CLaSp: In-Context Layer Skip for Self-Speculative Decoding
Authors: name: Longze Chen, name: Renke Shan, name: Huiming Wang, name: Lu Wang, name: Ziqiang Liu, name: Run Luo, name: Jiawei Wang, name: Hamid Alinejad-Rokny, name: Min Yang

Speculative decoding (SD) is a promising method for accelerating the decoding
process of Large Language Models (LLMs). The efficiency of SD primarily hinges
on the consistency between the draft model and the verify model. However,
existing drafting approaches typically require additional modules to be
trained, which can be challenging to implement and ensure compatibility across
various LLMs. In this paper, we propose CLaSp, an in-context layer-skipping
strategy for self-speculative decoding. Unlike prior methods, CLaSp does not
require additional drafting modules or extra training. Instead, it employs a
plug-and-play mechanism by skipping intermediate layers of the verify model to
construct a compressed draft model. Specifically, we develop a dynamic
programming algorithm that optimizes the layer-skipping process by leveraging
the complete hidden states from the last verification stage as an objective.
This enables CLaSp to dynamically adjust its layer-skipping strategy after each
verification stage, without relying on pre-optimized sets of skipped layers.
Experimental results across diverse downstream tasks demonstrate that CLaSp
achieves a speedup of 1.3x ~ 1.7x on LLaMA3 series models without altering the
original distribution of the generated text.