Big Bird: Transformers for Longer Sequences (Paper Explained)

#ai #nlp #attention

The quadratic resource requirements of the attention mechanism are the main roadblock in scaling up transformers to long sequences. This paper replaces the full quadratic attention mechanism by a combination of random attention, window attention, and global attention. Not only does this allow the processing of longer sequences, translating to state-of-the-art experimental results, but also the paper shows that BigBird comes with theoretical guarantees of universal approximation and turing completeness.

OUTLINE:
0:00 - Intro & Overview
1:50 - Quadratic Memory in Full Attention
4:55 - Architecture Overview
6:35 - Random Attention
10:10 - Window Attention
13:45 - Global Attention
15:40 - Architecture Summary
17:10 - Theoretical Result
22:00 - Experimental Parameters
25:35 - Structured Block Computations
29:30 - Recap
31:50 - Experimental Results
34:05 - Conclusion

Paper: https://arxiv.org/abs/2007.14062

My Video on Attention: https://youtu.be/iDulhoQ2pro
My Video on BERT: https://youtu.be/-9evrZnBorM
My Video on Longformer: https://youtu.be/_8KNb5iqblE
... and its memory requirements: https://youtu.be/gJR28onlqzs

Abstract:
Transformers-based models, such as BERT, have been one of the most successful deep learning models for NLP. Unfortunately, one of their core limitations is the quadratic dependency (mainly in terms of memory) on the sequence length due to their full attention mechanism. To remedy this, we propose, BigBird, a sparse attention mechanism that reduces this quadratic dependency to linear. We show that BigBird is a universal approximator of sequence functions and is Turing complete, thereby preserving these properties of the quadratic, full attention model. Along the way, our theoretical analysis reveals some of the benefits of having O(1) global tokens (such as CLS), that attend to the entire sequence as part of the sparse attention mechanism. The proposed sparse attention can handle sequences of length up to 8x of what was previously possible using similar hardware. As a consequence of the capability to handle longer context, BigBird drastically improves performance on various NLP tasks such as question answering and summarization. We also propose novel applications to genomics data.

Authors: Manzil Zaheer, Guru Guruganesh, Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, Amr Ahmed

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Видео Big Bird: Transformers for Longer Sequences (Paper Explained) канала Yannic Kilcher