Fine-tuning/backend/app/config.py

import os
from functools import lru_cache
from pathlib import Path

from pydantic import Field, field_validator
from pydantic_settings import (
    BaseSettings,
    EnvSettingsSource,
    DotEnvSettingsSource,
    SettingsConfigDict,
)


class _CommaListMixin:
    """Mixin for handling comma-separated list values in env/dotenv sources."""

    def decode_complex_value(self, field_name, field, value):
        if field_name == "backend_cors_origins" and isinstance(value, str):
            return [v.strip() for v in value.split(",") if v.strip()]
        return super().decode_complex_value(field_name, field, value)


class DotEnvSourceWithCommaLists(_CommaListMixin, DotEnvSettingsSource):
    """Dotenv source that handles comma-separated lists."""


class EnvSettingsSourceWithCommaLists(_CommaListMixin, EnvSettingsSource):
    """Env source that handles comma-separated lists."""


class Settings(BaseSettings):
    model_config = SettingsConfigDict(
        env_file=str(Path(__file__).resolve().parent.parent / ".env"),
        env_file_encoding="utf-8",
        case_sensitive=False,
        extra="ignore",
    )

    @classmethod
    def settings_customise_sources(
        cls, settings_cls, init_settings, env_settings, dotenv_settings, file_secret_settings
    ):
        return (
            init_settings,
            DotEnvSourceWithCommaLists(settings_cls),
            EnvSettingsSourceWithCommaLists(settings_cls),
            file_secret_settings,
        )

    # --- 数据路径 ---
    data_dir: Path = Path("/root/Fine-tuning/backend/data")

    # --- HuggingFace / ModelScope ---
    hf_token: str = ""
    hf_endpoint: str = "https://huggingface.co"
    use_modelscope: bool = False
    modelscope_endpoint: str = "https://modelscope.cn"

    # --- GPU / 硬件 ---
    cuda_visible_devices: str = "3"
    inference_cuda_devices: str = "2,3"  # 推理使用的物理 GPU，逗号分隔（多卡 model parallelism）
    max_memory_per_gpu: str = "0"
    use_unsloth: bool = False

    # --- 后端 ---
    backend_host: str = "0.0.0.0"
    backend_port: int = 8000
    backend_env: str = "production"
    backend_log_level: str = "INFO"
    backend_cors_origins: list[str] = ["http://192.168.91.253:5173"]

    # --- 数据库 ---
    database_url: str = "postgresql+asyncpg://finetune:finetune123@localhost:5432/finetuning"

    # --- 训练默认参数 ---
    default_peft_method: str = "lora"
    default_epochs: int = 3
    default_batch_size: int = 4
    default_gradient_accumulation: int = 4
    default_lr: float = 2e-4
    default_max_seq_length: int = 2048
    default_warmup_ratio: float = 0.05
    default_save_strategy: str = "epoch"
    default_eval_strategy: str = "epoch"
    default_eval_steps: int = 100

    # --- LoRA ---
    lora_r: int = 16
    lora_alpha: int = 32
    lora_dropout: float = 0.05
    lora_target_modules: str = "all-linear"

    # --- QLoRA ---
    qlora_bits: int = 4
    qlora_type: str = "nf4"
    qlora_double_quant: bool = True

    # --- 上传限制 ---
    max_upload_size_mb: int = 500
    allowed_dataset_formats: str = "jsonl,csv,parquet,json"

    # --- 分布式计算节点 ---
    compute_node_host: str = ""  # 算力节点 IP，为空则本地执行
    compute_node_ssh_port: int = 22
    compute_node_ssh_user: str = "root"
    compute_node_ssh_password: str = ""  # SSH 密码（与密钥二选一）
    compute_node_ssh_key: str = ""  # SSH 私钥路径
    compute_node_docker_container: str = "finetune-trainer"  # 算力节点上的训练容器名
    compute_node_python: str = "/opt/conda/bin/python"
    compute_node_workdir: str = "/root/Fine-tuning/backend"
    compute_node_remote_data_dir: str = "/root/Fine-tuning/backend/data"
    compute_node_remote_env: str = "production"
    compute_node_ssh_timeout: int = 300  # SSH 命令超时（秒）

    # --- 标注平台 ---
    annotation_platform_base_url: str = "http://192.168.92.61:9003"
    annotation_platform_app_id: str = "nlKLQJdJK3f5ub7UDfQ_E71z2Lo3YSQx"
    annotation_platform_app_secret: str = "wh0HU_9T83rYMjfLFToNxFOKcrk_8H7Ba_27nNGlPqtTf9ROCytsOgp2ue0ol5mm"

    # --- SSO 统一认证 ---
    sso_base_url: str = "http://192.168.92.61:8200"
    sso_client_id: str = "hmDeOtXZVbeo2AZ-x58yPssZLg4Tcb1W"
    sso_client_secret: str = "pj9UirhGUFPsFnCizCz-Qo1dOGi3kxRIrDKKmJZu2aRCPgtTogTubDRW1weM4KNL"
    sso_redirect_uri: str = "http://192.168.92.151:3000/auth/callback"
    sso_frontend_url: str = "http://192.168.92.151:3000"
    sso_scope: str = "email"
    sso_logout_redirect_url: str = "http://192.168.92.61:9200/login"
    jwt_secret_key: str = "change-me-in-production-use-a-long-random-string"
    jwt_algorithm: str = "HS256"
    jwt_access_expire_minutes: int = 20
    jwt_refresh_expire_hours: int = 24

    @field_validator("backend_cors_origins", mode="before")
    @classmethod
    def parse_cors_origins(cls, v):
        if isinstance(v, str):
            return [origin.strip() for origin in v.split(",") if origin.strip()]
        return v

    @property
    def models_dir(self) -> Path:
        return self.data_dir / "models"

    @property
    def adapters_dir(self) -> Path:
        return self.data_dir / "adapters"

    @property
    def uploads_dir(self) -> Path:
        return self.data_dir / "uploads"

    @property
    def processed_dir(self) -> Path:
        return self.data_dir / "processed"

    @property
    def use_remote_compute(self) -> bool:
        """是否启用远程算力节点。"""
        return bool(self.compute_node_host)

    def ensure_dirs(self) -> None:
        self.data_dir.mkdir(parents=True, exist_ok=True)
        for d in [self.models_dir, self.adapters_dir, self.uploads_dir, self.processed_dir]:
            d.mkdir(parents=True, exist_ok=True)


@lru_cache
def get_settings() -> Settings:
    settings = Settings()
    settings.ensure_dirs()
    # 设置 HF 环境变量
    if settings.hf_token:
        os.environ["HF_TOKEN"] = settings.hf_token
    os.environ["HF_ENDPOINT"] = settings.hf_endpoint
    if settings.cuda_visible_devices:
        os.environ["CUDA_VISIBLE_DEVICES"] = settings.cuda_visible_devices
    return settings