PEP 447 – Add __getdescriptor__ method to metaclass
- Author:
- Ronald Oussoren <ronaldoussoren at mac.com>
- Status:
- Deferred
- Type:
- Standards Track
- Created:
- 12-Jun-2013
- Post-History:
- 02-Jul-2013, 15-Jul-2013, 29-Jul-2013, 22-Jul-2015
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 replaces this behavior and gives more control over attribute
lookup, especially when using a super object.
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
The default implementation of __getdescriptor__ looks in the class
dictionary:
class type:
def __getdescriptor__(cls, name):
try:
return cls.__dict__[name]
except KeyError:
raise AttributeError(name) from None
PEP Status
This PEP is deferred until someone has time to update this PEP and push it forward.
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, for example dynamic
proxy classes.
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, as well as
completely reimplementing PyObject_GenericGetAttr for normal attribute
access.
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.
Note
PyObjC cannot precalculate the contents of the class __dict__
because Objective-C classes can grow new methods at runtime. Furthermore,
Objective-C classes tend to contain a lot of methods while most Python
code will only use a small subset of them, this makes precalculating
unnecessarily expensive.
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 process 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 basically 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 "fortytwo"
obj = SillyObject()
assert obj.m() == "fortytwo"
As mentioned earlier in this PEP a more realistic use case of this
functionality is a __getdescriptor__ method that dynamically 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 type flag Py_TPFLAGS_GETDESCRIPTOR with value (1UL << 11) that
indicates that the new slot is present and to be used.
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).
Classes with a tp_getdescriptor slot must add Py_TPFLAGS_GETDESCRIPTOR
to tp_flags to indicate that new slot must be used.
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.
For the same reason _PyType_LookupId is renamed to _PyType_LookupId2.
A number of other functions in typeobject.c with the same issue do not get
an updated name because they are private to that file.
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.
The items listed below are only affected by custom __getdescriptor__
methods, the default implementation for object won’t cause problems
because that still only uses the class __dict__ and won’t cause visible
changes to the visible behaviour of the object.__getattribute__.
dirmight not show all attributesAs with a custom
__getattribute__method dir() might not see all (instance) attributes when using the__getdescriptor__()method to dynamically 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_staticmight not show all attributesThe function
inspect.getattr_staticintentionally 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 ofinspect.getattr_staticcan be different from that ofbuiltin.getattr.inspect.getmembersandinspect.classify_class_attrsBoth of these functions directly access the class __dict__ of classes along the MRO, and hence can be affected by a custom
__getdescriptor__method.Code with a custom
__getdescriptor__method that want to play nice with these methods also needs to ensure that the__dict__is set up correctly when that is accessed directly by Python code.Note that
inspect.getmembersis used bypydocand hence this can affect runtime documentation introspection.- Direct introspection of the class
__dict__Any code that directly access the class
__dict__for introspection can be affected by a custom__getdescriptor__method, see the previous item.
Performance impact
WARNING: The benchmark results in this section are old, and will be updated when I’ve ported the patch to the current trunk. I don’t expect significant changes to the results in this section.
Micro benchmarks
Issue 18181 has a micro benchmark as one of its attachments (pep447-micro-bench.py) that specifically tests the speed of attribute lookup, both directly and through super.
Note that attribute lookup with deep class hierarchies is significantly slower
when using a custom __getdescriptor__ method. This is because the
attribute lookup cache for CPython cannot be used when having this method.
Pybench
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 and 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.
Alternative placement of the new method
This PEP proposes to add __getdescriptor__ as a method on the metaclass.
An alternative would be to add it as a class method on the class itself
(similar to how __new__ is a staticmethod of the class and not a method
of the metaclass).
The advantage of using a method on the metaclass is that will give an error
when two classes on the MRO have different metaclasses that may have different
behaviors for __getdescriptor__. With a normal classmethod that problem
would pass undetected while it might cause subtle errors when running the code.
History
- 23-Jul-2015: Added type flag
Py_TPFLAGS_GETDESCRIPTORafter talking with Guido.The new flag is primarily useful to avoid crashing when loading an extension for an older version of CPython and could have positive speed implications as well.
- Jul-2014: renamed slot to
__getdescriptor__, the old name didn’t match the naming style of other slots and was less descriptive.
Discussion threads
- The initial version of the PEP was send with Message-ID mailto:75030FAC-6918-4E94-95DA-67A88D53E6F5@mac.com
- Further discussion starting at a message with Message-ID mailto:5BB87CC4-F31B-4213-AAAC-0C0CE738460C@mac.com
- And more discussion starting at message with Message-ID mailto:00AA7433-C853-4101-9718-060468EBAC54@mac.com
References
- Issue 18181 contains an out of date prototype implementation
Copyright
This document has been placed in the public domain.
Source: https://github.com/python/peps/blob/main/peps/pep-0447.rst
Last modified: 2025-02-01 08:55:40 GMT