[SciPy-Dev] Enabling use of scipy.special from Numba

[Stanley Seibert]

This is great!  Thanks for doing this.  (We're aiming to get the Numba
release with the required patches for overloading NumPy functions out in
about 2 weeks.)

On Sun, Jul 7, 2019 at 1:18 AM Joshua Wilson <josh.craig.wilson at gmail.com>
wrote:

> Final update-an initial release of the package is up on PyPI:
>
> https://pypi.org/project/numba-special/
>
> There's more work to be done, but you can see a list what can
> currently be used from Numba here:
>
> https://numba-special.readthedocs.io/en/latest/numba_special.html
>
> On Thu, Jul 4, 2019 at 4:00 PM Ralf Gommers <ralf.gommers at gmail.com>
> wrote:
> >
> >
> >
> > On Thu, Jul 4, 2019 at 12:23 AM Joshua Wilson <
> josh.craig.wilson at gmail.com> wrote:
> >>
> >> Ok, returning to the special functions in Numba topic: here's a small
> >> repo that generates overloads for a few special functions:
> >>
> >> https://github.com/person142/numba_special/tree/master/numba_special
> >>
> >> Taking Nathaniel's point that coming up with an ad-hoc metadata format
> >> is probably not a good idea, it instead uses a little bit of Cython to
> >> pull out the function pointers:
> >>
> >>
> https://github.com/person142/numba_special/blob/master/numba_special/function_pointers.pyx
> >>
> >> and then you grab those on the Numba side to create the overloads:
> >>
> >>
> https://github.com/person142/numba_special/blob/master/numba_special/numba_overloads.py
> >>
> >> You can see that the overloads are working as expected in the tests:
> >>
> >>
> https://github.com/person142/numba_special/blob/master/numba_special/tests/test_functions.py
> >>
> >> (Note that the development version of Numba is required because of
> >> this issue: https://github.com/numba/numba/issues/4133)
> >>
> >> Of course the pointers/overloads that you're creating are highly
> >> structured, so you can just generate the necessary glue code:
> >>
> >>
> https://github.com/person142/numba_special/blob/master/generate_overloads.py
> >>
> >> though it only handles simple function signatures right now. All in
> >> all I think it works pretty well-you only use 100% officially
> >> supported pieces of all the libraries involved. I'll probably work on
> >> extending it more in the next couple of days.
> >
> >
> > Very nice, thanks Josh. That's all pretty clean and understandable code.
> >
> > Cheers,
> > Ralf
> >
> >>
> >> On Thu, Jun 20, 2019 at 5:31 PM Nathaniel Smith <njs at pobox.com> wrote:
> >> >
> >> > On Thu, Jun 20, 2019 at 1:41 PM Stanley Seibert <
> sseibert at anaconda.com> wrote:
> >> > >
> >> > > Well, we'd prefer not to have an unnecessary indirection (and the
> overhead of making a nested call to the scalar inside the length 1 loop)
> since the user is doing their own iteration in Numba, but if that's the
> only option, we can certainly work around it.
> >> >
> >> > Well, it's computers, so there's never just one option :-). But if you
> >> > can consume existing ufuncs and get 95% of the performance, then it
> >> > might not be worth building custom infrastructure to expose
> >> > scipy.special's raw scalar functions + an ad hoc metadata format + ad
> >> > hoc tools to consume the metadata. Intuitively it seems like a few
> >> > extra integer operations at entry and exit might not be noticeable at
> >> > all compared to the cost of computing a bessel function or whatever,
> >> > but I haven't checked.
> >> >
> >> > -n
> >> >
> >> > --
> >> > Nathaniel J. Smith -- https://vorpus.org
> >> > _______________________________________________
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