Parameter-Efficient Orthogonal Finetuning via Butterfly Factorization

TL;DR

This paper introduces BOFT, a parameter-efficient orthogonal finetuning method using butterfly structures, enabling effective adaptation of large models to downstream tasks with fewer trainable parameters.

Contribution

The paper proposes a novel butterfly-based orthogonal parameterization for finetuning, subsuming OFT, and demonstrates its effectiveness across vision, language, and diffusion models.

Findings

BOFT reduces the number of trainable parameters significantly.

BOFT achieves comparable or better performance than existing methods.

Extensive experiments validate BOFT's efficiency and versatility.

Abstract

Large foundation models are becoming ubiquitous, but training them from scratch is prohibitively expensive. Thus, efficiently adapting these powerful models to downstream tasks is increasingly important. In this paper, we study a principled finetuning paradigm -- Orthogonal Finetuning (OFT) -- for downstream task adaptation. Despite demonstrating good generalizability, OFT still uses a fairly large number of trainable parameters due to the high dimensionality of orthogonal matrices. To address this, we start by examining OFT from an information transmission perspective, and then identify a few key desiderata that enable better parameter-efficiency. Inspired by how the Cooley-Tukey fast Fourier transform algorithm enables efficient information transmission, we propose an efficient orthogonal parameterization using butterfly structures. We apply this parameterization to OFT, creating a novel parameter-efficient finetuning method, called Orthogonal Butterfly (BOFT). By subsuming OFT as a special case, BOFT introduces a generalized orthogonal finetuning framework. Finally, we conduct an extensive empirical study of adapting large vision transformers, large language models, and text-to-image diffusion models to various downstream tasks in vision and language.