On Tue, Jul 2, 2019 at 1:45 AM Juan Nunez-Iglesias <j...@fastmail.com> wrote:

> 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": > > http://www.numpy.org/neps/nep-0018-array-function-protocol.html#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. > > Do you mean "fallback coercion in NumPy itself", or "at all"? Right now there's lots of valid code around, e.g. `np.median(some_dask_array)`. Users will keep wanting to do that. Forcing everyone to write `np.median(np.array(some_dask_array))` serves no purpose. So the coercion has to be somewhere. You're arguing that that's up to Dask et al I think? Putting it in Dask right now still doesn't address Juan's backwards compat concern, but perhaps that could be bridged with a Dask bugfix release and some short-lived pain. I'm not convinced that this shouldn't be fixed in NumPy though. Your concern "reliably implement "strict" overrides of NumPy's API" is a bit abstract. Overriding the _whole_ NumPy API is definitely undesirable. If we'd have a reference list somewhere about every function that is handled with __array_function__, then would that address your concern? Such a list could be auto-generated fairly easily. > > 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. > This is one of the reasons I'm working on https://github.com/Quansight-Labs/rnumpy. It doesn't make sense for any library to copy the whole NumPy API, it's way too large with lots of stuff in there that's only there for backwards compat and has a better alternative or shouldn't be in NumPy in the first place. Cheers, Ralf

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