LoRA Done RITE: Robust Invariant Transformation Equilibration for LoRA Optimization

• URL: http://arxiv.org/abs/2410.20625v1
• Date: Sun, 27 Oct 2024 22:57:12 GMT
• Title: LoRA Done RITE: Robust Invariant Transformation Equilibration for LoRA Optimization
• Authors: Jui-Nan Yen, Si Si, Zhao Meng, Felix Yu, Sai Surya Duvvuri, Inderjit S. Dhillon, Cho-Jui Hsieh, Sanjiv Kumar,
• Abstract summary: Low-rank adaption (LoRA) is a widely used parameter-efficient finetuning method for LLM that reduces memory requirements. This paper introduces LoRA-RITE, a novel adaptive matrix preconditioning method for LoRA optimization.
• Score: 78.93425154518705
• License:
• Abstract: Low-rank adaption (LoRA) is a widely used parameter-efficient finetuning method for LLM that reduces memory requirements. However, current LoRA optimizers lack transformation invariance, meaning the actual updates to the weights depends on how the two LoRA factors are scaled or rotated. This deficiency leads to inefficient learning and sub-optimal solutions in practice. This paper introduces LoRA-RITE, a novel adaptive matrix preconditioning method for LoRA optimization, which can achieve transformation invariance and remain computationally efficient. We provide theoretical analysis to demonstrate the benefit of our method and conduct experiments on various LLM tasks with different models including Gemma 2B, 7B, and mT5-XXL. The results demonstrate consistent improvements against existing optimizers. For example, replacing Adam with LoRA-RITE during LoRA fine-tuning of Gemma-2B yielded 4.6\% accuracy gain on Super-Natural Instructions and 3.5\% accuracy gain across other four LLM benchmarks (HellaSwag, ArcChallenge, GSM8K, OpenBookQA).

Related papers

• Less is More: Extreme Gradient Boost Rank-1 Adaption for Efficient Finetuning of LLMs [75.11449420928139]
  Fine-tuning Large Language Models (LLMs) has become a crucial technique for adapting pre-trained models to downstream tasks. Low-Rank Adaptation (LoRA) has emerged as a promising solution, but there exists a gap between the practical performance of low-rank adaptations and its theoretical optimum. We propose eXtreme Gradient Boosting LoRA, a novel framework that bridges this gap by leveraging the power of ensemble learning.
  arXiv  Detail & Related papers  (2024-10-25T17:07:13Z)
• Randomized Asymmetric Chain of LoRA: The First Meaningful Theoretical Framework for Low-Rank Adaptation [58.288682735160585]
  Low-Rank Adaptation (LoRA) is a popular technique for finetuning models. LoRA often under performs when compared to full- parameter fine-tuning. We present a framework that rigorously analyzes the adaptation rates of LoRA methods.
  arXiv  Detail & Related papers  (2024-10-10T18:51:53Z)
• LoRA-Pro: Are Low-Rank Adapters Properly Optimized? [121.0693322732454]
  Low-rank adaptation, also known as LoRA, has emerged as a prominent method for parameter-efficient fine-tuning of foundation models. Despite its computational efficiency, LoRA still yields inferior performance compared to full fine-tuning. We introduce LoRA-Pro, a method that enhances LoRA's performance by strategically adjusting the gradients of low-rank matrices.
  arXiv  Detail & Related papers  (2024-07-25T17:57:12Z)
• RoLoRA: Fine-tuning Rotated Outlier-free LLMs for Effective Weight-Activation Quantization [38.23587031169402]
  We propose RoLoRA, the first LoRA-based scheme for effective weight-activation quantization. We evaluate RoLoRA across LLaMA2-7B/13B, LLaMA3-8B models, achieving up to 29.5% absolute accuracy gain of 4-bit weight-activation quantized LLaMA2- 13B.
  arXiv  Detail & Related papers  (2024-07-10T20:52:18Z)
• LoRA-GA: Low-Rank Adaptation with Gradient Approximation [5.685201910521295]
  Fine-tuning large-scale pretrained models is prohibitively expensive in terms of computational and memory costs. LoRA offers a cost-effective alternative by fine-tuning an auxiliary low-rank model that has significantly fewer parameters. LoRA converges at a considerably slower rate compared to full fine-tuning, leading to increased overall compute and often worse test performance.
  arXiv  Detail & Related papers  (2024-07-06T08:37:21Z)
• LoRA-XS: Low-Rank Adaptation with Extremely Small Number of Parameters [11.23006032094776]
  We introduce LoRA-XS, a novel low-rank adaptation method that considerably reduces the trainable parameters while showing superior or competitive performance. LoRA-XS achieves a remarkable reduction of trainable parameters by over 100x in 7B models compared to LoRA.
  arXiv  Detail & Related papers  (2024-05-27T19:07:13Z)
• DoRA: Weight-Decomposed Low-Rank Adaptation [57.68678247436207]
  We 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.
  arXiv  Detail & Related papers  (2024-02-14T17:59:34Z)
• Chain of LoRA: Efficient Fine-tuning of Language Models via Residual Learning [31.036465632204663]
  We introduce Chain of LoRA, an iterative optimization framework inspired by the Frank-Wolfe algorithm. We demonstrate that COLA can consistently outperform LoRA without additional computational or memory costs.
  arXiv  Detail & Related papers  (2024-01-08T14:26:49Z)
• LoRAPrune: Structured Pruning Meets Low-Rank Parameter-Efficient Fine-Tuning [56.88751562302793]
  Low-rank adaption (LoRA) has emerged to fine-tune large language models (LLMs) LoRAPrune is a new framework that delivers an accurate structured pruned model in a highly memory-efficient manner. LoRAPrune achieves a reduction in perplexity by 4.81 on WikiText2 and 3.46 on PTB, while also decreasing memory usage by 52.6%.
  arXiv  Detail & Related papers  (2023-05-28T15:15:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.