[Numpy-discussion] Adding to the non-dispatched implementation of NumPy methods
shoyer at gmail.com
Mon Apr 22 12:22:02 EDT 2019
Are there still concerns here? If not, I would love to move ahead with
these changes so we can get this into NumPy 1.17.
On Tue, Apr 16, 2019 at 10:23 AM Stephan Hoyer <shoyer at gmail.com> wrote:
> __numpy_implementation__ is indeed simply a slot for third-parties to
> access NumPy's implementation. It should be considered "NumPy's current
> implementation", not "NumPy's implementation as of 1.14". Of course, in
> practice these will remain very similar, because we are already very
> conservative about how we change NumPy.
> I would love to have clean well-defined coercion semantics for every NumPy
> function, which would be implicitly adopted by `__numpy_implementation__`
> (e.g., we could say that every function always coerces its arguments with
> `np.asarray()`). But I think that's an orthogonal issue. We have been
> supporting some ad-hoc duck typing in NumPy for a long time (e.g., the
> `.sum()` method which is called by `np.sum()`). Removing that would require
> a deprecation cycle, which may indeed be warranted once we're sure we're
> happy with __array_function__. But I don't think the deprecation cycle will
> be any worse if the implementation is also exposed via
> We should definitely still think about a cleaner "core" implementation of
> NumPy functions in terms of a minimal core. One recent example of this can
> be found JAX (see
> This would be something appropriate to put into a more generic function
> attribute on NumPy functions, perhaps `__array_implementation__`. But I
> don't think formalizing `__numpy_implementation__` as a way to get access
> to NumPy's default implementation will limit our future options here.
> On Tue, Apr 16, 2019 at 6:44 AM Marten van Kerkwijk <
> m.h.vankerkwijk at gmail.com> wrote:
>> I somewhat share Nathaniel's worry that by providing
>> `__numpy_implementation__` we essentially get stuck with the
>> implementations we have currently, rather than having the hoped-for freedom
>> to remove all the `np.asarray` coercion. In that respect, an advantage of
>> using `_wrapped` is that it is clearly a private method, so anybody is
>> automatically forewarned that this can change.
>> In principle, ndarray.__array_function__ would be more logical, but as
>> noted in the PR, the problem is that it is non-trivial for a regular
>> __array_function__ implementation to coerce all the arguments to ndarray
>> Which suggests that perhaps what is missing is a general routine that
>> does that, i.e., that re-uses the dispatcher.
>> -- Marten
>> NumPy-Discussion mailing list
>> NumPy-Discussion at python.org
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