pytorch fails signature tests with odd error message

[asmeurer]

This was discussed on slack. There are errors like

FAILED array_api_tests/test_signatures.py::test_func_signature[max] - hypothesis.errors.InvalidArgument: Could not create full array of dtype=torch.uint8 with fill value 0
FAILED array_api_tests/test_signatures.py::test_func_signature[mean] - hypothesis.errors.InvalidArgument: Could not create full array of dtype=torch.float32 with fill value 0.0
FAILED array_api_tests/test_signatures.py::test_func_signature[min] - hypothesis.errors.InvalidArgument: Could not create full array of dtype=torch.uint8 with fill value 0
FAILED array_api_tests/test_signatures.py::test_func_signature[prod] - hypothesis.errors.InvalidArgument: Could not create full array of dtype=torch.uint8 with fill value 0
FAILED array_api_tests/test_signatures.py::test_func_signature[std] - hypothesis.errors.InvalidArgument: Could not create full array of dtype=torch.float32 with fill value 0.0
FAILED array_api_tests/test_signatures.py::test_func_signature[sum] - hypothesis.errors.InvalidArgument: Could not create full array of dtype=torch.uint8 with fill value 0
FAILED array_api_tests/test_signatures.py::test_func_signature[var] - hypothesis.errors.InvalidArgument: Could not create full array of dtype=torch.float32 with fill value 0.0

which are not easy to read

The problem is that hypothesis tries to pass an integer to full, but torch only supports a tuple. I think a simple fix would be if hypothesis only tried passing a tuple to full to avoid this issue.

[asmeurer]

This error actually occurs throughout the test suite, not just in the signature tests.

[honno]

I think a simple fix would be if hypothesis only tried passing a tuple to full to avoid this issue.

Not a big fan of changes like this, where hypothesis.extra.array_api is nominally built with the mindset to happily do whatever the spec allows (such as passing ints as the shape arg). But given this is probably not going to change anytime soon for pytorch, most likely a worthy change beyond just for this test suite, so I'll submit a patch.

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For onlookers, ideally we could fallback to using inspect, but alas

• https://github.com/pytorch/pytorch/issues/28233

so we instead pass arrays for un-inspectable functions. They could be manual examples, but at the time I found it convenient to use Hypothesis, as it could easily translate a functions valid input dtypes and create an array from that... using manual examples could be revisited too, esp. if there's some blocker to changing Hypothesis behaviour.