Layer Normalization Variants are extensions and modifications of the standard LayerNorm — adapting the normalization computation for specific architectures, modalities, or efficiency requirements.

Key Variants

• Pre-Norm: LayerNorm applied before the attention/FFN (used in GPT-2+). More stable for deep transformers.
• Post-Norm: LayerNorm applied after the attention/FFN (original Transformer). Better final quality but harder to train deeply.
• RMSNorm: Removes the mean-centering step. Only normalizes by root mean square. Used in LLaMA, Gemma.
• DeepNorm: Scales residual connections to enable training 1000-layer transformers.
• QK-Norm: Applies LayerNorm to query and key vectors in attention (prevents attention logit growth).

Why It Matters

• Architecture-Dependent: The choice of normalization variant significantly impacts training stability and final performance.
• Scaling: Pre-Norm + RMSNorm is standard for billion-parameter LLMs due to training stability.
• Research: Active area with new variants proposed regularly as architectures evolve.

LayerNorm Variants are the normalization toolkit for transformers — each variant tuned for a specific architectural need.