Lossless Acceleration of Large Language Model via Adaptive N-gram Parallel Decoding
<|reference_start|>Lossless Acceleration of Large Language Model via Adaptive N-gram Parallel Decoding: While Large Language Models (LLMs) have shown remarkable abilities, they are hindered by significant resource consumption and considerable latency due to autoregressive processing. In this study, we introduce Adaptive N-gram Parallel Decoding (ANPD), an innovative and lossless approach that accelerates inference by allowing the simultaneous generation of multiple tokens. ANPD incorporates a two-stage approach: it begins with a rapid drafting phase that employs an N-gram module, which adapts based on the current interactive context, followed by a verification phase, during which the original LLM assesses and confirms the proposed tokens. Consequently, ANPD preserves the integrity of the LLM's original output while enhancing processing speed. We further leverage a multi-level architecture for the N-gram module to enhance the precision of the initial draft, consequently reducing inference latency. ANPD eliminates the need for retraining or extra GPU memory, making it an efficient and plug-and-play enhancement. In our experiments, models such as LLaMA and its fine-tuned variants have shown speed improvements up to 3.67x, validating the effectiveness of our proposed ANPD.<|reference_end|>
@article{ou2024lossless,
    title={Lossless Acceleration of Large Language Model via Adaptive N-gram
  Parallel Decoding},
    author={Jie Ou, Yueming Chen, Wenhong Tian},
    journal={arXiv preprint arXiv:2404.08698},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.08698},
    primaryClass={cs.CL cs.LG}
}