Summary of Hydra: Sequentially-dependent Draft Heads For Medusa Decoding, by Zachary Ankner et al.
Hydra: Sequentially-Dependent Draft Heads for Medusa Decoding
by Zachary Ankner, Rishab Parthasarathy, Aniruddha Nrusimha, Christopher Rinard, Jonathan Ragan-Kelley, William Brandon
First submitted to arxiv on: 7 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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Summary difficulty | Written by | Summary |
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High | Paper authors | High Difficulty Summary Read the original abstract here |
Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper tackles the memory bandwidth limitations of autoregressive language model inference by proposing Hydra heads, a new type of draft head that improves the accuracy of speculative decoding. Building on previous work in Medusa decoding, Hydra heads are sequentially-dependent lightweight models that operate on the base model’s hidden states to propose candidate continuations of an input sequence. The authors explore different training objectives and architectures for Hydra heads and present a carefully tuned recipe called Hydra++ that achieves significant improvements in decoding throughput compared to existing methods. |
Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes language models faster by creating new “heads” that help predict what comes next in a sentence. These heads, called Hydra heads, are like mini-brains that work together with the main model to make better predictions. This is important because current models can get stuck trying to figure out what comes next, which slows them down. The authors show that their new Hydra++ recipe makes the prediction process faster by up to 2.7 times! They’re sharing their code so others can use it too. |
Keywords
* Artificial intelligence * Autoregressive * Inference * Language model