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# 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: str = "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
"""
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=[name.strip() for name in lora_target.split(",")],
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)
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