Add torch option for linalg for NMA and transform#2208
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jamesmkrieger
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January 22, 2026 13:41
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This isn't ready yet. Ensemble superpose method failed because I didn't convert to tensor before svd, just like superpose function |
importLA was preferring torch.linalg (from 'try torch linalg'), which silently broke PCA/NMA
in any torch environment: solveEig's non-scipy branch returns the FULL ascending spectrum but
never applied the eigvals index subset, so callers sliced the smallest modes -> calcModes gave
0 modes; performSVD passed a numpy array to torch.linalg.svd -> TypeError.
Fixes:
- importLA prefers scipy > numpy > torch (torch last); add setLinalgBackend/getLinalgBackend to
force a backend ('torch' selects the experimental GPU path, uses CUDA when available).
- solveEig _eigh: numpy/torch branch now applies the eigvals index range (mirrors scipy
subset_by_index) so downstream selects the intended modes; torch runs on CUDA when available.
- performSVD: convert deviations to a (CUDA) tensor for the torch backend and back.
Verified: scipy/numpy/torch backends all return identical, correct eigenvalues for calcModes
and performSVD.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
jamesmkrieger
requested review from
karolamik13
and removed request for
AnthonyBogetti
July 21, 2026 18:22
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We may want to not make it default because it's not actually faster, but here's a start