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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
38 lines
1.0 KiB
Python
38 lines
1.0 KiB
Python
# Owner(s): ["module: dynamo"]
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import pytest
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from torch.testing._internal.common_utils import (
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run_tests,
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TEST_WITH_TORCHDYNAMO,
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TestCase,
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)
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# If we are going to trace through these, we should use NumPy
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# If testing on eager mode, we use torch._numpy
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if TEST_WITH_TORCHDYNAMO:
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import numpy as np
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from numpy.testing import assert_equal
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else:
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import torch._numpy as np
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from torch._numpy.testing import assert_equal
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class TestAppend(TestCase):
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# tests taken from np.append docstring
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def test_basic(self):
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result = np.append([1, 2, 3], [[4, 5, 6], [7, 8, 9]])
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assert_equal(result, np.arange(1, 10, dtype=int))
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# When `axis` is specified, `values` must have the correct shape.
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result = np.append([[1, 2, 3], [4, 5, 6]], [[7, 8, 9]], axis=0)
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assert_equal(result, np.arange(1, 10, dtype=int).reshape((3, 3)))
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with pytest.raises((RuntimeError, ValueError)):
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np.append([[1, 2, 3], [4, 5, 6]], [7, 8, 9], axis=0)
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if __name__ == "__main__":
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run_tests()
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