[Numpy-discussion] NEP: Dispatch Mechanism for NumPy’s high level API

Marten van Kerkwijk m.h.vankerkwijk at gmail.com
Sun Jun 3 20:45:56 EDT 2018

This in certainly true in general, but given the complete flexibility of
__array_function__ there's no way we can make every check convenient. The
best we can do is make it easy to handle the common cases, where the
argument position does not matter.

I think those cases may not be as common as you think - most functions are
not like `concatenate` & co... Indeed, it might be good to add some other
examples to the NEP. Looing at the list of functions which do not work with
Quantity currently: Maybe `np.dot`, `np.choose`, and `np.vectorize`?

> Possibly, a solution would rely on the same structure as used for the
> "dance". But as a general point, I don't see the advantage of passing types
> rather than arguments - less information for no benefit.

> Maybe this is premature optimization, but there will certainly be fewer
unique types than arguments to check for types. I suspect this may make for
a noticeable difference in performance in use cases involving a large
number of argument.

One also needs to worry about the cost of contructing `types`, though I
guess this could be minimal if it is a `set`. Or should it be the keys of a
`dict`, with the value something meaningful that has to be calculated
anyway (like a list of sequence numbers); this may all depend a bit on the
implementation of "dance" - the information it gathers might as well get
passed on.

> For example, suppose np.concatenate() is called on a list of 10,000 dask
arrays. Now dask.array.Array.__array_function__ needs to check all
arguments to decide whether it can use dask.array.concatenate() or needs to
return NotImplemented. By using the `types` argument, it only needs to do
isinstance() checks on the single argument in `types`, rather than all
10,000 overloaded function arguments

It is probably a good idea to add some of these considerations to the NEP.

-- Marten
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