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  • rgommers · 1 ✖

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  • `as_shared_dtype` converts scalars to 0d `numpy` arrays if chunked `cupy` is involved · 1 ✖

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  • NONE · 1 ✖
id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
1516494141 https://github.com/pydata/xarray/issues/7721#issuecomment-1516494141 https://api.github.com/repos/pydata/xarray/issues/7721 IC_kwDOAMm_X85aY909 rgommers 98330 2023-04-20T15:04:17Z 2023-04-20T15:04:17Z NONE

So really, my question is: how do we support python scalars for libraries that only implement __array_namespace__, given that stopping to do so would be a major breaking change?

I was considering this question for SciPy (xref scipy#18286) this week, and I think I'm happy with this strategy: 1. Cast all "array-like" inputs like Python scalars, lists/sequences, and generators, to numpy.ndarray. 2. Require "same array type" input, forbid mixing numpy-cupy, numpy-pytorch, cupy-pytorch, etc. - this will raise an exception 3. As a result, cupy-pyscalar and pytorch-pyscalar will also raise an exception.

What that results in is an API that's backwards-compatible for numpy and array-like usage, and much stricter when using other array libraries. That strictness to me is a good thing, because: - that's what CuPy, PyTorch & co themselves do, and it works well there - it avoids the complexity raised by arbitrary mixing, which results in questions like the one raised in this issue. - in case you do need to use a scalar from within a function inside your own library, just convert it explicitly to the desired array type with xp.asarray(a_scalar) giving you a 0-D array of the correct type (add dtype=x.dtype to make sure dtypes match if that matters)

 
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   `as_shared_dtype` converts scalars to 0d `numpy` arrays if chunked `cupy` is involved 1655290694

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