EF21: A New, Simpler, Theoretically Better, and Practically Faster Error Feedback

TL;DR

EF21 introduces a simplified and theoretically sound error feedback mechanism for distributed training, outperforming previous methods in convergence speed and applicability without relying on strong assumptions.

Contribution

We propose EF21, a new error feedback method with improved theoretical guarantees and practical performance, applicable to heterogeneous data and nonconvex problems.

Findings

EF21 achieves an $O(1/T)$ convergence rate for nonconvex problems.

EF21 attains a linear convergence rate for PL functions.

EF21 outperforms previous EF methods in practice and theory.

Abstract

Error feedback (EF), also known as error compensation, is an immensely popular convergence stabilization mechanism in the context of distributed training of supervised machine learning models enhanced by the use of contractive communication compression mechanisms, such as Top-$k$. First proposed by Seide et al (2014) as a heuristic, EF resisted any theoretical understanding until recently [Stich et al., 2018, Alistarh et al., 2018]. However, all existing analyses either i) apply to the single node setting only, ii) rely on very strong and often unreasonable assumptions, such global boundedness of the gradients, or iterate-dependent assumptions that cannot be checked a-priori and may not hold in practice, or iii) circumvent these issues via the introduction of additional unbiased compressors, which increase the communication cost. In this work we fix all these deficiencies by proposing and analyzing a new EF mechanism, which we call EF21, which consistently and substantially outperforms EF in practice. Our theoretical analysis relies on standard assumptions only, works in the distributed heterogeneous data setting, and leads to better and more meaningful rates. In particular, we prove that EF21 enjoys a fast $O(1/T)$ convergence rate for smooth nonconvex problems, beating the previous bound of $O(1/T^{2/3})$, which was shown a bounded gradients assumption. We further improve this to a fast linear rate for PL functions, which is the first linear convergence result for an EF-type method not relying on unbiased compressors. Since EF has a large number of applications where it reigns supreme, we believe that our 2021 variant, EF21, can a large impact on the practice of communication efficient distributed learning.