Dynamic Speculation Lookahead Accelerates Speculative Decoding of Large Language Models
<|reference_start|>Dynamic Speculation Lookahead Accelerates Speculative Decoding of Large Language Models: Speculative decoding is commonly used for reducing the inference latency of large language models. Its effectiveness depends highly on the speculation lookahead (SL)-the number of tokens generated by the draft model at each iteration. In this work we show that the common practice of using the same SL for all iterations (static SL) is suboptimal. We introduce DISCO (DynamIc SpeCulation lookahead Optimization), a novel method for dynamically selecting the SL. Our experiments with four datasets show that DISCO reaches an average speedup of 10% compared to the best static SL baseline, while generating the exact same text.<|reference_end|>
@article{mamou2024dynamic,
    title={Dynamic Speculation Lookahead Accelerates Speculative Decoding of Large
  Language Models},
    author={Jonathan Mamou and Oren Pereg and Daniel Korat and Moshe Berchansky
  and Nadav Timor and Moshe Wasserblat and Roy Schwartz},
    journal={arXiv preprint arXiv:2405.04304},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.04304},
    primaryClass={cs.CL}
}