[Numpy-discussion] Allowing broadcasting of code dimensions in generalized ufuncs

Stephan Hoyer shoyer at gmail.com
Fri Jun 15 14:17:09 EDT 2018

On Mon, Jun 11, 2018 at 11:59 PM Eric Wieser <wieser.eric+numpy at gmail.com>

> I don’t understand your alternative here. If we overload np.matmul using
> *array_function*, then it would not use *ether* of these options for
> writing the operation in terms of other gufuncs. It would simply look for
> an *array_function* attribute, and call that method instead.
> Let me explain that suggestion a little more clearly.
>    1. There’d be a linalg.matmul2d that performs the real matrix case,
>    which would be easy to make as a ufunc right now.
>    2. __matmul__ and __rmatmul__ would just call np.matmul, as they
>    currently do (for consistency between np.matmul and operator.matmul,
>    needed in python pre- at -operator)
>    3. np.matmul would be implemented as:
>    @do_array_function_overridesdef matmul(a, b):
>        if a.ndim != 1 and b.ndim != 1:
>            return matmul2d(a, b)
>        elif a.ndim != 1:
>            return matmul2d(a, b[:,None])[...,0]
>        elif b.ndim != 1:
>            return matmul2d(a[None,:], b)
>        else:
>            # this one probably deserves its own ufunf
>            return matmul2d(a[None,:], b[:,None])[0,0]
>    4. Quantity can just override __array_ufunc__ as with any other ufunc
>    5. DataArray, knowing the above doesn’t work, would implement
>    something like
>    @matmul.register_array_function(DataArray)def __array_function__(a, b):
>        if a.ndim != 1 and b.ndim != 1:
>            return matmul2d(a, b)
>        else:
>            # either:
>            # - add/remove dummy dimensions in a dataarray-specific way
>            # - downcast to ndarray and do the dimension juggling there
> Advantages of this approach:
>    -
>    Neither the ufunc machinery, nor __array_ufunc__, nor the inner loop,
>    need to know about optional dimensions.
>    -
>    We get a matmul2d ufunc, that all subclasses support out of the box if
>    they support matmul
> Eric
OK, this sounds pretty reasonable to me -- assuming we manage to figure out
the __array_function__ proposal!

There's one additional ingredient we would need to make this work well:
some way to guarantee that "ndim" and indexing operations are available
without casting to a base numpy array.

For now, np.asanyarray() would probably suffice, but that isn't quite right
(e.g., this would fail for np.matrix).

In the long term, I think we need a new coercion protocol for "duck"
arrays. Nathaniel Smith and I started writing a NEP on this, but it isn't
quite ready yet.

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