Training Neural Networks from Scratch with Parallel Low-Rank Adapters
Minyoung Huh, Brian Cheung, Jeremy Bernstein, Phillip Isola, Pulkit, Agrawal
TL;DR
This paper introduces LTE, a novel bi-level optimization algorithm that extends low-rank adaptation (LoRA) for parallel pre-training of neural networks, significantly reducing synchronization costs and demonstrating competitive results on vision transformers.
Contribution
The paper proposes LTE, enabling parallel low-rank adaptation during pre-training, addressing limitations of standard LoRA and improving scalability of deep learning models.
Findings
LTE achieves competitive pre-training performance.
Parallel training reduces synchronization overhead.
Effective on vision transformer models.
Abstract
The scalability of deep learning models is fundamentally limited by computing resources, memory, and communication. Although methods like low-rank adaptation (LoRA) have reduced the cost of model finetuning, its application in model pre-training remains largely unexplored. This paper explores extending LoRA to model pre-training, identifying the inherent constraints and limitations of standard LoRA in this context. We introduce LoRA-the-Explorer (LTE), a novel bi-level optimization algorithm designed to enable parallel training of multiple low-rank heads across computing nodes, thereby reducing the need for frequent synchronization. Our approach includes extensive experimentation on vision transformers using various vision datasets, demonstrating that LTE is competitive with standard pre-training.
Peer Reviews
Decision·Submitted to ICLR 2024
***Federated Learning-Inspired Approach*** - The paper introduces a novel algorithm aimed at updating the initial parameters following the independent training of parallel LoRAs for several iterations. ***Innovative Concept*** - The concept introduced is quite refreshing, departing from the conventional practice of directly updating the original parameters and instead approximating them from a combination of low-rank matrices. This approach bears similarities to the concept of a "mixture of exp
***Insufficient Experimentation*** - The paper lacks comprehensive experimentation, as it fails to include a comparison with competing methods or an initial set of experiments to validate the effectiveness of their proposed approach. For instance, the absence of comparisons to full fine-tuning or the use of a single LoRA with a pre-trained model in a conventional context is notable. The inclusion of more experiments would significantly enhance the paper's credibility. ***Lack of Elaboration***
1. The paper delves into an underexplored area by investigating the potential of parallel low-rank updates for memory-efficient and communication-efficient model pre-training. This is highly relevant in the context of contemporary computational constraints. 2. The introduction of multi-head low-rank adapters that integrate into model parameters constitutes a novel contribution to the field. This idea could generalize to multiple training paradigms, thereby adding considerable value to existing
1. The paper acknowledges its own limitation as a proof-of-concept work. Although the idea is compelling, there is insufficient evidence to support its feasibility for large models or complex tasks. 2. The manuscript would benefit from an in-depth theoretical analysis that substantiates the proposed approach, thereby addressing its current shortcomings.
This work boldly attempts to leverage the highly popular low-rank fine-tuning method (LoRA) for pre-training models from scratch. The paper carefully studies the performance degradation by drop-in LoRA, acknowledges the limitations, and offers future opportunities for the community to explore this line of research. The central idea of the proposed work is to approximate full-rank weight as a linear combination of low-rank weights, termed multi-head LoRA (MHLoRA). Section 3.1 is easy to understan
1. It will be worthwhile to examine, compare, and contrast a parallel body of work ReLoRA (Stack More Layers Differently: High-Rank Training Through Low-Rank Updates, Lialin et al., arXiv, July 2023) that presents a similar core idea. 2. Section 1 presentation can be further improved. The main findings and contributions in the middle seem to break the flow and could be considered a closing paragraph. Moreover, some of the items in these findings are the organization of the paper (eg, the last
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Taxonomy
TopicsNeural Networks and Applications