onnx: fix LayerNorm output dtype mismatch with F16 inputs#2276
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The LayerNorm op's `wire` expansion casts `normalized` back to
fact.datum_type *before* applying scale/bias, then multiplies that
result with `cast_scale` (which is still in self.datum_type, F32).
With F16 inputs this becomes F16 × F32, whose output is downgraded to
F32 by `mul()`. The inference rule then asserts
`outputs[0].datum_type == inputs[0].datum_type` (F16) against the
actual F32 output, failing `into_typed()` with:
Output mismatch after rewiring expansion for output #0:
expected 1,256,384,F16 got 1,256,384,F32
Fix: defer the cast back to fact.datum_type until after all scale/bias
operations. Now the expansion stays entirely in self.datum_type (F32)
through normalized × scale + bias, and casts only the final result.
Behavior is unchanged for F32 inputs (the final cast is a no-op when
fact.datum_type == self.datum_type).
Reproduced with sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
exported via `optimum.exporters.onnx.main_export(..., dtype="fp16")`
and loaded with `into_optimized().into_runnable()`.
kali
approved these changes
May 26, 2026
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The LayerNorm op's
wireexpansion castsnormalizedback to fact.datum_type before applying scale/bias, then multiplies that result withcast_scale(which is still in self.datum_type, F32).With F16 inputs this becomes F16 × F32, whose output is downgraded to F32 by
mul(). The inference rule then assertsoutputs[0].datum_type == inputs[0].datum_type(F16) against the actual F32 output, failinginto_typed()with:Fix: defer the cast back to fact.datum_type until after all scale/bias operations. Now the expansion stays entirely in self.datum_type (F32) through normalized × scale + bias, and casts only the final result.
Behavior is unchanged for F32 inputs (the final cast is a no-op when fact.datum_type == self.datum_type).
Reproduced with sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 exported via
optimum.exporters.onnx.main_export(..., dtype="fp16")and loaded withinto_optimized().into_runnable().