Preserve finite gradients when Smooth L1 beta is zero

[Afloat (Afloat16)]

With beta=0, the current torch.where computes the quadratic branch with a zero denominator even though the forward result selects L1. Backward can then produce NaN gradients, including for exact predictions.

Use PyTorch's native Smooth L1 implementation, which defines the zero-beta L1 limit while preserving the existing positive-beta mean reduction.

Regression tests check zero-beta values and gradients (including all exact matches), and compare positive-beta values/gradients with an explicit piecewise oracle. Run python -m unittest discover -s tests -p test_trellis2_smooth_l1_zero.py -v.

Validation: native CPU regression tests pass on this branch; the same tests fail against the pinned original source. Validation run.

Exact-head validation: all 3 native CPU regression tests pass at bda7c9bdc428167a5398a919abaf5441ae8079e4. Restoring the original production files at 75fbf0183001ed9876c8dbb35de6b68552ee08bd makes the same complete suite fail on the original behavior, with no skipped tests or import failures. Candidate and original-source logs.

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