Language Models as Zero-shot Lossless Gradient Compressors: Towards General Neural Parameter Prior Models

2024-09-26

AI Summary

To address the high communication overhead of gradients in distributed training, this paper proposes LM-GC—the first method to directly leverage large language models (LLMs) as zero-shot priors for lossless gradient compression. Its core innovation lies in a structure-preserving gradient-to-text mapping that converts continuous gradients into high-information-density token sequences, combined with arithmetic coding and symbolic representation to enable high-fidelity probabilistic modeling without supervision. Compared to state-of-the-art lossless compression methods, LM-GC achieves average compression ratio improvements of 10%–17.2% across multiple datasets and model architectures, while improving token efficiency by 38×. Moreover, it is fully compatible with lossy techniques such as quantization and sparsification, establishing a novel paradigm for efficient distributed training.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Natural Language Processing: (Large) Language Models
• Computer Vision: Large Vision Models

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Despite the widespread use of statistical prior models in various fields, such models for neural network gradients have long been overlooked. The inherent challenge stems from their high-dimensional structures and complex interdependencies, which complicate effective modeling. In this work, we demonstrate the potential of large language models (LLMs) to act as gradient priors in a zero-shot setting. We examine the property by considering lossless gradient compression -- a critical application in distributed learning -- that depends heavily on precise probability modeling. To achieve this, we introduce LM-GC, a novel method that integrates LLMs with arithmetic coding. Our technique converts plain gradients into text-like formats, enhancing token efficiency by up to 38 times compared to their plain representations. We ensure that this data conversion maintains a close alignment with the structure of plain gradients and the symbols commonly recognized by LLMs. Our experiments indicate that LM-GC surpasses existing state-of-the-art lossless compression methods, improving compression rates by 10% up to 17.2% across various datasets and architectures. Additionally, our approach shows promising compatibility with lossy compression techniques such as quantization and sparsification. These findings highlight the significant potential of LLMs as a model for effectively handling gradients. Code is available at https://github.com/hui-po-wang/LM-GC.

Problem

• Gradient Compression
• Distributed Learning
• Model Training Efficiency

Innovation

• LLM-based Gradient Compression
• Arithmetic Encoding
• Synergy with Lossy Compression Methods