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RFC: https://github.com/pytorch/rfcs/pull/54 First commit is the contents of https://github.com/Quansight-Labs/numpy_pytorch_interop/ We have already been using this in core for the last few months as a external dependency. This PR pulls all these into core. In the next commits, I do a number of things in this order - Fix a few small issues - Make the tests that this PR adds pass - Bend backwards until lintrunner passes - Remove the optional dependency on `torch_np` and simply rely on the upstreamed code - Fix a number dynamo tests that were passing before (they were not tasting anything I think) and are not passing now. Missing from this PR (but not blocking): - Have a flag that deactivates tracing NumPy functions and simply breaks. There used to be one but after the merge stopped working and I removed it. @lezcano to investigate. - https://github.com/pytorch/pytorch/pull/106431#issuecomment-1667079543. @voznesenskym to submit a fix after we merge. All the tests in `tests/torch_np` take about 75s to run. This was a work by @ev-br, @rgommers @honno and I. I did not create this PR via ghstack (which would have been convenient) as this is a collaboration, and ghstack doesn't allow for shared contributions. Pull Request resolved: https://github.com/pytorch/pytorch/pull/106211 Approved by: https://github.com/ezyang
80 lines
2.1 KiB
Python
80 lines
2.1 KiB
Python
# Owner(s): ["module: dynamo"]
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import sys
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import pytest
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import torch._numpy as tnp
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def pytest_configure(config):
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config.addinivalue_line("markers", "slow: very slow tests")
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def pytest_addoption(parser):
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parser.addoption("--runslow", action="store_true", help="run slow tests")
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parser.addoption("--nonp", action="store_true", help="error when NumPy is accessed")
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class Inaccessible:
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def __getattribute__(self, attr):
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raise RuntimeError(f"Using --nonp but accessed np.{attr}")
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def pytest_sessionstart(session):
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if session.config.getoption("--nonp"):
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sys.modules["numpy"] = Inaccessible()
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def pytest_generate_tests(metafunc):
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"""
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Hook to parametrize test cases
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See https://docs.pytest.org/en/6.2.x/parametrize.html#pytest-generate-tests
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The logic here allows us to test with both NumPy-proper and torch._numpy.
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Normally we'd just test torch._numpy, e.g.
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import torch._numpy as np
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...
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def test_foo():
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np.array([42])
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...
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but this hook allows us to test NumPy-proper as well, e.g.
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def test_foo(np):
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np.array([42])
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...
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np is a pytest parameter, which is either NumPy-proper or torch._numpy. This
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allows us to sanity check our own tests, so that tested behaviour is
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consistent with NumPy-proper.
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pytest will have test names respective to the library being tested, e.g.
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$ pytest --collect-only
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test_foo[torch._numpy]
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test_foo[numpy]
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"""
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np_params = [tnp]
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try:
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import numpy as np
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except ImportError:
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pass
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else:
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if not isinstance(np, Inaccessible): # i.e. --nonp was used
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np_params.append(np)
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if "np" in metafunc.fixturenames:
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metafunc.parametrize("np", np_params)
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def pytest_collection_modifyitems(config, items):
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if not config.getoption("--runslow"):
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skip_slow = pytest.mark.skip(reason="slow test, use --runslow to run")
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for item in items:
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if "slow" in item.keywords:
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item.add_marker(skip_slow)
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