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Layer Normalization vs Batch Normalization

Difference between Batch Normalization and Layer Normalization. LN operates across feature dimensions for each sample independently.

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Limitations of Batch Normalization

Batch Normalization (BN) helps training efficiency by positioning activations in a trainable way. However, it has limitations with small batch sizes since it operates across the batch dimension and normalizes activations for each feature/channel across the batch. Smaller batch sizes result in inaccurate statistics. This is particularly problematic in LLMs that require small mini-batches due to memory constraints.

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Layer Normalization

For architectures such as recurrent networks and transformers, we apply Layer Normalization. The layer normalization of an input vector x∈Rdx \in \mathbb{R}^d is computed as: LayerNorm(x)=γ⊙x−μσ2+ϵ+β\text{LayerNorm}(x) = \gamma \odot \frac{x - \mu}{\sqrt{\sigma^2 + \epsilon}} + \beta where the mean μ\mu and variance σ2\sigma^2 are: μ=1d∑i=1dxi,σ2=1d∑i=1d(xi−μ)2\mu = \frac{1}{d} \sum_{i=1}^{d} x_i, \quad \sigma^2 = \frac{1}{d} \sum_{i=1}^{d} (x_i - \mu)^2 Here:
  • γ\gamma and β\beta are learnable parameters (of shape dd)
  • ϵ\epsilon is a small constant for numerical stability
  • ⊙\odot denotes element-wise multiplication

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Key Difference from Batch Normalization

Layer Normalization operates across the feature dimensions for each sample independently, normalizing the activations in a trainable way. This is effectively the transpose of Batch Normalization:

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When to Use Layer Normalization

  • Recurrent Neural Networks (RNNs): Variable sequence lengths make BN difficult
  • Transformers: Self-attention mechanisms benefit from LN
  • Small batch training: When memory constraints limit batch size
  • Online learning: Single-sample updates

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References

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PyTorch reference

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