  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.
