[Numpy-discussion] Adding to the non-dispatched implementation of NumPy methods

Stephan Hoyer shoyer at gmail.com
Tue Apr 16 13:23:13 EDT 2019

__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
> itself.
> Which suggests that perhaps what is missing is a general routine that does
> that, i.e., that re-uses the dispatcher.
> -- Marten
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