ENH: is_lazy_array() #228
ENH: is_lazy_array() #228
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tests/test_common.py
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| @@ -92,6 +93,70 @@ def test_is_writeable_array_numpy(): | |||
| assert not is_writeable_array(x) | |||
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| @pytest.mark.parametrize("library", all_libraries) | |||
| # Unknown Array API compatible object. Note that this test may have dire consequences | ||
| # in terms of performance, e.g. for a lazy object that eagerly computes the graph | ||
| # on __bool__ (dask is one such example, which however is special-cased above). |
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This is a good argument for replacing everything below this point with a blind return False. Please discuss if you'd prefer it that way.
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This seems fine as is to me, since it's more correct. In case it's a problem for a library that's not explicitly handled, then support for it can be added, or a discussion can happen then - at least we'll learn something.
array_api_compat/common/_helpers.py
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| if ( | ||
| is_numpy_array(x) | ||
| or is_cupy_array(x) | ||
| or is_torch_array(x) |
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FIXME is this correct and safe? I don't know enough about torch.compile myself.
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It works, at least for most backends, since TorchDynamo can handle graph breaks. If we have to make a choice here, then I'd say what you wrote is correct for PyTorch.
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| # Unknown Array API compatible object. Note that this test may have dire consequences | ||
| # in terms of performance, e.g. for a lazy object that eagerly computes the graph | ||
| # on __bool__ (dask is one such example, which however is special-cased above). | ||
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array_api_strict reaches this point
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LGTM, thanks @crusaderky. And is_lazy_array seems okay as a helper function in this library rather than in array-api-extra.
@ev-br @asmeurer @lucascolley any of you want to take a look?
Closes #225