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[MRG] Stabilize non-Torch matrix square roots for rank-deficient covariances - #900
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Codecov Report✅ All modified and coverable lines are covered by tests. Additional details and impacted files@@ Coverage Diff @@
## master #900 +/- ##
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- Coverage 97.00% 97.00% -0.01%
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Files 130 130
Lines 26842 26849 +7
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+ Hits 26039 26045 +6
- Misses 803 804 +1 🚀 New features to boost your workflow:
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Types of changes
Motivation and context
Addresses #889. Positive semi-definite covariance matrices can produce tiny negative eigenvalues from numerical roundoff. Taking their square root in the NumPy, JAX, CuPy, and TensorFlow backends then creates NaNs, which can make Gaussian Wasserstein computations fail. The Torch backend already clamps these eigenvalues before the square root.
Changes
The singular-source mapping semantics remain unchanged; this patch addresses the numerical roundoff failure in the matrix square root.
How has this been tested (if it applies)
PR checklist