HybridNorm: Towards Stable and Efficient Transformer Training via Hybrid
  Normalization
Authors: name: Zhijian Zhuo, name: Yutao Zeng, name: Ya Wang, name: Sijun Zhang, name: Jian Yang, name: Xiaoqing Li, name: Xun Zhou, name: Jinwen Ma

Transformers have become the de facto architecture for a wide range of
machine learning tasks, particularly in large language models (LLMs). Despite
their remarkable performance, challenges remain in training deep transformer
networks, especially regarding the location of layer normalization. While
Pre-Norm structures facilitate easier training due to their more prominent
identity path, they often yield suboptimal performance compared to Post-Norm.
In this paper, we propose HybridNorm, a straightforward yet
effective hybrid normalization strategy that integrates the advantages of both
Pre-Norm and Post-Norm approaches. Specifically, HybridNorm employs QKV
normalization within the attention mechanism and Post-Norm in the feed-forward
network (FFN) of each transformer block. This design not only stabilizes
training but also enhances performance, particularly in the context of LLMs.
Comprehensive experiments in both dense and sparse architectures show that
HybridNorm consistently outperforms both Pre-Norm and Post-Norm approaches,
achieving state-of-the-art results across various benchmarks. These findings
highlight the potential of HybridNorm as a more stable and effective technique
for improving the training and performance of deep transformer models. %Code
will be made publicly available. Code is available at
https://github.com/BryceZhuo/HybridNorm.