Sequoia is a novel algorithm designed to accelerate large language model (LLM) inference by introducing scalable tree structures, robust sampling and verification methods, and hardware-aware optimization. It achieves impressive speedups of up to 4.04× on GPU and 9.96× with offloading settings. The method addresses key limitations in existing speculative decoding approaches by optimizing tree construction, sampling techniques, and hardware-specific parameters.
The paper outlines the challenges faced in accelerating LLM inference due to I/O bottlenecks and inefficient hardware utilization. It introduces Sequoia as a solution that leverages dynamic programming for optimal tree structure discovery, innovative sampling methods for robust performance across hyperparameters, and hardware-aware optimization for maximum speedup.
By comparing Sequoia with existing methods like SpecInfer and top-k sampling, the study demonstrates superior scalability in generating tokens per decoding step. Additionally, it showcases the robustness of Sequoia's sampling algorithm across different temperatures and top-p values while achieving significant speedups on various hardware configurations.
Furthermore, the hardware-aware tree optimizer in Sequoia proves to be effective in selecting optimal tree sizes and depths based on specific hardware settings. This approach results in substantial improvements in end-to-end speedups compared to unconstrained tree structures.
Overall, Sequoia presents a comprehensive solution for enhancing LLM inference efficiency through advanced speculative decoding techniques tailored for scalability, robustness, and hardware optimization.
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by Zhuoming Che... о arxiv.org 03-01-2024
https://arxiv.org/pdf/2402.12374.pdfГлибші Запити