On Layer Normalization in the Transformer Architecture

TL;DR

This paper analyzes how the placement of layer normalization in Transformers affects training stability and proposes that Pre-LN Transformers can be trained without warm-up stages, reducing training time and hyper-parameter tuning.

Contribution

The paper provides a theoretical explanation for the effectiveness of layer normalization placement and demonstrates that Pre-LN Transformers can be trained without warm-up, unlike Post-LN models.

Findings

Pre-LN Transformers have well-behaved gradients at initialization.

Removing warm-up stages does not harm performance for Pre-LN models.

Pre-LN models train faster and require less hyper-parameter tuning.

Abstract

The Transformer is widely used in natural language processing tasks. To train a Transformer however, one usually needs a carefully designed learning rate warm-up stage, which is shown to be crucial to the final performance but will slow down the optimization and bring more hyper-parameter tunings. In this paper, we first study theoretically why the learning rate warm-up stage is essential and show that the location of layer normalization matters. Specifically, we prove with mean field theory that at initialization, for the original-designed Post-LN Transformer, which places the layer normalization between the residual blocks, the expected gradients of the parameters near the output layer are large. Therefore, using a large learning rate on those gradients makes the training unstable. The warm-up stage is practically helpful for avoiding this problem. On the other hand, our theory also shows that if the layer normalization is put inside the residual blocks (recently proposed as Pre-LN Transformer), the gradients are well-behaved at initialization. This motivates us to remove the warm-up stage for the training of Pre-LN Transformers. We show in our experiments that Pre-LN Transformers without the warm-up stage can reach comparable results with baselines while requiring significantly less training time and hyper-parameter tuning on a wide range of applications.