ENH: is_lazy_array() #228
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ENH: is_lazy_array() #228
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crusaderky
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Jan 6, 2025
crusaderky
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crusaderky
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Jan 6, 2025
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) | |||
crusaderky
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Jan 6, 2025
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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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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.
crusaderky
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Jan 6, 2025
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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jakevdp
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Jan 7, 2025
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crusaderky
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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
crusaderky
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crusaderky
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crusaderky
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crusaderky
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| @pytest.mark.parametrize("shape", [(math.nan,), (1, math.nan), (None, ), (1, None)]) | ||
| def test_is_lazy_array_nan_size(shape, monkeypatch): | ||
| """Test is_lazy_array() on an unknown Array API compliant object | ||
| with NaN (like Dask) or None (like ndonnx) in its shape |
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To be simplified after #231
crusaderky
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Jan 7, 2025
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| # Select a single point of the array | ||
| s = size(x) | ||
| if s is None or math.isnan(s): |
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To be simplified after #231
This was referenced Jan 8, 2025
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Closes #225