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5ed4a423de
Use conditional imports: when running under dynamo, import the original NumPy not torch._numpy. This is what we want to trace, not our implementation.
With this, the test suite passes with and without `PYTORCH_TEST_WITH_DYNAMO=1` (modulo a couple of test modules which are not meant to be compiled, e.g. `test_nep50_examples`). There are two new decorators, `x{fail,pass}ifTorchDynamo`, the `xpass` in most cases indicates a graph break and a fallback to eager for things we do not implement.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/110401
Approved by: https://github.com/lezcano
18 lines
520 B
INI
18 lines
520 B
INI
[pytest]
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addopts =
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# show summary of all tests that did not pass
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-rEfX
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# Make tracebacks shorter
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--tb=native
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# capture only Python print and C++ py::print, but not C output (low-level Python errors)
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--capture=sys
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# don't suppress warnings, but don't shove them all to the end either
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-p no:warnings
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testpaths =
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test
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junit_logging_reruns = all
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filterwarnings =
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ignore:Module already imported so cannot be rewritten.*hypothesis:pytest.PytestAssertRewriteWarning
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strict_xfail = True
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