[Numpy-discussion] __array_function related regression for 1.17.0rc1
jni at fastmail.com
Tue Jul 2 04:43:37 EDT 2019
On Tue, 2 Jul 2019, at 4:34 PM, Stephan Hoyer wrote:
> This is addressed in the NEP, see bullet 1 under "Partial implementation of NumPy's API":
> My concern is that fallback coercion behavior makes it difficult to reliably implement "strict" overrides of NumPy's API. Fallback coercion is certainly useful for interactive use, but it isn't really appropriate for libraries.
> In contrast to putting this into NumPy, if a library like dask prefers to issue warnings or even keep around fallback coercion indefinitely (not that I would recommend it), they can do that by putting it in their __array_function__ implementation.
I get the above concerns, and thanks for bringing them up, Stephan, as I'd only skimmed the NEP the first time around and missed them. Nevertheless, the fact is that the current behaviour breaks user code that was perfectly valid until NumPy 1.16, which seems, well, insane. So, warning for a few versions followed raising seems like the only way forward to me. The NEP explicitly states “We would like to gain experience with how `__array_function__` is actually used before making decisions that would be difficult to roll back.” I think that this breakage *is* that experience, and the decision right now should be not to break user code with no warning period.
I'm also wondering where the list of functions that must be implemented can be found, so that libraries like dask and CuPy can be sure that they have a complete implementation, and further typeerrors won't be raised with their arrays.
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