All verified against torch, which IS available here - only spconv was not. So unlike
the submanifold conv these compare against upstream's real semantics rather than a
hand-written paraphrase. 6/6 pass, max err 9.5e-7.
The norm distinction is the trap: LayerNorm32 is applied to x.feats (per-voxel over
channels) while SparseGroupNorm32 reshapes [N_b,C] -> [1,C,N_b] per batch item, so its
statistics span channels-in-group AND voxels. Both produce identical shapes, so a mixup
is numerically silent - there is an explicit test asserting the group norm does NOT
match a per-voxel group_norm.
Architecture confirmed from the converted weights rather than constructor defaults:
no rope, no qk_rms_norm, transformer norms non-affine, ResBlock norm1 affine/norm2 not.
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