87 lines
3.1 KiB
Python
87 lines
3.1 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/v0.11.0/examples/pissa_finetuning/preprocess.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 LoraConfig, TaskType, get_peft_model
|
|
from transformers import AutoModelForCausalLM, AutoTokenizer
|
|
|
|
|
|
if TYPE_CHECKING:
|
|
from transformers import PreTrainedModel
|
|
|
|
|
|
def quantize_pissa(
|
|
model_name_or_path: str,
|
|
output_dir: str,
|
|
pissa_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 Principal Singular values and Singular vectors Adaptation (PiSSA)
|
|
Usage: python pissa_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")
|
|
|
|
lora_config = LoraConfig(
|
|
task_type=TaskType.CAUSAL_LM,
|
|
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="pissa" if pissa_iter == -1 else "pissa_niter_{}".format(pissa_iter),
|
|
)
|
|
|
|
# Init PiSSA model
|
|
peft_model = get_peft_model(model, lora_config)
|
|
pissa_dir = os.path.join(output_dir, "pissa_init")
|
|
|
|
# Save PiSSA model
|
|
setattr(peft_model.peft_config["default"], "init_lora_weights", True) # don't apply pissa again
|
|
peft_model.save_pretrained(pissa_dir, safe_serialization=save_safetensors)
|
|
print("Adapter weights saved in {}".format(pissa_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(pissa_dir))
|
|
print("finetuning_type: lora")
|
|
print("pissa_init: false")
|
|
print("pissa_convert: true")
|
|
print("- and optionally with:")
|
|
print("quantization_bit: 4")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
fire.Fire(quantize_pissa)
|