PEP: 447 Title: Add __getdescriptor__ method to metaclass Version: $Revision$ Last-Modified: $Date$ Author: Ronald Oussoren Status: Draft Type: Standards Track Content-Type: text/x-rst Created: 12-Jun-2013 Post-History: 2-Jul-2013, 15-Jul-2013, 29-Jul-2013 Abstract ======== Currently ``object.__getattribute__`` and ``super.__getattribute__`` peek in the ``__dict__`` of classes on the MRO for a class when looking for an attribute. This PEP adds an optional ``__getdescriptor__`` method to a metaclass that can be used to override this behavior. That is, the MRO walking loop in ``_PyType_Lookup`` and ``super.__getattribute__`` gets changed from:: def lookup(mro_list, name): for cls in mro_list: if name in cls.__dict__: return cls.__dict__ return NotFound to:: def lookup(mro_list, name): for cls in mro_list: try: return cls.__getdescriptor__(name) except AttributeError: pass return NotFound Rationale ========= It is currently not possible to influence how the `super class`_ looks up attributes (that is, ``super.__getattribute__`` unconditionally peeks in the class ``__dict__``), and that can be problematic for dynamic classes that can grow new methods on demand. The ``__getdescriptor__`` method makes it possible to dynamically add attributes even when looking them up using the `super class`_. The new method affects ``object.__getattribute__`` (and `PyObject_GenericGetAttr`_) as well for consistency and to have a single place to implement dynamic attribute resolution for classes. Background ---------- The current behavior of ``super.__getattribute__`` causes problems for classes that are dynamic proxies for other (non-Python) classes or types, an example of which is `PyObjC`_. PyObjC creates a Python class for every class in the Objective-C runtime, and looks up methods in the Objective-C runtime when they are used. This works fine for normal access, but doesn't work for access with ``super`` objects. Because of this PyObjC currently includes a custom ``super`` that must be used with its classes. The API in this PEP makes it possible to remove the custom ``super`` and simplifies the implementation because the custom lookup behavior can be added in a central location. The superclass attribute lookup hook ==================================== Both ``super.__getattribute__`` and ``object.__getattribute__`` (or `PyObject_GenericGetAttr`_ and in particular ``_PyType_Lookup`` in C code) walk an object's MRO and currently peek in the class' ``__dict__`` to look up attributes. With this proposal both lookup methods no longer peek in the class ``__dict__`` but call the special method ``__getdescriptor__``, which is a slot defined on the metaclass. The default implementation of that method looks up the name the class ``__dict__``, which means that attribute lookup is unchanged unless a metatype actually defines the new special method. Aside: Attribute resolution algorithm in Python ----------------------------------------------- The attribute resolution proces as implemented by ``object.__getattribute__`` (or PyObject_GenericGetAttr`` in CPython's implementation) is fairly straightforward, but not entirely so without reading C code. The current CPython implementation of object.__getattribute__ is basicly equivalent to the following (pseudo-) Python code (excluding some house keeping and speed tricks):: def _PyType_Lookup(tp, name): mro = tp.mro() assert isinstance(mro, tuple) for base in mro: assert isinstance(base, type) # PEP 447 will change these lines: try: return base.__dict__[name] except KeyError: pass return None class object: def __getattribute__(self, name): assert isinstance(name, str) tp = type(self) descr = _PyType_Lookup(tp, name) f = None if descr is not None: f = descr.__get__ if f is not None and descr.__set__ is not None: # Data descriptor return f(descr, self, type(self)) dict = self.__dict__ if dict is not None: try: return self.__dict__[name] except KeyError: pass if f is not None: # Non-data descriptor return f(descr, self, type(self)) if descr is not None: # Regular class attribute return descr raise AttributeError(name) class super: def __getattribute__(self, name): assert isinstance(name, unicode) if name != '__class__': starttype = self.__self_type__ mro = startype.mro() try: idx = mro.index(self.__thisclass__) except ValueError: pass else: for base in mro[idx+1:]: # PEP 447 will change these lines: try: descr = base.__dict__[name] except KeyError: continue f = descr.__get__ if f is not None: return f(descr, None if (self.__self__ is self.__self_type__) else self.__self__, starttype) else: return descr return object.__getattribute__(self, name) This PEP should change the dict lookup at the lines starting at "# PEP 447" with a method call to perform the actual lookup, making is possible to affect that lookup both for normal attribute access and access through the `super proxy`_. Note that specific classes can already completely override the default behaviour by implementing their own ``__getattribute__`` slot (with or without calling the super class implementation). In Python code -------------- A meta type can define a method ``__getdescriptor__`` that is called during attribute resolution by both ``super.__getattribute__`` and ``object.__getattribute``:: class MetaType(type): def __getdescriptor__(cls, name): try: return cls.__dict__[name] except KeyError: raise AttributeError(name) from None The ``__getdescriptor__`` method has as its arguments a class (which is an instance of the meta type) and the name of the attribute that is looked up. It should return the value of the attribute without invoking descriptors, and should raise `AttributeError`_ when the name cannot be found. The `type`_ class provides a default implementation for ``__getdescriptor__``, that looks up the name in the class dictionary. Example usage ............. The code below implements a silly metaclass that redirects attribute lookup to uppercase versions of names:: class UpperCaseAccess (type): def __getdescriptor__(cls, name): try: return cls.__dict__[name.upper()] except KeyError: raise AttributeError(name) from None class SillyObject (metaclass=UpperCaseAccess): def m(self): return 42 def M(self): return "fourtytwo" obj = SillyObject() assert obj.m() == "fortytwo" As mentioned earlier in this PEP a more realistic use case of this functionallity is a ``__getdescriptor__`` method that dynamicly populates the class ``__dict__`` based on attribute access, primarily when it is not possible to reliably keep the class dict in sync with its source, for example because the source used to populate ``__dict__`` is dynamic as well and does not have triggers that can be used to detect changes to that source. An example of that are the class bridges in PyObjC: the class bridge is a Python object (class) that represents an Objective-C class and conceptually has a Python method for every Objective-C method in the Objective-C class. As with Python it is possible to add new methods to an Objective-C class, or replace existing ones, and there are no callbacks that can be used to detect this. In C code --------- A new slot ``tp_getdescriptor`` is added to the ``PyTypeObject`` struct, this slot corresponds to the ``__getdescriptor__`` method on `type`_. The slot has the following prototype:: PyObject* (*getdescriptorfunc)(PyTypeObject* cls, PyObject* name); This method should lookup *name* in the namespace of *cls*, without looking at superclasses, and should not invoke descriptors. The method returns ``NULL`` without setting an exception when the *name* cannot be found, and returns a new reference otherwise (not a borrowed reference). Use of this hook by the interpreter ----------------------------------- The new method is required for metatypes and as such is defined on `type_`. Both ``super.__getattribute__`` and ``object.__getattribute__``/`PyObject_GenericGetAttr`_ (through ``_PyType_Lookup``) use the this ``__getdescriptor__`` method when walking the MRO. Other changes to the implementation ----------------------------------- The change for `PyObject_GenericGetAttr`_ will be done by changing the private function ``_PyType_Lookup``. This currently returns a borrowed reference, but must return a new reference when the ``__getdescriptor__`` method is present. Because of this ``_PyType_Lookup`` will be renamed to ``_PyType_LookupName``, this will cause compile-time errors for all out-of-tree users of this private API. The attribute lookup cache in ``Objects/typeobject.c`` is disabled for classes that have a metaclass that overrides ``__getdescriptor__``, because using the cache might not be valid for such classes. Impact of this PEP on introspection ----------------------------------- Use of the method introduced in this PEP can affect introspection of classes with a metaclass that uses a custom ``__getdescriptor__`` method. This section lists those changes. * ``dir`` might not show all attributes As with a custom ``__getattribute__`` method ``dir()`` might not see all (instance) attributes when using the ``__getdescriptor__()`` method to dynamicly resolve attributes. The solution for that is quite simple: classes using ``__getdescriptor__`` should also implement ``__dir__`` if they want full support for the builtin ``dir`` function. * ``inspect.getattr_static`` might not show all attributes The function ``inspect.getattr_static`` intentionally does not invoke ``__getattribute__`` and descriptors to avoid invoking user code during introspection with this function. The ``__getdescriptor__`` method will also be ignored and is another way in which the result of ``inspect.getattr_static`` can be different from that of ``builtin.getattr``. * ``inspect.getmembers`` and ``inspect.get_class_attrs`` Both of these functions directly access the class __dict__ of classes along the MRO, and hence can be affected by a custom ``__getdescriptor__`` method. **TODO**: I haven't fully worked out what the impact of this is, and if there are mitigations for those using either updates to these functions, or additional methods that users should implement to be fully compatible with these functions. Performance impact ------------------ The pybench output below compares an implementation of this PEP with the regular source tree, both based on changeset a5681f50bae2, run on an idle machine an Core i7 processor running Centos 6.4. Even though the machine was idle there were clear differences between runs, I've seen difference in "minimum time" vary from -0.1% to +1.5%, with similar (but slightly smaller) differences in the "average time" difference. :: ------------------------------------------------------------------------------- PYBENCH 2.1 ------------------------------------------------------------------------------- * using CPython 3.4.0a0 (default, Jul 29 2013, 13:01:34) [GCC 4.4.7 20120313 (Red Hat 4.4.7-3)] * disabled garbage collection * system check interval set to maximum: 2147483647 * using timer: time.perf_counter * timer: resolution=1e-09, implementation=clock_gettime(CLOCK_MONOTONIC) ------------------------------------------------------------------------------- Benchmark: pep447.pybench ------------------------------------------------------------------------------- Rounds: 10 Warp: 10 Timer: time.perf_counter Machine Details: Platform ID: Linux-2.6.32-358.114.1.openstack.el6.x86_64-x86_64-with-centos-6.4-Final Processor: x86_64 Python: Implementation: CPython Executable: /tmp/default-pep447/bin/python3 Version: 3.4.0a0 Compiler: GCC 4.4.7 20120313 (Red Hat 4.4.7-3) Bits: 64bit Build: Jul 29 2013 14:09:12 (#default) Unicode: UCS4 ------------------------------------------------------------------------------- Comparing with: default.pybench ------------------------------------------------------------------------------- Rounds: 10 Warp: 10 Timer: time.perf_counter Machine Details: Platform ID: Linux-2.6.32-358.114.1.openstack.el6.x86_64-x86_64-with-centos-6.4-Final Processor: x86_64 Python: Implementation: CPython Executable: /tmp/default/bin/python3 Version: 3.4.0a0 Compiler: GCC 4.4.7 20120313 (Red Hat 4.4.7-3) Bits: 64bit Build: Jul 29 2013 13:01:34 (#default) Unicode: UCS4 Test minimum run-time average run-time this other diff this other diff ------------------------------------------------------------------------------- BuiltinFunctionCalls: 45ms 44ms +1.3% 45ms 44ms +1.3% BuiltinMethodLookup: 26ms 27ms -2.4% 27ms 27ms -2.2% CompareFloats: 33ms 34ms -0.7% 33ms 34ms -1.1% CompareFloatsIntegers: 66ms 67ms -0.9% 66ms 67ms -0.8% CompareIntegers: 51ms 50ms +0.9% 51ms 50ms +0.8% CompareInternedStrings: 34ms 33ms +0.4% 34ms 34ms -0.4% CompareLongs: 29ms 29ms -0.1% 29ms 29ms -0.0% CompareStrings: 43ms 44ms -1.8% 44ms 44ms -1.8% ComplexPythonFunctionCalls: 44ms 42ms +3.9% 44ms 42ms +4.1% ConcatStrings: 33ms 33ms -0.4% 33ms 33ms -1.0% CreateInstances: 47ms 48ms -2.9% 47ms 49ms -3.4% CreateNewInstances: 35ms 36ms -2.5% 36ms 36ms -2.5% CreateStringsWithConcat: 69ms 70ms -0.7% 69ms 70ms -0.9% DictCreation: 52ms 50ms +3.1% 52ms 50ms +3.0% DictWithFloatKeys: 40ms 44ms -10.1% 43ms 45ms -5.8% DictWithIntegerKeys: 32ms 36ms -11.2% 35ms 37ms -4.6% DictWithStringKeys: 29ms 34ms -15.7% 35ms 40ms -11.0% ForLoops: 30ms 29ms +2.2% 30ms 29ms +2.2% IfThenElse: 38ms 41ms -6.7% 38ms 41ms -6.9% ListSlicing: 36ms 36ms -0.7% 36ms 37ms -1.3% NestedForLoops: 43ms 45ms -3.1% 43ms 45ms -3.2% NestedListComprehensions: 39ms 40ms -1.7% 39ms 40ms -2.1% NormalClassAttribute: 86ms 82ms +5.1% 86ms 82ms +5.0% NormalInstanceAttribute: 42ms 42ms +0.3% 42ms 42ms +0.0% PythonFunctionCalls: 39ms 38ms +3.5% 39ms 38ms +2.8% PythonMethodCalls: 51ms 49ms +3.0% 51ms 50ms +2.8% Recursion: 67ms 68ms -1.4% 67ms 68ms -1.4% SecondImport: 41ms 36ms +12.5% 41ms 36ms +12.6% SecondPackageImport: 45ms 40ms +13.1% 45ms 40ms +13.2% SecondSubmoduleImport: 92ms 95ms -2.4% 95ms 98ms -3.6% SimpleComplexArithmetic: 28ms 28ms -0.1% 28ms 28ms -0.2% SimpleDictManipulation: 57ms 57ms -1.0% 57ms 58ms -1.0% SimpleFloatArithmetic: 29ms 28ms +4.7% 29ms 28ms +4.9% SimpleIntFloatArithmetic: 37ms 41ms -8.5% 37ms 41ms -8.7% SimpleIntegerArithmetic: 37ms 41ms -9.4% 37ms 42ms -10.2% SimpleListComprehensions: 33ms 33ms -1.9% 33ms 34ms -2.9% SimpleListManipulation: 28ms 30ms -4.3% 29ms 30ms -4.1% SimpleLongArithmetic: 26ms 26ms +0.5% 26ms 26ms +0.5% SmallLists: 40ms 40ms +0.1% 40ms 40ms +0.1% SmallTuples: 46ms 47ms -2.4% 46ms 48ms -3.0% SpecialClassAttribute: 126ms 120ms +4.7% 126ms 121ms +4.4% SpecialInstanceAttribute: 42ms 42ms +0.6% 42ms 42ms +0.8% StringMappings: 94ms 91ms +3.9% 94ms 91ms +3.8% StringPredicates: 48ms 49ms -1.7% 48ms 49ms -2.1% StringSlicing: 45ms 45ms +1.4% 46ms 45ms +1.5% TryExcept: 23ms 22ms +4.9% 23ms 22ms +4.8% TryFinally: 32ms 32ms -0.1% 32ms 32ms +0.1% TryRaiseExcept: 17ms 17ms +0.9% 17ms 17ms +0.5% TupleSlicing: 49ms 48ms +1.1% 49ms 49ms +1.0% WithFinally: 48ms 47ms +2.3% 48ms 47ms +2.4% WithRaiseExcept: 45ms 44ms +0.8% 45ms 45ms +0.5% ------------------------------------------------------------------------------- Totals: 2284ms 2287ms -0.1% 2306ms 2308ms -0.1% (this=pep447.pybench, other=default.pybench) A run of the benchmark suite (with option "-b 2n3") also seems to indicate that the performance impact is minimal:: Report on Linux fangorn.local 2.6.32-358.114.1.openstack.el6.x86_64 #1 SMP Wed Jul 3 02:11:25 EDT 2013 x86_64 x86_64 Total CPU cores: 8 ### call_method_slots ### Min: 0.304120 -> 0.282791: 1.08x faster Avg: 0.304394 -> 0.282906: 1.08x faster Significant (t=2329.92) Stddev: 0.00016 -> 0.00004: 4.1814x smaller ### call_simple ### Min: 0.249268 -> 0.221175: 1.13x faster Avg: 0.249789 -> 0.221387: 1.13x faster Significant (t=2770.11) Stddev: 0.00012 -> 0.00013: 1.1101x larger ### django_v2 ### Min: 0.632590 -> 0.601519: 1.05x faster Avg: 0.635085 -> 0.602653: 1.05x faster Significant (t=321.32) Stddev: 0.00087 -> 0.00051: 1.6933x smaller ### fannkuch ### Min: 1.033181 -> 0.999779: 1.03x faster Avg: 1.036457 -> 1.001840: 1.03x faster Significant (t=260.31) Stddev: 0.00113 -> 0.00070: 1.6112x smaller ### go ### Min: 0.526714 -> 0.544428: 1.03x slower Avg: 0.529649 -> 0.547626: 1.03x slower Significant (t=-93.32) Stddev: 0.00136 -> 0.00136: 1.0028x smaller ### iterative_count ### Min: 0.109748 -> 0.116513: 1.06x slower Avg: 0.109816 -> 0.117202: 1.07x slower Significant (t=-357.08) Stddev: 0.00008 -> 0.00019: 2.3664x larger ### json_dump_v2 ### Min: 2.554462 -> 2.609141: 1.02x slower Avg: 2.564472 -> 2.620013: 1.02x slower Significant (t=-76.93) Stddev: 0.00538 -> 0.00481: 1.1194x smaller ### meteor_contest ### Min: 0.196336 -> 0.191925: 1.02x faster Avg: 0.196878 -> 0.192698: 1.02x faster Significant (t=61.86) Stddev: 0.00053 -> 0.00041: 1.2925x smaller ### nbody ### Min: 0.228039 -> 0.235551: 1.03x slower Avg: 0.228857 -> 0.236052: 1.03x slower Significant (t=-54.15) Stddev: 0.00130 -> 0.00029: 4.4810x smaller ### pathlib ### Min: 0.108501 -> 0.105339: 1.03x faster Avg: 0.109084 -> 0.105619: 1.03x faster Significant (t=311.08) Stddev: 0.00022 -> 0.00011: 1.9314x smaller ### regex_effbot ### Min: 0.057905 -> 0.056447: 1.03x faster Avg: 0.058055 -> 0.056760: 1.02x faster Significant (t=79.22) Stddev: 0.00006 -> 0.00015: 2.7741x larger ### silent_logging ### Min: 0.070810 -> 0.072436: 1.02x slower Avg: 0.070899 -> 0.072609: 1.02x slower Significant (t=-191.59) Stddev: 0.00004 -> 0.00008: 2.2640x larger ### spectral_norm ### Min: 0.290255 -> 0.299286: 1.03x slower Avg: 0.290335 -> 0.299541: 1.03x slower Significant (t=-572.10) Stddev: 0.00005 -> 0.00015: 2.8547x larger ### threaded_count ### Min: 0.107215 -> 0.115206: 1.07x slower Avg: 0.107488 -> 0.115996: 1.08x slower Significant (t=-109.39) Stddev: 0.00016 -> 0.00076: 4.8665x larger The following not significant results are hidden, use -v to show them: call_method, call_method_unknown, chaos, fastpickle, fastunpickle, float, formatted_logging, hexiom2, json_load, normal_startup, nqueens, pidigits, raytrace, regex_compile, regex_v8, richards, simple_logging, startup_nosite, telco, unpack_sequence. Alternative proposals --------------------- ``__getattribute_super__`` .......................... An earlier version of this PEP used the following static method on classes:: def __getattribute_super__(cls, name, object, owner): pass This method performed name lookup as well as invoking descriptors and was necessarily limited to working only with ``super.__getattribute__``. Reuse ``tp_getattro`` ..................... It would be nice to avoid adding a new slot, thus keeping the API simpler and easier to understand. A comment on `Issue 18181`_ asked about reusing the ``tp_getattro`` slot, that is super could call the ``tp_getattro`` slot of all methods along the MRO. That won't work because ``tp_getattro`` will look in the instance ``__dict__`` before it tries to resolve attributes using classes in the MRO. This would mean that using ``tp_getattro`` instead of peeking the class dictionaries changes the semantics of the `super class`_. References ========== * `Issue 18181`_ contains a prototype implementation Copyright ========= This document has been placed in the public domain. .. _`Issue 18181`: http://bugs.python.org/issue18181 .. _`super class`: http://docs.python.org/3/library/functions.html#super .. _`super proxy`: http://docs.python.org/3/library/functions.html#super .. _`NotImplemented`: http://docs.python.org/3/library/constants.html#NotImplemented .. _`PyObject_GenericGetAttr`: http://docs.python.org/3/c-api/object.html#PyObject_GenericGetAttr .. _`type`: http://docs.python.org/3/library/functions.html#type .. _`AttributeError`: http://docs.python.org/3/library/exceptions.html#AttributeError .. _`PyObjC`: http://pyobjc.sourceforge.net/ .. _`classmethod`: http://docs.python.org/3/library/functions.html#classmethod