ASPEN: High-Throughput LoRA Fine-Tuning of Large Language Models with a Single GPU

• URL: http://arxiv.org/abs/2312.02515v1
• Date: Tue, 5 Dec 2023 05:38:38 GMT
• Title: ASPEN: High-Throughput LoRA Fine-Tuning of Large Language Models with a Single GPU
• Authors: Zhengmao Ye and Dengchun Li and Jingqi Tian and Tingfeng Lan and Jie Zuo and Lei Duan and Hui Lu and Yexi Jiang and Jian Sha and Ke Zhang and Mingjie Tang
• Abstract summary: We present ASPEN, a framework for fine-tuning transformer-based large language models (LLMs) ASPEN efficiently trains multiple jobs on a single GPU using the LoRA method, leveraging shared pre-trained model and adaptive scheduling. Experiments show that ASPEN saves 53% of GPU memory when training multiple LLaMA-7B models on NVIDIA A100 80GB GPU.
• Score: 4.198627205271621
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Transformer-based large language models (LLMs) have demonstrated outstanding performance across diverse domains, particularly when fine-turned for specific domains. Recent studies suggest that the resources required for fine-tuning LLMs can be economized through parameter-efficient methods such as Low-Rank Adaptation (LoRA). While LoRA effectively reduces computational burdens and resource demands, it currently supports only a single-job fine-tuning setup. In this paper, we present ASPEN, a high-throughput framework for fine-tuning LLMs. ASPEN efficiently trains multiple jobs on a single GPU using the LoRA method, leveraging shared pre-trained model and adaptive scheduling. ASPEN is compatible with transformer-based language models like LLaMA and ChatGLM, etc. Experiments show that ASPEN saves 53% of GPU memory when training multiple LLaMA-7B models on NVIDIA A100 80GB GPU and boosts training throughput by about 17% compared to existing methods when training with various pre-trained models on different GPUs. The adaptive scheduling algorithm reduces turnaround time by 24%, end-to-end training latency by 12%, prioritizing jobs and preventing out-of-memory issues.

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