Started fine on the mechanics: mean and variance computed across the feature dimension for each sample independently, then you normalize and apply the learnable scale and shift.
Start by clearly defining Layer Normalization and walking through the computation of mean and variance across the feature dimension for each sample. Then explain the learnable scale and shift parameters and the normalization formula. Finally, contrast with Batch Normalization and highlight why LayerNorm is better suited for Transformers, emphasizing independence from batch size and sequence length.
Pro tip: Mention that LayerNorm's per-sample normalization makes it robust to variable sequence lengths and small batch sizes, which is crucial for training large Transformer models like those used in production at Snapchat.
Explain that LayerNorm normalizes the activations across the feature dimension for each individual sample, independently of other samples in the batch.
Describe how the mean and variance are calculated over the feature dimension (or specified normalized dimensions) for each sample, typically using the formula: μ = (1/H) Σ x_i, σ² = (1/H) Σ (x_i - μ)².
Show the normalization formula: y = γ * (x - μ) / √(σ² + ε) + β, where γ and β are learnable scale and shift parameters, and ε is a small constant for numerical stability.
Contrast LayerNorm with BatchNorm: BatchNorm computes statistics across the batch dimension, which depends on batch size and is problematic for variable-length sequences; LayerNorm computes per sample, making it independent of batch size.
Highlight that Transformers process sequences with varying lengths and often use small batches; LayerNorm's per-sample normalization ensures consistent behavior during training and inference, and avoids issues with padding and batch statistics.
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