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. > > - N > _______________________________________________ > cython-devel mailing list > cython-devel@python.org > http://mail.python.org/mailman/listinfo/cython-devel
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"). _______________________________________________ cython-devel mailing list cython-devel@python.org http://mail.python.org/mailman/listinfo/cython-devel
