Wandering Thoughts archives


Nailing down new-style classes and types in Python

Since I keep confusing myself, it's time to write this stuff down once and for all to make sure I have it straight (even if some or all of it is in the official documentation).

One writes Python code to define classes; it's right there in the language syntax, where you write 'class A(object): ...'. Defining a class creates a type object for that class, which is an instance of type; this C-level object holds necessary information about the class and how it's actually implemented. This type object is what is bound to the class name; if you define a class A, 'type(A)' will then report <type 'type'>.

Classes have a class inheritance hierarchy, which is ultimately rooted at object (including for C-level classes). However, strictly speaking there is no type hierarchy as far as I know; all types are simply instances of type (including type itself). Further, the type non-hierarchy is of course unrelated to the class hierarchy. This means that isinstance(A, type) is True but issubclass(A, type) is both False and the wrong question (unless you really do have a subclass of type somewhere in your code).

(Among other things I believe that this means that 'type(type(obj))' is always 'type' for any arbitrary Python object, since all objects have a type and all types are instances of type.)

The Python documentation sometimes talks about a 'type hierarchy'. What it means is either 'the conceptual hierarchy of various built-in types', such as the various forms of numbers, mutable sequences, and so on, or 'the class inheritance hierarchy of built-in types', since a few are subclasses of others and everyone is a subclass of object.

(Some languages really do have a hierarchy of all types, with real (abstract) types for things like 'all numeric types' or 'all mutable sequence types', but Python does not. You can see this by inspecting the __mro__ attribute on built in types to see the classes involved in their method resolution order; the MRO of a type like int is just itself and object. Only a few built in types are subclasses of other types.)

PS: yes, almost all of this is in the Python documentation or is implied by it. Writing it down anyways helps me get it straight in my own head.

PPS: I believe that technically it would be possible for a sufficiently perverse extension module to create a valid new style C-level class that was not a subclass of object. Don't do that, and if you did I expect that things would blow up sooner or later.

Sidebar: the real difference between classes and types

If you use repr() on user-defined classes and on built in types (eg 'repr(A)' and 'repr(str)'), you'll notice that it reports them differently. This is a bit odd once you think about it, since they are both instances of type and so are using the same repr() function, yet one reports it is a 'class' and the other reports it is a 'type'.

In CPython, the difference between the two is whether the C-level type instance structure is flagged as having been allocated on the heap or not. A heap-allocated type instance is a class as far as type.__repr__() is concerned; a statically allocated one is a type. All classes defined in Python are allocated on the heap, like all other Python-created objects, and so report as classes. Most 'types' defined in C-level extension modules are statically defined and so get called types, but I believe that with sufficient work you could create a C-level type that had a heap allocated type instance and was reported as a class.

(It's easy enough to keep it from being garbage collected out from underneath your extension module; you just artificially increase its reference count.)

python/ClassesAndTypes written at 23:07:07; Add Comment

How CPython implements __slots__ (part 1): storage

At an abstract level, each instance of a conventional class has a __dict__ member that is a conventional Python dictionary, and instance attributes are created and manipulated by manipulating this dictionary; the dictionary key is the attribute name and the value is the attribute's value. __slots__ eliminates this dictionary and instead has a fixed list of attributes that instances of the class know about. All of this is in the documentation. What the documentation won't tell you is how the machine level storage for all of this actually works. That's what today's entry is about.

In CPython, class instances start out as a more or less opaque C structure that is specific to the C-level type that your class inherits from (we saw this before). However, the general CPython type infrastructure for new-style classes reserves the right to add some extra space on the end of your type's opaque blob for its own purposes. If your class has a __slots__, this code adds some extra space after the C structure blob to store what is effectively an array of pointers to Python objects. These entries are used to point to the values of each __slots__ attribute (if there is no value set, the corresponding entry is NULL and the CPython code reacts appropriately).

While somewhat complicated, this approach minimizes the memory overhead for class instances. If you allocated the array of slot value pointers separately, you would have a second memory allocation and you'd need an extra pointer in the base object structure to point to the separate array. And because all instances of the class have exactly the same slots, you can put all information on the names of slots and how to access them on the class, instead of having to have it also attached on the instance.

If you have a class that both has a non-empty __slots__ and tries to inherit from certain built in types, you will get the error:

nonempty __slots__ not supported for subtype of '<type>'

The Python documentation mentions this but does not explain the details of what is going on, which have to do with this storage approach.

Most C-level types have a fixed size C structure; however, the type infrastructure has general support for types that have a fixed size header structure plus some number of (fixed size) items immediately after the header. Because the information on how to access slot values is attached to the class, not the instance, the CPython code requires that all slot value pointers have a constant offset from the start of the instance object. This requires that all instance objects for a type have the same fixed size, which is not the case for instances of 'base + items' C-level types. Hence the message you get here.

You can still have an empty __slots__ even for 'base + items' types, because this doesn't require allocating any slot value pointers; it just turns off the creation of the __dict__ dictionary.

(Well, usually.)

Sidebar: how __dict__ itself is (usually) implemented

One might innocently think that __dict__ would be implemented by having something like an ob_dict pointer in the basic Python C-level object structure. As it happens, CPython is both more clever and more sleazy than this. The storage for the pointer to the __dict__ dictionary is actually usually created through this same 'add things on the end of the type's blob' code, and the C structure for the type itself has a field that says what offset this pointer is to be found at. This saves a pointer when __slots__ turns off __dict__ and probably has other implementation advantages that I don't know about.

You might wonder how this works for base + items types. That's where the sleaze comes in: CPython has special magic support to make this work for the __dict__ offset. If I'm reading the code right, it switches to indexing the offset from the end of the object instead of the start.

(If you want the gory details, see _PyObject_GetDictPtr in Objects/object.c in the CPython source code.)

If you want to see some of this sausage's insides, look at the __dictoffset__ attribute of any new-style class. For bonus points, create a class that inherits from, say, str and then look at its __dictoffset__. Note that almost all built-in types will show a 0 for this value for reasons that do not fit into this sidebar.

python/HowSlotsWorkI written at 01:12:01; Add Comment

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