Toward Understanding Why Adam Converges Faster Than SGD for Transformers

TL;DR

This paper investigates why Adam converges faster than SGD for training transformers, introducing the concept of directional sharpness and proposing coordinate-wise clipping to improve convergence speed.

Contribution

It introduces directional sharpness as a key factor in optimization performance and proposes coordinate-wise clipping to enhance SGD convergence.

Findings

Adam exhibits lower directional sharpness than SGD.

Coordinate-wise clipping reduces sharpness and accelerates convergence.

Clipping improves local loss reduction in scenarios with few problematic coordinates.

Abstract

While stochastic gradient descent (SGD) is still the most popular optimization algorithm in deep learning, adaptive algorithms such as Adam have established empirical advantages over SGD in some deep learning applications such as training transformers. However, it remains a question that why Adam converges significantly faster than SGD in these scenarios. In this paper, we propose one explanation of why Adam converges faster than SGD using a new concept directional sharpness. We argue that the performance of optimization algorithms is closely related to the directional sharpness of the update steps, and show SGD has much worse directional sharpness compared to adaptive algorithms. We further observe that only a small fraction of the coordinates causes the bad sharpness and slow convergence of SGD, and propose to use coordinate-wise clipping as a solution to SGD and other optimization algorithms. We demonstrate the effect of coordinate-wise clipping on sharpness reduction and speeding up the convergence of optimization algorithms under various settings. We show that coordinate-wise clipping improves the local loss reduction when only a small fraction of the coordinates has bad sharpness. We conclude that the sharpness reduction effect of adaptive coordinate-wise scaling is the reason for Adam's success in practice and suggest the use of coordinate-wise clipping as a universal technique to speed up deep learning optimization.