BUG: nan returned by np.linalg.det while it should be 0 on arm64 mac

Describe the issue:

np.linalg.det on a 4x4 real matrix returns nan, while it should be 0.

If the same code example is run on an intel64 linux machine, the determinant is zero for both matrices.

This happens both with conda-forge and pypi numpy, on both python 3.9 and 3.10.

Reproduce the code example:

import numpy as np
np.linalg.det(np.array([[1.0000000000000000e+000, 0.0000000000000000e+000,
         0.0000000000000000e+000, 0.0000000000000000e+000],
        [0.0000000000000000e+000, 1.0000000000000000e+000,
         3.8307904270117927e-146, 1.4674955295685193e-291],
        [0.0000000000000000e+000, 3.8307904270117927e-146,
         1.4674955295685193e-291, 0.0000000000000000e+000],
        [0.0000000000000000e+000, 1.4674955295685193e-291,
         0.0000000000000000e+000, 0.0000000000000000e+000]]))
/opt/homebrew/Caskroom/miniforge/base/envs/numpy-test/lib/python3.10/site-packages/numpy/linalg/linalg.py:2154: RuntimeWarning: overflow encountered in det
  r = _umath_linalg.det(a, signature=signature)
/opt/homebrew/Caskroom/miniforge/base/envs/numpy-test/lib/python3.10/site-packages/numpy/linalg/linalg.py:2154: RuntimeWarning: invalid value encountered in det
  r = _umath_linalg.det(a, signature=signature)
nan

This one instead works fine:

np.linalg.det(np.array([[1.0000000000000000e+000, 0.0000000000000000e+000,
         0.0000000000000000e+000, 0.0000000000000000e+000],
        [0.0000000000000000e+000, 1.0000000000000000e+000,
         3.8307904315347111e-146, 1.4674955330337903e-291],
        [0.0000000000000000e+000, 3.8307904315347111e-146,
         1.4674955330337899e-291, 0.0000000000000000e+000],
        [0.0000000000000000e+000, 1.4674955330337903e-291,
         0.0000000000000000e+000, 0.0000000000000000e+000]]))

I’m also attaching a .zip file with the matrices saved as .npy files in case there are numerical precision problems: numpy_nan.zip.

test_nan.npy gives the nan while for test_ok.npy the determinant is correct.

Error message:

No response

NumPy/Python version information:

1.23.1 3.10.0 | packaged by conda-forge | (default, Nov 20 2021, 02:27:15) [Clang 11.1.0 ]

Output of np.show_config():

>>> np.show_config()
openblas64__info:
    libraries = ['openblas64_', 'openblas64_']
    library_dirs = ['/usr/local/lib']
    language = c
    define_macros = [('HAVE_CBLAS', None), ('BLAS_SYMBOL_SUFFIX', '64_'), ('HAVE_BLAS_ILP64', None)]
    runtime_library_dirs = ['/usr/local/lib']
blas_ilp64_opt_info:
    libraries = ['openblas64_', 'openblas64_']
    library_dirs = ['/usr/local/lib']
    language = c
    define_macros = [('HAVE_CBLAS', None), ('BLAS_SYMBOL_SUFFIX', '64_'), ('HAVE_BLAS_ILP64', None)]
    runtime_library_dirs = ['/usr/local/lib']
openblas64__lapack_info:
    libraries = ['openblas64_', 'openblas64_']
    library_dirs = ['/usr/local/lib']
    language = c
    define_macros = [('HAVE_CBLAS', None), ('BLAS_SYMBOL_SUFFIX', '64_'), ('HAVE_BLAS_ILP64', None), ('HAVE_LAPACKE', None)]
    runtime_library_dirs = ['/usr/local/lib']
lapack_ilp64_opt_info:
    libraries = ['openblas64_', 'openblas64_']
    library_dirs = ['/usr/local/lib']
    language = c
    define_macros = [('HAVE_CBLAS', None), ('BLAS_SYMBOL_SUFFIX', '64_'), ('HAVE_BLAS_ILP64', None), ('HAVE_LAPACKE', None)]
    runtime_library_dirs = ['/usr/local/lib']
Supported SIMD extensions in this NumPy install:
    baseline = NEON,NEON_FP16,NEON_VFPV4,ASIMD
    found = ASIMDHP
    not found = ASIMDDP,ASIMDFHM

[mattip]

How do I get the latest openblas?

You can build from source (not recommended) or wait a few weeks until the conda forge feedstock is updated, and then install that. Note that it seems you are only using the pip installed numpy, to install from conda forge you would have to do

pip uninstall numpy
conda install openblas==0.3.21
conda install --force-reinstall numpy

[mattip]

From the output above, you are running the arm64 variant and using the homebrew-provided OpenBLAS 0.3.20. It is strange that the pip-installed numpy does not show anything, I think you need to import numpy first:

python -c "import numpy, threadpoolctl, pprint; \
pprint.pprint(threadpoolctl.threadpool_info())"

OpenBLAS released 0.3.21 yesterday, I wonder if that will change anything.