3. 定义扩展类型:已分类主题


这是 C 类型 PyTypeObject 的定义,省略了只用于 调试构建 的字段:

typedef struct _typeobject {
    const char *tp_name; /* For printing, in format "<module>.<name>" */
    Py_ssize_t tp_basicsize, tp_itemsize; /* For allocation */

    /* Methods to implement standard operations */

    destructor tp_dealloc;
    Py_ssize_t tp_vectorcall_offset;
    getattrfunc tp_getattr;
    setattrfunc tp_setattr;
    PyAsyncMethods *tp_as_async; /* formerly known as tp_compare (Python 2)
                                    or tp_reserved (Python 3) */
    reprfunc tp_repr;

    /* Method suites for standard classes */

    PyNumberMethods *tp_as_number;
    PySequenceMethods *tp_as_sequence;
    PyMappingMethods *tp_as_mapping;

    /* More standard operations (here for binary compatibility) */

    hashfunc tp_hash;
    ternaryfunc tp_call;
    reprfunc tp_str;
    getattrofunc tp_getattro;
    setattrofunc tp_setattro;

    /* Functions to access object as input/output buffer */
    PyBufferProcs *tp_as_buffer;

    /* Flags to define presence of optional/expanded features */
    unsigned long tp_flags;

    const char *tp_doc; /* Documentation string */

    /* Assigned meaning in release 2.0 */
    /* call function for all accessible objects */
    traverseproc tp_traverse;

    /* delete references to contained objects */
    inquiry tp_clear;

    /* Assigned meaning in release 2.1 */
    /* rich comparisons */
    richcmpfunc tp_richcompare;

    /* weak reference enabler */
    Py_ssize_t tp_weaklistoffset;

    /* Iterators */
    getiterfunc tp_iter;
    iternextfunc tp_iternext;

    /* Attribute descriptor and subclassing stuff */
    struct PyMethodDef *tp_methods;
    struct PyMemberDef *tp_members;
    struct PyGetSetDef *tp_getset;
    // Strong reference on a heap type, borrowed reference on a static type
    struct _typeobject *tp_base;
    PyObject *tp_dict;
    descrgetfunc tp_descr_get;
    descrsetfunc tp_descr_set;
    Py_ssize_t tp_dictoffset;
    initproc tp_init;
    allocfunc tp_alloc;
    newfunc tp_new;
    freefunc tp_free; /* Low-level free-memory routine */
    inquiry tp_is_gc; /* For PyObject_IS_GC */
    PyObject *tp_bases;
    PyObject *tp_mro; /* method resolution order */
    PyObject *tp_cache;
    PyObject *tp_subclasses;
    PyObject *tp_weaklist;
    destructor tp_del;

    /* Type attribute cache version tag. Added in version 2.6 */
    unsigned int tp_version_tag;

    destructor tp_finalize;
    vectorcallfunc tp_vectorcall;
} PyTypeObject;

这里有 很多 方法。但是不要太担心,如果你要定义一个类型,通常只需要实现少量的方法。


const char *tp_name; /* For printing */

类型的名字 - 上一章提到过的,会出现在很多地方,几乎全部都是为了诊断目的。尝试选择一个好名字,对于诊断很有帮助。

Py_ssize_t tp_basicsize, tp_itemsize; /* For allocation */

这些字段告诉运行时在创造这个类型的新对象时需要分配多少内存。Python为了可变长度的结构(想下:字符串,元组)有些内置支持,这是 tp_itemsize 字段存在的原由。这部分稍后解释。

const char *tp_doc;

这里你可以放置一段字符串(或者它的地址),当你想在Python脚本引用 obj.__doc__ 时返回这段文档字符串。

现在我们来看一下基本类型方法 - 大多数扩展类型将实现的方法。

3.1. 终结和内存释放

destructor tp_dealloc;

当您的类型实例的引用计数减少为零并且Python解释器想要回收它时,将调用此函数。如果你的类型有内存可供释放或执行其他清理,你可以把它放在这里。 对象本身也需要在这里释放。 以下是此函数的示例:

static void
newdatatype_dealloc(newdatatypeobject *obj)
    Py_TYPE(obj)->tp_free((PyObject *)obj);

If your type supports garbage collection, the destructor should call PyObject_GC_UnTrack() before clearing any member fields:

static void
newdatatype_dealloc(newdatatypeobject *obj)
    Py_TYPE(obj)->tp_free((PyObject *)obj);

一个重要的释放器函数实现要求是把所有未决异常放着不动。这很重要是因为释放器会被解释器频繁的调用,当栈异常退出时(而非正常返回),不会有任何办法保护释放器看到一个异常尚未被设置。此事释放器的任何行为都会导致额外增加的Python代码来检查异常是否被设置。这可能导致解释器的误导性错误。正确的保护方法是,在任何不安全的操作前,保存未决异常,然后在其完成后恢复。者可以通过 PyErr_Fetch()PyErr_Restore() 函数来实现:

static void
my_dealloc(PyObject *obj)
    MyObject *self = (MyObject *) obj;
    PyObject *cbresult;

    if (self->my_callback != NULL) {
        PyObject *err_type, *err_value, *err_traceback;

        /* This saves the current exception state */
        PyErr_Fetch(&err_type, &err_value, &err_traceback);

        cbresult = PyObject_CallNoArgs(self->my_callback);
        if (cbresult == NULL)

        /* This restores the saved exception state */
        PyErr_Restore(err_type, err_value, err_traceback);



There are limitations to what you can safely do in a deallocator function. First, if your type supports garbage collection (using tp_traverse and/or tp_clear), some of the object's members can have been cleared or finalized by the time tp_dealloc is called. Second, in tp_dealloc, your object is in an unstable state: its reference count is equal to zero. Any call to a non-trivial object or API (as in the example above) might end up calling tp_dealloc again, causing a double free and a crash.

从 Python 3.4 开始,推荐不要在 tp_dealloc 放复杂的终结代码,而是使用新的 tp_finalize 类型方法。


PEP 442 解释了新的终结方案。

3.2. 对象展示

在 Python 中,有两种方式可以生成对象的文本表示: repr() 函数和 str() 函数。 (print() 函数会直接调用 str()。) 这些处理程序都是可选的。

reprfunc tp_repr;
reprfunc tp_str;

tp_repr 处理程序应该返回一个字符串对象,其中包含调用它的实例的表示形式。 下面是一个简单的例子:

static PyObject *
newdatatype_repr(newdatatypeobject * obj)
    return PyUnicode_FromFormat("Repr-ified_newdatatype{{size:%d}}",

如果没有指定 tp_repr 处理程序,解释器将提供一个使用 tp_name 的表示形式以及对象的惟一标识值。

The tp_str handler is to str() what the tp_repr handler described above is to repr(); that is, it is called when Python code calls str() on an instance of your object. Its implementation is very similar to the tp_repr function, but the resulting string is intended for human consumption. If tp_str is not specified, the tp_repr handler is used instead.


static PyObject *
newdatatype_str(newdatatypeobject * obj)
    return PyUnicode_FromFormat("Stringified_newdatatype{{size:%d}}",

3.3. 属性管理

For every object which can support attributes, the corresponding type must provide the functions that control how the attributes are resolved. There needs to be a function which can retrieve attributes (if any are defined), and another to set attributes (if setting attributes is allowed). Removing an attribute is a special case, for which the new value passed to the handler is NULL.

Python supports two pairs of attribute handlers; a type that supports attributes only needs to implement the functions for one pair. The difference is that one pair takes the name of the attribute as a char*, while the other accepts a PyObject*. Each type can use whichever pair makes more sense for the implementation's convenience.

getattrfunc  tp_getattr;        /* char * version */
setattrfunc  tp_setattr;
/* ... */
getattrofunc tp_getattro;       /* PyObject * version */
setattrofunc tp_setattro;

If accessing attributes of an object is always a simple operation (this will be explained shortly), there are generic implementations which can be used to provide the PyObject* version of the attribute management functions. The actual need for type-specific attribute handlers almost completely disappeared starting with Python 2.2, though there are many examples which have not been updated to use some of the new generic mechanism that is available.

3.3.1. 泛型属性管理

大多数扩展类型只使用 简单 属性,那么,是什么让属性变得“简单”呢?只需要满足下面几个条件:

  1. 当调用 PyType_Ready() 时,必须知道属性的名称。

  2. 不需要特殊的处理来记录属性是否被查找或设置,也不需要根据值采取操作。


When PyType_Ready() is called, it uses three tables referenced by the type object to create descriptors which are placed in the dictionary of the type object. Each descriptor controls access to one attribute of the instance object. Each of the tables is optional; if all three are NULL, instances of the type will only have attributes that are inherited from their base type, and should leave the tp_getattro and tp_setattro fields NULL as well, allowing the base type to handle attributes.


struct PyMethodDef *tp_methods;
struct PyMemberDef *tp_members;
struct PyGetSetDef *tp_getset;

If tp_methods is not NULL, it must refer to an array of PyMethodDef structures. Each entry in the table is an instance of this structure:

typedef struct PyMethodDef {
    const char  *ml_name;       /* method name */
    PyCFunction  ml_meth;       /* implementation function */
    int          ml_flags;      /* flags */
    const char  *ml_doc;        /* docstring */
} PyMethodDef;

One entry should be defined for each method provided by the type; no entries are needed for methods inherited from a base type. One additional entry is needed at the end; it is a sentinel that marks the end of the array. The ml_name field of the sentinel must be NULL.

The second table is used to define attributes which map directly to data stored in the instance. A variety of primitive C types are supported, and access may be read-only or read-write. The structures in the table are defined as:

typedef struct PyMemberDef {
    const char *name;
    int         type;
    int         offset;
    int         flags;
    const char *doc;
} PyMemberDef;

For each entry in the table, a descriptor will be constructed and added to the type which will be able to extract a value from the instance structure. The type field should contain one of the type codes defined in the structmember.h header; the value will be used to determine how to convert Python values to and from C values. The flags field is used to store flags which control how the attribute can be accessed.

以下标志常量定义在:file: ' structmember.h ';它们可以使用bitwise-OR组合。






Emit an object.__getattr__ audit events before reading.

3.10 版更變: RESTRICTED, READ_RESTRICTED and WRITE_RESTRICTED are deprecated. However, READ_RESTRICTED is an alias for PY_AUDIT_READ, so fields that specify either RESTRICTED or READ_RESTRICTED will also raise an audit event.

An interesting advantage of using the tp_members table to build descriptors that are used at runtime is that any attribute defined this way can have an associated doc string simply by providing the text in the table. An application can use the introspection API to retrieve the descriptor from the class object, and get the doc string using its __doc__ attribute.

As with the tp_methods table, a sentinel entry with a name value of NULL is required.

3.3.2. Type-specific Attribute Management

For simplicity, only the char* version will be demonstrated here; the type of the name parameter is the only difference between the char* and PyObject* flavors of the interface. This example effectively does the same thing as the generic example above, but does not use the generic support added in Python 2.2. It explains how the handler functions are called, so that if you do need to extend their functionality, you'll understand what needs to be done.

The tp_getattr handler is called when the object requires an attribute look-up. It is called in the same situations where the __getattr__() method of a class would be called.


static PyObject *
newdatatype_getattr(newdatatypeobject *obj, char *name)
    if (strcmp(name, "data") == 0)
        return PyLong_FromLong(obj->data);

                 "'%.50s' object has no attribute '%.400s'",
                 tp->tp_name, name);
    return NULL;

The tp_setattr handler is called when the __setattr__() or __delattr__() method of a class instance would be called. When an attribute should be deleted, the third parameter will be NULL. Here is an example that simply raises an exception; if this were really all you wanted, the tp_setattr handler should be set to NULL.

static int
newdatatype_setattr(newdatatypeobject *obj, char *name, PyObject *v)
    PyErr_Format(PyExc_RuntimeError, "Read-only attribute: %s", name);
    return -1;

3.4. Object Comparison

richcmpfunc tp_richcompare;

The tp_richcompare handler is called when comparisons are needed. It is analogous to the rich comparison methods, like __lt__(), and also called by PyObject_RichCompare() and PyObject_RichCompareBool().

This function is called with two Python objects and the operator as arguments, where the operator is one of Py_EQ, Py_NE, Py_LE, Py_GE, Py_LT or Py_GT. It should compare the two objects with respect to the specified operator and return Py_True or Py_False if the comparison is successful, Py_NotImplemented to indicate that comparison is not implemented and the other object's comparison method should be tried, or NULL if an exception was set.

Here is a sample implementation, for a datatype that is considered equal if the size of an internal pointer is equal:

static PyObject *
newdatatype_richcmp(PyObject *obj1, PyObject *obj2, int op)
    PyObject *result;
    int c, size1, size2;

    /* code to make sure that both arguments are of type
       newdatatype omitted */

    size1 = obj1->obj_UnderlyingDatatypePtr->size;
    size2 = obj2->obj_UnderlyingDatatypePtr->size;

    switch (op) {
    case Py_LT: c = size1 <  size2; break;
    case Py_LE: c = size1 <= size2; break;
    case Py_EQ: c = size1 == size2; break;
    case Py_NE: c = size1 != size2; break;
    case Py_GT: c = size1 >  size2; break;
    case Py_GE: c = size1 >= size2; break;
    result = c ? Py_True : Py_False;
    return result;

3.5. Abstract Protocol Support

Python supports a variety of abstract 'protocols;' the specific interfaces provided to use these interfaces are documented in 抽象物件層.

A number of these abstract interfaces were defined early in the development of the Python implementation. In particular, the number, mapping, and sequence protocols have been part of Python since the beginning. Other protocols have been added over time. For protocols which depend on several handler routines from the type implementation, the older protocols have been defined as optional blocks of handlers referenced by the type object. For newer protocols there are additional slots in the main type object, with a flag bit being set to indicate that the slots are present and should be checked by the interpreter. (The flag bit does not indicate that the slot values are non-NULL. The flag may be set to indicate the presence of a slot, but a slot may still be unfilled.)

PyNumberMethods   *tp_as_number;
PySequenceMethods *tp_as_sequence;
PyMappingMethods  *tp_as_mapping;

If you wish your object to be able to act like a number, a sequence, or a mapping object, then you place the address of a structure that implements the C type PyNumberMethods, PySequenceMethods, or PyMappingMethods, respectively. It is up to you to fill in this structure with appropriate values. You can find examples of the use of each of these in the Objects directory of the Python source distribution.

hashfunc tp_hash;

This function, if you choose to provide it, should return a hash number for an instance of your data type. Here is a simple example:

static Py_hash_t
newdatatype_hash(newdatatypeobject *obj)
    Py_hash_t result;
    result = obj->some_size + 32767 * obj->some_number;
    if (result == -1)
       result = -2;
    return result;

Py_hash_t is a signed integer type with a platform-varying width. Returning -1 from tp_hash indicates an error, which is why you should be careful to avoid returning it when hash computation is successful, as seen above.

ternaryfunc tp_call;

This function is called when an instance of your data type is "called", for example, if obj1 is an instance of your data type and the Python script contains obj1('hello'), the tp_call handler is invoked.

This function takes three arguments:

  1. self is the instance of the data type which is the subject of the call. If the call is obj1('hello'), then self is obj1.

  2. args is a tuple containing the arguments to the call. You can use PyArg_ParseTuple() to extract the arguments.

  3. kwds is a dictionary of keyword arguments that were passed. If this is non-NULL and you support keyword arguments, use PyArg_ParseTupleAndKeywords() to extract the arguments. If you do not want to support keyword arguments and this is non-NULL, raise a TypeError with a message saying that keyword arguments are not supported.

Here is a toy tp_call implementation:

static PyObject *
newdatatype_call(newdatatypeobject *self, PyObject *args, PyObject *kwds)
    PyObject *result;
    const char *arg1;
    const char *arg2;
    const char *arg3;

    if (!PyArg_ParseTuple(args, "sss:call", &arg1, &arg2, &arg3)) {
        return NULL;
    result = PyUnicode_FromFormat(
        "Returning -- value: [%d] arg1: [%s] arg2: [%s] arg3: [%s]\n",
        arg1, arg2, arg3);
    return result;
/* Iterators */
getiterfunc tp_iter;
iternextfunc tp_iternext;

These functions provide support for the iterator protocol. Both handlers take exactly one parameter, the instance for which they are being called, and return a new reference. In the case of an error, they should set an exception and return NULL. tp_iter corresponds to the Python __iter__() method, while tp_iternext corresponds to the Python __next__() method.

Any iterable object must implement the tp_iter handler, which must return an iterator object. Here the same guidelines apply as for Python classes:

  • For collections (such as lists and tuples) which can support multiple independent iterators, a new iterator should be created and returned by each call to tp_iter.

  • Objects which can only be iterated over once (usually due to side effects of iteration, such as file objects) can implement tp_iter by returning a new reference to themselves -- and should also therefore implement the tp_iternext handler.

Any iterator object should implement both tp_iter and tp_iternext. An iterator's tp_iter handler should return a new reference to the iterator. Its tp_iternext handler should return a new reference to the next object in the iteration, if there is one. If the iteration has reached the end, tp_iternext may return NULL without setting an exception, or it may set StopIteration in addition to returning NULL; avoiding the exception can yield slightly better performance. If an actual error occurs, tp_iternext should always set an exception and return NULL.

3.6. Weak Reference Support

One of the goals of Python's weak reference implementation is to allow any type to participate in the weak reference mechanism without incurring the overhead on performance-critical objects (such as numbers).


Documentation for the weakref module.

For an object to be weakly referencable, the extension type must do two things:

  1. Include a PyObject* field in the C object structure dedicated to the weak reference mechanism. The object's constructor should leave it NULL (which is automatic when using the default tp_alloc).

  2. Set the tp_weaklistoffset type member to the offset of the aforementioned field in the C object structure, so that the interpreter knows how to access and modify that field.

Concretely, here is how a trivial object structure would be augmented with the required field:

typedef struct {
    PyObject *weakreflist;  /* List of weak references */
} TrivialObject;

And the corresponding member in the statically-declared type object:

static PyTypeObject TrivialType = {
    PyVarObject_HEAD_INIT(NULL, 0)
    /* ... other members omitted for brevity ... */
    .tp_weaklistoffset = offsetof(TrivialObject, weakreflist),

The only further addition is that tp_dealloc needs to clear any weak references (by calling PyObject_ClearWeakRefs()) if the field is non-NULL:

static void
Trivial_dealloc(TrivialObject *self)
    /* Clear weakrefs first before calling any destructors */
    if (self->weakreflist != NULL)
        PyObject_ClearWeakRefs((PyObject *) self);
    /* ... remainder of destruction code omitted for brevity ... */
    Py_TYPE(self)->tp_free((PyObject *) self);

3.7. 更多建议

In order to learn how to implement any specific method for your new data type, get the CPython source code. Go to the Objects directory, then search the C source files for tp_ plus the function you want (for example, tp_richcompare). You will find examples of the function you want to implement.

When you need to verify that an object is a concrete instance of the type you are implementing, use the PyObject_TypeCheck() function. A sample of its use might be something like the following:

if (!PyObject_TypeCheck(some_object, &MyType)) {
    PyErr_SetString(PyExc_TypeError, "arg #1 not a mything");
    return NULL;