Layer Normalization

This is a PyTorch implementation of Layer Normalization.

Limitations of Batch Normalization

• You need to maintain running means.
• Tricky for RNNs. Do you need different normalizations for each step?
• Doesn't work with small batch sizes; large NLP models are usually trained with small batch sizes.
• Need to compute means and variances across devices in distributed training.

Layer Normalization

Layer normalization is a simpler normalization method that works on a wider range of settings. Layer normalization transforms the inputs to have zero mean and unit variance across the features. Note that batch normalization fixes the zero mean and unit variance for each element. Layer normalization does it for each batch across all elements.

Layer normalization is generally used for NLP tasks.

We have used layer normalization in most of the transformer implementations.