Test failures in test_tensordot

[bnavigator]

Describe the bug
Running the unit tests for the openSUSE rpm package of python-sparse 0.14 results in test failures

To Reproduce

docker pull opensuse/tumbleweed
docker run -it --name sparsetest opensuse/tumbleweed

in docker shell:

zypper ref
zypper install python310-dask-array python310-numba python310-numpy python310-pytest python310-scipy wget
wget https://files.pythonhosted.org/packages/source/s/sparse/sparse-0.14.0.tar.gz
tar xf sparse-0.14.0.tar.gz 
cd sparse-0.14.0
PYTHONPATH=$PWD pytest -v

958ff8a328b4:/sparse-0.14.0 # PYTHONPATH=$PWD pytest -v
=============================================================================== test session starts ================================================================================
platform linux -- Python 3.10.10, pytest-7.3.1, pluggy-1.0.0 -- /usr/bin/python3.10
cachedir: .pytest_cache
rootdir: /sparse-0.14.0
collected 5496 items 
...
===================================================================================== FAILURES =====================================================================================
_________________________________________________________________ test_tensordot[coo-gcxs-a_shape1-b_shape1-axes1] _________________________________________________________________

a_shape = (3, 4), b_shape = (4, 3), axes = (0, 1), a_format = 'coo', b_format = 'gcxs'

    @pytest.mark.parametrize(
        "a_shape,b_shape,axes",
        [
            [(3, 4), (4, 3), (1, 0)],
            [(3, 4), (4, 3), (0, 1)],
            [(3, 4, 5), (4, 3), (1, 0)],
            [(3, 4), (5, 4, 3), (1, 1)],
            [(3, 4), (5, 4, 3), ((0, 1), (2, 1))],
            [(3, 4), (5, 4, 3), ((1, 0), (1, 2))],
            [(3, 4, 5), (4,), (1, 0)],
            [(4,), (3, 4, 5), (0, 1)],
            [(4,), (4,), (0, 0)],
            [(4,), (4,), 0],
        ],
    )
    @pytest.mark.parametrize(
        "a_format, b_format",
        [("coo", "coo"), ("coo", "gcxs"), ("gcxs", "coo"), ("gcxs", "gcxs")],
    )
    def test_tensordot(a_shape, b_shape, axes, a_format, b_format):
        sa = sparse.random(a_shape, density=0.5, format=a_format)
        sb = sparse.random(b_shape, density=0.5, format=b_format)
    
        a = sa.todense()
        b = sb.todense()
    
        a_b = np.tensordot(a, b, axes)
    
        # tests for return_type=None
        sa_sb = sparse.tensordot(sa, sb, axes)
        sa_b = sparse.tensordot(sa, b, axes)
        a_sb = sparse.tensordot(a, sb, axes)
    
        assert_eq(a_b, sa_sb)
        assert_eq(a_b, sa_b)
        assert_eq(a_b, a_sb)
        if all(isinstance(arr, COO) for arr in [sa, sb]):
            assert isinstance(sa_sb, COO)
        else:
            assert isinstance(sa_sb, GCXS)
        assert isinstance(sa_b, np.ndarray)
        assert isinstance(a_sb, np.ndarray)
    
        # tests for return_type=COO
        sa_b = sparse.tensordot(sa, b, axes, return_type=COO)
>       a_sb = sparse.tensordot(a, sb, axes, return_type=COO)

sparse/tests/test_dot.py:58: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
sparse/_common.py:199: in tensordot
    res = _dot(at, bt, return_type)
sparse/_common.py:426: in _dot
    out = GCXS(
sparse/_compressed/compressed.py:193: in __init__
    self._prune()
sparse/_compressed/compressed.py:840: in _prune
    np.cumsum(np.bincount(coords[0], minlength=row_size), out=indptr[1:])
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

args = (array([94149847811728,             -1,              3,              1,
                    1,              2,              2,              2,
                    3,              3,              3,              3]),)
kwargs = {'minlength': 4}
relevant_args = (array([94149847811728,             -1,              3,              1,
                    1,              2,              2,              2,
                    3,              3,              3,              3]), None)

>   ???
E   ValueError: 'list' argument must have no negative elements

<__array_function__ internals>:200: ValueError
...
============================================================================= short test summary info ==============================================================================
FAILED sparse/tests/test_dot.py::test_tensordot[coo-gcxs-a_shape1-b_shape1-axes1] - ValueError: 'list' argument must have no negative elements
FAILED sparse/tests/test_dot.py::test_tensordot[coo-gcxs-a_shape3-b_shape3-axes3] - numpy.core._exceptions._ArrayMemoryError: Unable to allocate 685. TiB for an array with shape (94149851715509,) and data type int64
FAILED sparse/tests/test_dot.py::test_tensordot[coo-gcxs-a_shape7-b_shape7-axes7] - ValueError: array is too big; `arr.size * arr.dtype.itemsize` is larger than the maximum possible size.
FAILED sparse/tests/test_dot.py::test_tensordot[gcxs-coo-a_shape1-b_shape1-axes1] - numpy.core._exceptions._ArrayMemoryError: Unable to allocate 1021. TiB for an array with shape (140281763544423,) and data type int64
FAILED sparse/tests/test_dot.py::test_tensordot[gcxs-gcxs-a_shape9-b_shape9-0] - ValueError: 'list' argument must have no negative elements
====================================================== 5 failed, 5406 passed, 34 xfailed, 51 xpassed, 3683 warnings in 55.28s ======================================================

Expected behavior
Successful test suite

System

• OS and version: openSUSE Tumbleweed Linux
• sparse version 0.1.4
• NumPy version 1.24.2
• Numba version 0.57

Full build log from opensuse build service: osc-build-sparse.txt

[bnavigator]

For completeness, current master fails with the same errors, plus a few more:

# same docker setup as above
zypper in git-core
git clone https://github.com/pydata/sparse.git
cd sparse
# remove coverage flags
sed -i /addopts/d pytest.ini
PYTHONPATH=$PWD pytest -v

958ff8a328b4:/sparse # PYTHONPATH=$PWD pytest -v
=============================================================================== test session starts ================================================================================
platform linux -- Python 3.10.10, pytest-7.3.1, pluggy-1.0.0 -- /usr/bin/python3.10
cachedir: .pytest_cache
rootdir: /sparse
configfile: pytest.ini
testpaths: sparse
collected 5523 items                                                                                                                                                               
...
===================================================================================== FAILURES =====================================================================================
_____________________________________________________________________________ TestBasic.test_roundtrip _____________________________________________________________________________

self = <test_coo_numba.TestBasic object at 0x7f8e7da62cb0>

    def test_roundtrip(self):
        c1 = sparse.COO(np.eye(3), fill_value=1)
>       c2 = identity(c1)

sparse/tests/test_coo_numba.py:41: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib64/python3.10/site-packages/numba/core/dispatcher.py:468: in _compile_for_args
    error_rewrite(e, 'typing')
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

e = TypingError('Failed in nopython mode pipeline (step: nopython frontend)\nnon-precise type pyobject\nDuring: typing of ...caused by the following argument(s):\n- argument 0: Cannot determine Numba type of <class \'sparse._coo.core.COO\'>\n')
issue_type = 'typing'

    def error_rewrite(e, issue_type):
        """
        Rewrite and raise Exception `e` with help supplied based on the
        specified issue_type.
        """
        if config.SHOW_HELP:
            help_msg = errors.error_extras[issue_type]
            e.patch_message('\n'.join((str(e).rstrip(), help_msg)))
        if config.FULL_TRACEBACKS:
            raise e
        else:
>           raise e.with_traceback(None)
E           numba.core.errors.TypingError: Failed in nopython mode pipeline (step: nopython frontend)
E           non-precise type pyobject
E           During: typing of argument at /sparse/sparse/tests/test_coo_numba.py (8)
E           
E           File "sparse/tests/test_coo_numba.py", line 8:
E           
E           @numba.njit
E           ^ 
E           
E           This error may have been caused by the following argument(s):
E           - argument 0: Cannot determine Numba type of <class 'sparse._coo.core.COO'>

/usr/lib64/python3.10/site-packages/numba/core/dispatcher.py:409: TypingError
...
============================================================================= short test summary info ==============================================================================
FAILED sparse/tests/test_coo_numba.py::TestBasic::test_roundtrip - numba.core.errors.TypingError: Failed in nopython mode pipeline (step: nopython frontend)
FAILED sparse/tests/test_coo_numba.py::TestBasic::test_roundtrip_constant - numba.core.errors.TypingError: Failed in nopython mode pipeline (step: nopython frontend)
FAILED sparse/tests/test_coo_numba.py::TestBasic::test_unpack_attrs - numba.core.errors.TypingError: Failed in nopython mode pipeline (step: nopython frontend)
FAILED sparse/tests/test_coo_numba.py::TestBasic::test_repack_attrs - numba.core.errors.TypingError: Failed in nopython mode pipeline (step: nopython frontend)
FAILED sparse/tests/test_dot.py::test_tensordot[coo-gcxs-a_shape1-b_shape1-axes1] - ValueError: 'list' argument must have no negative elements
FAILED sparse/tests/test_dot.py::test_tensordot[coo-gcxs-a_shape3-b_shape3-axes3] - numpy.core._exceptions._ArrayMemoryError: Unable to allocate 685. TiB for an array with shape (94151847520437,) and data type int64
FAILED sparse/tests/test_dot.py::test_tensordot[coo-gcxs-a_shape7-b_shape7-axes7] - ValueError: array is too big; `arr.size * arr.dtype.itemsize` is larger than the maximum possible size.
FAILED sparse/tests/test_dot.py::test_tensordot[coo-gcxs-a_shape9-b_shape9-0] - ValueError: 'list' argument must have no negative elements
FAILED sparse/tests/test_dot.py::test_tensordot[gcxs-coo-a_shape1-b_shape1-axes1] - ValueError: array is too big; `arr.size * arr.dtype.itemsize` is larger than the maximum possible size.
====================================================== 9 failed, 5429 passed, 34 xfailed, 51 xpassed, 280 warnings in 55.48s =======================================================

[hameerabbasi]

Could you perhaps pin Numba to 0.56 instead of 0.57 and check if that works?

I believe @eric-wieser wrote the failing code.

Eric, is this used anywhere to the best of your knowledge?

[eric-wieser]

Yes, I think I use this downstream in clifford. I'll try to find some time to diagnose this; my guess is that numba just changed their API somehow

[bnavigator]

Going back to Numba 0.56 is not an option for us, specifically because of the lack of Python 3.11 support. But yes, downgrading numba via pip (which also downgrades numpy) results in passed tests.

You can view this bug report as a humble indication that sparse needs some adjustments in order to keep up with the numba and numpy APIs.

[hameerabbasi]

Sure thing -- Just determining what needs changing and on which side at the moment. 😉

[esc]

docker pull opensuse/tumbleweed
docker run -it --name sparsetest opensuse/tumbleweed

in docker shell:

zypper ref
zypper install python310-dask-array python310-numba python310-numpy python310-pytest python310-scipy wget
wget https://files.pythonhosted.org/packages/source/s/sparse/sparse-0.14.0.tar.gz
tar xf sparse-0.14.0.tar.gz 
cd sparse-0.14.0
PYTHONPATH=$PWD pytest -v

System

• OS and version: openSUSE Tumbleweed Linux
• sparse version 0.1.4
• NumPy version 1.24.2
• Numba version 0.57

Full build log from opensuse build service: osc-build-sparse.txt

I am unable to reproduce this given the instructions above. The debugging information is ambiguous. When I execute the commands you list I receive Numba 0.56.4 -- not 0.57.0 -- and so the bug doesn't manifest.

[bnavigator]

Numba in Tumbleweed is at 0.57.0 for sure. You must have forgotten to pull the latest tumbleweed docker image.

e5338e6c78b5:/sparse-0.14.0 # cat /etc/os-release 
NAME="openSUSE Tumbleweed"
# VERSION="20230607"
ID="opensuse-tumbleweed"
ID_LIKE="opensuse suse"
VERSION_ID="20230607"
PRETTY_NAME="openSUSE Tumbleweed"
ANSI_COLOR="0;32"
CPE_NAME="cpe:/o:opensuse:tumbleweed:20230607"
BUG_REPORT_URL="https://bugzilla.opensuse.org"
SUPPORT_URL="https://bugs.opensuse.org"
HOME_URL="https://www.opensuse.org"
DOCUMENTATION_URL="https://en.opensuse.org/Portal:Tumbleweed"
LOGO="distributor-logo-Tumbleweed"
e5338e6c78b5:/sparse-0.14.0 # zypper info python310-numba
Loading repository data...
Reading installed packages...


Information for package python310-numba:
----------------------------------------
Repository     : openSUSE-Tumbleweed-Oss
Name           : python310-numba
Version        : 0.57.0-1.1
Arch           : x86_64
Vendor         : openSUSE
Installed Size : 22.5 MiB
Installed      : Yes
Status         : up-to-date
Source package : python-numba-0.57.0-1.1.src
Upstream URL   : https://numba.pydata.org/
Summary        : NumPy-aware optimizing compiler for Python using LLVM
Description    : 
    Numba is a NumPy-aware optimizing compiler for Python. It uses the
    LLVM compiler infrastructure to compile Python syntax to
    machine code.

    It is aware of NumPy arrays as typed memory regions and so can speed-up
    code using NumPy arrays.  Other, less well-typed code will be translated
    to Python C-API calls, effectively removing the "interpreter", but not removing
    the dynamic indirection.

    Numba is also not a tracing JIT.  It *compiles* your code before it gets
    run, either using run-time type information or type information you provide
    in the decorator.

    Numba is a mechanism for producing machine code from Python syntax and typed
    data structures such as those that exist in NumPy.

e5338e6c78b5:/sparse-0.14.0 # 

[hameerabbasi]

I'll try to reproduce this tomorrow or the day after with my local machine, however I believe openSUSE rolled back support since the bug (and perhaps others).

[esc]

zypper info python310-numba

ffdb52c0c496:/sparse-0.14.0 # cat /etc/os-release
NAME="openSUSE Tumbleweed"
# VERSION="20230604"
ID="opensuse-tumbleweed"
ID_LIKE="opensuse suse"
VERSION_ID="20230604"
PRETTY_NAME="openSUSE Tumbleweed"
ANSI_COLOR="0;32"
CPE_NAME="cpe:/o:opensuse:tumbleweed:20230604"
BUG_REPORT_URL="https://bugzilla.opensuse.org"
SUPPORT_URL="https://bugs.opensuse.org"
HOME_URL="https://www.opensuse.org"
DOCUMENTATION_URL="https://en.opensuse.org/Portal:Tumbleweed"
LOGO="distributor-logo-Tumbleweed"
ffdb52c0c496:/sparse-0.14.0 # zypper info python310-numba
Loading repository data...
Reading installed packages...


Information for package python310-numba:
----------------------------------------
Repository     : openSUSE-Tumbleweed-Oss
Name           : python310-numba
Version        : 0.57.0-1.1
Arch           : aarch64
Vendor         : openSUSE
Installed Size : 23.0 MiB
Installed      : Yes
Status         : out-of-date (version 0.56.4-2.1 installed)
Source package : python-numba-0.57.0-1.1.src
Upstream URL   : https://numba.pydata.org/
Summary        : NumPy-aware optimizing compiler for Python using LLVM
Description    :
    Numba is a NumPy-aware optimizing compiler for Python. It uses the
    LLVM compiler infrastructure to compile Python syntax to
    machine code.

    It is aware of NumPy arrays as typed memory regions and so can speed-up
    code using NumPy arrays.  Other, less well-typed code will be translated
    to Python C-API calls, effectively removing the "interpreter", but not removing
    the dynamic indirection.

    Numba is also not a tracing JIT.  It *compiles* your code before it gets
    run, either using run-time type information or type information you provide
    in the decorator.

    Numba is a mechanism for producing machine code from Python syntax and typed
    data structures such as those that exist in NumPy.

But then

ffdb52c0c496:/sparse-0.14.0 # numba -s | grep "Numba Version"
/usr/bin/python3.10: No module named pip
Numba Version                                 : 0.56.4

[bnavigator]

Arch           : aarch64
...
Status         : out-of-date (version 0.56.4-2.1 installed)

There is your problem. Aarch64 is not on the latest rolling release.

[esc]

Arch           : aarch64
...
Status         : out-of-date (version 0.56.4-2.1 installed)

There is your problem. Aarch64 is not on the latest rolling release.

Oh, I see, I only have this hardware available at present...

I tried updating the package manually, and now pytest segfaults for me..

ffdb52c0c496:/sparse-0.14.0 # zypper install python310-numba
Loading repository data...
Reading installed packages...
Resolving package dependencies...

The following 2 packages are going to be upgraded:
  python310-llvmlite python310-numba

The following NEW package is going to be installed:
  libLLVM14

2 packages to upgrade, 1 new.
Overall download size: 24.2 MiB. Already cached: 0 B. After the operation, additional 91.5 MiB will be used.
Continue? [y/n/v/...? shows all options] (y): y
Retrieving: libLLVM14-14.0.6-9.1.aarch64 (openSUSE-Tumbleweed-Oss)                                                                                     (1/3),  19.7 MiB
Retrieving: libLLVM14-14.0.6-9.1.aarch64.rpm ..........................................................................................................[done (717.4 KiB/s)]
Retrieving: python310-llvmlite-0.40.0-1.1.aarch64 (openSUSE-Tumbleweed-Oss)                                                                            (2/3), 270.5 KiB
Retrieving: python310-llvmlite-0.40.0-1.1.aarch64.rpm .................................................................................................[done (267.4 KiB/s)]
Retrieving: python310-numba-0.57.0-1.1.aarch64 (openSUSE-Tumbleweed-Oss)                                                                               (3/3),   4.2 MiB
Retrieving: python310-numba-0.57.0-1.1.aarch64.rpm ....................................................................................................[done (710.1 KiB/s)]

Checking for file conflicts: ........................................................................................................................................[done]
(1/3) Installing: libLLVM14-14.0.6-9.1.aarch64 ......................................................................................................................[done]
(2/3) Installing: python310-llvmlite-0.40.0-1.1.aarch64 .............................................................................................................[done]
warning: file pycc: remove failed: No such file or directory
(3/3) Installing: python310-numba-0.57.0-1.1.aarch64 ................................................................................................................[done]

ffdb52c0c496:/sparse-0.14.0 # PYTHONPATH=$PWD pytest -v
\=========================================================================== test session starts ===========================================================================
platform linux -- Python 3.10.10, pytest-7.3.1, pluggy-1.0.0 -- /usr/bin/python3.10
cachedir: .pytest_cache
rootdir: /sparse-0.14.0
collected 5496 items

sparse/tests/test_array_function.py::test_unary[mean] Fatal Python error: Segmentation fault

Current thread 0x0000ffffb66c2020 (most recent call first):
  File "/sparse-0.14.0/sparse/_coo/indexing.py", line 179 in _mask
  File "/sparse-0.14.0/sparse/_coo/indexing.py", line 77 in getitem
  File "/sparse-0.14.0/sparse/_sparse_array.py", line 386 in reduce
  File "/sparse-0.14.0/sparse/_sparse_array.py", line 278 in _reduce
  File "/sparse-0.14.0/sparse/_sparse_array.py", line 307 in __array_ufunc__
  File "/sparse-0.14.0/sparse/_sparse_array.py", line 419 in sum
  File "/sparse-0.14.0/sparse/_sparse_array.py", line 686 in mean
  File "/sparse-0.14.0/sparse/_sparse_array.py", line 268 in __array_function__
  File "<__array_function__ internals>", line 200 in mean
  File "/sparse-0.14.0/sparse/tests/test_array_function.py", line 27 in test_unary
  File "/usr/lib/python3.10/site-packages/_pytest/python.py", line 194 in pytest_pyfunc_call
  File "/usr/lib/python3.10/site-packages/pluggy/_callers.py", line 39 in _multicall
  File "/usr/lib/python3.10/site-packages/pluggy/_manager.py", line 80 in _hookexec
  File "/usr/lib/python3.10/site-packages/pluggy/_hooks.py", line 265 in __call__
  File "/usr/lib/python3.10/site-packages/_pytest/python.py", line 1799 in runtest
  File "/usr/lib/python3.10/site-packages/_pytest/runner.py", line 169 in pytest_runtest_call
  File "/usr/lib/python3.10/site-packages/pluggy/_callers.py", line 39 in _multicall
  File "/usr/lib/python3.10/site-packages/pluggy/_manager.py", line 80 in _hookexec
  File "/usr/lib/python3.10/site-packages/pluggy/_hooks.py", line 265 in __call__
  File "/usr/lib/python3.10/site-packages/_pytest/runner.py", line 262 in <lambda>
  File "/usr/lib/python3.10/site-packages/_pytest/runner.py", line 341 in from_call
  File "/usr/lib/python3.10/site-packages/_pytest/runner.py", line 261 in call_runtest_hook
  File "/usr/lib/python3.10/site-packages/_pytest/runner.py", line 222 in call_and_report
  File "/usr/lib/python3.10/site-packages/_pytest/runner.py", line 133 in runtestprotocol
  File "/usr/lib/python3.10/site-packages/_pytest/runner.py", line 114 in pytest_runtest_protocol
  File "/usr/lib/python3.10/site-packages/pluggy/_callers.py", line 39 in _multicall
  File "/usr/lib/python3.10/site-packages/pluggy/_manager.py", line 80 in _hookexec
  File "/usr/lib/python3.10/site-packages/pluggy/_hooks.py", line 265 in __call__
  File "/usr/lib/python3.10/site-packages/_pytest/main.py", line 348 in pytest_runtestloop
  File "/usr/lib/python3.10/site-packages/pluggy/_callers.py", line 39 in _multicall
  File "/usr/lib/python3.10/site-packages/pluggy/_manager.py", line 80 in _hookexec
  File "/usr/lib/python3.10/site-packages/pluggy/_hooks.py", line 265 in __call__
  File "/usr/lib/python3.10/site-packages/_pytest/main.py", line 323 in _main
  File "/usr/lib/python3.10/site-packages/_pytest/main.py", line 269 in wrap_session
  File "/usr/lib/python3.10/site-packages/_pytest/main.py", line 316 in pytest_cmdline_main
  File "/usr/lib/python3.10/site-packages/pluggy/_callers.py", line 39 in _multicall
  File "/usr/lib/python3.10/site-packages/pluggy/_manager.py", line 80 in _hookexec
  File "/usr/lib/python3.10/site-packages/pluggy/_hooks.py", line 265 in __call__
  File "/usr/lib/python3.10/site-packages/_pytest/config/__init__.py", line 166 in main
  File "/usr/lib/python3.10/site-packages/_pytest/config/__init__.py", line 189 in console_main
  File "/usr/bin/pytest-3.10", line 33 in <module>

Extension modules: numpy.core._multiarray_umath, numpy.core._multiarray_tests, numpy.linalg._umath_linalg, numpy.fft._pocketfft_internal, numpy.random._common, numpy.random.bit_generator, numpy.random._bounded_integers, numpy.random._mt19937, numpy.random.mtrand, numpy.random._philox, numpy.random._pcg64, numpy.random._sfc64, numpy.random._generator, scipy._lib._ccallback_c, yaml._yaml, numba.core.typeconv._typeconv, numba._helperlib, numba._dynfunc, numba._dispatcher, numba.core.runtime._nrt_python, numba.np.ufunc._internal, numba.experimental.jitclass._box, scipy.sparse._sparsetools, _csparsetools, scipy.sparse._csparsetools, scipy.sparse.linalg._isolve._iterative, scipy.linalg._fblas, scipy.linalg._flapack, scipy.linalg._cythonized_array_utils, scipy.linalg._flinalg, scipy.linalg._solve_toeplitz, scipy.linalg._matfuncs_sqrtm_triu, scipy.linalg.cython_lapack, scipy.linalg.cython_blas, scipy.linalg._matfuncs_expm, scipy.linalg._decomp_update, scipy.sparse.linalg._dsolve._superlu, scipy.sparse.linalg._eigen.arpack._arpack, scipy.sparse.csgraph._tools, scipy.sparse.csgraph._shortest_path, scipy.sparse.csgraph._traversal, scipy.sparse.csgraph._min_spanning_tree, scipy.sparse.csgraph._flow, scipy.sparse.csgraph._matching, scipy.sparse.csgraph._reordering, numpy.linalg.lapack_lite, scipy.spatial._ckdtree, scipy._lib.messagestream, scipy.spatial._qhull, scipy.spatial._voronoi, scipy.spatial._distance_wrap, scipy.spatial._hausdorff, scipy.special._ufuncs_cxx, scipy.special._ufuncs, scipy.special._specfun, scipy.special._comb, scipy.special._ellip_harm_2, scipy.spatial.transform._rotation, scipy.ndimage._nd_image, _ni_label, scipy.ndimage._ni_label, scipy.optimize._minpack2, scipy.optimize._group_columns, scipy.optimize._trlib._trlib, scipy.optimize._lbfgsb, _moduleTNC, scipy.optimize._moduleTNC, scipy.optimize._cobyla, scipy.optimize._slsqp, scipy.optimize._minpack, scipy.optimize._lsq.givens_elimination, scipy.optimize._zeros, scipy.optimize.__nnls, scipy.optimize._highs.cython.src._highs_wrapper, scipy.optimize._highs._highs_wrapper, scipy.optimize._highs.cython.src._highs_constants, scipy.optimize._highs._highs_constants, scipy.linalg._interpolative, scipy.optimize._bglu_dense, scipy.optimize._lsap, scipy.optimize._direct, scipy.integrate._odepack, scipy.integrate._quadpack, scipy.integrate._vode, scipy.integrate._dop, scipy.integrate._lsoda, scipy.special.cython_special, scipy.stats._stats, scipy.stats.beta_ufunc, scipy.stats._boost.beta_ufunc, scipy.stats.binom_ufunc, scipy.stats._boost.binom_ufunc, scipy.stats.nbinom_ufunc, scipy.stats._boost.nbinom_ufunc, scipy.stats.hypergeom_ufunc, scipy.stats._boost.hypergeom_ufunc, scipy.stats.ncf_ufunc, scipy.stats._boost.ncf_ufunc, scipy.stats.ncx2_ufunc, scipy.stats._boost.ncx2_ufunc, scipy.stats.nct_ufunc, scipy.stats._boost.nct_ufunc, scipy.stats.skewnorm_ufunc, scipy.stats._boost.skewnorm_ufunc, scipy.stats.invgauss_ufunc, scipy.stats._boost.invgauss_ufunc, scipy.interpolate._fitpack, scipy.interpolate.dfitpack, scipy.interpolate._bspl, scipy.interpolate._ppoly, scipy.interpolate.interpnd, scipy.interpolate._rbfinterp_pythran, scipy.interpolate._rgi_cython, scipy.stats._biasedurn, scipy.stats._levy_stable.levyst, scipy.stats._stats_pythran, scipy._lib._uarray._uarray, scipy.stats._statlib, scipy.stats._mvn, scipy.stats._sobol, scipy.stats._qmc_cy, scipy.stats._rcont.rcont (total: 122)
Segmentation fault

[bnavigator]

Try doing a full zypper dup, or just use a virtual environmen without openSUSE on your end. Maybe the wheels or conda packages for arm suit your needs better and still reproduce the errror.

[esc]

I'll try to reproduce this tomorrow or the day after with my local machine, however I believe openSUSE rolled back support since the bug (and perhaps others).

OK. Thank you. Unfortunately this means, any potential fixes will not make it into 0.57.1 -- we have already delayed the release by a few days trying to fix and debug this, and we need to get on with the release. Unfortunately, everyone who looked at this came to the conclusion, that this is probably not on the Numba side.

@kc611 will update the corresponding Numba issue numba/numba#8993 with details on what else was tried.

If this does turn out to be on the Numba side, we will release a 0.57.2 with a fix.

[hameerabbasi]

@bnavigator Would you be willing to make a PR based on numba/numba#8993 (comment)?

If not, I can do it in the coming week; that's when I get my desktop back.