Read the equation
The sigmoid 1/(1+exp(−z)) is applied to each expert logit independently. It compresses each finite real logit into (0,1), without normalizing across experts. These values are routing affinities; they become relative mixture weights only after gathering selected entries and normalizing them.
Terms & definitions
| Term | Meaning in this source |
|---|---|
| logits | Raw expert logits |
| scores | Independent sigmoid affinities |
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
logits = F.linear(hidden_states.type(torch.float32),
self.weight.type(torch.float32), None)
if self.scoring_func == "sigmoid":
scores = logits.sigmoid()
else:
raise NotImplementedError(
f"insupportable scoring function for MoE gating: {self.scoring_func}"Exact original notation
scores = logits.sigmoid()