--- Begin Message ---
Source: python-sparse
Version: 0.13.0-1
Severity: serious
X-Debbugs-CC: nu...@packages.debian.org
Tags: sid trixie
User: debian...@lists.debian.org
Usertags: needs-update
Control: affects -1 src:numba
Dear maintainer(s),
With a recent upload of numba the autopkgtest of python-sparse fails in
testing when that autopkgtest is run with the binary packages of numba
from unstable. It passes when run with only packages from testing. In
tabular form:
pass fail
numba from testing 0.57.1+dfsg-1
python-sparse from testing 0.13.0-1
all others from testing from testing
I copied some of the output at the bottom of this report.
Currently this regression is blocking the migration of numba to testing
[1]. Of course, numba shouldn't just break your autopkgtest (or even
worse, your package), but it seems to me that numba isn't going to fix
the situation.
If this is a real problem in your package (and not only in your
autopkgtest), the right binary package(s) from numba should really add a
versioned Breaks on the unfixed version of (one of your) package(s).
Note: the Breaks is nice even if the issue is only in the autopkgtest as
it helps the migration software to figure out the right versions to
combine in the tests.
More information about this bug and the reason for filing it can be found on
https://wiki.debian.org/ContinuousIntegration/RegressionEmailInformation
Paul
[1] https://qa.debian.org/excuses.php?package=numba
https://ci.debian.net/data/autopkgtest/testing/amd64/p/python-sparse/38059394/log.gz
115s =================================== FAILURES
===================================
115s _______________ test_tensordot[coo-gcxs-a_shape1-b_shape1-axes1]
_______________
115s
115s a_shape = (3, 4), b_shape = (4, 3), axes = (0, 1), a_format = 'coo'
115s b_format = 'gcxs'
115s
115s @pytest.mark.parametrize(
115s "a_shape,b_shape,axes",
115s [
115s [(3, 4), (4, 3), (1, 0)],
115s [(3, 4), (4, 3), (0, 1)],
115s [(3, 4, 5), (4, 3), (1, 0)],
115s [(3, 4), (5, 4, 3), (1, 1)],
115s [(3, 4), (5, 4, 3), ((0, 1), (2, 1))],
115s [(3, 4), (5, 4, 3), ((1, 0), (1, 2))],
115s [(3, 4, 5), (4,), (1, 0)],
115s [(4,), (3, 4, 5), (0, 1)],
115s [(4,), (4,), (0, 0)],
115s [(4,), (4,), 0],
115s ],
115s )
115s @pytest.mark.parametrize(
115s "a_format, b_format",
115s [("coo", "coo"), ("coo", "gcxs"), ("gcxs", "coo"), ("gcxs",
"gcxs")],
115s )
115s def test_tensordot(a_shape, b_shape, axes, a_format, b_format):
115s sa = sparse.random(a_shape, density=0.5, format=a_format)
115s sb = sparse.random(b_shape, density=0.5, format=b_format)
115s
115s a = sa.todense()
115s b = sb.todense()
115s
115s a_b = np.tensordot(a, b, axes)
115s
115s # tests for return_type=None
115s sa_sb = sparse.tensordot(sa, sb, axes)
115s sa_b = sparse.tensordot(sa, b, axes)
115s a_sb = sparse.tensordot(a, sb, axes)
115s
115s assert_eq(a_b, sa_sb)
115s assert_eq(a_b, sa_b)
115s assert_eq(a_b, a_sb)
115s if all(isinstance(arr, COO) for arr in [sa, sb]):
115s assert isinstance(sa_sb, COO)
115s else:
115s assert isinstance(sa_sb, GCXS)
115s assert isinstance(sa_b, np.ndarray)
115s assert isinstance(a_sb, np.ndarray)
115s
115s # tests for return_type=COO
115s sa_b = sparse.tensordot(sa, b, axes, return_type=COO)
115s > a_sb = sparse.tensordot(a, sb, axes, return_type=COO)
115s
115s tests/test_dot.py:58:
115s _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _ _ _
115s /usr/lib/python3/dist-packages/sparse/_common.py:198: in tensordot
115s res = _dot(at, bt, return_type)
115s /usr/lib/python3/dist-packages/sparse/_common.py:425: in _dot
115s out = GCXS(
115s
/usr/lib/python3/dist-packages/sparse/_compressed/compressed.py:195: in
__init__
115s self._prune()
115s
/usr/lib/python3/dist-packages/sparse/_compressed/compressed.py:817: in
_prune
115s np.cumsum(np.bincount(coords[0], minlength=row_size),
out=indptr[1:])
115s _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _ _ _
115s
115s args = (array([7304680739967889201, 3701732606794297453,
1,
115s 1, 2, 2,
115s 3, 3]),)
115s kwargs = {'minlength': 4}
115s relevant_args = (array([7304680739967889201, 3701732606794297453,
1,
115s 1, 2, 2,
115s 3, 3]), None)
115s
115s > ???
115s E ValueError: array is too big; `arr.size * arr.dtype.itemsize`
is larger than the maximum possible size.
115s
115s <__array_function__ internals>:200: ValueError
115s _______________ test_tensordot[coo-gcxs-a_shape3-b_shape3-axes3]
_______________
115s
115s a_shape = (3, 4), b_shape = (5, 4, 3), axes = (1, 1), a_format = 'coo'
115s b_format = 'gcxs'
115s
115s @pytest.mark.parametrize(
115s "a_shape,b_shape,axes",
115s [
115s [(3, 4), (4, 3), (1, 0)],
115s [(3, 4), (4, 3), (0, 1)],
115s [(3, 4, 5), (4, 3), (1, 0)],
115s [(3, 4), (5, 4, 3), (1, 1)],
115s [(3, 4), (5, 4, 3), ((0, 1), (2, 1))],
115s [(3, 4), (5, 4, 3), ((1, 0), (1, 2))],
115s [(3, 4, 5), (4,), (1, 0)],
115s [(4,), (3, 4, 5), (0, 1)],
115s [(4,), (4,), (0, 0)],
115s [(4,), (4,), 0],
115s ],
115s )
115s @pytest.mark.parametrize(
115s "a_format, b_format",
115s [("coo", "coo"), ("coo", "gcxs"), ("gcxs", "coo"), ("gcxs",
"gcxs")],
115s )
115s def test_tensordot(a_shape, b_shape, axes, a_format, b_format):
115s sa = sparse.random(a_shape, density=0.5, format=a_format)
115s sb = sparse.random(b_shape, density=0.5, format=b_format)
115s
115s a = sa.todense()
115s b = sb.todense()
115s
115s a_b = np.tensordot(a, b, axes)
115s
115s # tests for return_type=None
115s sa_sb = sparse.tensordot(sa, sb, axes)
115s sa_b = sparse.tensordot(sa, b, axes)
115s a_sb = sparse.tensordot(a, sb, axes)
115s
115s assert_eq(a_b, sa_sb)
115s assert_eq(a_b, sa_b)
115s assert_eq(a_b, a_sb)
115s if all(isinstance(arr, COO) for arr in [sa, sb]):
115s assert isinstance(sa_sb, COO)
115s else:
115s assert isinstance(sa_sb, GCXS)
115s assert isinstance(sa_b, np.ndarray)
115s assert isinstance(a_sb, np.ndarray)
115s
115s # tests for return_type=COO
115s sa_b = sparse.tensordot(sa, b, axes, return_type=COO)
115s > a_sb = sparse.tensordot(a, sb, axes, return_type=COO)
115s
115s tests/test_dot.py:58:
115s _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _ _ _
115s /usr/lib/python3/dist-packages/sparse/_common.py:198: in tensordot
115s res = _dot(at, bt, return_type)
115s /usr/lib/python3/dist-packages/sparse/_common.py:425: in _dot
115s out = GCXS(
115s
/usr/lib/python3/dist-packages/sparse/_compressed/compressed.py:195: in
__init__
115s self._prune()
115s
/usr/lib/python3/dist-packages/sparse/_compressed/compressed.py:817: in
_prune
115s np.cumsum(np.bincount(coords[0], minlength=row_size),
out=indptr[1:])
115s _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _ _ _
115s
115s args = (array([ 92935672, 2, 8, 4294969460,
92935696,
115s 4294967297, 95773504, 25769803777,... 1, 1,
1, 1,
115s 1, 1, 1, 1,
1]),)
115s kwargs = {'minlength': 3}
115s relevant_args = (array([ 92935672, 2, 8,
4294969460, 92935696,
115s 4294967297, 95773504, 25769803777,... 1,
1, 1,
115s 1, 1, 1, 1,
1]), None)
115s
115s > ???
115s E numpy.core._exceptions._ArrayMemoryError: Unable to allocate
256. GiB for an array with shape (34359738371,) and data type int64
115s
115s <__array_function__ internals>:200: MemoryError
115s _______________ test_tensordot[coo-gcxs-a_shape7-b_shape7-axes7]
_______________
115s
115s a_shape = (4,), b_shape = (3, 4, 5), axes = (0, 1), a_format = 'coo'
115s b_format = 'gcxs'
115s
115s @pytest.mark.parametrize(
115s "a_shape,b_shape,axes",
115s [
115s [(3, 4), (4, 3), (1, 0)],
115s [(3, 4), (4, 3), (0, 1)],
115s [(3, 4, 5), (4, 3), (1, 0)],
115s [(3, 4), (5, 4, 3), (1, 1)],
115s [(3, 4), (5, 4, 3), ((0, 1), (2, 1))],
115s [(3, 4), (5, 4, 3), ((1, 0), (1, 2))],
115s [(3, 4, 5), (4,), (1, 0)],
115s [(4,), (3, 4, 5), (0, 1)],
115s [(4,), (4,), (0, 0)],
115s [(4,), (4,), 0],
115s ],
115s )
115s @pytest.mark.parametrize(
115s "a_format, b_format",
115s [("coo", "coo"), ("coo", "gcxs"), ("gcxs", "coo"), ("gcxs",
"gcxs")],
115s )
115s def test_tensordot(a_shape, b_shape, axes, a_format, b_format):
115s sa = sparse.random(a_shape, density=0.5, format=a_format)
115s sb = sparse.random(b_shape, density=0.5, format=b_format)
115s
115s a = sa.todense()
115s b = sb.todense()
115s
115s a_b = np.tensordot(a, b, axes)
115s
115s # tests for return_type=None
115s sa_sb = sparse.tensordot(sa, sb, axes)
115s sa_b = sparse.tensordot(sa, b, axes)
115s a_sb = sparse.tensordot(a, sb, axes)
115s
115s assert_eq(a_b, sa_sb)
115s assert_eq(a_b, sa_b)
115s assert_eq(a_b, a_sb)
115s if all(isinstance(arr, COO) for arr in [sa, sb]):
115s assert isinstance(sa_sb, COO)
115s else:
115s assert isinstance(sa_sb, GCXS)
115s assert isinstance(sa_b, np.ndarray)
115s assert isinstance(a_sb, np.ndarray)
115s
115s # tests for return_type=COO
115s sa_b = sparse.tensordot(sa, b, axes, return_type=COO)
115s > a_sb = sparse.tensordot(a, sb, axes, return_type=COO)
115s
115s tests/test_dot.py:58:
115s _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _ _ _
115s /usr/lib/python3/dist-packages/sparse/_common.py:198: in tensordot
115s res = _dot(at, bt, return_type)
115s /usr/lib/python3/dist-packages/sparse/_common.py:425: in _dot
115s out = GCXS(
115s
/usr/lib/python3/dist-packages/sparse/_compressed/compressed.py:195: in
__init__
115s self._prune()
115s
/usr/lib/python3/dist-packages/sparse/_compressed/compressed.py:817: in
_prune
115s np.cumsum(np.bincount(coords[0], minlength=row_size),
out=indptr[1:])
115s _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _ _ _
115s
115s args = (array([140611529669312, 140611525615744, 140611529695520,
140611526270736,
115s 140611529954992, 140611529497968, 140611530646432,
140611530966048,
115s 0, 4294967296]),)
115s kwargs = {'minlength': 1}
115s relevant_args = (array([140611529669312, 140611525615744,
140611529695520, 140611526270736,
115s 140611529954992, 140611529497968, 140611530646432,
140611530966048,
115s 0, 4294967296]), None)
115s
115s > ???
115s E numpy.core._exceptions._ArrayMemoryError: Unable to allocate
1023. TiB for an array with shape (140611530966049,) and data type int64
115s
115s <__array_function__ internals>:200: MemoryError
OpenPGP_signature.asc
Description: OpenPGP digital signature
--- End Message ---
