Compare RMSNorm and LayerNorm in Transformer Blocks

Contrast LayerNorm with RMSNorm as used within a transformer module. Provide their mathematical expressions, clarify the impact of skipping mean removal, and consider the motivations that make RMSNorm a favorable builder's pick.

Constraints & Assumptions

Carry out normalization along the hidden feature axis of an individual token. Rely on the usual affine LayerNorm and scale-exclusive RMSNorm specs; actual implementations might differ in learned bias, epsilon positioning, and numerical precision.

Clarifying Questions

What dimensions get normalized? Does the method incorporate a trainable bias? Does normalization occur prior to or following the sublayer? Which floating-point format is applied during summation steps?

What a Strong Answer Covers

Separate variance-based from mean-squared normalization, note their invariant properties and compute cost gaps, and refrain from stating that either scheme universally yields superior networks.

Follow-up Questions

How does the output change when an identical constant is summed to each feature? In what way do epsilon and reduced-precision math alter the outcome? Can RMSNorm ensure its output has zero mean?

Overview: Set alongside the LayerNorm and RMSNorm formulas, their centering, shift traits, feature-axis scope, numeric precision, and the pros and cons of a leaner normalization approach.