DNT: a Deeply Normalized Transformer that can be trained by Momentum SGD

TL;DR

This paper introduces Deeply Normalized Transformers (DNT), enabling training with vanilla momentum SGD by normalization techniques that stabilize gradient distributions, matching the performance of AdamW-based training.

Contribution

DNT's novel normalization approach allows Transformers to be trained effectively with momentum SGD, simplifying training and maintaining high performance.

Findings

DNT outperforms ViT and GPT in experiments.

DNT can be trained with vanilla mSGDW effectively.

Normalization stabilizes gradient distributions in Transformers.

Abstract

Transformers have become the de facto backbone of modern deep learning, yet their training typically demands an advanced optimizer with adaptive learning rate like AdamW, rather than a momentum SGDW (mSGDW). Previous works show that it is mainly due to a heavy-tailed distribution of the gradients. In this paper, we introduce a Deeply Normalized Transformer (DNT), which is meticulously engineered to overcome this limitation enabling seamless training with vanilla mSGDW while yielding comparable performance to the Transformers trained via AdamW. To be specific, in DNT, we strategically integrate normalization techniques at proper positions in the Transformers to effectively modulate the Jacobian matrices of each layer, balance the influence of weights, activations, and their interactions, and thus enable the distributions of gradients concentrated. We provide both theoretical justifications of the normalization technique used in our DNT and extensive empirical evaluation on two popular Transformer architectures to validate that: a) DNT outperforms its counterparts (\ie, ViT and GPT), and b) DNT can be effectively trained with vanilla mSGDW.