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Root Mean Square Layer Normalization (RMSNorm) is a normalization technique that normalizes the input by the root mean square of its elements. Variants:
  • Standard RMSNorm: basic RMS normalization that scales input by RMS and applies learned weight parameters
  • Fused Add RMSNorm: adds residual connection before normalization in a single fused operation
Axes (2 dimensions):
  • batch_size: variable
  • hidden_size: constant
Inputs (2 or 3 tensors):
  • hidden_states: [batch_size, hidden_size]
  • weight: [hidden_size]
  • For Fused Add RMSNorm only:
    • residual: [batch_size, hidden_size]
Outputs (1 tensor):
  • output: [batch_size, hidden_size]
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