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arXiv · 2402.09268

Transformers, parallel computation, and logarithmic depth

Clayton Sanford·Daniel Hsu·Matus Telgarsky
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Abstract

We show that a constant number of self-attention layers can efficiently simulate, and be simulated by, a constant number of communication rounds of Massively Parallel Computation. As a consequence, we show that logarithmic depth is sufficient for transformers to solve basic computational tasks that cannot be efficiently solved by several other neural sequence models and sub-quadratic transformer approximations. We thus establish parallelism as a key distinguishing property of transformers.

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Clayton Sanford, Daniel Hsu, Matus Telgarsky. 2024-02-14. Transformers, parallel computation, and logarithmic depth. https://arxiv.org/abs/2402.09268

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