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The scale fit computed its normalisation constant 1/sum(F_obs^2) via .double(), which raises on MPS: the backend has no float64, and PYTORCH_ENABLE_MPS_FALLBACK covers unimplemented ops, not unsupported dtypes. This broke every test reaching get_scales()/refine_scaler() on the Accelerator CI job, and equally breaks a real TORCHREF_DEVICE=mps refinement. The float64 was unnecessary rather than misplaced. The constant rescales every term of the objective identically, so its own precision cancels out of the minimiser, the gradient direction and the likelihood:penalty ratio; it only has to be finite, positive and O(1)-producing. float32 gives it to ~5e-8 relative on real work sets (torch.sum reduces pairwise, so error grows like log(N)*eps, not N*eps). Also stop the batched direct-summation paths allocating their accumulator as complex128 regardless of configuration, which raises on MPS once max_memory_gb splits the reflection loop and made the batched output dtype disagree with the unbatched one. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ldd5qF6gWrhgHMiv7FcfW1
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Version 0.6.4
nllto unit-weightlslog_scalewith a Chebyshev polynomialc_isoin sin(theta)/lambdak_sol exp(-ln2 (ss/ss_half)^n), merged sigmoid exponential form