Ouroboros: Speculative Decoding with Large Model Enhanced Drafting
Weilin Zhao Yuxiang Huang Xu Han Chaojun Xiao Zhiyuan Liu Maosong Sun
Drafting-then-verifying decoding methods such as speculative decoding are
widely adopted training-free methods to accelerate the inference of large
language models (LLMs). Instead of employing an autoregressive process to
decode tokens sequentially, speculative decoding initially creates drafts with
an efficient small model. Then LLMs are required to conduct verification and
correction in a non-autoregressive fashion to minimize time overhead.
Generating longer drafts can lead to even more significant speedups once
verified, but also incurs substantial trial and error costs if it fails.
Suffering from the high verification failure probability, existing decoding
methods cannot draft too much content for verification at one time, achieving
sub-optimal inference acceleration. In this paper, we introduce Ouroboros,
which constructs a phrase candidate pool from the verification process of LLMs
to provide candidates for draft generation of the small model. Thereby,
Ouroboros can further improve the efficiency and effectiveness of the initial
drafts. The experimental results on typical text generation tasks show that
Ouroboros achieves speedups of up to 1.9x and 2.8x compared to lookahead
decoding and speculative decoding, respectively. The source code of Ouroboros
is available at https://github.com/thunlp/Ouroboros.