On Mon, May 28, 2012 at 11:55 AM, mark florisson <markflorisso...@gmail.com> wrote: > On 28 May 2012 11:41, Nathaniel Smith <n...@pobox.com> wrote: >> On Mon, May 28, 2012 at 10:13 AM, mark florisson >> <markflorisso...@gmail.com> wrote: >>> On 28 May 2012 09:54, mark florisson <markflorisso...@gmail.com> wrote: >>>> On 27 May 2012 23:12, Nathaniel Smith <n...@pobox.com> wrote: >>>>> On Sun, May 27, 2012 at 10:24 PM, Dag Sverre Seljebotn >>>>> <d.s.seljeb...@astro.uio.no> wrote: >>>>>> On 05/18/2012 10:30 AM, Dag Sverre Seljebotn wrote: >>>>>>> >>>>>>> On 05/18/2012 12:57 AM, Nick Coghlan wrote: >>>>>>>> >>>>>>>> I think the main things we'd be looking for would be: >>>>>>>> - a clear explanation of why a new metaclass is considered too complex >>>>>>>> a >>>>>>>> solution >>>>>>>> - what the implications are for classes that have nothing to do with >>>>>>>> the >>>>>>>> SciPy/NumPy ecosystem >>>>>>>> - how subclassing would behave (both at the class and metaclass level) >>>>>>>> >>>>>>>> Yes, defining a new metaclass for fast signature exchange has its >>>>>>>> challenges - but it means that *our* concerns about maintaining >>>>>>>> consistent behaviour in the default object model and avoiding adverse >>>>>>>> effects on code that doesn't need the new behaviour are addressed >>>>>>>> automatically. >>>>>>>> >>>>>>>> Also, I'd consider a functioning reference implementation using a >>>>>>>> custom >>>>>>>> metaclass a requirement before we considered modifying type anyway, so >>>>>>>> I >>>>>>>> think that's the best thing to pursue next rather than a PEP. It also >>>>>>>> has the virtue of letting you choose which Python versions to target >>>>>>>> and >>>>>>>> iterating at a faster rate than CPython. >>>>>>> >>>>>>> >>>>>>> This seems right on target. I could make a utility code C header for >>>>>>> such a metaclass, and then the different libraries can all include it >>>>>>> and handshake on which implementation becomes the real one through >>>>>>> sys.modules during module initialization. That way an eventual PEP will >>>>>>> only be a natural incremental step to make things more polished, whether >>>>>>> that happens by making such a metaclass part of the standard library or >>>>>>> by extending PyTypeObject. >>>>>> >>>>>> >>>>>> So I finally got around to implementing this: >>>>>> >>>>>> https://github.com/dagss/pyextensibletype >>>>>> >>>>>> Documentation now in a draft in the NumFOCUS SEP repo, which I believe >>>>>> is a >>>>>> better place to store cross-project standards like this. (The NumPy >>>>>> docstring standard will be SEP 100). >>>>>> >>>>>> https://github.com/numfocus/sep/blob/master/sep200.rst >>>>>> >>>>>> Summary: >>>>>> >>>>>> - No common runtime dependency >>>>>> >>>>>> - 1 ns overhead per lookup (that's for the custom slot *alone*, no >>>>>> fast-callable signature matching or similar) >>>>>> >>>>>> - Slight annoyance: Types that want to use the metaclass must be a >>>>>> PyHeapExtensibleType, to make the binary layout work with how CPython >>>>>> makes >>>>>> subclasses from Python scripts >>>>>> >>>>>> My conclusion: I think the metaclass approach should work really well. >>>>> >>>>> Few quick comments on skimming the code: >>>>> >>>>> The complicated nested #ifdef for __builtin_expect could be simplified to >>>>> #if defined(__GNUC__) && (__GNUC__ > 2 || __GNUC_MINOR__ > 95) >>>>> >>>>> PyCustomSlots_Check should be called PyCustomSlots_CheckExact, surely? >>>>> And given that, how can this code work if someone does subclass this >>>>> metaclass? >>>> >>>> I think we should provide a wrapper for PyType_Ready, which just >>>> copies the pointer to the table and the count directly into the >>>> subclass. If a user then wishes to add stuff, the user can allocate a >>>> new memory region dynamically, memcpy the base class' stuff in there, >>>> and append some entries. >>> >>> Maybe we should also allow each custom type to set a deallocator, >>> since they are then heap types which can go out of scope. The >>> metaclass can then call this deallocator to deallocate the table. >> >> Custom types are plain old Python objects, they can use tp_dealloc. >> > If I set etp_custom_slots to something allocated on the heap, then the > (shared) metaclass would have to deallocate it. The tp_dealloc of the > type itself would be called for its instances (which can be used to > deallocate dynamically allocated memory in the objects if you use a > custom slot "pointer offset").
Oh, I see. Right, the natural way to handle this would be have each user define their own metaclass with the behavior they want. Another argument for supporting multiple metaclasses simultaneously I guess... - N _______________________________________________ cython-devel mailing list cython-devel@python.org http://mail.python.org/mailman/listinfo/cython-devel
