forked from p04798526/LLaMA-Factory-Mirror
90 lines
3.3 KiB
Python
90 lines
3.3 KiB
Python
# coding=utf-8
|
|
# Copyright 2024 HuggingFace Inc. and the LlamaFactory team.
|
|
#
|
|
# This code is based on the HuggingFace's PEFT library.
|
|
# https://github.com/huggingface/peft/blob/v0.10.0/examples/loftq_finetuning/quantize_save_load.py
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
import os
|
|
from typing import TYPE_CHECKING
|
|
|
|
import fire
|
|
from peft import LoftQConfig, LoraConfig, TaskType, get_peft_model
|
|
from transformers import AutoModelForCausalLM, AutoTokenizer
|
|
|
|
|
|
if TYPE_CHECKING:
|
|
from transformers import PreTrainedModel
|
|
|
|
|
|
def quantize_loftq(
|
|
model_name_or_path: str,
|
|
output_dir: str,
|
|
loftq_bits: int = 4,
|
|
loftq_iter: int = 4,
|
|
lora_alpha: int = None,
|
|
lora_rank: int = 16,
|
|
lora_dropout: float = 0,
|
|
lora_target: tuple = ("q_proj", "v_proj"),
|
|
save_safetensors: bool = True,
|
|
):
|
|
r"""
|
|
Initializes LoRA weights with LoRA-fine-tuning-aware Quantization (LoftQ)
|
|
Usage: python loftq_init.py --model_name_or_path path_to_model --output_dir output_dir
|
|
"""
|
|
if isinstance(lora_target, str):
|
|
lora_target = [name.strip() for name in lora_target.split(",")]
|
|
|
|
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=lora_dropout,
|
|
target_modules=lora_target,
|
|
init_lora_weights="loftq",
|
|
loftq_config=loftq_config,
|
|
)
|
|
|
|
# Init LoftQ model
|
|
print("Initializing LoftQ weights, it may be take several minutes, wait patiently.")
|
|
peft_model = get_peft_model(model, lora_config)
|
|
loftq_dir = os.path.join(output_dir, "loftq_init")
|
|
|
|
# Save LoftQ model
|
|
setattr(peft_model.peft_config["default"], "base_model_name_or_path", os.path.abspath(output_dir))
|
|
setattr(peft_model.peft_config["default"], "init_lora_weights", True) # don't apply loftq again
|
|
peft_model.save_pretrained(loftq_dir, safe_serialization=save_safetensors)
|
|
print("Adapter weights saved in {}".format(loftq_dir))
|
|
|
|
# Save base model
|
|
base_model: "PreTrainedModel" = peft_model.unload()
|
|
base_model.save_pretrained(output_dir, safe_serialization=save_safetensors)
|
|
tokenizer.save_pretrained(output_dir)
|
|
print("Model weights saved in {}".format(output_dir))
|
|
|
|
print("- Fine-tune this model with:")
|
|
print("model_name_or_path: {}".format(output_dir))
|
|
print("adapter_name_or_path: {}".format(loftq_dir))
|
|
print("finetuning_type: lora")
|
|
print("quantization_bit: {}".format(loftq_bits))
|
|
|
|
|
|
if __name__ == "__main__":
|
|
fire.Fire(quantize_loftq)
|