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LayerNorm

Layer Normalization

Normalizes inputs across the feature dimension, computing mean and variance over the last dimension. Essential component in transformer architectures.

Parameters:

  • dim: Normalization dimension (size of the feature axis)

Shape Contract:

  • Input: [*shape, dim] where dim is the normalized dimension
  • Output: [*shape, dim] same shape as input

Notes:

  • Normalizes over last dimension only (per-example normalization)
  • Includes learnable scale (gamma) and shift (beta) parameters
  • More stable than BatchNorm for variable-length sequences
  • Standard in transformers (BERT, GPT, etc.)

Signature​

neuron LayerNorm(dim)

Ports​

Inputs:

  • default: [*shape, dim]

Outputs:

  • default: [*shape, dim]

Implementation​

"from core import normalization/LayerNorm"
Source { source: "core", path: "normalization/LayerNorm" }