DoRA: Weight-Decomposed Low-Rank Adaptation
DoRA enhances LoRA's learning capacity via weight decomposition, outperforming LoRA on multiple tasks.
Key Findings
Methodology
DoRA decomposes pre-trained weights into magnitude and direction components for fine-tuning, specifically using LoRA for directional updates to efficiently minimize trainable parameters. This method enhances LoRA's learning capacity and training stability without additional inference overhead.
Key Results
- On LLaMA-7B, DoRA improved by 3.7% over LoRA on commonsense reasoning tasks, and by 2.9% on LLaMA2-7B.
- In visual instruction tuning, DoRA improved by 0.6% over LoRA on LLaVA-7B.
- In image/video-text understanding, DoRA improved by 0.9% over LoRA on VL-BART.
Significance
DoRA reveals the fundamental differences between full fine-tuning and LoRA through weight decomposition analysis, proposing a novel low-rank adaptation method that enhances LoRA's learning capacity and stability. It consistently outperforms LoRA across various downstream tasks without increasing inference latency, impacting both academia and industry.
Technical Contribution
DoRA introduces weight decomposition analysis, decomposing pre-trained weights into magnitude and direction components for fine-tuning, significantly enhancing LoRA's learning capacity and stability, providing new theoretical guarantees and engineering possibilities.
Novelty
DoRA is the first method to reveal differences between full fine-tuning and LoRA through weight decomposition analysis, proposing a novel low-rank adaptation method that significantly improves LoRA's performance.
Limitations
- DoRA may still not fully match the performance of full fine-tuning in some complex tasks.
- The effectiveness of directional updates relies on LoRA.
Future Work
Future research can explore DoRA's applicability to more tasks and models, and further optimize its directional component update strategy.
AI Executive Summary
In machine learning, parameter-efficient fine-tuning methods like LoRA are popular for avoiding additional inference costs, yet they still lag behind full fine-tuning in accuracy. This paper introduces a novel weight decomposition analysis to uncover the fundamental differences between full fine-tuning and LoRA.
DoRA decomposes pre-trained weights into magnitude and direction components for fine-tuning, specifically using LoRA for directional updates to efficiently minimize trainable parameters. Experimental results show that DoRA consistently outperforms LoRA across various downstream tasks without additional inference latency.
The introduction of DoRA not only enhances LoRA's learning capacity and stability but also provides new theoretical guarantees and engineering possibilities for parameter-efficient fine-tuning methods. Future research can explore DoRA's applicability to more tasks and models, and further optimize its directional component update strategy.
Deep Analysis
Background
As models and datasets grow in size, the cost of fine-tuning the entire model becomes prohibitively large. Parameter-efficient fine-tuning (PEFT) methods emerged to fine-tune pre-trained models with minimal parameters. LoRA is notably popular for its simplicity and efficacy but still lags behind full fine-tuning in performance.
Core Problem
LoRA lags behind full fine-tuning in performance, primarily due to the limited number of trainable parameters. This paper aims to reveal the fundamental differences between LoRA and full fine-tuning through weight decomposition analysis to enhance LoRA's learning capacity.
Innovation
DoRA introduces weight decomposition analysis, decomposing pre-trained weights into magnitude and direction components for fine-tuning, specifically using LoRA for directional updates. This method enhances LoRA's learning capacity and stability without additional inference overhead.
Methodology
- �� Decompose pre-trained weights into magnitude and direction components
- �� Use LoRA for directional updates
- �� Keep magnitude components trainable
- �� Validate DoRA's performance across various downstream tasks
Experiments
Experiments were conducted on LLaMA, LLaVA, and VL-BART, with tasks including commonsense reasoning, visual instruction tuning, and image/video-text understanding. Baselines included LoRA and other PEFT methods.
Results
DoRA improved by 3.7% over LoRA on commonsense reasoning tasks, by 0.6% on visual instruction tuning tasks, and by 0.9% on image/video-text understanding tasks.
Applications
DoRA can be applied to scenarios requiring efficient fine-tuning, such as natural language processing and multimodal tasks, especially in resource-constrained environments.
Limitations & Outlook
DoRA may still not fully match the performance of full fine-tuning in some complex tasks, and the effectiveness of directional updates relies on LoRA.
Plain Language Accessible to non-experts
Imagine you're in a kitchen cooking. Full fine-tuning is like redesigning the entire kitchen, while DoRA is like rearranging the positions and functions of existing appliances to improve efficiency. In this way, DoRA enhances performance without additional costs.
ELI14 Explained like you're 14
Hey there! Imagine you're playing a game, and your character has lots of skills. Full fine-tuning is like upgrading all the skills, while DoRA is like upgrading only the most important skills to become stronger! Isn't that cool?
Glossary
LoRA (Low-Rank Adaptation)
A parameter-efficient fine-tuning method using low-rank matrices to approximate weight changes.
Used to reduce the number of trainable parameters during fine-tuning.
Weight Decomposition
A method of decomposing pre-trained weights into magnitude and direction components.
Used to analyze differences between full fine-tuning and LoRA.
DoRA (Weight-Decomposed Low-Rank Adaptation)
A novel method that enhances LoRA performance through weight decomposition.
Outperforms LoRA across various downstream tasks.
Full Fine-Tuning
A method of fine-tuning all model parameters to adapt to downstream tasks.
Typically requires more computational resources compared to LoRA.
Parameter-Efficient Fine-Tuning (PEFT)
Methods that fine-tune models with minimal trainable parameters.
Used to reduce the cost of fine-tuning large models.
Open Questions Unanswered questions from this research
- 1 How can DoRA's directional component update strategy be optimized for more tasks and models?
- 2 What is the potential for performance improvement in more complex tasks with DoRA?
Applications
Immediate Applications
Natural Language Processing
Improve model fine-tuning efficiency and performance in resource-constrained environments using DoRA.
Long-term Vision
Multimodal Tasks
DoRA has the potential to significantly enhance performance in image, video, and text understanding tasks.
Abstract
Among the widely used parameter-efficient fine-tuning (PEFT) methods, LoRA and its variants have gained considerable popularity because of avoiding additional inference costs. However, there still often exists an accuracy gap between these methods and full fine-tuning (FT). In this work, we first introduce a novel weight decomposition analysis to investigate the inherent differences between FT and LoRA. Aiming to resemble the learning capacity of FT from the findings, we propose Weight-Decomposed Low-Rank Adaptation (DoRA). DoRA decomposes the pre-trained weight into two components, magnitude and direction, for fine-tuning, specifically employing LoRA for directional updates to efficiently minimize the number of trainable parameters. By employing \ours, we enhance both the learning capacity and training stability of LoRA while avoiding any additional inference overhead. \ours~consistently outperforms LoRA on fine-tuning LLaMA, LLaVA, and VL-BART on various downstream tasks, such as commonsense reasoning, visual instruction tuning, and image/video-text understanding. Code is available at https://github.com/NVlabs/DoRA.