Summary of Amphista: Bi-directional Multi-head Decoding For Accelerating Llm Inference, by Zeping Li et al.
Amphista: Bi-directional Multi-head Decoding for Accelerating LLM Inference
by Zeping Li, Xinlong Yang, Ziheng Gao, Ji Liu, Guanchen Li, Zhuang Liu, Dong Li, Jinzhang Peng, Lu Tian, Emad Barsoum
First submitted to arxiv on: 19 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces Amphista, an enhanced speculative decoding framework that builds upon Medusa to overcome the limitations of large language models (LLMs) using autoregressive decoding. Specifically, Amphista incorporates bi-directional attention and Auto-embedding Blocks for parallel inference, as well as Staged Adaptation Layers for seamless transition between autoregressive and non-autoregressive inference. The authors demonstrate the effectiveness of Amphista on Vicuna models using MT-Bench and Spec-Bench, achieving substantial acceleration while maintaining generation quality. Compared to vanilla autoregressive decoding and Medusa, Amphista delivers up to 2.75speedup in wall-clock time. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research introduces a new way for computers to understand language more quickly. The problem is that current language models are slow because they process information one step at a time. The authors created a new framework called Amphista, which allows the computer to process information simultaneously, making it much faster. They tested this new approach on a special dataset and found that it was significantly faster than the old way, while still maintaining good quality. |
Keywords
» Artificial intelligence » Attention » Autoregressive » Embedding » Inference
