Preserve Complex Bingham single-point autograd - #5258
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August 7, 2026 17:53
FlorianPfaff
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Bug
ComplexBinghamDistribution.pdf(...)keeps backend-native values for batched evaluation, but the single-point path returnedfloat(p[0]).On the PyTorch backend, a complex query tensor with
requires_grad=Truetherefore produced a Pythonfloat. That conversion silently detached the likelihood from the computation graph, so callers could not differentiate a single-point Complex Bingham density even though the equivalent batched path remained differentiable.Fix
Return the backend scalar
p[0]directly for single-point evaluation instead of converting it to Pythonfloat.This preserves the numerical value while retaining the active backend's scalar type and PyTorch autograd history.
Regression coverage
Add a PyTorch-backend regression that evaluates a unit-norm complex point with
requires_grad=Trueand verifies:float64on the same device as the query;Validation
float(...)turns a differentiable PyTorch scalar into a plain Python float and prevents.backward();main(f196ec41d1d8681e279936db9f1a0a3abfa276e7) and is 2 commits ahead / 0 behind;