Compress then Serve: Serving Thousands of LoRA Adapters with Little Overhead
(2407.00066)Abstract
Fine-tuning LLMs with low-rank adapters (LoRAs) has become common practice, often yielding numerous copies of the same LLM differing only in their LoRA updates. This paradigm presents challenges for systems that serve real-time responses to queries that each involve a different LoRA. Prior works optimize the design of such systems but still require continuous loading and offloading of LoRAs, as it is infeasible to store thousands of LoRAs in GPU memory. To mitigate this issue, we investigate the efficacy of compression when serving LoRA adapters. We consider compressing adapters individually via SVD and propose a method for joint compression of LoRAs into a shared basis paired with LoRA-specific scaling matrices. Our experiments with up to 500 LoRAs demonstrate that compressed LoRAs preserve performance while offering major throughput gains in realistic serving scenarios with over a thousand LoRAs, maintaining 75% of the throughput of serving a single LoRA.
Overview
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The paper introduces an efficient approach to manage Low-Rank Adaptations (LoRA) in LLMs by compressing multiple adapters to retain performance and enhance throughput.
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Two compression methods, individual SVD compression and joint diagonalization, are proposed, with the latter focusing on sharing a basis among LoRAs to maximize parameter savings.
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Through empirical evaluation and theoretical guarantees, it demonstrates that over 1000 LoRAs can be served competitively, maintaining significant throughput improvements compared to single LoRA serving.
A Comprehensive Review of "Compress then Serve: Serving Thousands of LoRA Adapters with Little Overhead"
The paper "Compress then Serve: Serving Thousands of LoRA Adapters with Little Overhead" presents an innovative approach to managing the computational challenges introduced by the proliferation of Low-Rank Adaptations (LoRA) in LLMs. By formalizing and empirically examining the problem of retaining the performance of compressor LoRAs and optimizing the throughput, the authors offer substantial improvements to the efficiency of serving systems.
Key Contributions
The paper's primary contributions are:
- Formulation of LoRA Compression Problem: The authors introduce a systematic framework for compressing multiple LoRA adapters, focusing on two primary goals: preserving the performance of the original LoRAs and enhancing the throughput of serving many LoRAs under computational constraints.
- Proposed Methods for Compression: Two methods are proposed – individual SVD compression and joint diagonalization. The latter method, in particular, explores efficient sharing of a basis while using LoRA-specific scaling matrices, leveraging shared structures across LoRAs.
- Theoretical Guarantees: The paper establishes theoretical guarantees for the reconstruction error inherent in the compression formulations and correlates this error with empirical performance.
- Empirical Evaluation: By training 500 high-quality LoRAs and integrating the compression techniques into a state-of-the-art LLM serving system, the authors demonstrate that over 1000 LoRAs can be served competitively, maintaining up to 75% of the throughput of serving a single LoRA.
Detailed Findings
Rank-Based Compression Schemes:
- Individual SVD Compression: This technique involves independently reducing each LoRA adapter's rank, producing a direct but limited parameter reduction.
- Joint Diagonalization: The authors’ innovation lies in the joint diagonalization approach, where a shared basis is optimized. This method is shown to effectively capture shared structural components among multiple LoRAs, yielding significant parameter savings while retaining performance.
Empirical Results:
- The paper reports that joint diagonalization can achieve extreme parameter count reduction with minimal impact on computational performances, such as reduced GPU parameter workload and total parameter savings.
- Experiments demonstrate the advantages of compression in real-world scenarios. For instance, throughput is notably improved when serving a large number of compressed LoRAs, doubling throughput in some scenarios compared to uncompressed cases.
Theoretical Implications
The paper theorizes that the shared basis approach provides a more effective compression scheme by clustering related components, effectively retaining essential features while discarding redundancies. This is supported by bounds on reconstruction error and experimental correlations between reconstruction error and downstream task performance.
Practical Implications
Practically, the proposed methods are designed to mitigate the trade-offs between parameter efficiency and performance. When incorporated into LLM serving systems such as vLLM, compressed LoRAs introduce substantial improvements in throughput, reducing computational load and memory constraints. This optimization holds significant promise for scalable AI services, particularly in environments where rapid switching between multiple task-specific models is necessary.
Future Directions
The research opens several avenues for future exploration:
- Enhanced Scheduling Algorithms: Given the observed scheduling bottlenecks, developing algorithms that efficiently manage compressed LoRAs on GPUs could further elevate system throughput.
- Generalization to Other Models: Extending the compression techniques to other types of adapters and foundation models could validate the general utility of the proposed methods.
- Evaluation on Diverse Tasks: Spanning a breadth of problem domains and language tasks, especially incorporating cross-lingual capabilities, would provide deeper insights into the compressibility and performance retention of LoRAs.
Conclusion
"Compress then Serve: Serving Thousands of LoRA Adapters with Little Overhead" provides a rigorous and practical framework for enhancing the efficiency of large language model serving systems via strategic compression. By grounding its approach in both theoretical analysis and empirical validation, the paper makes a robust case for the feasibility and benefits of its proposed methods. This work represents a significant step toward scalable, efficient AI systems capable of managing a diverse array of task-specific models with reduced computational overhead.