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This paper presents LoopServe, an adaptive dual-phase inference acceleration framework for efficient inference of large-scale language models (LLMs) in multi-turn conversations. LoopServe efficiently manages conversational context by introducing online sparsification with dynamic importance selection (in the prefilling phase) and adaptive key-value compression (in the decoding phase). Furthermore, we propose a new benchmark consisting of 11 multi-turn datasets that reflect realistic query positions and conversation dependencies, and experimentally demonstrate that LoopServe outperforms existing methods in terms of performance and acceleration.
Takeaways, Limitations
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Takeaways:
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We present a novel framework to improve the inference speed and efficiency of multi-turn conversational LLM.
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Implement dynamic context management through online sparsification and adaptive key-value compression.
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Presenting an acceleration methodology that can adapt to real-world conversation patterns.
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A new benchmark proposal covering diverse multi-turn datasets.
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Demonstrated superior performance and acceleration compared to existing methodologies.
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Limitations:
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Lack of detailed information on the specific numerical values of the acceleration effects and the degree of performance improvement presented in the paper.
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Absence of discussion of LoopServe's complexity and implementation difficulties.
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Lack of specification of dependencies on specific LLM architectures and hardware environments.
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Further research is needed on generalizability across different domains and tasks.
