Fine-tuning/backend/app/services/eval_service.py

import json
import uuid
from datetime import datetime
from typing import Any

from app.config import get_settings
from app.core.db import async_session, EvalResultModel
from app.core.logging import logger
from app.core.remote_executor import ssh_exec
from sqlalchemy import select

settings = get_settings()


async def run_evaluation(job_id: str, config: dict[str, Any]) -> dict[str, Any]:
    """在已训练的 adapter 上运行评估（perplexity）。"""
    eval_id = str(uuid.uuid4())

    # 远程训练模式：把评估任务也发到远程容器执行
    if settings.use_remote_compute:
        logger.info(f"Running remote evaluation for job {job_id}")
        return await _run_remote_evaluation(eval_id, job_id)

    adapter_path = settings.adapters_dir / job_id

    if not adapter_path.exists():
        return {"id": eval_id, "job_id": job_id, "metrics": {}, "created_at": "", "error": "Adapter not found"}

    try:
        import torch
        from transformers import AutoModelForCausalLM, AutoTokenizer

        # 加载 base model + adapter
        model = AutoModelForCausalLM.from_pretrained(adapter_path, torch_dtype=torch.float16, device_map="auto")
        tokenizer = AutoTokenizer.from_pretrained(adapter_path, trust_remote_code=True)

        # 加载评估数据
        async with async_session() as session:
            from app.core.db import TrainingJobModel
            result = await session.execute(select(TrainingJobModel).where(TrainingJobModel.id == job_id))
            record = result.scalar_one_or_none()

        if record:
            dataset_path = record.dataset_id

        metrics = {}
        model.eval()

        # 计算 perplexity（使用 adapter 自身的数据或默认样例）
        sample_texts = [
            "The quick brown fox jumps over the lazy dog.",
            "Hello, how are you doing today?",
        ]
        losses = []
        with torch.no_grad():
            for text in sample_texts:
                inputs = tokenizer(text, return_tensors="pt").to(model.device)
                outputs = model(**inputs, labels=inputs["input_ids"])
                losses.append(outputs.loss.item())

        avg_loss = sum(losses) / len(losses) if losses else 0
        perplexity = torch.exp(torch.tensor(avg_loss)).item() if avg_loss > 0 else 0

        metrics = {
            "eval_loss": round(avg_loss, 4),
            "perplexity": round(perplexity, 2),
            "num_samples": len(sample_texts),
        }

        # 保存结果
        eval_record = EvalResultModel(
            id=eval_id,
            job_id=job_id,
            metrics=json.dumps(metrics),
            created_at=datetime.utcnow(),
        )
        async with async_session() as session:
            session.add(eval_record)
            await session.commit()

        logger.info(f"Evaluation completed for job {job_id}: {metrics}")
        return {"id": eval_id, "job_id": job_id, "metrics": metrics, "created_at": eval_record.created_at.isoformat()}

    except Exception as e:
        logger.error(f"Evaluation failed for job {job_id}: {e}")
        return {"id": eval_id, "job_id": job_id, "metrics": {}, "created_at": "", "error": str(e)}


async def _run_remote_evaluation(eval_id: str, job_id: str) -> dict[str, Any]:
    """通过 SSH 在远程容器里执行评估。"""
    remote_cmd = (
        f"docker exec "
        f"-e MACA_MPS_MODE=1 "
        f"-e CUDA_VISIBLE_DEVICES=2,3 "
        f"-w {settings.compute_node_workdir} "
        f"{settings.compute_node_docker_container} "
        f"{settings.compute_node_python} -c \""
        "import asyncio, json; "
        "from app.core.remote_eval import run_remote_eval; "
        f"result = asyncio.run(run_remote_eval('{job_id}')); "
        "print(json.dumps(result, ensure_ascii=False))\" 2>&1"
    )

    code, stdout, stderr = ssh_exec(remote_cmd, timeout=300)

    if code != 0:
        logger.error(f"Remote evaluation failed: {stderr}")
        return {"id": eval_id, "job_id": job_id, "metrics": {}, "created_at": "", "error": stderr.strip()}

    # 提取最后一行 JSON
    for line in reversed(stdout.strip().split("\n")):
        line = line.strip()
        if line.startswith("{"):
            try:
                result = json.loads(line)
                # 保存结果到本地数据库
                eval_record = EvalResultModel(
                    id=eval_id,
                    job_id=job_id,
                    metrics=json.dumps(result.get("metrics", {})),
                    created_at=datetime.utcnow(),
                )
                async with async_session() as session:
                    session.add(eval_record)
                    await session.commit()
                return {"id": eval_id, "job_id": job_id, "metrics": result.get("metrics", {}),
                        "created_at": eval_record.created_at.isoformat()}
            except json.JSONDecodeError:
                continue

    return {"id": eval_id, "job_id": job_id, "metrics": {}, "created_at": "", "error": f"Invalid response: {stdout[:500]}"}


async def get_evaluation_results(eval_id: str) -> dict[str, Any]:
    """获取已完成评估的结果。"""
    async with async_session() as session:
        result = await session.execute(select(EvalResultModel).where(EvalResultModel.id == eval_id))
        record = result.scalar_one_or_none()
        if record:
            return {
                "id": record.id,
                "job_id": record.job_id,
                "metrics": json.loads(record.metrics) if record.metrics else {},
                "created_at": record.created_at.isoformat(),
            }
    return {"id": eval_id, "job_id": "", "metrics": {}, "created_at": ""}