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Bug#1009200: marked as done (pytorch: (autopkgtest) needs update for python3.10: 'float' object cannot be interpreted as an integer)

Your message dated Mon, 12 Sep 2022 21:44:26 +0000
with message-id <E1oXrEA-001wF9-SH@fasolo.debian.org>
and subject line Bug#1009200: fixed in pytorch 1.12.0-1
has caused the Debian Bug report #1009200,
regarding pytorch: (autopkgtest) needs update for python3.10: 'float' object cannot be interpreted as an integer
to be marked as done.

This means that you claim that the problem has been dealt with.
If this is not the case it is now your responsibility to reopen the
Bug report if necessary, and/or fix the problem forthwith.

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-- 
1009200: https://bugs.debian.org/cgi-bin/bugreport.cgi?bug=1009200
Debian Bug Tracking System
Contact owner@bugs.debian.org with problems
--- Begin Message --- 
Source: pytorch
Version: 1.8.1-5
Severity: serious
Tags: sid bookworm
User: debian-ci@lists.debian.org
Usertags: needs-update
User: debian-python@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: 

                       pass            fail
python3-defaults       from testing    3.10.4-1
pytorch                from testing    1.8.1-5
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 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 = <test_distributions.TestDistributions 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
test/distributions/test_distributions.py::TestJit::test_entropy
test/distributions/test_distributions.py::TestJit::test_enumerate_support
test/distributions/test_distributions.py::TestJit::test_log_prob
test/distributions/test_distributions.py::TestJit::test_mean
test/distributions/test_distributions.py::TestJit::test_rsample
test/distributions/test_distributions.py::TestJit::test_sample
test/distributions/test_distributions.py::TestJit::test_variance
/usr/lib/python3/dist-packages/torch/distributions/distribution.py:52: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs! 
    if not constraint.check(getattr(self, param)).all():

test/distributions/test_distributions.py::TestJit::test_cdf
test/distributions/test_distributions.py::TestJit::test_entropy
test/distributions/test_distributions.py::TestJit::test_enumerate_support
test/distributions/test_distributions.py::TestJit::test_log_prob
test/distributions/test_distributions.py::TestJit::test_mean
test/distributions/test_distributions.py::TestJit::test_sample
test/distributions/test_distributions.py::TestJit::test_variance
/usr/lib/python3/dist-packages/torch/distributions/geometric.py:38: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs! 
    if not self.probs.gt(0).all():

test/distributions/test_distributions.py::TestJit::test_cdf
test/distributions/test_distributions.py::TestJit::test_log_prob
/usr/lib/python3/dist-packages/torch/distributions/distribution.py:265: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs! 
    if i != 1 and j != 1 and i != j:

test/distributions/test_distributions.py::TestJit::test_cdf
test/distributions/test_distributions.py::TestJit::test_log_prob
/usr/lib/python3/dist-packages/torch/distributions/distribution.py:276: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs! 
    if not support.check(value).all():

test/distributions/test_distributions.py::TestJit::test_cdf
test/distributions/test_distributions.py::TestJit::test_entropy
test/distributions/test_distributions.py::TestJit::test_enumerate_support
test/distributions/test_distributions.py::TestJit::test_log_prob
test/distributions/test_distributions.py::TestJit::test_mean
test/distributions/test_distributions.py::TestJit::test_rsample
test/distributions/test_distributions.py::TestJit::test_sample
test/distributions/test_distributions.py::TestJit::test_variance
/usr/lib/python3/dist-packages/torch/distributions/utils.py:37: TracerWarning: torch.tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect. new_values = [v if isinstance(v, torch.Tensor) or has_torch_function((v,)) else torch.tensor(v, **options) 

test/distributions/test_distributions.py::TestJit::test_cdf
test/distributions/test_distributions.py::TestJit::test_entropy
test/distributions/test_distributions.py::TestJit::test_enumerate_support
test/distributions/test_distributions.py::TestJit::test_log_prob
test/distributions/test_distributions.py::TestJit::test_mean
test/distributions/test_distributions.py::TestJit::test_rsample
test/distributions/test_distributions.py::TestJit::test_sample
test/distributions/test_distributions.py::TestJit::test_variance
/usr/lib/python3/dist-packages/torch/distributions/uniform.py:50: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs! 
    if self._validate_args and not torch.lt(self.low, self.high).all():

test/distributions/test_distributions.py::TestJit::test_cdf
test/distributions/test_distributions.py::TestJit::test_entropy
test/distributions/test_distributions.py::TestJit::test_enumerate_support
test/distributions/test_distributions.py::TestJit::test_log_prob
test/distributions/test_distributions.py::TestJit::test_mean
test/distributions/test_distributions.py::TestJit::test_rsample
test/distributions/test_distributions.py::TestJit::test_sample
test/distributions/test_distributions.py::TestJit::test_variance
/usr/lib/python3/dist-packages/torch/distributions/transformed_distribution.py:65: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs! 
    if base_shape != expanded_base_shape:

test/distributions/test_distributions.py::TestJit::test_cdf
test/distributions/test_distributions.py::TestJit::test_log_prob
/usr/lib/python3/dist-packages/torch/distributions/distribution.py:258: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs! 
    if value.size()[event_dim_start:] != self._event_shape:

test/distributions/test_distributions.py::TestJit::test_cdf
test/distributions/test_distributions.py::TestJit::test_enumerate_support
test/distributions/test_distributions.py::TestJit::test_mean
test/distributions/test_distributions.py::TestJit::test_rsample
test/distributions/test_distributions.py::TestJit::test_sample
test/distributions/test_distributions.py::TestJit::test_variance
/usr/lib/python3/dist-packages/torch/distributions/lowrank_multivariate_normal.py:89: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs! 
    if cov_factor.shape[-2:-1] != event_shape:

test/distributions/test_distributions.py::TestJit::test_cdf
test/distributions/test_distributions.py::TestJit::test_enumerate_support
test/distributions/test_distributions.py::TestJit::test_mean
test/distributions/test_distributions.py::TestJit::test_rsample
test/distributions/test_distributions.py::TestJit::test_sample
test/distributions/test_distributions.py::TestJit::test_variance
/usr/lib/python3/dist-packages/torch/distributions/lowrank_multivariate_normal.py:92: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs! 
    if cov_diag.shape[-1:] != event_shape:

test/distributions/test_distributions.py::TestJit::test_cdf
test/distributions/test_distributions.py::TestJit::test_enumerate_support
test/distributions/test_distributions.py::TestJit::test_mean
test/distributions/test_distributions.py::TestJit::test_rsample
test/distributions/test_distributions.py::TestJit::test_sample
test/distributions/test_distributions.py::TestJit::test_variance
/usr/lib/python3/dist-packages/torch/tensor.py:587: RuntimeWarning: Iterating over a tensor might cause the trace to be incorrect. Passing a tensor of different shape won't change the number of iterations executed (and might lead to errors or silently give incorrect results). warnings.warn('Iterating over a tensor might cause the trace to be incorrect. ' 

test/distributions/test_distributions.py::TestJit::test_entropy
test/distributions/test_distributions.py::TestJit::test_log_prob
/usr/lib/python3/dist-packages/torch/nn/functional.py:2826: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs! 
    if not (target.size() == input.size()):

test/distributions/test_distributions.py::TestJit::test_log_prob
/usr/lib/python3/dist-packages/torch/distributions/gamma.py:66: TracerWarning: torch.as_tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect. value = torch.as_tensor(value, dtype=self.rate.dtype, device=self.rate.device) 

test/distributions/test_distributions.py::TestJit::test_log_prob
/usr/lib/python3/dist-packages/torch/distributions/half_cauchy.py:55: TracerWarning: torch.as_tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect. 
    value = torch.as_tensor(value, dtype=self.base_dist.scale.dtype,

test/distributions/test_distributions.py::TestJit::test_log_prob
/usr/lib/python3/dist-packages/torch/distributions/relaxed_categorical.py:80: TracerWarning: Converting a tensor to a Python float might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs! 
    log_scale = (torch.full_like(self.temperature, float(K)).lgamma() -

-- Docs: https://docs.pytest.org/en/stable/warnings.html
=========================== short test summary info ============================ FAILED distributions/test_distributions.py::TestDistributions::test_invalid_parameter_broadcasting =========== 1 failed, 147 passed, 59 skipped, 69 warnings in 21.10s ============ 
Traceback (most recent call last):
File "/tmp/autopkgtest-lxc.uzyuxcob/downtmp/build.HAJ/src/test/run_test.py", line 926, in <module> 
    main()
File "/tmp/autopkgtest-lxc.uzyuxcob/downtmp/build.HAJ/src/test/run_test.py", line 905, in main 
    raise RuntimeError(err_message)
RuntimeError: distributions/test_distributions.py failed!
autopkgtest [19:25:23]: test 10_of_49__pytest__test_distributions

Attachment: OpenPGP_signature
Description: OpenPGP digital signature


--- End Message ---
--- Begin Message --- 
Source: pytorch
Source-Version: 1.12.0-1
Done: Mo Zhou <lumin@debian.org>

We believe that the bug you reported is fixed in the latest version of
pytorch, which is due to be installed in the Debian FTP archive.

A summary of the changes between this version and the previous one is
attached.

Thank you for reporting the bug, which will now be closed.  If you
have further comments please address them to 1009200@bugs.debian.org,
and the maintainer will reopen the bug report if appropriate.

Debian distribution maintenance software
pp.
Mo Zhou <lumin@debian.org> (supplier of updated pytorch package)

(This message was generated automatically at their request; if you
believe that there is a problem with it please contact the archive
administrators by mailing ftpmaster@ftp-master.debian.org)


-----BEGIN PGP SIGNED MESSAGE-----
Hash: SHA512

Format: 1.8
Date: Mon, 12 Sep 2022 17:16:34 -0400
Source: pytorch
Architecture: source
Version: 1.12.0-1
Distribution: unstable
Urgency: medium
Maintainer: Debian Deep Learning Team <debian-ai@lists.debian.org>
Changed-By: Mo Zhou <lumin@debian.org>
Closes: 1004782 1009200
Changes:
 pytorch (1.12.0-1) unstable; urgency=medium
 .
   * New upstream version 1.12.0 (June 28 2022) (Closes: #1004782, #1009200)
   * Autopkgtest: use openblas or blis instead of reference blas.
   * Patch to fix wrong shebang in several .py files.
   * Refresh existing patches.
   * Update lintian overrides.
   * Upload to unstable.
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