[Torch] Fix batch norm decomposition dtype for mixed-precision inputs#4483
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[Torch] Fix batch norm decomposition dtype for mixed-precision inputs#4483
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Use the running stats dtype instead of the input dtype when reshaping `running_mean`/`running_var` in `DecomposeAtenNativeBatchNormOp`. When the input is e.g. bf16 with f32 running stats, the reshape was producing `aten.view` ops with a bf16 result type from an f32 input, which is invalid. Fixes llvm#4480 Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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`aten.view` maps to the shape-only overload (`aten::view(Tensor, SymInt[])`), which preserves dtype. Without a verifier, invalid IR with mismatched input/output element types reaches `genericViewLikeFold` and crashes with an assertion failure in `DenseElementsAttr::get`. Fixes llvm#4479 Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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DecomposeAtenNativeBatchNormOpused the input dtype when reshapingrunning_mean/running_varfrom[C]to[1,C,1,...]. When the input has a different dtype from the running stats (e.g. bf16 input with f32 running stats), this produced invalidaten.viewops that change the element type.Fix by using each value's own dtype for its reshape result type. Also add a verifier to
aten.viewthat rejects element type mismatches between input and output.Fixes #4480
Fixes #4479