Sparse LLMs at inference: 6x faster transformers! | DEJAVU paper explained

Contextual sparsity: Take an LLM and make it sparse at inference time. In this video, we explain how the DEJAVU method implements contextual sparsity.
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📜 Liu, Zichang, Jue Wang, Tri Dao, Tianyi Zhou, Binhang Yuan, Zhao Song, Anshumali Shrivastava et al. "Deja vu: Contextual sparsity for efficient llms at inference time." In International Conference on Machine Learning, pp. 22137-22176. PMLR, 2023. https://arxiv.org/abs/2310.17157

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Outline:
00:00 DEJAVU explained
02:58 Sparse neural networks
04:06 Why static sparsity hurts
04:43 Contextual sparsity
05:40 DEJAVU method
07:59 Speedups!
08:52 MoE: Connection to Mixture of Experts
09:38 Theoretical insights: Why can we make MLPs sparsity
10:36 Why can we make attention sparse?
11:38 Attention does Mean-shift clustering!

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Video editing: Nils Trost
Music 🎵 : Sunday Rain - Cheel