forked from p04798526/LLaMA-Factory-Mirror
83 lines
3.1 KiB
Python
83 lines
3.1 KiB
Python
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# coding=utf-8
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# Initializes LoRA weights with LoRA-fine-tuning-aware Quantization (LoftQ)
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# Usage: python loftq_init.py --model_name_or_path path_to_model --save_dir output_dir
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# Inspired by: https://github.com/huggingface/peft/blob/main/examples/loftq_finetuning/quantize_save_load.py
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import os
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from typing import TYPE_CHECKING, Optional
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import fire
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import torch
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import torch.nn as nn
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from peft import LoftQConfig, LoraConfig, TaskType, get_peft_model
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from transformers import AutoModelForCausalLM, AutoTokenizer
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if TYPE_CHECKING:
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from transformers import PreTrainedModel
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class Shell(nn.Module):
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def __init__(self, weight: torch.Tensor, bias: Optional[torch.Tensor] = None):
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super().__init__()
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self.weight = nn.Parameter(weight, requires_grad=False)
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if bias is not None:
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self.bias = nn.Parameter(bias, requires_grad=False)
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def unwrap_model(model: nn.Module, pattern=".base_layer") -> None:
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for name in {k.split(pattern)[0] for k, _ in model.named_modules() if pattern in k}:
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parent_name = ".".join(name.split(".")[:-1])
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child_name = name.split(".")[-1]
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parent_module = model.get_submodule(parent_name)
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child_module = getattr(parent_module, child_name)
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base_layer = getattr(child_module, "base_layer")
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weight = getattr(base_layer, "weight", None)
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bias = getattr(base_layer, "bias", None)
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setattr(parent_module, child_name, Shell(weight, bias))
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print("Model unwrapped.")
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def quantize_loftq(
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model_name_or_path: str,
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save_dir: str,
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loftq_bits: Optional[int] = 4,
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loftq_iter: Optional[int] = 1,
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lora_alpha: Optional[int] = None,
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lora_rank: Optional[int] = 16,
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lora_target: Optional[str] = "q_proj,v_proj",
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save_safetensors: Optional[bool] = False,
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):
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tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained(model_name_or_path, trust_remote_code=True, torch_dtype="auto")
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loftq_config = LoftQConfig(loftq_bits=loftq_bits, loftq_iter=loftq_iter)
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lora_config = LoraConfig(
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task_type=TaskType.CAUSAL_LM,
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inference_mode=True,
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r=lora_rank,
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lora_alpha=lora_alpha if lora_alpha is not None else lora_rank * 2,
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lora_dropout=0.1,
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target_modules=[name.strip() for name in lora_target.split(",")],
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init_lora_weights="loftq",
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loftq_config=loftq_config,
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)
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# Init LoftQ model
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lora_model = get_peft_model(model, lora_config)
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base_model: "PreTrainedModel" = lora_model.get_base_model()
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# Save LoftQ model
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setattr(lora_model.base_model.peft_config["default"], "base_model_name_or_path", save_dir)
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setattr(lora_model.base_model.peft_config["default"], "init_lora_weights", True)
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lora_model.save_pretrained(os.path.join(save_dir, "adapters"), safe_serialization=save_safetensors)
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# Save base model
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unwrap_model(base_model)
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base_model.save_pretrained(save_dir, safe_serialization=save_safetensors)
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tokenizer.save_pretrained(save_dir)
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if __name__ == "__main__":
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fire.Fire(quantize_loftq)
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