Fine-tuning/backend/app/core/job_queue.py

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:
            return

        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")

            await engine.preprocess_dataset(dataset_path, processed_path, task_type=task_type, template=template)

            # 判断是否远程执行
            if settings.use_remote_compute:
                # 远程训练模式 — 数据集路径已由上面的代码查好
                if not dataset_path:
                    dataset_path = self._find_dataset_path(dataset_id)
                if not dataset_path:
                    raise FileNotFoundError(f"Dataset not found: {dataset_id}")

                self.update_job(job_id, status=JobStatus.TRAINING)
                await self._notify_callbacks()

                from app.core.remote_executor import run_training_remote, is_process_running
                pid = run_training_remote(job_id, model_id, model_type, dataset_path, config)

                if not pid:
                    raise RuntimeError("Failed to launch remote training")

                # 轮询共享日志文件解析进度
                await self._poll_remote_progress(job_id, pid)

                logger.info(f"Remote training launched for job {job_id}")
            else:
                # 本地训练模式
                await engine.load_model(model_id, quantization="4bit" if peft_method == "qlora" else None)
                peft_config = engine.get_peft_config(peft_method, config)

                self.update_job(job_id, status=JobStatus.TRAINING)
                await self._notify_callbacks()

                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

    async def _poll_remote_progress(self, job_id: str, pid: str):
        """通过 SSH 读取远程日志文件，解析训练进度（非阻塞）。"""
        from app.config import get_settings
        from app.core.websocket import send_progress, send_epoch_done, send_completed, send_error
        from app.core.remote_executor import ssh_exec, is_process_running

        settings = get_settings()
        remote_log = f"{settings.compute_node_remote_data_dir}/logs/{job_id}.jsonl"
        last_bytes = 0
        poll_interval = 5
        max_polls = 8640

        for _ in range(max_polls):
            if self.is_cancelled(job_id):
                _s = get_settings()
                await asyncio.to_thread(ssh_exec, f"docker exec {_s.compute_node_docker_container} bash -c 'kill {pid} 2>/dev/null'", timeout=10)
                self.update_job(job_id, status=JobStatus.CANCELLED)
                await self._notify_callbacks()
                await send_error(job_id, "Training cancelled")
                return

            # 检查进程是否还在运行（非阻塞）
            process_alive = await asyncio.to_thread(is_process_running, pid)

            # 通过 SSH 远程读取日志文件（非阻塞）
            cat_cmd = f"docker exec {settings.compute_node_docker_container} bash -c 'wc -c < {remote_log} 2>/dev/null || echo 0'"
            code, size_out, _ = await asyncio.to_thread(ssh_exec, cat_cmd, timeout=30)
            try:
                file_size = int(size_out.strip()) if code == 0 and size_out.strip() else 0
            except ValueError:
                file_size = 0

            if file_size > last_bytes:
                read_cmd = f"docker exec {settings.compute_node_docker_container} bash -c 'tail -c +{last_bytes + 1} {remote_log} 2>/dev/null'"
                code, log_content, _ = await asyncio.to_thread(ssh_exec, read_cmd, timeout=30)

                if code == 0 and log_content.strip():
                    for line in log_content.strip().split("\n"):
                        line = line.strip()
                        if not line:
                            continue
                        try:
                            entry = json.loads(line)
                        except json.JSONDecodeError:
                            continue

                        entry_type = entry.get("type")
                        if entry_type == "progress":
                            self.update_job(job_id,
                                            current_step=entry.get("step", 0),
                                            total_steps=entry.get("total_steps", 0),
                                            loss=entry.get("loss"),
                                            progress=round(entry.get("step", 0) / max(entry.get("total_steps", 1), 1) * 100, 1))
                            await self._notify_callbacks()
                            await send_progress(job_id, **{k: v for k, v in entry.items() if k != "type"})

                        elif entry_type == "epoch_begin":
                            self.update_job(job_id, current_epoch=entry.get("epoch", 0))
                            await self._notify_callbacks()

                        elif entry_type == "epoch_done":
                            await self._notify_callbacks()
                            await send_epoch_done(job_id, **{k: v for k, v in entry.items() if k not in ("type", "ts")})

                        elif entry_type == "completed":
                            adapter_path = entry.get("adapter_path", str(settings.adapters_dir / job_id))
                            self.update_job(job_id,
                                            status=JobStatus.COMPLETED,
                                            adapter_path=adapter_path,
                                            progress=100.0)
                            await self._notify_callbacks()
                            await send_completed(job_id, **{k: v for k, v in entry.items() if k not in ("type", "ts")})
                            return

                        elif entry_type == "error":
                            error_msg = entry.get("message", "Unknown error")
                            logger.error(f"Remote job {job_id} failed: {error_msg}")
                            self.update_job(job_id,
                                            status=JobStatus.FAILED,
                                            error_message=error_msg)
                            await self._notify_callbacks()
                            await send_error(job_id, error_msg)
                            return

                    last_bytes = file_size

            # 进程已退出但日志里没有 completed/error
            if not process_alive:
                await asyncio.sleep(2)
                if not await asyncio.to_thread(is_process_running, pid):
                    error_msg = f"Remote process exited unexpectedly (pid={pid})"
                    logger.error(f"Remote job {job_id} failed: {error_msg}")
                    self.update_job(job_id,
                                    status=JobStatus.FAILED,
                                    error_message=error_msg)
                    await self._notify_callbacks()
                    await send_error(job_id, error_msg)
                    return

            await asyncio.sleep(poll_interval)

        # 超时
        error_msg = "Remote training timed out"
        logger.error(f"Remote job {job_id} failed: {error_msg}")
        self.update_job(job_id, status=JobStatus.FAILED, error_message=error_msg)
        await self._notify_callbacks()
        await send_error(job_id, error_msg)

    @property
    def jobs(self) -> dict[str, TrainingJob]:
        return dict(self._jobs)


# 全局单例
job_queue = JobQueue(max_concurrent=2)