ParallelSpec: Parallel Drafter for Efficient Speculative Decoding

Zilin Xiao, Hongming Zhang, Tao Ge, Siru Ouyang, Vicente Ordonez, Dong Yu

Keywords: large language model inference, speculative decoding

TL;DR: Compelling alternative to auto-regressive drafting; it can be integrated into speculative decoding that requires additional alignment stage.

Abstract: Speculative decoding has proven to be an efficient solution to large language model (LLM) inference, where the small drafter predicts future tokens at a low cost, and the target model is leveraged to verify them in parallel. However, most existing works still draft tokens auto-regressively to maintain sequential dependency in language modeling, which we consider a huge computational burden in speculative decoding. We present ParallelSpec, an alternative to auto-regressive drafting strategies in state-of-the-art speculative decoding approaches. In contrast to auto-regressive drafting in the speculative stage, we train a parallel drafter to serve as an efficient speculative model. ParallelSpec learns to efficiently predict multiple future tokens in parallel using a single model, and it can be integrated into any speculative decoding framework that requires aligning the output distributions of the drafter and the target model with minimal training cost. Experimental results show that ParallelSpec accelerates baseline methods in latency up to 62% for Medusa and 9-17\% for EAGLE on text generation benchmarks, and it achieves 2.84$\times$ overall speedup on the Llama-2-13B model using third-party evaluation criteria.

Primary Area: foundation or frontier models, including LLMs

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Submission Number: 4021