[Numpy-discussion] @ operator
Nathaniel Smith
njs at pobox.com
Thu Sep 11 00:08:49 EDT 2014
On Wed, Sep 10, 2014 at 4:53 PM, Charles R Harris
<charlesr.harris at gmail.com> wrote:
>
> On Wed, Sep 10, 2014 at 10:52 AM, Pauli Virtanen <pav at iki.fi> wrote:
>>
>> 09.09.2014, 22:52, Charles R Harris kirjoitti:
>> > 1. Should the operator accept array_like for one of the arguments?
>> > 2. Does it need to handle __numpy_ufunc__, or will
>> > __array_priority__ serve?
>>
>> I think the __matmul__ operator implementation should follow that of
>> __mul__.
>>
>> [clip]
>> > 3. Do we want PyArray_Matmul in the numpy API?
>> > 4. Should a matmul function be supplied by the multiarray module?
>> >
>> > If 3 and 4 are wanted, should they use the __numpy_ufunc__ machinery, or
>> > will __array_priority__ serve?
>>
>> dot() function deals with __numpy_ufunc__, and the matmul() function
>> should behave similarly.
>>
>> It seems dot() uses __array_priority__ for selection of output return
>> subclass, so matmul() probably needs do the same thing.
>>
>> > Note that the type number operators, __add__ and such, currently use
>> > __numpy_ufunc__ in combination with __array_priority__, this in addition
>> > to
>> > the fact that they are by default using ufuncs that do the same. I'd
>> > rather
>> > that the __*__ operators simply rely on __array_priority__.
>>
>> The whole business of __array_priority__ and __numpy_ufunc__ in the
>> binary ops is solely about when __<op>__ should yield the execution to
>> __r<op>__ of the other object.
>>
>> The rule of operation currently is: "__rmul__ before __numpy_ufunc__"
>>
>> If you remove the __numpy_ufunc__ handling, it becomes: "__rmul__ before
>> __numpy_ufunc__, except if array_priority happens to be smaller than
>> that of the other class and your class is not an ndarray subclass".
>>
>> The following binops also do not IIRC respect __array_priority__ in
>> preferring right-hand operand:
>>
>> - in-place operations
>> - comparisons
>>
>> One question here is whether it's possible to change the behavior of
>> __array_priority__ here at all, or whether changes are possible only in
>> the context of adding new attributes telling Numpy what to do.
>
> I was tempted to make it a generalized ufunc, which would take care of a lot
> of things, but there is a lot of overhead in those functions. Sounds like
> the easiest thing is to make it similar to dot, although having an inplace
> versions complicates the type selection a bit.
Can we please fix the overhead instead of adding more half-complete
implementations of the same concepts? I feel like this usually ends up
slowing things down in the end, as optimization efforts get divided...
My vote is:
__matmul__/__rmatmul__ do the standard dispatch stuff that all __op__
methods do (so I guess check __array_priority__ or whatever it is we
always do). I'd also be okay with ignoring __array_priority__ on the
grounds that __numpy_ufunc__ is better, and there's no existing code
relying on __array_priority__ support in __matmul__.
Having decided that we are actually going to run, they dispatch
unconditionally to np.newdot(a, b) (or newdot(a, b, out=a) for the
in-place version), similarly to how e.g. __add__ dispatches to np.add.
newdot acts like a standard gufunc with all the standard niceties,
including __numpy_ufunc__ dispatch.
("newdot" here is intended as a placeholder name, maybe it should be
np.linalg.matmul or something else to be bikeshed later. I also vote
that eventually 'dot' become an alias for this function, but whether
to do that is an orthogonal discussion for later.)
--
Nathaniel J. Smith
Postdoctoral researcher - Informatics - University of Edinburgh
http://vorpus.org
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