On Apr 12, 2012, at 4:51 PM, Dag Sverre Seljebotn wrote: > On 04/12/2012 11:13 PM, Travis Oliphant wrote: >> Dag, >> >> Thanks for the link to your CEP. This is the first time I've seen it. You >> probably referenced it before, but I hadn't seen it. >> >> That CEP seems along the lines of what I was thinking of. We can make >> scipy follow that CEP and NumPy as well in places that it needs function >> pointers. >> >> I can certainly get behind it with Numba and recommend it to SciPy (and >> write the scipy.integrate.quad function to support it). >> >> Thanks for the CEP. > > Great. I'll pass this message on to the Cython list and see if anybody > wants to provide input (but given the scope, it should be minor tweaks > and easy to accommodate in whatever code you write). > > You will fill in more of the holes as you implement this in Numba and > SciPy of course (my feeling is they will support it before Cython; let's > say I hope this happens within the next year).
Very nice. This will help immensely I think. It's actually just what I was looking for. Just to be clear, by " pad to sizeof(void*) alignment", you mean that after the first 2 bytes there are (sizeof(void*) - 2) bytes before the first function pointer in the memory block pointed to by the PyCObject / Capsule? Thanks, -Travis > > Dag > > > > >> >> -Travis >> >> >> >> >> >> On Apr 12, 2012, at 2:08 PM, Dag Sverre Seljebotn wrote: >> >>> On 04/12/2012 07:24 PM, Nathaniel Smith wrote: >>>> On Wed, Apr 11, 2012 at 10:23 PM, Travis Oliphant<teoliph...@gmail.com> >>>> wrote: >>>>>>> In the mean-time, I think we could do as Robert essentially suggested >>>>>>> and just use Capsule Objects around an agreed-upon simple C-structure: >>>>>>> >>>>>>> int id /* Some number that can be used as a "type-check" */ >>>>>>> void *func; >>>>>>> char *string; >>>>>>> >>>>>>> We can then just create some nice functions to go to and from this form >>>>>>> in NumPy ctypeslib and then use this while the Python PEP gets written >>>>>>> and adopted. >>>>>> >>>>>> What is not clear to me is how one get from the Python callable to the >>>>>> capsule. >>>>> >>>>> This varies substantially based on the tool. Numba would do it's work >>>>> and create the capsule object using it's approach. Cython would use a >>>>> different approach. >>>>> >>>>> I would also propose to have in NumPy some basic functions that go >>>>> back-and forth between this representation, ctypes, and any other useful >>>>> representations that might emerge. >>>>> >>>>>> >>>>>> Or do you simply intend to pass a non-callable capsule as an argument in >>>>>> place of the callback? >>>>> >>>>> I had simply intended to allow a non-callable capsule argument to be >>>>> passed in instead of another call-back to any SciPy or NumPy function >>>>> that can take a raw C-function pointer. >>>> >>>> If the cython folks are worried about type-checking overhead, then >>>> PyCapsule seems sub-optimal, because it's unnecessarily complicated to >>>> determine what sort of PyCapsule you have, and then extract the actual >>>> C struct. (At a minimum, it requires two calls to non-inlineable >>>> functions, plus an unnecessary pointer indirection.) >>> >>> I think this discussion is moot -- the way I reverse-engineer Travis is >>> that there's no time for a cross-project discussion about this now. >>> That's not too bad, Cython will go its own way (eventually), and perhaps >>> we can merge in the future... >>> >>> But for the entertainment value: >>> >>> In my CEP [1] I descripe two access mechanisms, one slow (for which I >>> think capsules is fine), and a faster one. >>> >>> Obviously, only the slow mechanism will be implemented first. >>> >>> So the only things I'd like changed in how Travis' want to do this is >>> >>> a) Storing the signature string data in the struct, rather than as a char*; >>> >>> void *func >>> char string[1]; // variable-size-allocated and null-terminated >>> >>> b) Allow for multiple signatures in the same capsule, i.e. "dd->d", >>> "ff->f", in the same capsule. >>> >>>> A tiny little custom class in a tiny little library that everyone can >>>> share might be better? (Bonus: a custom class could define a __call__ >>>> method that used ctypes to call the function directly, for interactive >>>> convenience/testing/etc.) >>> >>> Having NumPy and Cython depend on a common library, and getting that to >>> work for users, seems rather utopic to me. And if I propose that Cython >>> have a hard dependency of NumPy for a feature as basic as calli.ng a >>> callback object then certain people will be very angry. >>> >>> Anyway, in my CEP I went to great pains to avoid having to do this, with >>> a global registration mechanism for multiple such types. >>> >>> Regarding your idea for the __call__, that's the exact opposite of what >>> I'm doing in the CEP. I'm pretty sure that what I described is what we >>> want for Cython; we will never tell our users to pass capsules around. >>> What I want is this: >>> >>> @numba >>> def f(x): return 2 * x >>> >>> @cython.inline >>> def g(x): return 3 * x >>> >>> print f(3) >>> print g(3) >>> print scipy.integrate.quad(f, 0.2, 3) # fast! >>> print scipy.integrate.quad(g, 0.2, 3) # fast! >>> >>> # If you really want a capsule: >>> print f.__nativecall__ >>> >>> Dag >>> >>> [1] http://wiki.cython.org/enhancements/cep1000 >>> _______________________________________________ >>> NumPy-Discussion mailing list >>> NumPy-Discussion@scipy.org >>> http://mail.scipy.org/mailman/listinfo/numpy-discussion >> >> _______________________________________________ >> NumPy-Discussion mailing list >> NumPy-Discussion@scipy.org >> http://mail.scipy.org/mailman/listinfo/numpy-discussion > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion