Summary of Layerskip: Enabling Early Exit Inference and Self-speculative Decoding, by Mostafa Elhoushi et al.
LayerSkip: Enabling Early Exit Inference and Self-Speculative Decoding
by Mostafa Elhoushi, Akshat Shrivastava, Diana Liskovich, Basil Hosmer, Bram Wasti, Liangzhen Lai, Anas Mahmoud, Bilge Acun, Saurabh Agarwal, Ahmed Roman, Ahmed A Aly, Beidi Chen, Carole-Jean Wu
First submitted to arxiv on: 25 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| 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 presents LayerSkip, an end-to-end solution to accelerate inference for large language models (LLMs). The authors achieve this by applying layer dropout during training, with varying dropout rates and early exit loss. This recipe enables earlier layers to be more accurate in exiting the model without introducing additional modules. During inference, they propose a novel self-speculative decoding approach that leverages shared compute and activations between verification and draft stages. Experiments are conducted on various Llama models for pretraining, continual pretraining, finetuning, and specific tasks, demonstrating speedups of up to 2.16x on summarization, 1.82x on coding, and 2.0x on semantic parsing. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LayerSkip is a new way to make big language models run faster. It helps earlier parts of the model get better at knowing when to stop working by training them in a special way. This makes it more efficient without adding any extra pieces to the model. During actual use, LayerSkip uses a clever decoding method that saves memory and uses shared computer power. The authors tested their approach on different language models and tasks, showing speedups of up to 2 times. |
Keywords
» Artificial intelligence » Dropout » Inference » Llama » Pretraining » Semantic parsing » Summarization
