peft/examples/lorafa_finetune

LoRA-FA: Memory-efficient Low-rank Adaptation for Large Language Models Fine-tuning

Introduction

LoRA-FA is a noval Parameter-efficient Fine-tuning method, which freezes the projection down layer (matrix A) during LoRA training process and thus lead to less accelerator memory consumption by eliminating the need for storing the activations of input tensors (X). Furthermore, LoRA-FA narrows the gap between the update amount of pre-trained weights when using the low-rank fine-tuning method and the full fine-tuning method. In conclusion, LoRA-FA reduces the memory consumption and leads to superior performance compared to vanilla LoRA.

Quick start

import torch
from peft import LoraConfig, get_peft_model
from peft.optimizers import create_lorafa_optimizer
from transformers import AutoTokenizer, AutoModelForCausalLM, Trainer
from datasets import load_dataset

model = AutoModelForCausalLM.from_pretrained("meta-llama/Meta-Llama-3-8B-Instruct")
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3-8B-Instruct")
dataset = load_dataset("timdettmers/openassistant-guanaco", split="train")

lora_rank = 16
lora_alpha = 32

lora_config = LoraConfig(
    r=lora_rank,
    lora_alpha=lora_alpha,
    bias="none",
)
peft_model = get_peft_model(model, lora_config)
optimizer = create_lorafa_optimizer(
    model=peft_model,
    r=lora_rank,
    lora_alpha=lora_alpha,
    lr=7e-5,
)
# you can also use scheduler, we recommend get_cosine_schedule_with_warmup from transformers
# for better model performance
scheduler = None

trainer = transformers.Trainer(
    model=peft_model,
    train_dataset=dataset,
    dataset_text_field="text",
    max_seq_length=2048,
    processing_class=tokenizer,
    optimizers=(optimizer, None),
)
trainer.train()
peft_model.save_pretrained("lorafa-llama-3-8b-inst")

The only change in your code is to pass the LoRA-FA optimizer to the trainer (if training with trainer). Do not forget from peft.optimizers import create_lorafa_optimizer!

Example

In this dir, we also provide you a simple example for fine-tuning with LoRA-FA optimizer.

Run on CPU, single-accelerator or multi-accelerator

This 👇 by default will load the model in peft set up with LoRA config, and train the model with LoRA-FA optimizer.

0. CPU

You can simply run LoRA-FA as below:

python lorafa_finetuning.py --base_model_name_or_path meta-llama/Meta-Llama-3-8B --dataset_name_or_path meta-math/MetaMathQA-40K --output_dir path/to/output --lorafa

1. Single-accelerator

Run the finetuning script on 1 accelerator:

export CUDA_VISIBLE_DEVICES=0  # force to use CUDA GPU 0
export ZE_AFFINITY_MASK=0      # force to use Intel XPU 0

python lorafa_finetuning.py --base_model_name_or_path meta-llama/Meta-Llama-3-8B --dataset_name_or_path meta-math/MetaMathQA-40K --output_dir path/to/output --lorafa

2. Multi-accelerator

LoRA-FA can also be run on multi-accelerator, with 🤗 Accelerate:

export CUDA_VISIBLE_DEVICES=0,1,2,3 # force to use CUDA GPU 0,1,2,3
export ZE_AFFINITY_MASK=0,1,2,3     # force to use Intel XPU 0,1,2,3

accelerate launch lorafa_finetuning.py --base_model_name_or_path meta-llama/Meta-Llama-3-8B --dataset_name_or_path meta-math/MetaMathQA-40K --output_dir path/to/output --lorafa

The accelerate launch will automatically configure multi-accelerator for you. You can also utilize accelerate launch in single-accelerator scenario.

Use the model from 🤗

You can load and use the model as any other 🤗 models.

from transformers import AutoModel
model = AutoModel.from_pretrained("meta-llama/Llama-2-7b-chat-hf")

Best practice in fine-tuning Llama using LoRA-FA: the hyper-params

Sometimes, achieving optimal LoRA fine-tuning can be challenging due to the larger number of hyperparameters to consider compared to full fine-tuning. For instance, not only do we need to adjust the commonly used learning rate, but the ideal LoRA rank may also vary depending on the specific model and task. Additionally, there are other factors to consider, such as LoRA alpha and sequence length. To assist with this, we have created a repository of reproducible best practices in the LoRA-FA examples for reference. This resource showcases the optimal LoRA-FA fine-tuning hyperparameters for different models across various datasets. By doing so, we significantly reduce the time and effort spent on hyperparameter tuning, and it may also provide insights for tuning other training hyperparameters. We encourage you to experiment and fine-tune on your own downstream tasks as well.

LoRA-FA's advantages and limitations

By eliminating the activation of adapter A, LoRA-FA uses less memory for fine-tuning compared to LoRA. For instance, when fine-tuning Llama-2-7b-chat-hf with a batch size of 8 and a sequence length of 1024, LoRA-FA requires 36GB of memory to store activations. This allows it to run successfully on an 80GB accelerator. In contrast, LoRA requires at least 60GB of memory for activations, leading to an Out of Memory (OOM) error. Additionally, the memory consumption of LoRA-FA is not sensitive to the rank, allowing for performance improvements by increasing the LoRA rank without additional memory usage. LoRA-FA further narrows the performance gap with Full-FT by minimizing the discrepancy between the low-rank gradient and the full gradient, enabling it to achieve performance that is on par with or even superior to vanilla LoRA.

Despite its advantages, LoRA-FA is inherently limited by its low-rank approximation nature and potential issues with catastrophic forgetting. The gradient approximation can impact training throughput. Addressing these limitations, especially in terms of approximation accuracy and forgetting phenomena, presents a promising direction for future research.

Citation

@misc{zhang2023lorafamemoryefficientlowrankadaptation,
      title={LoRA-FA: Memory-efficient Low-rank Adaptation for Large Language Models Fine-tuning}, 
      author={Longteng Zhang and Lin Zhang and Shaohuai Shi and Xiaowen Chu and Bo Li},
      year={2023},
      eprint={2308.03303},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://huggingface.co/papers/2308.03303}, 
}