On Thu, Feb 6, 2020 at 12:20 PM Sebastian Berg <
sebastian@sipsolutions.net> wrote:
Another thing about backward compatibility: What is our vision there
actually?
This NEP will *not* give the *end user* the option to opt-in! Here,
opt-in is really reserved to the *library user* (e.g. sklearn). (I did
not realize this clearly before)
Thinking about that for a bit now, that seems like the right choice.
But it also means that the library requires an easy way of giving a
FutureWarning, to notify the end-user of the upcoming change. The end-
user will easily be able to convert to a NumPy array to keep the old
behaviour.
Once this warning is given (maybe during `get_array_module()`, the
array module object/context would preferably be passed around,
hopefully even between libraries. That provides a reasonable way to
opt-in to the new behaviour without a warning (mainly for library
users, end-users can silence the warning if they wish so).
I don't think NumPy needs to do anything about warnings. It is straightforward for libraries that want to use use get_array_module() to issue their own warnings before calling get_array_module(), if desired.
Or alternatively, if a library is about to add a new __array_module__ method, it is straightforward to issue a warning inside the new __array_module__ method before returning the NumPy functions.
I don't think this is quite enough. Sebastian points out a fairly important issue. One of the main rationales for the whole NEP, and the argument in multiple places (
https://numpy.org/neps/nep-0037-array-module.html#opt-in-vs-opt-out-for-users) is that it's now opt-in while __array_function__ was opt-out. This isn't really true - the problem is simply *moved*, from the duck array libraries to the array-consuming libraries. The end user will still see the backwards incompatible change, with no way to turn it off. It will be easier with __array_module__ to warn users, but this should be expanded on in the NEP.
Also, I'm still not sure I agree with the tone of the discussion on this topic. It's very heavily inspired by what the JAX devs are telling you (the NEP still says PyTorch and scipy.sparse as well, but that's not true in both cases). If you ask Dask and CuPy for example, they're quite happy with __array_function__ and there haven't been many complaints about backwards compat breakage.
Cheers,
Ralf
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