Multi-vendor GPU (PR4): cuperiod doctor + PyTorch install/backend docs - #12
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tjayasinghe
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Jun 30, 2026
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PR4 of the multi-vendor GPU effort — the install UX for the torch backend. - cli: new `cuperiod doctor` command — reports installed backends, the NVIDIA CUDA fast paths, the portable torch backend and each device it sees (CUDA/ROCm/MPS/XPU/CPU) with the precision each uses, and what `backend="auto"` resolves to per method. It sets KMP_DUPLICATE_LIB_OK for its own read-only device probe only (it does no numerics), so it can't OMP-abort on Windows; the library still never sets it for compute paths. - docs/installation: `[torch]` extra, per-accelerator PyTorch wheel guidance (the plain wheel is CPU-only), the Apple-MPS float32 note, and a Windows OpenMP-clash warning with the KMP_DUPLICATE_LIB_OK workaround. - docs/guide/backends: torch in the selector table; a "portable PyTorch backend" section covering devices, the device/precision settings, and the fast-on-NVIDIA / portable-everywhere split. - docs/guide/cli: document `doctor`. Verified: `cuperiod doctor` runs clean on a CPU-only Windows box without any external env (torch:cpu -> float64; auto -> finufft/numba/numpy). Docs build with `-W`. 161 passed, 8 skipped; ruff + mypy clean. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The PR5 local adversarial review surfaced three float32-only bugs in the portable torch backend, all masked because every torch test ran float64-on-CPU: - BLS cast absolute BJD time to float32 *before* subtracting t_min, destroying sub-0.25-day timing on real light curves (silent wrong periodogram; this is the default path on Apple MPS). Subtract the origin in float64 before the device cast and restore the absolute transit_time on the host (mirrors gls._prep). - MHAOV multiband dropped `precision`, silently ignoring it (and, on MPS, downcasting to float32 instead of raising). Forward precision to aov_multiband_power. - MHAOV's fixed 1e-10 diagonal ridge underflowed the ~N Gram diagonal in float32, so linalg.solve raised on a singular matrix at degenerate frequencies. Scale the ridge by eps(dtype)*n_points. Add float32 regression tests (three proven to fail pre-fix) and the requires_torch_gpu marker. Document the remaining hardware-gated limitations (XPU fp64 probe, torch-GPU VRAM auto-sizing, GPU argmax tie-breaks of extras) in the changelog. NVIDIA fast paths verified unchanged; 165 passed, ruff+mypy clean. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The portable String-Length body sorted folded phases with whichever argsort the backend namespace provided: the numpy path used raw numpy.argsort (quicksort, unstable) while the torch path used the array-API argsort (stable). On exactly-tied phases the two ordered points differently, so the string length diverged — and because quicksort's tie order is platform-dependent, the numpy-vs-torch parity test passed locally but failed in CI across OS/Python. Route the numpy and cupy paths through the array-API namespace too, so every backend uses the standard stable sort and ties keep a backend- and platform-independent order. Add a regression test that forces heavy phase ties (deterministically failing pre-fix). Surfaced by the PR5 review (the argsort tie divergence) and confirmed by CI. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
tjayasinghe
changed the base branch from
feat/torch-bonus-methods
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dev/multi-vendor-gpu
June 30, 2026 21:56
This was referenced Jun 30, 2026
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