Sparse Expert Models (Switch Transformers, GLAM, and more... w/ the Authors)

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3 years ago
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nlp sparsity transformers

#nlp #sparsity #transformers

This video is an interview with Barret Zoph and William Fedus of Google Brain about Sparse Expert Models.
Sparse Expert models have been hugely successful at distributing parts of models, mostly Transformers, across large array of machines and use a routing function to effectively route signals between them. This means that even though these models have a huge number of parameters, the computational load for a given signal does not increase because the model is only sparsely activated. Sparse expert models, such as Switch Transformers and GLAM can scale up to trillions of parameters and bring a number of desirable properties. We discuss everything from the fundamentals, history, strengths and weaknesses, up to the current state of the art of these models.

OUTLINE:
0:00 - Intro
0:30 - What are sparse expert models?
4:25 - Start of Interview
5:55 - What do you mean by sparse experts?
8:10 - How does routing work in these models?
12:10 - What is the history of sparse experts?
14:45 - What does an individual expert learn?
19:25 - When are these models appropriate?
22:30 - How comparable are sparse to dense models?
26:30 - How does the pathways system connect to this?
28:45 - What improvements did GLAM make?
31:30 - The "designing sparse experts" paper
37:45 - Can experts be frozen during training?
41:20 - Can the routing function be improved?
47:15 - Can experts be distributed beyond data centers?
50:20 - Are there sparse experts for other domains than NLP?
52:15 - Are sparse and dense models in competition?
53:35 - Where do we go from here?
56:30 - How can people get started with this?

Papers:
Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity (https://arxiv.org/abs/2101.03961)
GLaM: Efficient Scaling of Language Models with Mixture-of-Experts (https://arxiv.org/abs/2112.06905)
Designing Effective Sparse Expert Models (https://arxiv.org/abs/2202.08906)

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