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