Read the equation
Multiply each feature by 1/√(mean(x²)+epsilon). Positive epsilon prevents division by zero and changes exact unit-RMS normalization near the zero vector. Unlike LayerNorm, this operation does not center the features.
Terms & definitions
| Term | Meaning in this source |
|---|---|
| variance | Previously computed mean square |
| variance_epsilon | Positive numerical stabilizer |
How it works
Read the definition from its right-hand expression to the quantity on the left. Its surrounding source records the intended input, state, or parameter context. Where the source leaves an operation unspecified, this page preserves that gap.
Assumptions & limits
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Original context
Read the surrounding source passage
input_dtype = hidden_states.dtype
hidden_states = hidden_states.to(torch.float32)
variance = hidden_states.pow(2).mean(-1, keepdim=True)
hidden_states = hidden_states * torch.rsqrt(variance +
self.variance_epsilon)
return self.weight * hidden_states.to(input_dtype)Exact original notation
hidden_states = hidden_states * torch.rsqrt(variance +
self.variance_epsilon)Related builds
Sources & provenance
hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)