Source: pytorch
Version: 1.8.1-5
Severity: serious
Tags: sid bookworm
User: debian...@lists.debian.org
Usertags: needs-update
User: debian-pyt...@lists.debian.org
Usertags: python3.10
Control: affects -1 src:python3-defaults
Dear maintainer(s),
We are in the transition of making python3.10 the default Python
versions [0]. With a recent upload of python3-defaults the autopkgtest
of pytorch fails in testing when that autopkgtest is run with the binary
packages of python3-defaults from unstable. It passes when run with only
packages from testing. In tabular form:
passfail
python3-defaults from testing3.10.4-1
pytorchfrom testing1.8.1-5
all others from testingfrom testing
I copied some of the output at the bottom of this report.
Currently this regression is blocking the migration of python3-defaults
to testing [1]. https://docs.python.org/3/whatsnew/3.10.html lists
what's new in Python3.10, it may help to identify what needs to be updated.
More information about this bug and the reason for filing it can be found on
https://wiki.debian.org/ContinuousIntegration/RegressionEmailInformation
Paul
[0] https://bugs.debian.org/1006836
[1] https://qa.debian.org/excuses.php?package=python3-defaults
https://ci.debian.net/data/autopkgtest/testing/amd64/p/pytorch/20675875/log.gz
=== FAILURES
===
TestDistributions.test_invalid_parameter_broadcasting
_
self = testMethod=test_invalid_parameter_broadcasting>
def test_invalid_parameter_broadcasting(self):
# invalid broadcasting cases; should throw error
# example type (distribution class, distribution params)
invalid_examples = [
(Normal, {
'loc': torch.tensor([[0, 0]]),
'scale': torch.tensor([1, 1, 1, 1])
}),
(Normal, {
'loc': torch.tensor([[[0, 0, 0], [0, 0, 0]]]),
'scale': torch.tensor([1, 1])
}),
(FisherSnedecor, {
'df1': torch.tensor([1, 1]),
'df2': torch.tensor([1, 1, 1]),
}),
(Gumbel, {
'loc': torch.tensor([[0, 0]]),
'scale': torch.tensor([1, 1, 1, 1])
}),
(Gumbel, {
'loc': torch.tensor([[[0, 0, 0], [0, 0, 0]]]),
'scale': torch.tensor([1, 1])
}),
(Gamma, {
'concentration': torch.tensor([0, 0]),
'rate': torch.tensor([1, 1, 1])
}),
(Kumaraswamy, {
'concentration1': torch.tensor([[1, 1]]),
'concentration0': torch.tensor([1, 1, 1, 1])
}),
(Kumaraswamy, {
'concentration1': torch.tensor([[[1, 1, 1], [1, 1, 1]]]),
'concentration0': torch.tensor([1, 1])
}),
(Laplace, {
'loc': torch.tensor([0, 0]),
'scale': torch.tensor([1, 1, 1])
}),
(Pareto, {
'scale': torch.tensor([1, 1]),
'alpha': torch.tensor([1, 1, 1])
}),
(StudentT, {
'df': torch.tensor([1, 1]),
'scale': torch.tensor([1, 1, 1])
}),
(StudentT, {
'df': torch.tensor([1, 1]),
'loc': torch.tensor([1, 1, 1])
})
]
for dist, kwargs in invalid_examples:
self.assertRaises(RuntimeError, dist, **kwargs)
distributions/test_distributions.py:2871: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
/usr/lib/python3/dist-packages/torch/distributions/studentT.py:45: in
__init__
self.df, self.loc, self.scale = broadcast_all(df, loc, scale)
/usr/lib/python3/dist-packages/torch/distributions/utils.py:37: in
broadcast_all
new_values = [v if isinstance(v, torch.Tensor) or
has_torch_function((v,)) else torch.tensor(v, **options)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _
new_values = [v if isinstance(v, torch.Tensor) or has_torch_function((v,))
else torch.tensor(v, **options)
for v in values]
E TypeError: 'float' object cannot be interpreted as an integer
/usr/lib/python3/dist-packages/torch/distributions/utils.py:37: TypeError
=== warnings summary
===
../../../../../../usr/lib/python3/dist-packages/torch/testing/_internal/common_cuda.py:9
/usr/lib/python3/dist-packages/torch/testing/_internal/common_cuda.py:9:
DeprecationWarning: The distutils package is deprecated and slated for
removal in Python 3.12. Use setuptools or check PEP 632 for potential
alternatives
from distutils.version import LooseVersion
test/distributions/test_distributions.py::TestJit::test_cdf
