examples/olora_finetuning/README.md

OLoRA: Orthonormal Low Rank Adaptation of Large Language Models

Introduction

OLoRA is a novel approach that leverages orthonormal low rank adaptation through QR decomposition. Unlike the default LoRA implementation, OLoRA decomposes original weights into their $\mathbf{Q}$ and $\mathbf{R}$ parts, and then uses the first rank rows of $\mathbf{R}$ and the first rank columns of $\mathbf{Q}$ to initialize $\mathbf{A}$ and $\mathbf{B}$, respectively. This results in significantly faster convergence, more stable training, and superior performance.

Quick start

import torch
from peft import LoraConfig, get_peft_model
from transformers import AutoTokenizer, AutoModelForCausalLM
from trl import SFTConfig, SFTTrainer
from datasets import load_dataset

model = AutoModelForCausalLM.from_pretrained("facebook/opt-350m", torch_dtype=torch.bfloat16, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m")
dataset = load_dataset("imdb", split="train[:1%]")
lora_config = LoraConfig(
    init_lora_weights="olora"
)
peft_model = get_peft_model(model, lora_config)
training_args = SFTConfig(dataset_text_field="text", max_seq_length=128)
trainer = SFTTrainer(
    model=peft_model,
    train_dataset=dataset,
    processing_class=tokenizer,
)
trainer.train()
peft_model.save_pretrained("olora-opt-350m")

There is no additional change needed to your standard LoRA procedure, except for specifying init_lora_weights = "olora" option in your lora configuration.

Additionally you can refer to olora finetuning script. Run the script simply by running:

python3 examples/olora_finetuning/olora_finetuning.py --base_model facebook/opt-350m

OLoRA also supports quantization. To use 4-bit quantization try:

python3 examples/olora_finetuning/olora_finetuning.py --base_model facebook/opt-350m --quantize

or you can just pass a quantized model without the quantize flag.

If you want to run DDP by accelerate, please run accelerate config to set your ddp config, and run:

accelerate launch examples/olora_finetuning/olora_finetuning.py --base_model facebook/opt-350m

please add --device_map cpu if you want to run finetune on CPU.

If you want to train a quantized model like AWQ and GPTQ which do not support olora init method, please pass --init_lora_weights gaussian. For example:

python3 examples/olora_finetuning/olora_finetuning.py --base_model hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4 --init_lora_weights gaussian

Use the model

You can load and use the model as any other 🤗 PEFT model

from peft import PeftModel
model = AutoModelForCausalLM.from_pretrained("facebook/opt-350m")
tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m")
olora_model = PeftModel.from_pretrained(model, "olora-opt-350m")

OLoRA and LoRA

OLoRA differs from LoRA in that it mutates the original weights. To utilize multiple adapters simultaneously, you can leverage the path_initial_model_for_weight_conversion option. Below is a simple template illustrating how to convert OLoRA to conventional LoRA:

base_model = AutoModel.from_pretrained("facebook/opt-350m")
olora_config = LoraConfig(
    ...
    init_lora_weights = "olora" # Initialize the model with OLoRA
)
olora_model = get_peft_model(base_model, olora_config)
init_path = <path-to-untrained-olora-model>
olora_model.save_pretrained(init_path) # Save the model *before* performing any training

# Train the model
train(olora_model) # Your training loop

#Save the model after training
olora_model.save_pretrained(output_dir, path_initial_model_for_weight_conversion=init_path) 

After completing training, you can save and convert your OLoRA model to a conventional LoRA model by setting path_initial_model_for_weight_conversion to init_path, that is the path of your untrained OLoRA model. This conversion enables you to use multiple adapters with your LoRA model. Note that this conversion is not supported if rslora is used in combination with rank_pattern or alpha_pattern.

Citation

@misc{büyükakyüz2024olora,
      title={OLoRA: Orthonormal Low-Rank Adaptation of Large Language Models}, 
      author={Kerim Büyükakyüz},
      year={2024},
      eprint={2406.01775},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}