One QuantLLM for ALL: Fine-tuning Quantized LLMs Once for Efficient Deployments

• URL: http://arxiv.org/abs/2405.20202v1
• Date: Thu, 30 May 2024 16:05:15 GMT
• Title: One QuantLLM for ALL: Fine-tuning Quantized LLMs Once for Efficient Deployments
• Authors: Ke Yi, Yuhui Xu, Heng Chang, Chen Tang, Yuan Meng, Tong Zhang, Jia Li,
• Abstract summary: Large Language Models (LLMs) have advanced rapidly but face significant memory demands. Current methods typically require lengthy training to alleviate the performance degradation from quantization loss. We make an initial attempt to extend the once-for-all framework to large language models.
• Score: 43.107261545706415
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Models (LLMs) have advanced rapidly but face significant memory demands. While quantization has shown promise for LLMs, current methods typically require lengthy training to alleviate the performance degradation from quantization loss. However, deploying LLMs across diverse scenarios with different resource constraints, e.g., servers and personal computers, requires repeated training per application, which amplifies the lengthy training problem. Given that, it is advantageous to train a once-for-all (OFA) supernet capable of yielding diverse optimal subnets for downstream applications through one-shot training. Nonetheless, the scale of current language models impedes efficiency and amplifies interference from weight sharing between subnets. We make an initial attempt to extend the once-for-all framework to large language models. Specifically, we decouple shared weights to eliminate the interference and incorporate Low-Rank adapters for training efficiency. Furthermore, we observe the imbalance allocation of training resources from the traditional uniform sampling. A non-parametric scheduler is introduced to adjust the sampling rate for each quantization configuration, achieving a more balanced allocation among subnets with varying demands. We validate the approach on LLaMA2 families, and downstream evaluation confirms our ability to maintain high performance while significantly reducing deployment time faced with multiple scenarios.

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