import asyncio import json from datetime import datetime, timezone from enum import Enum from typing import Any, Callable, Coroutine, Optional from pydantic import BaseModel, Field from app.core.logging import logger class JobStatus(str, Enum): PENDING = "pending" QUEUED = "queued" PREPROCESSING = "preprocessing" TRAINING = "training" COMPLETED = "completed" EVALUATING = "evaluating" EVALUATION_DONE = "evaluation_done" FAILED = "failed" CANCELLED = "cancelled" @property def is_terminal(self) -> bool: return self in (self.COMPLETED, self.FAILED, self.CANCELLED, self.EVALUATION_DONE) class TrainingJob(BaseModel): id: str model_id: str model_type: str peft_method: str dataset_id: str config: dict = Field(default_factory=dict) status: JobStatus = JobStatus.PENDING progress: float = 0.0 current_epoch: int = 0 current_step: int = 0 total_steps: int = 0 loss: float | None = None adapter_path: str | None = None error_message: str | None = None created_at: str = Field(default_factory=lambda: datetime.now(timezone.utc).isoformat()) started_at: str | None = None finished_at: str | None = None class JobQueue: """异步任务队列，支持取消和并发控制。""" def __init__(self, max_concurrent: int = 2): self._queue: asyncio.Queue[str] = asyncio.Queue() self._jobs: dict[str, TrainingJob] = {} self._cancel_events: dict[str, asyncio.Event] = {} self._callbacks: list[Callable[[TrainingJob], Coroutine[Any, Any, None]]] = [] self._max_concurrent = max_concurrent self._workers: list[asyncio.Task] = [] self._running = False async def start(self): """启动后台 worker。""" if self._running: return self._running = True for _ in range(self._max_concurrent): worker = asyncio.create_task(self._worker_loop()) self._workers.append(worker) logger.info(f"JobQueue started with {self._max_concurrent} workers") async def stop(self): """停止所有 worker。""" self._running = False for event in self._cancel_events.values(): event.set() for worker in self._workers: worker.cancel() self._workers.clear() logger.info("JobQueue stopped") async def enqueue(self, job_id: str, job: TrainingJob): """将任务加入队列。""" self._jobs[job_id] = job self._cancel_events[job_id] = asyncio.Event() await self._queue.put(job_id) logger.info(f"Job {job_id} enqueued") async def dequeue(self) -> str: """从队列中取出任务 ID。""" return await self._queue.get() def mark_done(self, job_id: str): """标记任务完成。""" self._queue.task_done() self._cancel_events.pop(job_id, None) def get_job(self, job_id: str) -> Optional[TrainingJob]: return self._jobs.get(job_id) def update_job(self, job_id: str, **kwargs): if job_id in self._jobs: job = self._jobs[job_id] for key, val in kwargs.items(): if hasattr(job, key): setattr(job, key, val) def is_cancelled(self, job_id: str) -> bool: event = self._cancel_events.get(job_id) return event is not None and event.is_set() async def cancel(self, job_id: str): """取消任务。""" if job_id in self._cancel_events: self._cancel_events[job_id].set() self.update_job(job_id, status=JobStatus.CANCELLED) await self._notify_callbacks() logger.info(f"Job {job_id} cancelled") def register_callback(self, callback: Callable[[TrainingJob], Coroutine[Any, Any, None]]): """注册状态变更回调（用于更新数据库等）。""" self._callbacks.append(callback) async def _notify_callbacks(self): for cb in self._callbacks: try: for job in self._jobs.values(): await cb(job) except Exception as e: logger.error(f"JobQueue callback error: {e}") async def _worker_loop(self): """worker 循环：不断从队列取任务并执行。""" while self._running: try: job_id = await asyncio.wait_for(self._queue.get(), timeout=1.0) except asyncio.TimeoutError: continue try: await self._run_job(job_id) except Exception as e: logger.error(f"Job {job_id} failed: {e}") self.update_job(job_id, status=JobStatus.FAILED, error_message=str(e)) finally: self._queue.task_done() async def _run_job(self, job_id: str): """执行单个任务：预处理 → 训练 → 完成。""" job = self._jobs.get(job_id) if not job: logger.warning(f"Job {job_id} not found in queue, skipping") return logger.info(f"Job {job_id}: Starting execution (status=QUEUED)") self.update_job(job_id, status=JobStatus.QUEUED) await self._notify_callbacks() if self.is_cancelled(job_id): return self.update_job(job_id, status=JobStatus.PREPROCESSING, started_at=datetime.now(timezone.utc).isoformat()) await self._notify_callbacks() if self.is_cancelled(job_id): return try: config = job.config model_id = job.model_id model_type = job.model_type peft_method = job.peft_method dataset_id = config.get("dataset_id", job.dataset_id) from app.config import get_settings settings = get_settings() # 查找数据集文件路径 dataset_path = await self._lookup_dataset_db(dataset_id) if not dataset_path: dataset_path = self._find_dataset_path(dataset_id) if not dataset_path: raise FileNotFoundError(f"Dataset not found: {dataset_id}") # 选择引擎 engine = self._get_engine(model_type) # 预处理数据集（始终在本地执行） processed_path = str(settings.processed_dir / f"{job_id}_processed.jsonl") task_type = config.get("task_type", "sft") template = config.get("dataset_template", "alpaca") logger.info(f"Job {job_id}: Preprocessing dataset (task_type={task_type}, template={template})") await engine.preprocess_dataset(dataset_path, processed_path, task_type=task_type, template=template) logger.info(f"Job {job_id}: Preprocessing completed, output: {processed_path}") logger.info(f"Job {job_id}: Loading model {model_id} (peft={peft_method})") await engine.load_model(model_id, quantization="4bit" if peft_method == "qlora" else None) logger.info(f"Job {job_id}: Model loaded, building PEFT config") peft_config = engine.get_peft_config(peft_method, config) logger.info(f"Job {job_id}: PEFT config built, starting training...") self.update_job(job_id, status=JobStatus.TRAINING) await self._notify_callbacks() logger.info(f"Job {job_id}: Calling engine.train()...") adapter_path = await engine.train( job_id=job_id, dataset_path=processed_path, peft_config=peft_config, training_args=config, ) self.update_job(job_id, status=JobStatus.COMPLETED, adapter_path=adapter_path) await self._notify_callbacks() logger.info(f"Job {job_id} completed successfully") except asyncio.CancelledError: self.update_job(job_id, status=JobStatus.CANCELLED) await self._notify_callbacks() except Exception as e: logger.error(f"Job {job_id} failed: {e}") self.update_job(job_id, status=JobStatus.FAILED, error_message=str(e)) await self._notify_callbacks() def _find_dataset_path(self, dataset_id: str) -> str | None: """根据 dataset_id 查找文件路径（数据库或 uploads 目录）。""" from app.config import get_settings from pathlib import Path settings = get_settings() # 尝试从 uploads 目录查找 upload_path = settings.uploads_dir / dataset_id if upload_path.exists(): return str(upload_path) # 如果 dataset_id 本身是路径 if Path(dataset_id).exists(): return dataset_id return None async def _lookup_dataset_db(self, dataset_id: str) -> str | None: """从数据库查找数据集路径。""" from app.core.db import async_session, DatasetRecord from sqlalchemy import select async with async_session() as session: result = await session.execute(select(DatasetRecord).where( (DatasetRecord.id == dataset_id) | (DatasetRecord.name == dataset_id) )) record = result.scalar_one_or_none() if record: return record.file_path return None def _get_engine(self, model_type: str): """根据模型类型选择训练引擎。""" if model_type == "vision": from app.engines.vision_engine import vision_engine return vision_engine elif model_type == "multimodal": from app.engines.multimodal_engine import multimodal_engine return multimodal_engine else: from app.engines.text_engine import text_engine return text_engine @property def jobs(self) -> dict[str, TrainingJob]: return dict(self._jobs) # 全局单例 job_queue = JobQueue(max_concurrent=2)