Dev Tools · 2h ago
LayerNorm vs BatchNorm: Why Transformers Normalize Per Token
LayerNorm and BatchNorm both standardize activations then rescale, but differ in the axis of normalization. BatchNorm averages across batch samples, causing issues with small batches and variable-length sequences. LayerNorm averages across features per sample, making it batch-size independent and ideal for Transformers.
Meridian48 take
This is a clear, practical explanation of a fundamental design choice that enables Transformers to handle variable-length sequences and single-sample inference without breaking.
Read the full reporting
LayerNorm vs BatchNorm: why Transformers normalize per token, not per batch →
DEV Community
normalizationtransformers