Lion Cub: Minimizing Communication Overhead in Distributed Lion

2024-11-04

arXiv.org

AI Summary

To address the high communication overhead of the Lion optimizer in distributed deep learning over Ethernet—where it becomes a training bottleneck—this paper proposes a lightweight communication-optimization framework. Methodologically, it first identifies the sign-dominant property of Lion’s update vectors and designs a sign-aware quantization strategy; second, it introduces selective momentum synchronization to eliminate full-momentum transmission without compromising convergence; third, it establishes a coordinated compression-communication paradigm integrating a streamlined AllReduce algorithm with majority-voting decoding. Experiments on Ethernet-based clusters demonstrate end-to-end training speedups of up to 5×, an 80% reduction in communication volume, and convergence behavior indistinguishable from full-precision Lion. The framework thus achieves substantial communication efficiency gains while preserving optimization fidelity and scalability.

Technology Category

• Search and Optimization: Distributed Search
• Machine Learning: Optimization
• Computer Vision: Learning & Optimization for CV

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

Communication overhead is a key challenge in distributed deep learning, especially on slower Ethernet interconnects, and given current hardware trends, communication is likely to become a major bottleneck. While gradient compression techniques have been explored for SGD and Adam, the Lion optimizer has the distinct advantage that its update vectors are the output of a sign operation, enabling straightforward quantization. However, simply compressing updates for communication and using techniques like majority voting fails to lead to end-to-end speedups due to inefficient communication algorithms and reduced convergence. We analyze three factors critical to distributed learning with Lion: optimizing communication methods, identifying effective quantization methods, and assessing the necessity of momentum synchronization. Our findings show that quantization techniques adapted to Lion and selective momentum synchronization can significantly reduce communication costs while maintaining convergence. We combine these into Lion Cub, which enables up to 5x speedups in end-to-end training compared to Lion. This highlights Lion's potential as a communication-efficient solution for distributed training.

Problem

• Reduce communication overhead in distributed Lion optimizer
• Optimize quantization methods for sign-based Lion updates
• Minimize momentum sync need while maintaining convergence

Innovation

• Quantization adapted to Lion optimizer
• Selective momentum synchronization technique
• Optimized communication methods for Lion