(self) -> bool:
return bool(self.active_tasks or self.waiting_queue)
def wait_for_tasks(self, timeout: float = 1.0):
with self._lock:
if self.waiting_queue or self.active_tasks:
return
self._task_event.clear()
self._task_event.wait(timeout=timeout)
def get_active_tasks(self) -> List[Task]:
with self._lock:
return list(self.active_tasks)
def get_waiting_tasks(self) -> List[Task]:
with self._lock:
return list(self.waiting_queue)
def clear_queues(self):
with self._lock:
self.waiting_queue.clear()
self.active_tasks.clear()
def wake(self):
self._task_event.set()