Contents

• Abstract
• Introduction
• About metatypes
• Making a type a factory for its instances
• Preparing a type for subtyping
• Creating a subtype of a built-in type in C
• Subtyping in Python
• Multiple inheritance
• MRO: Method resolution order (the lookup rule)
• XXX To be done
  • open issues
• Implementation
• References
• Copyright

Abstract

This PEP proposes additions to the type object API that will allow the creation of subtypes of built-in types, in C and in Python.

[Editor’s note: the ideas described in this PEP have been incorporated into Python. The PEP no longer accurately describes the implementation.]

Introduction

Traditionally, types in Python have been created statically, by declaring a global variable of type PyTypeObject and initializing it with a static initializer. The slots in the type object describe all aspects of a Python type that are relevant to the Python interpreter. A few slots contain dimensional information (like the basic allocation size of instances), others contain various flags, but most slots are pointers to functions to implement various kinds of behaviors. A NULL pointer means that the type does not implement the specific behavior; in that case the system may provide a default behavior or raise an exception when the behavior is invoked for an instance of the type. Some collections of function pointers that are usually defined together are obtained indirectly via a pointer to an additional structure containing more function pointers.

While the details of initializing a PyTypeObject structure haven’t been documented as such, they are easily gleaned from the examples in the source code, and I am assuming that the reader is sufficiently familiar with the traditional way of creating new Python types in C.

This PEP will introduce the following features:

• a type can be a factory function for its instances
• types can be subtyped in C
• types can be subtyped in Python with the class statement
• multiple inheritance from types is supported (insofar as practical – you still can’t multiply inherit from list and dictionary)
• the standard coercion functions (int, tuple, str etc.) will be redefined to be the corresponding type objects, which serve as their own factory functions
• a class statement can contain a __metaclass__ declaration, specifying the metaclass to be used to create the new class
• a class statement can contain a __slots__ declaration, specifying the specific names of the instance variables supported

This PEP builds on PEP 252, which adds standard introspection to types; for example, when a particular type object initializes the tp_hash slot, that type object has a __hash__ method when introspected. PEP 252 also adds a dictionary to type objects which contains all methods. At the Python level, this dictionary is read-only for built-in types; at the C level, it is accessible directly (but it should not be modified except as part of initialization).

For binary compatibility, a flag bit in the tp_flags slot indicates the existence of the various new slots in the type object introduced below. Types that don’t have the Py_TPFLAGS_HAVE_CLASS bit set in their tp_flags slot are assumed to have NULL values for all the subtyping slots. (Warning: the current implementation prototype is not yet consistent in its checking of this flag bit. This should be fixed before the final release.)

In current Python, a distinction is made between types and classes. This PEP together with PEP 254 will remove that distinction. However, for backwards compatibility the distinction will probably remain for years to come, and without PEP 254, the distinction is still large: types ultimately have a built-in type as a base class, while classes ultimately derive from a user-defined class. Therefore, in the rest of this PEP, I will use the word type whenever I can – including base type or supertype, derived type or subtype, and metatype. However, sometimes the terminology necessarily blends, for example an object’s type is given by its __class__ attribute, and subtyping in Python is spelled with a class statement. If further distinction is necessary, user-defined classes can be referred to as “classic” classes.

About metatypes

Inevitably the discussion comes to metatypes (or metaclasses). Metatypes are nothing new in Python: Python has always been able to talk about the type of a type:

>>> a = 0
>>> type(a)
<type 'int'>
>>> type(type(a))
<type 'type'>
>>> type(type(type(a)))
<type 'type'>
>>>

In this example, type(a) is a “regular” type, and type(type(a)) is a metatype. While as distributed all types have the same metatype (PyType_Type, which is also its own metatype), this is not a requirement, and in fact a useful and relevant 3rd party extension (ExtensionClasses by Jim Fulton) creates an additional metatype. The type of classic classes, known as types.ClassType, can also be considered a distinct metatype.

A feature closely connected to metatypes is the “Don Beaudry hook”, which says that if a metatype is callable, its instances (which are regular types) can be subclassed (really subtyped) using a Python class statement. I will use this rule to support subtyping of built-in types, and in fact it greatly simplifies the logic of class creation to always simply call the metatype. When no base class is specified, a default metatype is called – the default metatype is the “ClassType” object, so the class statement will behave as before in the normal case. (This default can be changed per module by setting the global variable __metaclass__.)

Python uses the concept of metatypes or metaclasses in a different way than Smalltalk. In Smalltalk-80, there is a hierarchy of metaclasses that mirrors the hierarchy of regular classes, metaclasses map 1-1 to classes (except for some funny business at the root of the hierarchy), and each class statement creates both a regular class and its metaclass, putting class methods in the metaclass and instance methods in the regular class.

Nice though this may be in the context of Smalltalk, it’s not compatible with the traditional use of metatypes in Python, and I prefer to continue in the Python way. This means that Python metatypes are typically written in C, and may be shared between many regular types. (It will be possible to subtype metatypes in Python, so it won’t be absolutely necessary to write C to use metatypes; but the power of Python metatypes will be limited. For example, Python code will never be allowed to allocate raw memory and initialize it at will.)

Metatypes determine various policies for types, such as what happens when a type is called, how dynamic types are (whether a type’s __dict__ can be modified after it is created), what the method resolution order is, how instance attributes are looked up, and so on.

I’ll argue that left-to-right depth-first is not the best solution when you want to get the most use from multiple inheritance.

I’ll argue that with multiple inheritance, the metatype of the subtype must be a descendant of the metatypes of all base types.

I’ll come back to metatypes later.

Making a type a factory for its instances

Traditionally, for each type there is at least one C factory function that creates instances of the type (PyTuple_New(), PyInt_FromLong() and so on). These factory functions take care of both allocating memory for the object and initializing that memory. As of Python 2.0, they also have to interface with the garbage collection subsystem, if the type chooses to participate in garbage collection (which is optional, but strongly recommended for so-called “container” types: types that may contain references to other objects, and hence may participate in reference cycles).

In this proposal, type objects can be factory functions for their instances, making the types directly callable from Python. This mimics the way classes are instantiated. The C APIs for creating instances of various built-in types will remain valid and in some cases more efficient. Not all types will become their own factory functions.

The type object has a new slot, tp_new, which can act as a factory for instances of the type. Types are now callable, because the tp_call slot is set in PyType_Type (the metatype); the function looks for the tp_new slot of the type that is being called.

Explanation: the tp_call slot of a regular type object (such as PyInt_Type or PyList_Type) defines what happens when instances of that type are called; in particular, the tp_call slot in the function type, PyFunction_Type, is the key to making functions callable. As another example, PyInt_Type.tp_call is NULL, because integers are not callable. The new paradigm makes type objects callable. Since type objects are instances of their metatype (PyType_Type), the metatype’s tp_call slot (PyType_Type.tp_call) points to a function that is invoked when any type object is called. Now, since each type has to do something different to create an instance of itself, PyType_Type.tp_call immediately defers to the tp_new slot of the type that is being called. PyType_Type itself is also callable: its tp_new slot creates a new type. This is used by the class statement (formalizing the Don Beaudry hook, see above). And what makes PyType_Type callable? The tp_call slot of its metatype – but since it is its own metatype, that is its own tp_call slot!

If the type’s tp_new slot is NULL, an exception is raised. Otherwise, the tp_new slot is called. The signature for the tp_new slot is

PyObject *tp_new(PyTypeObject *type,
                 PyObject *args,
                 PyObject *kwds)

where ‘type’ is the type whose tp_new slot is called, and ‘args’ and ‘kwds’ are the sequential and keyword arguments to the call, passed unchanged from tp_call. (The ‘type’ argument is used in combination with inheritance, see below.)

There are no constraints on the object type that is returned, although by convention it should be an instance of the given type. It is not necessary that a new object is returned; a reference to an existing object is fine too. The return value should always be a new reference, owned by the caller.

Once the tp_new slot has returned an object, further initialization is attempted by calling the tp_init() slot of the resulting object’s type, if not NULL. This has the following signature:

int tp_init(PyObject *self,
            PyObject *args,
            PyObject *kwds)

It corresponds more closely to the __init__() method of classic classes, and in fact is mapped to that by the slot/special-method correspondence rules. The difference in responsibilities between the tp_new() slot and the tp_init() slot lies in the invariants they ensure. The tp_new() slot should ensure only the most essential invariants, without which the C code that implements the objects would break. The tp_init() slot should be used for overridable user-specific initializations. Take for example the dictionary type. The implementation has an internal pointer to a hash table which should never be NULL. This invariant is taken care of by the tp_new() slot for dictionaries. The dictionary tp_init() slot, on the other hand, could be used to give the dictionary an initial set of keys and values based on the arguments passed in.

Note that for immutable object types, the initialization cannot be done by the tp_init() slot: this would provide the Python user with a way to change the initialization. Therefore, immutable objects typically have an empty tp_init() implementation and do all their initialization in their tp_new() slot.

You may wonder why the tp_new() slot shouldn’t call the tp_init() slot itself. The reason is that in certain circumstances (like support for persistent objects), it is important to be able to create an object of a particular type without initializing it any further than necessary. This may conveniently be done by calling the tp_new() slot without calling tp_init(). It is also possible that tp_init() is not called, or called more than once – its operation should be robust even in these anomalous cases.

For some objects, tp_new() may return an existing object. For example, the factory function for integers caches the integers -1 through 99. This is permissible only when the type argument to tp_new() is the type that defined the tp_new() function (in the example, if type == &PyInt_Type), and when the tp_init() slot for this type does nothing. If the type argument differs, the tp_new() call is initiated by a derived type’s tp_new() to create the object and initialize the base type portion of the object; in this case tp_new() should always return a new object (or raise an exception).

Both tp_new() and tp_init() should receive exactly the same ‘args’ and ‘kwds’ arguments, and both should check that the arguments are acceptable, because they may be called independently.

There’s a third slot related to object creation: tp_alloc(). Its responsibility is to allocate the memory for the object, initialize the reference count (ob_refcnt) and the type pointer (ob_type), and initialize the rest of the object to all zeros. It should also register the object with the garbage collection subsystem if the type supports garbage collection. This slot exists so that derived types can override the memory allocation policy (like which heap is being used) separately from the initialization code. The signature is:

PyObject *tp_alloc(PyTypeObject *type, int nitems)

The type argument is the type of the new object. The nitems argument is normally zero, except for objects with a variable allocation size (basically strings, tuples, and longs). The allocation size is given by the following expression:

type->tp_basicsize  +  nitems * type->tp_itemsize

The tp_alloc slot is only used for subclassable types. The tp_new() function of the base class must call the tp_alloc() slot of the type passed in as its first argument. It is the tp_new() function’s responsibility to calculate the number of items. The tp_alloc() slot will set the ob_size member of the new object if the type->tp_itemsize member is nonzero.

(Note: in certain debugging compilation modes, the type structure used to have members named tp_alloc and a tp_free slot already, counters for the number of allocations and deallocations. These are renamed to tp_allocs and tp_deallocs.)

Standard implementations for tp_alloc() and tp_new() are available. PyType_GenericAlloc() allocates an object from the standard heap and initializes it properly. It uses the above formula to determine the amount of memory to allocate, and takes care of GC registration. The only reason not to use this implementation would be to allocate objects from a different heap (as is done by some very small frequently used objects like ints and tuples). PyType_GenericNew() adds very little: it just calls the type’s tp_alloc() slot with zero for nitems. But for mutable types that do all their initialization in their tp_init() slot, this may be just the ticket.

Preparing a type for subtyping

The idea behind subtyping is very similar to that of single inheritance in C++. A base type is described by a structure declaration (similar to the C++ class declaration) plus a type object (similar to the C++ vtable). A derived type can extend the structure (but must leave the names, order and type of the members of the base structure unchanged) and can override certain slots in the type object, leaving others the same. (Unlike C++ vtables, all Python type objects have the same memory layout.)

The base type must do the following:

• Add the flag value Py_TPFLAGS_BASETYPE to tp_flags.
• Declare and use tp_new(), tp_alloc() and optional tp_init() slots.
• Declare and use tp_dealloc() and tp_free().
• Export its object structure declaration.
• Export a subtyping-aware type-checking macro.

The requirements and signatures for tp_new(), tp_alloc() and tp_init() have already been discussed above: tp_alloc() should allocate the memory and initialize it to mostly zeros; tp_new() should call the tp_alloc() slot and then proceed to do the minimally required initialization; tp_init() should be used for more extensive initialization of mutable objects.

It should come as no surprise that there are similar conventions at the end of an object’s lifetime. The slots involved are tp_dealloc() (familiar to all who have ever implemented a Python extension type) and tp_free(), the new kid on the block. (The names aren’t quite symmetric; tp_free() corresponds to tp_alloc(), which is fine, but tp_dealloc() corresponds to tp_new(). Maybe the tp_dealloc slot should be renamed?)

The tp_free() slot should be used to free the memory and unregister the object with the garbage collection subsystem, and can be overridden by a derived class; tp_dealloc() should deinitialize the object (usually by calling Py_XDECREF() for various sub-objects) and then call tp_free() to deallocate the memory. The signature for tp_dealloc() is the same as it always was:

void tp_dealloc(PyObject *object)

The signature for tp_free() is the same:

void tp_free(PyObject *object)

(In a previous version of this PEP, there was also a role reserved for the tp_clear() slot. This turned out to be a bad idea.)

To be usefully subtyped in C, a type must export the structure declaration for its instances through a header file, as it is needed to derive a subtype. The type object for the base type must also be exported.

If the base type has a type-checking macro (like PyDict_Check()), this macro should be made to recognize subtypes. This can be done by using the new PyObject_TypeCheck(object, type) macro, which calls a function that follows the base class links.

The PyObject_TypeCheck() macro contains a slight optimization: it first compares object->ob_type directly to the type argument, and if this is a match, bypasses the function call. This should make it fast enough for most situations.

Note that this change in the type-checking macro means that C functions that require an instance of the base type may be invoked with instances of the derived type. Before enabling subtyping of a particular type, its code should be checked to make sure that this won’t break anything. It has proved useful in the prototype to add another type-checking macro for the built-in Python object types, to check for exact type match too (for example, PyDict_Check(x) is true if x is an instance of dictionary or of a dictionary subclass, while PyDict_CheckExact(x) is true only if x is a dictionary).

Creating a subtype of a built-in type in C

The simplest form of subtyping is subtyping in C. It is the simplest form because we can require the C code to be aware of some of the problems, and it’s acceptable for C code that doesn’t follow the rules to dump core. For added simplicity, it is limited to single inheritance.

Let’s assume we’re deriving from a mutable base type whose tp_itemsize is zero. The subtype code is not GC-aware, although it may inherit GC-awareness from the base type (this is automatic). The base type’s allocation uses the standard heap.

The derived type begins by declaring a type structure which contains the base type’s structure. For example, here’s the type structure for a subtype of the built-in list type:

typedef struct {
    PyListObject list;
    int state;
} spamlistobject;

Note that the base type structure member (here PyListObject) must be the first member of the structure; any following members are additions. Also note that the base type is not referenced via a pointer; the actual contents of its structure must be included! (The goal is for the memory layout of the beginning of the subtype instance to be the same as that of the base type instance.)

Next, the derived type must declare a type object and initialize it. Most of the slots in the type object may be initialized to zero, which is a signal that the base type slot must be copied into it. Some slots that must be initialized properly:

• The object header must be filled in as usual; the type should be &PyType_Type.
• The tp_basicsize slot must be set to the size of the subtype instance struct (in the above example: sizeof(spamlistobject)).
• The tp_base slot must be set to the address of the base type’s type object.
• If the derived slot defines any pointer members, the tp_dealloc slot function requires special attention, see below; otherwise, it can be set to zero, to inherit the base type’s deallocation function.
• The tp_flags slot must be set to the usual Py_TPFLAGS_DEFAULT value.
• The tp_name slot must be set; it is recommended to set tp_doc as well (these are not inherited).

If the subtype defines no additional structure members (it only defines new behavior, no new data), the tp_basicsize and the tp_dealloc slots may be left set to zero.

The subtype’s tp_dealloc slot deserves special attention. If the derived type defines no additional pointer members that need to be DECREF’ed or freed when the object is deallocated, it can be set to zero. Otherwise, the subtype’s tp_dealloc() function must call Py_XDECREF() for any PyObject * members and the correct memory freeing function for any other pointers it owns, and then call the base class’s tp_dealloc() slot. This call has to be made via the base type’s type structure, for example, when deriving from the standard list type:

PyList_Type.tp_dealloc(self);

If the subtype wants to use a different allocation heap than the base type, the subtype must override both the tp_alloc() and the tp_free() slots. These will be called by the base class’s tp_new() and tp_dealloc() slots, respectively.

To complete the initialization of the type, PyType_InitDict() must be called. This replaces slots initialized to zero in the subtype with the value of the corresponding base type slots. (It also fills in tp_dict, the type’s dictionary, and does various other initializations necessary for type objects.)

A subtype is not usable until PyType_InitDict() is called for it; this is best done during module initialization, assuming the subtype belongs to a module. An alternative for subtypes added to the Python core (which don’t live in a particular module) would be to initialize the subtype in their constructor function. It is allowed to call PyType_InitDict() more than once; the second and further calls have no effect. To avoid unnecessary calls, a test for tp_dict==NULL can be made.

(During initialization of the Python interpreter, some types are actually used before they are initialized. As long as the slots that are actually needed are initialized, especially tp_dealloc, this works, but it is fragile and not recommended as a general practice.)

To create a subtype instance, the subtype’s tp_new() slot is called. This should first call the base type’s tp_new() slot and then initialize the subtype’s additional data members. To further initialize the instance, the tp_init() slot is typically called. Note that the tp_new() slot should not call the tp_init() slot; this is up to tp_new()’s caller (typically a factory function). There are circumstances where it is appropriate not to call tp_init().

If a subtype defines a tp_init() slot, the tp_init() slot should normally first call the base type’s tp_init() slot.

(XXX There should be a paragraph or two about argument passing here.)

Subtyping in Python

The next step is to allow subtyping of selected built-in types through a class statement in Python. Limiting ourselves to single inheritance for now, here is what happens for a simple class statement:

class C(B):
    var1 = 1
    def method1(self): pass
    # etc.

The body of the class statement is executed in a fresh environment (basically, a new dictionary used as local namespace), and then C is created. The following explains how C is created.

Assume B is a type object. Since type objects are objects, and every object has a type, B has a type. Since B is itself a type, we also call its type its metatype. B’s metatype is accessible via type(B) or B.__class__ (the latter notation is new for types; it is introduced in PEP 252). Let’s say this metatype is M (for Metatype). The class statement will create a new type, C. Since C will be a type object just like B, we view the creation of C as an instantiation of the metatype, M. The information that needs to be provided for the creation of a subclass is:

• its name (in this example the string “C”);
• its bases (a singleton tuple containing B);
• the results of executing the class body, in the form of a dictionary (for example {"var1": 1, "method1": <functionmethod1 at ...>, ...}).

The class statement will result in the following call:

C = M("C", (B,), dict)

where dict is the dictionary resulting from execution of the class body. In other words, the metatype (M) is called.

Note that even though the example has only one base, we still pass in a (singleton) sequence of bases; this makes the interface uniform with the multiple-inheritance case.

In current Python, this is called the “Don Beaudry hook” after its inventor; it is an exceptional case that is only invoked when a base class is not a regular class. For a regular base class (or when no base class is specified), current Python calls PyClass_New(), the C level factory function for classes, directly.

Under the new system this is changed so that Python always determines a metatype and calls it as given above. When one or more bases are given, the type of the first base is used as the metatype; when no base is given, a default metatype is chosen. By setting the default metatype to PyClass_Type, the metatype of “classic” classes, the classic behavior of the class statement is retained. This default can be changed per module by setting the global variable __metaclass__.

There are two further refinements here. First, a useful feature is to be able to specify a metatype directly. If the class suite defines a variable __metaclass__, that is the metatype to call. (Note that setting __metaclass__ at the module level only affects class statements without a base class and without an explicit __metaclass__ declaration; but setting __metaclass__ in a class suite overrides the default metatype unconditionally.)

Second, with multiple bases, not all bases need to have the same metatype. This is called a metaclass conflict 1. Some metaclass conflicts can be resolved by searching through the set of bases for a metatype that derives from all other given metatypes. If such a metatype cannot be found, an exception is raised and the class statement fails.

This conflict resolution can be implemented by the metatype constructors: the class statement just calls the metatype of the first base (or that specified by the __metaclass__ variable), and this metatype’s constructor looks for the most derived metatype. If that is itself, it proceeds; otherwise, it calls that metatype’s constructor. (Ultimate flexibility: another metatype might choose to require that all bases have the same metatype, or that there’s only one base class, or whatever.)

(In 1, a new metaclass is automatically derived that is a subclass of all given metaclasses. But since it is questionable in Python how conflicting method definitions of the various metaclasses should be merged, I don’t think this is feasible. Should the need arise, the user can derive such a metaclass manually and specify it using the __metaclass__ variable. It is also possible to have a new metaclass that does this.)

Note that calling M requires that M itself has a type: the meta-metatype. And the meta-metatype has a type, the meta-meta-metatype. And so on. This is normally cut short at some level by making a metatype be its own metatype. This is indeed what happens in Python: the ob_type reference in PyType_Type is set to &PyType_Type. In the absence of third party metatypes, PyType_Type is the only metatype in the Python interpreter.

(In a previous version of this PEP, there was one additional meta-level, and there was a meta-metatype called “turtle”. This turned out to be unnecessary.)

In any case, the work for creating C is done by M’s tp_new() slot. It allocates space for an “extended” type structure, containing: the type object; the auxiliary structures (as_sequence etc.); the string object containing the type name (to ensure that this object isn’t deallocated while the type object is still referencing it); and some auxiliary storage (to be described later). It initializes this storage to zeros except for a few crucial slots (for example, tp_name is set to point to the type name) and then sets the tp_base slot to point to B. Then PyType_InitDict() is called to inherit B’s slots. Finally, C’s tp_dict slot is updated with the contents of the namespace dictionary (the third argument to the call to M).

Multiple inheritance

The Python class statement supports multiple inheritance, and we will also support multiple inheritance involving built-in types.

However, there are some restrictions. The C runtime architecture doesn’t make it feasible to have a meaningful subtype of two different built-in types except in a few degenerate cases. Changing the C runtime to support fully general multiple inheritance would be too much of an upheaval of the code base.

The main problem with multiple inheritance from different built-in types stems from the fact that the C implementation of built-in types accesses structure members directly; the C compiler generates an offset relative to the object pointer and that’s that. For example, the list and dictionary type structures each declare a number of different but overlapping structure members. A C function accessing an object expecting a list won’t work when passed a dictionary, and vice versa, and there’s not much we could do about this without rewriting all code that accesses lists and dictionaries. This would be too much work, so we won’t do this.

The problem with multiple inheritance is caused by conflicting structure member allocations. Classes defined in Python normally don’t store their instance variables in structure members: they are stored in an instance dictionary. This is the key to a partial solution. Suppose we have the following two classes:

class A(dictionary):
    def foo(self): pass

class B(dictionary):
    def bar(self): pass

class C(A, B): pass

(Here, ‘dictionary’ is the type of built-in dictionary objects, a.k.a. type({}) or {}.__class__ or types.DictType.) If we look at the structure layout, we find that an A instance has the layout of a dictionary followed by the __dict__ pointer, and a B instance has the same layout; since there are no structure member layout conflicts, this is okay.

Here’s another example:

class X(object):
    def foo(self): pass

class Y(dictionary):
    def bar(self): pass

class Z(X, Y): pass

(Here, ‘object’ is the base for all built-in types; its structure layout only contains the ob_refcnt and ob_type members.) This example is more complicated, because the __dict__ pointer for X instances has a different offset than that for Y instances. Where is the __dict__ pointer for Z instances? The answer is that the offset for the __dict__ pointer is not hardcoded, it is stored in the type object.

Suppose on a particular machine an ‘object’ structure is 8 bytes long, and a ‘dictionary’ struct is 60 bytes, and an object pointer is 4 bytes. Then an X structure is 12 bytes (an object structure followed by a __dict__ pointer), and a Y structure is 64 bytes (a dictionary structure followed by a __dict__ pointer). The Z structure has the same layout as the Y structure in this example. Each type object (X, Y and Z) has a “__dict__ offset” which is used to find the __dict__ pointer. Thus, the recipe for looking up an instance variable is:

1. get the type of the instance
2. get the __dict__ offset from the type object
3. add the __dict__ offset to the instance pointer
4. look in the resulting address to find a dictionary reference
5. look up the instance variable name in that dictionary

Of course, this recipe can only be implemented in C, and I have left out some details. But this allows us to use multiple inheritance patterns similar to the ones we can use with classic classes.

XXX I should write up the complete algorithm here to determine base class compatibility, but I can’t be bothered right now. Look at best_base() in typeobject.c in the implementation mentioned below.

MRO: Method resolution order (the lookup rule)

With multiple inheritance comes the question of method resolution order: the order in which a class or type and its bases are searched looking for a method of a given name.

In classic Python, the rule is given by the following recursive function, also known as the left-to-right depth-first rule:

def classic_lookup(cls, name):
    if cls.__dict__.has_key(name):
        return cls.__dict__[name]
    for base in cls.__bases__:
        try:
            return classic_lookup(base, name)
        except AttributeError:
            pass
    raise AttributeError, name

The problem with this becomes apparent when we consider a “diamond diagram”:

      class A:
        ^ ^  def save(self): ...
       /   \
      /     \
     /       \
    /         \
class B     class C:
    ^         ^  def save(self): ...
     \       /
      \     /
       \   /
        \ /
      class D

Arrows point from a subtype to its base type(s). This particular diagram means B and C derive from A, and D derives from B and C (and hence also, indirectly, from A).

Assume that C overrides the method save(), which is defined in the base A. (C.save() probably calls A.save() and then saves some of its own state.) B and D don’t override save(). When we invoke save() on a D instance, which method is called? According to the classic lookup rule, A.save() is called, ignoring C.save()!

This is not good. It probably breaks C (its state doesn’t get saved), defeating the whole purpose of inheriting from C in the first place.

Why was this not a problem in classic Python? Diamond diagrams are rarely found in classic Python class hierarchies. Most class hierarchies use single inheritance, and multiple inheritance is usually confined to mix-in classes. In fact, the problem shown here is probably the reason why multiple inheritance is unpopular in classic Python.

Why will this be a problem in the new system? The ‘object’ type at the top of the type hierarchy defines a number of methods that can usefully be extended by subtypes, for example __getattr__().

(Aside: in classic Python, the __getattr__() method is not really the implementation for the get-attribute operation; it is a hook that only gets invoked when an attribute cannot be found by normal means. This has often been cited as a shortcoming – some class designs have a legitimate need for a __getattr__() method that gets called for all attribute references. But then of course this method has to be able to invoke the default implementation directly. The most natural way is to make the default implementation available as object.__getattr__(self, name).)

Thus, a classic class hierarchy like this:

class B     class C:
    ^         ^  def __getattr__(self, name): ...
     \       /
      \     /
       \   /
        \ /
      class D

will change into a diamond diagram under the new system:

      object:
        ^ ^  __getattr__()
       /   \
      /     \
     /       \
    /         \
class B     class C:
    ^         ^  def __getattr__(self, name): ...
     \       /
      \     /
       \   /
        \ /
      class D

and while in the original diagram C.__getattr__() is invoked, under the new system with the classic lookup rule, object.__getattr__() would be invoked!

Fortunately, there’s a lookup rule that’s better. It’s a bit difficult to explain, but it does the right thing in the diamond diagram, and it is the same as the classic lookup rule when there are no diamonds in the inheritance graph (when it is a tree).

The new lookup rule constructs a list of all classes in the inheritance diagram in the order in which they will be searched. This construction is done at class definition time to save time. To explain the new lookup rule, let’s first consider what such a list would look like for the classic lookup rule. Note that in the presence of diamonds the classic lookup visits some classes multiple times. For example, in the ABCD diamond diagram above, the classic lookup rule visits the classes in this order:

D, B, A, C, A

Note how A occurs twice in the list. The second occurrence is redundant, since anything that could be found there would already have been found when searching the first occurrence.

We use this observation to explain our new lookup rule. Using the classic lookup rule, construct the list of classes that would be searched, including duplicates. Now for each class that occurs in the list multiple times, remove all occurrences except for the last. The resulting list contains each ancestor class exactly once (including the most derived class, D in the example).

Searching for methods in this order will do the right thing for the diamond diagram. Because of the way the list is constructed, it does not change the search order in situations where no diamond is involved.

Isn’t this backwards incompatible? Won’t it break existing code? It would, if we changed the method resolution order for all classes. However, in Python 2.2, the new lookup rule will only be applied to types derived from built-in types, which is a new feature. Class statements without a base class create “classic classes”, and so do class statements whose base classes are themselves classic classes. For classic classes the classic lookup rule will be used. (To experiment with the new lookup rule for classic classes, you will be able to specify a different metaclass explicitly.) We’ll also provide a tool that analyzes a class hierarchy looking for methods that would be affected by a change in method resolution order.

XXX Another way to explain the motivation for the new MRO, due to Damian Conway: you never use the method defined in a base class if it is defined in a derived class that you haven’t explored yet (using the old search order).

XXX To be done

Additional topics to be discussed in this PEP:

• backwards compatibility issues!!!
• class methods and static methods
• cooperative methods and super()
• mapping between type object slots (tp_foo) and special methods (__foo__) (actually, this may belong in PEP 252)
• built-in names for built-in types (object, int, str, list etc.)
• __dict__ and __dictoffset__
• __slots__
• the HEAPTYPE flag bit
• GC support
• API docs for all the new functions
• how to use __new__
• writing metaclasses (using mro() etc.)
• high level user overview

Implementation

A prototype implementation of this PEP (and for PEP 252) is available from CVS, and in the series of Python 2.2 alpha and beta releases. For some examples of the features described here, see the file Lib/test/test_descr.py and the extension module Modules/xxsubtype.c.

References

1 (1, 2)

“Putting Metaclasses to Work”, by Ira R. Forman and Scott H. Danforth, Addison-Wesley 1999. (http://www.aw.com/product/0,2627,0201433052,00.html)

Copyright

This document has been placed in the public domain.