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BN_FFN_BN: Leveraging Batch Normalization for Vision Transformers

December 2021

tl;dr: Introducing BN into vision based transformers via a BN-FFN-BN structure.

Inherited from the NLP tasks, vision transformers take Layernorm (LN)as default normalization technique when applied to vision tasks.

This is an alternative to PowerNorm to replace LN in transformers.

BN is faster in inference than LN due to avoidance of calculating the mean and variance statistics during inference.
LN is suitable for NLP tasks as the input has variable length, and LN in NLP only calculates statistics in the channel dimension without involving batch or sequence length dimension.
Naively replacing LN with BN leads to crashes. The crashes are due to un-normalized FFN blocks. Thus a BN layer is added in-between the two linear layers in each FFN block. This leads to stabilization of training and 20% faster inference.

Normalization methods: batch-related and batch-irrelevant.
- LN is best suited for NLP tasks
- GN is good for small batch size and for dense prediction tasks
- IN is good for style transfers
For a 4D tensor input (NCHW)
- BN has 2C elements of statistics, where each mean and var are computed across NHW in training. In inference, the averaged values are used without recalculating.
- LN normalizes input along C leading to 2NHW statistics. It calculates statistics for each sample independently. This requires calculation in both training and evaluation.