[Numpy-discussion] Keep __array_function__ unexposed by default for 1.17?

Ralf Gommers ralf.gommers at gmail.com
Wed May 22 17:34:37 EDT 2019

On Wed, May 22, 2019 at 9:46 PM Marten van Kerkwijk <
m.h.vankerkwijk at gmail.com> wrote:

> Hi Stephan,
> I'm quite happy with the idea of turning on __array_function__ but
> postponing any formal solution to getting into the wrapped routines (i.e.,
> one can use __wrapped__, but it is an implementation detail that is not
> documented and comes with absolutely no guarantees).
> That way, 1.17 will be a release where we can think of how to address two
> different things:
> 1. Reduce the overhead costs for pure ndarray cases (i.e., mostly within
> numpy itself);
> 2. Simplify implementation in outside packages.
> On the performance front, I'm not quite sure what the state of the
> environment variable check is, but is it possible to just flip the default,
> i.e., for 1.17 one gets __array_function__ support turned on by default,
> but can turn it off if wanted?

This would be useful as a safety measure.

> All the best,
> Marten
> On Wed, May 22, 2019 at 11:53 AM Stephan Hoyer <shoyer at gmail.com> wrote:
>> Thanks for raising these concerns.
>> The full implications of my recent __skip_array_function__ proposal are
>> only now becoming evident to me now, looking at it's use in GH-13585.
>> Guaranteeing that it does not expand NumPy's API surface seems hard to
>> achieve without pervasive use of __skip_array_function__ internally.
>> Taking a step back, the sort of minor hacks [1] that motivated
>> __skip_array_function__ for me are annoying, but really not too bad -- they
>> are a small amount of additional code duplication in a proposal that
>> already requires a large amount of code duplication.
>> So let's roll back the recent NEP change adding __skip_array_function__
>> to the public interface [2]. Inside the few NumPy functions where
>> __array_function__ causes a measurable performance impact due to repeated
>> calls (most notably np.block, for which some benchmarks are 25% slower), we
>> can make use of the private __wrapped__ attribute.
Thanks Stephan, this sounds good.

>> I would still like to turn on __array_function__ in NumPy 1.17. At least,
>> let's try that for the release candidate and see how it goes.
I agree. I'd actually suggest flipping the switch asap and see if it causes
any issues for projects that test against numpy master in their CI, and the
people that like to live on the bleeding edge by installing master into
their environment.


The "all in" nature of __array_function__ without __skip_array_function__
>> will both limit its use to cases where it is strongly motivated, and also
>> limits the API implications for NumPy. There is still plenty of room for
>> expanding the protocol, but it's really hard to see what is necessary (and
>> prudent!) without actual use.
>> [1] e.g., see
>> https://github.com/google/jax/blob/62473351643cecb6c248a50601af163646ba7be6/jax/numpy/lax_numpy.py#L2440-L2459
>> [2] https://github.com/numpy/numpy/pull/13305
>> On Tue, May 21, 2019 at 11:44 PM Juan Nunez-Iglesias <jni.soma at gmail.com>
>> wrote:
>>> I just want to express my general support for Marten's concerns. As an
>>> "interested observer", I've been meaning to give `__array_function__` a try
>>> but haven't had the chance yet. So from my anecdotal experience I expect
>>> that more people need to play with this before setting the API in stone.
>>> At scikit-image we place a very strong emphasis on code simplicity and
>>> readability, so I also share Marten's concerns about code getting too
>>> complex. My impression reading the NEP was "whoa, this is hard, I'm glad
>>> smarter people than me are working on this, I'm sure it'll get simpler in
>>> time". But I haven't seen the simplicity materialise...
>>> On Wed, 22 May 2019, at 11:31 AM, Marten van Kerkwijk wrote:
>>> Hi All,
>>> For 1.17, there has been a big effort, especially by Stephan, to make
>>> __array_function__ sufficiently usable that it can be exposed. I think this
>>> is great, and still like the idea very much, but its impact on the numpy
>>> code base has gotten so big in the most recent PR (gh-13585) that I wonder
>>> if we shouldn't reconsider the approach, and at least for 1.17 stick with
>>> the status quo. Since that seems to be a bigger question than can be
>>> usefully addressed in the PR, I thought I would raise it here.
>>> Specifically, now not only does every numpy function have its dispatcher
>>> function, but also internally all numpy function calls are being done via
>>> the new `__skip_array_function__` attribute, to avoid further overrides. I
>>> think both changes make the code significantly less readable, thus, e.g.,
>>> making it even harder than it is already to attract new contributors.
>>> I think with this it is probably time to step back and check whether the
>>> implementation is in fact the right one. For instance, among the
>>> alternatives we originally considered was one that had the overridable
>>> versions of functions in the regular `numpy` namespace, and the once that
>>> would not themselves check in a different one. Alternatively, for some of
>>> the benefits provided by `__skip_array_function__`, there was a different
>>> suggestion to have a special return value, of `NotImplementedButCoercible`.
>>> Might these be better after all?
>>> More generally, I think we're suffering from the fact that several of us
>>> seem to have rather different final goals in mind  In particular, I'd like
>>> to move to a state where as much of the code as possible makes use of the
>>> simplest possible implementation, with only a few true base functions, so
>>> that all but those simplest functions will generally work on any type of
>>> array. Others, however, worry much more about making implementations (even
>>> more) part of the API.
>>> All the best,
>>> Marten
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