S-LoRA: Serving Thousands of Concurrent LoRA Adapters
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arXiv
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| Format: | Preprint |
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2023
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| _version_ | 1866910472204189696 |
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| author | Sheng, Ying Cao, Shiyi Li, Dacheng Hooper, Coleman Lee, Nicholas Yang, Shuo Chou, Christopher Zhu, Banghua Zheng, Lianmin Keutzer, Kurt Gonzalez, Joseph E. Stoica, Ion |
| author_facet | Sheng, Ying Cao, Shiyi Li, Dacheng Hooper, Coleman Lee, Nicholas Yang, Shuo Chou, Christopher Zhu, Banghua Zheng, Lianmin Keutzer, Kurt Gonzalez, Joseph E. Stoica, Ion |
| contents | The "pretrain-then-finetune" paradigm is commonly adopted in the deployment of large language models. Low-Rank Adaptation (LoRA), a parameter-efficient fine-tuning method, is often employed to adapt a base model to a multitude of tasks, resulting in a substantial collection of LoRA adapters derived from one base model. We observe that this paradigm presents significant opportunities for batched inference during serving. To capitalize on these opportunities, we present S-LoRA, a system designed for the scalable serving of many LoRA adapters. S-LoRA stores all adapters in the main memory and fetches the adapters used by the currently running queries to the GPU memory. To efficiently use the GPU memory and reduce fragmentation, S-LoRA proposes Unified Paging. Unified Paging uses a unified memory pool to manage dynamic adapter weights with different ranks and KV cache tensors with varying sequence lengths. Additionally, S-LoRA employs a novel tensor parallelism strategy and highly optimized custom CUDA kernels for heterogeneous batching of LoRA computation. Collectively, these features enable S-LoRA to serve thousands of LoRA adapters on a single GPU or across multiple GPUs with a small overhead. Compared to state-of-the-art libraries such as HuggingFace PEFT and vLLM (with naive support of LoRA serving), S-LoRA can improve the throughput by up to 4 times and increase the number of served adapters by several orders of magnitude. As a result, S-LoRA enables scalable serving of many task-specific fine-tuned models and offers the potential for large-scale customized fine-tuning services. The code is available at https://github.com/S-LoRA/S-LoRA |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_03285 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | S-LoRA: Serving Thousands of Concurrent LoRA Adapters Sheng, Ying Cao, Shiyi Li, Dacheng Hooper, Coleman Lee, Nicholas Yang, Shuo Chou, Christopher Zhu, Banghua Zheng, Lianmin Keutzer, Kurt Gonzalez, Joseph E. Stoica, Ion Machine Learning Artificial Intelligence Distributed, Parallel, and Cluster Computing The "pretrain-then-finetune" paradigm is commonly adopted in the deployment of large language models. Low-Rank Adaptation (LoRA), a parameter-efficient fine-tuning method, is often employed to adapt a base model to a multitude of tasks, resulting in a substantial collection of LoRA adapters derived from one base model. We observe that this paradigm presents significant opportunities for batched inference during serving. To capitalize on these opportunities, we present S-LoRA, a system designed for the scalable serving of many LoRA adapters. S-LoRA stores all adapters in the main memory and fetches the adapters used by the currently running queries to the GPU memory. To efficiently use the GPU memory and reduce fragmentation, S-LoRA proposes Unified Paging. Unified Paging uses a unified memory pool to manage dynamic adapter weights with different ranks and KV cache tensors with varying sequence lengths. Additionally, S-LoRA employs a novel tensor parallelism strategy and highly optimized custom CUDA kernels for heterogeneous batching of LoRA computation. Collectively, these features enable S-LoRA to serve thousands of LoRA adapters on a single GPU or across multiple GPUs with a small overhead. Compared to state-of-the-art libraries such as HuggingFace PEFT and vLLM (with naive support of LoRA serving), S-LoRA can improve the throughput by up to 4 times and increase the number of served adapters by several orders of magnitude. As a result, S-LoRA enables scalable serving of many task-specific fine-tuned models and offers the potential for large-scale customized fine-tuning services. The code is available at https://github.com/S-LoRA/S-LoRA |
| title | S-LoRA: Serving Thousands of Concurrent LoRA Adapters |
| topic | Machine Learning Artificial Intelligence Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2311.03285 |