V-LoRA: An Efficient and Flexible System Boosts Vision Applications with LoRA LMM

• URL: http://arxiv.org/abs/2411.00915v1
• Date: Fri, 01 Nov 2024 13:43:33 GMT
• Title: V-LoRA: An Efficient and Flexible System Boosts Vision Applications with LoRA LMM
• Authors: Liang Mi, Weijun Wang, Wenming Tu, Qingfeng He, Rui Kong, Xinyu Fang, Yazhu Dong, Yikang Zhang, Yunchun Li, Meng Li, Haipeng Dai, Guihai Chen, Yunxin Liu,
• Abstract summary: Low-rank adaptation (LoRA) offers a promising method to integrate external knowledge into large language models (LMMs) Existing LoRA model serving is excessively computationally expensive and causes extremely high latency. We present an end-to-end solution that empowers diverse vision tasks and enriches vision applications with LoRA LMMs.
• Score: 32.37720746437661
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Multimodal Models (LMMs) have shown significant progress in various complex vision tasks with the solid linguistic and reasoning capacity inherited from large language models (LMMs). Low-rank adaptation (LoRA) offers a promising method to integrate external knowledge into LMMs, compensating for their limitations on domain-specific tasks. However, the existing LoRA model serving is excessively computationally expensive and causes extremely high latency. In this paper, we present an end-to-end solution that empowers diverse vision tasks and enriches vision applications with LoRA LMMs. Our system, VaLoRA, enables accurate and efficient vision tasks by 1) an accuracy-aware LoRA adapter generation approach that generates LoRA adapters rich in domain-specific knowledge to meet application-specific accuracy requirements, 2) an adaptive-tiling LoRA adapters batching operator that efficiently computes concurrent heterogeneous LoRA adapters, and 3) a flexible LoRA adapter orchestration mechanism that manages application requests and LoRA adapters to achieve the lowest average response latency. We prototype VaLoRA on five popular vision tasks on three LMMs. Experiment results reveal that VaLoRA improves 24-62% of the accuracy compared to the original LMMs and reduces 20-89% of the latency compared to the state-of-the-art LoRA model serving systems.

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