[Cython] [Python-Dev] C-level duck typing

Nathaniel Smith njs at pobox.com
Mon May 28 12:41:37 CEST 2012


On Mon, May 28, 2012 at 10:13 AM, mark florisson
<markflorisson88 at gmail.com> wrote:
> On 28 May 2012 09:54, mark florisson <markflorisson88 at gmail.com> wrote:
>> On 27 May 2012 23:12, Nathaniel Smith <njs at pobox.com> wrote:
>>> On Sun, May 27, 2012 at 10:24 PM, Dag Sverre Seljebotn
>>> <d.s.seljebotn at 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


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