eDKM: An Efficient and Accurate Train-time Weight Clustering for Large Language Models

TL;DR

This paper introduces eDKM, a memory-efficient implementation of Differentiable KMeans Clustering, enabling effective train-time weight clustering for large language models, significantly reducing memory usage while maintaining high accuracy.

Contribution

It proposes a novel memory reduction technique for DKM, allowing large-scale LLM compression during training without excessive memory overhead.

Findings

Compressed LLaMA 7B from 12.6 GB to 2.5 GB using eDKM.

Reduced train-time memory footprint of a decoder layer by 130×.

Achieved competitive accuracy on LLM benchmarks.

Abstract

Since Large Language Models or LLMs have demonstrated high-quality performance on many complex language tasks, there is a great interest in bringing these LLMs to mobile devices for faster responses and better privacy protection. However, the size of LLMs (i.e., billions of parameters) requires highly effective compression to fit into storage-limited devices. Among many compression techniques, weight-clustering, a form of non-linear quantization, is one of the leading candidates for LLM compression, and supported by modern smartphones. Yet, its training overhead is prohibitively significant for LLM fine-tuning. Especially, Differentiable KMeans Clustering, or DKM, has shown the state-of-the-art trade-off between compression ratio and accuracy regression, but its large memory complexity makes it nearly impossible to apply to train-time LLM compression. In this paper, we propose a memory-efficient DKM implementation, eDKM powered by novel techniques to reduce the memory footprint of DKM by orders of magnitudes. For a given tensor to be saved on CPU for the backward pass of DKM, we compressed the tensor by applying uniquification and sharding after checking if there is no duplicated tensor previously copied to CPU. Our experimental results demonstrate that \prjname can fine-tune and compress a pretrained LLaMA 7B model from 12.6 GB to 2.5 GB (3bit/weight) with the Alpaca dataset by reducing the train-time memory footprint of a decoder layer by 130$\times$, while delivering good accuracy on broader LLM benchmarks (i.e., 77.7% for PIQA, 66.1% for Winograde, and so on).