Summary Serving Thousands of Concurrent LoRA Adapters arxiv.org
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S-LoRA is a high-performing system that efficiently serves LoRA adapters, minimizing fragmentation and surpassing other libraries in throughput.
Slides
Slide Presentation (12 slides)
Key Points
- S-LoRA is a system designed for the scalable serving of many LoRA adapters, using the Low-Rank Adaptation (LoRA) method.
- S-LoRA stores all adapters in main memory and fetches the adapters used by currently running queries to GPU memory.
- S-LoRA proposes Unified Paging, a memory management technique that reduces fragmentation and efficiently handles dynamic adapter weights and KV cache tensors.
- S-LoRA employs a tensor parallelism strategy and optimized CUDA kernels for batched LoRA computation, enabling it to serve thousands of LoRA adapters with minimal overhead.
- S-LoRA outperforms state-of-the-art libraries in terms of throughput, improving it by up to 4 times and increasing the number of served adapters significantly.
- S-LoRA's features, including Unified Paging, custom CUDA kernels, and tensor parallelism, contribute to its ability to serve thousands of LoRA adapters efficiently.
- S-LoRA's performance is evaluated on synthetic and real production workloads, consistently demonstrating superior performance compared to other systems.
- S-LoRA is a highly efficient system for serving thousands of LoRA adapters, with innovative design strategies and scalability.
Summaries
17 word summary
S-LoRA is an efficient system for serving LoRA adapters, reducing fragmentation and outperforming other libraries in throughput.
59 word summary
S-LoRA is a highly efficient system for serving many LoRA adapters. It reduces fragmentation by storing adapters in main memory and fetching only active ones to GPU memory. S-LoRA outperforms other libraries in terms of throughput and the number of served adapters. Its tensor parallelism strategy scaled well with minimal overhead, making it valuable for deploying large language models.
128 word summary
S-LoRA is a highly efficient system for serving many LoRA adapters. It reduces fragmentation by storing adapters in main memory and fetching only active ones to GPU memory. Unified Paging manages dynamic adapter weights and KV cache tensors, enabling larger batch sizes. S-LoRA uses custom CUDA kernels for batched LoRA computations and employs a tensor parallelism strategy for multi-GPU inference. It outperforms other libraries in terms of throughput and the number of served adapters in synthetic and real production workloads. S-LoRA's tensor parallelism strategy scaled well with minimal overhead. Ablation studies showed the effectiveness of S-LoRA's on-the-fly computation method and early abort strategy. Overall, S-LoRA is a groundbreaking system that addresses the challenges of serving thousands of LoRA adapters simultaneously, making it valuable for deploying large language models.
371 word summary
S-LoRA is a highly efficient system designed for serving many LoRA adapters. It stores adapters in main memory and fetches only the active ones to GPU memory, reducing fragmentation. Unified Paging manages dynamic adapter weights and KV cache tensors, further reducing fragmentation and enabling larger batch sizes. S-LoRA uses custom CUDA kernels for batched LoRA computations and employs a tensor parallelism strategy for multi-GPU inference.
In evaluations, S-LoRA outperforms other libraries in terms of throughput and the number of served adapters. It consistently demonstrates superior performance in synthetic and real production workloads. In a synthetic workload experiment, S-LoRA served up to 2,000 adapters with minimal overhead, while other systems could only handle a few due to GPU memory constraints. S-LoRA achieved up to 4 times higher throughput than vLLM-packed and up to 30 times higher than PEFT in this experiment. Similar results were observed in real-world workload traces, demonstrating S-LoRA's efficiency.
Experiments with different numbers of GPUs showed that S-LoRA's tensor parallelism strategy scaled well, with minimal overhead from LoRA communication. Transitioning from 2 GPUs to 4 GPUs significantly increased serving throughput. Ablation studies compared S-LoRA's on-the-fly computation method with an alternative design that merges adapter weights with the base model. The on-the-fly method maintained high performance even with multiple adapters, while the merging approach declined in performance due to time-consuming switching between adapters.
Another ablation study compared S-LoRA's early abort strategy with First Come First Serve (FCFS) and Last Come First Serve (LCFS). S-LoRA's early abort strategy outperformed both FCFS and LCFS, especially as the coefficient of variation increased. S-LoRA addresses the unique characteristics of LLM serving, such as auto-regressive characteristics and parameter-efficient adapters.
In conclusion, S-LoRA is a highly efficient system for serving thousands of LoRA adapters. Its innovative design strategies enable large-scale fine-tuning services, and its scalability and high throughput make it suitable for diverse requirements. The appendix provides additional experiment results, including the impact of the number of clusters on throughput and SLO attainment, as well as an analysis of the admission control strategy in S-LoRA. Overall, S-LoRA is a groundbreaking system that addresses the challenges of serving thousands of LoRA adapters simultaneously. Its performance and scalability make it a valuable tool for deploying large language models.
444 word summary
S-LoRA is a system designed for serving many LoRA adapters efficiently. It stores adapters in main memory and fetches only the active ones to GPU memory, reducing fragmentation. Unified Paging is introduced to manage dynamic adapter weights and KV cache tensors, further reducing fragmentation and allowing for larger batch sizes. S-LoRA uses custom CUDA kernels for batched LoRA computations and employs a tensor parallelism strategy for multi-GPU inference. In evaluations, S-LoRA outperforms other libraries in terms of throughput and the number of served adapters. It consistently demonstrates superior performance in synthetic and real production workloads.
S-LoRA achieves high throughput and supports a large number of adapters simultaneously. In a synthetic workload experiment, S-LoRA served up to 2,000 adapters with minimal overhead, while other systems could only handle a few due to GPU memory constraints. S-LoRA achieved up to 4 times higher throughput than vLLM-packed and up to 30 times higher than PEFT in this experiment. Similar results were observed in real-world workload traces, demonstrating S-LoRA's efficiency.
Experiments with different numbers of GPUs showed that S-LoRA's tensor parallelism strategy scaled well, with minimal overhead from LoRA communication. Transitioning from 2 GPUs to 4 GPUs significantly increased serving throughput. An ablation study compared S-LoRA's on-the-fly computation method with an alternative design that merges adapter weights with the base model. The on-the-fly method maintained high performance even with multiple adapters, while the merging approach declined in performance due to time-consuming switching between adapters.
Another ablation study compared S-LoRA's early abort strategy with First Come First Serve (FCFS) and Last Come First Serve (LCFS). S-LoRA's early abort strategy outperformed both FCFS and LCFS, especially as the coefficient of variation increased. The related work section highlighted the significance of transformer architecture and the development of specialized serving systems. S-LoRA addresses the unique characteristics of LLM serving, such as auto-regressive characteristics and parameter-efficient adapters.
In conclusion, S-LoRA is a highly efficient system for serving thousands of LoRA adapters. Its innovative design strategies enable large-scale fine-tuning services, and its scalability and high throughput make it suitable for diverse requirements. The research was supported by various sponsors, and the authors expressed their gratitude for the support and helpful discussions received throughout the study.
The appendix provides additional experiment results, including the impact of the number of clusters on throughput and SLO attainment, as well as an analysis of the admission control strategy in S-LoRA. The appendix also includes a proof for the optimality of serving the most recent elements in order.
Overall, S-LoRA is a groundbreaking system that addresses the challenges of serving thousands of LoRA adapters simultaneously. Its performance and scalability make it a valuable tool for deploying large language models.
947 word summary
S-LoRA is a system designed for the scalable serving of many LoRA adapters, which are derived from a base model using the Low-Rank Adaptation (LoRA) method. S-LoRA stores all adapters in main memory and fetches the adapters used by currently running queries to GPU memory. It proposes Unified Paging, a memory management technique that reduces fragmentation and efficiently handles dynamic adapter weights and KV cache tensors. S-LoRA also employs a tensor parallelism strategy and optimized CUDA kernels for batched LoRA computation. These features enable S-LoRA to serve thousands of LoRA adapters on a single GPU or across multiple GPUs with minimal overhead.
Large language models (LLMs) have become widely used in various applications, and fine-tuning a base model for specific tasks can result in numerous task-specific adapters. However, serving these fine-tuned variants at scale is a challenge that has not been explored. The original LoRA paper proposed merging the adapter weights with the base model to eliminate inference latency, but this approach reduces overall serving throughput. Additionally, the paper did not consider leveraging host memory to increase the number of adapters hosted by a single machine.
To address these challenges, S-LoRA separates the batchable base model computation from individual LoRA computations. It stores all adapters in main memory and fetches only the active adapters for the current batch to GPU memory. This approach allows for efficient memory management and reduces fragmentation. S-LoRA also introduces Unified Paging, which uses a unified memory pool to manage dynamic adapter weights and KV cache tensors. This reduces memory fragmentation and allows for larger batch sizes.
To handle the computation of multiple adapters with distinct ranks in non-contiguous memory, S-LoRA employs custom CUDA kernels that support batching LoRA computations with varying ranks and sequence lengths. These kernels operate directly on non-contiguous memory and align with the memory pool design, enabling efficient batched inference for LoRA. S-LoRA also introduces a novel tensor parallelism strategy to support multi-GPU inference of large transformer models. This strategy minimizes communication and memory overheads.
In evaluations, S-LoRA outperforms state-of-the-art libraries such as HuggingFace PEFT and vLLM in terms of throughput. It can improve throughput by up to 4 times and increase the number of served adapters by several orders of magnitude. S-LoRA can serve thousands of LoRA adapters on a single GPU or across multiple GPUs with minimal overhead. The performance of S-LoRA is evaluated on synthetic and real production workloads, and it consistently demonstrates superior performance compared to other systems.
Overall, S-LoRA enables the scalable serving of many task-specific fine-tuned models and offers the potential for large-scale customized fine-tuning services. Its features, including Unified Paging, custom CUDA kernels, and tensor parallelism, contribute to its ability to serve thousands of LoRA adapters efficiently. The code for S-LoRA is available on GitHub.
S-LoRA is a system that can serve thousands of LoRA adapters simultaneously with high throughput. It achieves this by minimizing overhead and utilizing innovative design strategies. Compared to existing systems like vLLM-packed and PEFT, S-LoRA outperforms in terms of throughput and the number of adapters it can support.
In a synthetic workload experiment, S-LoRA demonstrated its superior performance. It was able to serve up to 2,000 adapters simultaneously with minimal overhead, while vLLM-packed could only handle fewer than 5 adapters due to GPU memory constraints. S-LoRA achieved up to 4 times higher throughput than vLLM-packed and up to 30 times higher than PEFT.
The results were consistent in real-world workload traces as well. S-LoRA showed high throughput and SLO attainment, proving its efficiency in serving real-world workloads.
To test the scalability of S-LoRA's tensor parallelism strategy, experiments were conducted with different numbers of GPUs. The results showed that the added LoRA communication had minimal overhead compared to the computational overhead. When transitioning from 2 GPUs to 4 GPUs, the serving throughput increased significantly.
An ablation study was conducted to compare S-LoRA's on-the-fly computation method with an alternative design that merges adapter weights with the base model. The results showed that the merging approach performed better with one adapter, but its performance declined with more than 2 adapters due to time-consuming switching between adapters. On the other hand, S-LoRA's on-the-fly computation method maintained high performance even with multiple adapters.
Another ablation study focused on the early abort strategy of S-LoRA compared to First Come First Serve (FCFS) and Last Come First Serve (LCFS). The results showed that S-LoRA's early abort strategy outperformed both FCFS and LCFS, especially as the coefficient of variation (cv) increased.
The related work section highlighted the significance of transformer architecture and the development of specialized serving systems for it. Systems like PetS, Clipper, TensorFlow Serving, and Nexus have made advancements in batching mechanisms, memory optimizations, GPU kernel optimizations, and model parallelism. However, they overlook the auto-regressive characteristics and parameter-efficient adapters in LLM serving, which S-LoRA addresses.
In conclusion, S-LoRA is a highly efficient system for serving thousands of LoRA adapters. Its innovative design strategies, including tensor parallelism, adapter batching, and CUDA kernels, enable large-scale fine-tuning services. The system's scalability and high throughput make it suitable for deploying models tailored to diverse requirements.
The research was supported by various sponsors, and the authors expressed their gratitude for the support and helpful discussions received throughout the study.
The appendix provides additional experiment results, including the impact of number of clusters on throughput and SLO attainment, as well as an analysis of the admission control strategy in S-LoRA. The appendix also includes a proof for the optimality of serving the most recent l elements in order.
Overall, S-LoRA is a groundbreaking system that addresses the challenges of serving thousands of LoRA adapters simultaneously. Its performance and scalability make it a valuable tool for deploying large language models.