[Numpy-discussion] Adding to the non-dispatched implementation of NumPy methods
Nathaniel Smith
njs at pobox.com
Sun Apr 28 14:50:28 EDT 2019
On Sun, Apr 28, 2019, 08:41 Marten van Kerkwijk <m.h.vankerkwijk at gmail.com>
wrote:
> Hi Ralf,
>
> Thanks for the comments and summary slides. I think you're
> over-interpreting my wish to break people's code! I certainly believe - and
> think we all agree - that we remain as committed as ever to ensure that
> ```
> np.function(inputs)
> ```
> continues to work just as before. My main comment is that I want to ensure
> that no similar guarantee will exist for
> ```
> np.function.__wrapped__(inputs)
> ```
> (or whatever we call it). I think that is quite consistent with NEP-18,
> since as originally written there was not even the possibility to access
> the implementation directly (which was after long discussions about whether
> to allow it, including ideas like `import numpy.internal_apt as np`). In
> this respect, the current proposal is a large deviation from the original
> intent, so we need to be clear about what we are promising.
>
> In summary, I think the guarantees should be as follows:
> 1.If you call np.function and
> - do not define __array_function__, changes happen only via the usual
> cycle.
> - define __array_function__, you take responsibility for returning the
> result.
> 2. If you call np.function.__wrapped__ and
> - input only ndarray, changes happen only via the usual cycle;
> - input anything but ndarray, changes can happen in any release.
>
Let's just say that __skip_array_function__ is provisional, the same as
__array_function__ itself.
> On the larger picture,in your slides, the further split that happens is
> that if no override is present, the first thing that actually gets called
> is not the function implementation but rather `ndarray.__array_function__`.
>
This is tricky. I've definitely wanted to figure out some way the
conceptual model could be simplified by integrating __array_function__ into
regular dispatch to reduce special cases. (It's possible I suggested adding
ndarray.__array_dispatch__ in the first place?)
But, on further consideration, I don't think there's actually any way to
pretend that ndarray is just another duck array with an __array_function__
method. The big problem is:
np.concatenate([1, 2], [3, 4])
Here none of the arguments have __array_function__ methods. So the
implementation *has* to start by doing coercion. The coercion can't happen
inside __array_function__, because there is no __array_function__ until
after coercion.
So ndarray coercion and everything after it has to remain a special case.
ndarray.__array_function__ isn't fooling anyone.
Also: if we add Stephan's __skipping_array_function__ (or whatever we call
it), then that's also incompatible with the idea that
ndarray.__array_function__ is where the real work happens.
I'm starting to think ndarray.__array_function__ is a mistake – it was
supposed to simplify the conceptual model, by letting us handle the
fallback logic and the override logic using the same unified framework. But
it fails.
-n
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