Training Neural Networks from Scratch with Parallel Low-Rank Adapters (2402.16828v2)

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.

• The paper presents LTE, a bi-level optimization algorithm that employs parallel low-rank adapters to pre-train neural networks with lower memory and bandwidth requirements.
• It introduces a distributed framework where multiple low-rank heads are independently optimized, significantly reducing communication costs in decentralized settings.
• Empirical results validate that LTE enables training models three times larger with half the bandwidth and extends required training samples by 40% for convergence.

Exploring Efficient Pre-Training Strategies with Parallel Low-Rank Adapters

Introduction

The advancements in deep learning models necessitate improved training strategies to mitigate computational and memory constraints. This paper introduces LoRA-the-Explorer (LTE), an innovative bi-level optimization algorithm leveraging parallel low-rank adapters for neural network pre-training. By employing multiple low-rank heads across computing nodes, LTE significantly reduces memory requirements and communication bandwidth, addressing limitations in standard Low-Rank Adaptation (LoRA) methods for model pre-training.

Low-Rank Adapters in Model Pre-Training

The principal challenge addressed is extending the applicability of low-rank adapters beyond model finetuning to pre-training stages. Typically, LoRA has been effective for finetuning tasks by minimizing memory usage and facilitating training on limited hardware. This work, however, pioneers in applying a similar principle to pre-train models from scratch, introducing a parallelization scheme that retains the memory efficiency of LoRA while overcoming its inherent performance limitations when applied to model pre-training.

The LTE Algorithm

LTE proposes a parallel training framework where multiple low-rank heads are optimized independently across computing nodes. This approach allows significant reductions in communication needs, a critical advantage in distributed settings with bandwidth constraints. The main contributions and findings of this algorithm include:

• Demonstrating that parallel low-rank updates can approximate the performance of standard full-parameter pre-training.
• Employing federated averaging combined with low-rank adaptations to facilitate distributed training with minimal synchronization.
• Achieving considerable resource efficiency, enabling the training of models three times larger with approximately half the bandwidth compared to traditional methods.

Empirical Validation

Extensive experiments conducted on vision transformers using various datasets validate LTE's competitive edge. Particularly, it extends the training samples required for convergence by 40%, allowing the fitting of significantly larger models on devices with limited memory. This paves the way for leveraging multiple low-memory devices for faster training, presenting a cost-effective strategy for training large-scale models.

Implications and Future Directions

This research opens several avenues for further exploration, including dynamic determination of the number of ranks or heads for optimal performance and the development of heterogeneous parameterization techniques for LoRA. The possibility of employing varying ranks across different LoRA heads presents an intriguing area for future research, potentially enhancing model performance and training efficiency further.

Conclusion

LoRA-the-Explorer stands as a testament to the ongoing efforts in refining pre-training methodologies to accommodate the growing demands for computational efficiency in deep learning tasks. By harnessing the power of parallel low-rank adapters, LTE showcases a promising direction for training state-of-the-art neural networks in environments where computational resources are at a premium. The findings underscore the transformative potential of innovative optimization algorithms in making advanced AI models more accessible and sustainable.

Acknowledgements

The support from various grants and fellowships highlights the collaborative effort behind this paper, underscoring the importance of continued research in developing efficient training methods for deep learning models. This contribution not only advances our understanding of low-rank adaptations in neural network training but also sets the stage for broader applications in computational resource-constrained settings.