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

Eric Wieser wieser.eric+numpy at gmail.com
Tue Jun 12 02:59:36 EDT 2018

```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

-

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
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