0

Speculative Decoding: Exploiting Speculative Execution for Accelerating Seq2seq Generation

Speculative Decoding accelerates autoregressive decoding with a draft-then-verify approach, achieving up to 5x speedup for Transformers while maintaining generation quality.

Preview
Year
2022
Venue
arXiv 2022
Authors
6
Hosting
Abstract onlyARXIV-DEFAULT

Cite

Notes

Only stored in your browser.

Attribution

Abstract & full text
arxiv.org/abs/2203.16487v6ARXIV-DEFAULT
TL;DR
Semantic Scholar
Attribution policy →

Abstract

We propose Speculative Decoding (SpecDec), for the first time ever, to formally study exploiting the idea of speculative execution to accelerate autoregressive (AR) decoding. Speculative Decoding has two innovations: Spec-Drafter -- an independent model specially optimized for efficient and accurate drafting -- and Spec-Verification -- a reliable method for verifying the drafted tokens efficiently in the decoding paradigm. Experimental results on various seq2seq tasks including machine translation and abstractive summarization show our approach can achieve around 5\times speedup for the popular Transformer architectures with comparable generation quality to beam search decoding, refreshing the impression that the draft-then-verify paradigm introduces only 1.4\times\sim2\times speedup. In addition to the remarkable speedup, we also demonstrate 3 additional advantages of SpecDec, revealing its practical value for accelerating generative models in real-world applications. Our models and codes are available at https://github.com/hemingkx/SpecDec.

Authors

6