Fine-tuning/backend/app/peft/__init__.py

from typing import Any


def build_lora_config(params: dict[str, Any]):
    """返回实际的 peft.LoraConfig 对象。"""
    from peft import LoraConfig, TaskType

    target_modules = params.get("lora_target_modules", "all-linear")
    if isinstance(target_modules, str):
        if target_modules == "all-linear":
            target_modules = ["linear", "lm_head", "q_proj", "v_proj", "k_proj", "o_proj"]

    return LoraConfig(
        r=params.get("lora_r", 16),
        lora_alpha=params.get("lora_alpha", 32),
        lora_dropout=params.get("lora_dropout", 0.05),
        target_modules=target_modules,
        task_type=TaskType.CAUSAL_LM,
    )


def build_qlora_config(params: dict[str, Any]):
    """返回 (bitsandbytes_config, peft.LoraConfig) 二元组。"""
    from peft import LoraConfig, TaskType
    from transformers import BitsAndBytesConfig
    import torch

    bnb_params = BitsAndBytesConfig(
        load_in_4bit=params.get("qlora_bits", 4) == 4,
        load_in_8bit=params.get("qlora_bits", 4) == 8,
        bnb_4bit_quant_type=params.get("qlora_type", "nf4"),
        bnb_4bit_use_double_quant=params.get("qlora_double_quant", True),
        bnb_4bit_compute_dtype=torch.float16,
    )

    target_modules = params.get("lora_target_modules", "all-linear")
    if isinstance(target_modules, str) and target_modules == "all-linear":
        target_modules = ["linear", "lm_head", "q_proj", "v_proj", "k_proj", "o_proj"]

    lora_cfg = LoraConfig(
        r=params.get("lora_r", 16),
        lora_alpha=params.get("lora_alpha", 32),
        lora_dropout=params.get("lora_dropout", 0.05),
        target_modules=target_modules,
        task_type=TaskType.CAUSAL_LM,
    )

    return bnb_params, lora_cfg


def build_adalora_config(params: dict[str, Any]):
    """返回实际的 peft.AdaLoraConfig 对象。"""
    from peft import AdaLoraConfig, TaskType

    return AdaLoraConfig(
        init_r=params.get("adalora_init_r", 8),
        target_r=params.get("adalora_target_r", 16),
        beta1=params.get("adalora_beta1", 0.85),
        beta2=params.get("adalora_beta2", 0.85),
        task_type=TaskType.CAUSAL_LM,
    )