3PC: Three Point Compressors for Communication-Efficient Distributed Training and a Better Theory for Lazy Aggregation
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Date
2022
Publication Type
Conference Paper
ETH Bibliography
yes
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Abstract
We propose and study a new class of gradient compressors for communication-efficient training—three point compressors (3PC)—as well as efficient distributed nonconvex optimization algorithms that can take advantage of them. Unlike most established approaches, which rely on a static compressor choice (e.g., TopK), our class allows the compressors to evolve throughout the training process, with the aim of improving the theoretical communication complexity and practical efficiency of the underlying methods. We show that our general approach can recover the recently proposed state-of-the-art error feedback mechanism EF21 (Richtárik et al, 2021) and its theoretical properties as a special case, but also leads to a number of new efficient methods. Notably, our approach allows us to improve upon the state-of-the-art in the algorithmic and theoretical foundations of the lazy aggregation literature (Liu et al, 2017; Lan et al, 2017). As a by-product that may be of independent interest, we provide a new and fundamental link between the lazy aggregation and error feedback literature. A special feature of our work is that we do not require the compressors to be unbiased.
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Publication status
published
Book title
Proceedings of the 39th International Conference on Machine Learning
Journal / series
Volume
162
Pages / Article No.
18596 - 18648
Publisher
PMLR
Event
39th International Conference on Machine Learning (ICML 2022)
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Methods
Software
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Organisational unit
09729 - He, Niao / He, Niao
02219 - ETH AI Center / ETH AI Center