对象、类型和引用计数

Most Python/C API functions have one or more arguments as well as a return value of type PyObject*. This type is a pointer to an opaque data type representing an arbitrary Python object. Since all Python object types are treated the same way by the Python language in most situations (e.g., assignments, scope rules, and argument passing), it is only fitting that they should be represented by a single C type. Almost all Python objects live on the heap: you never declare an automatic or static variable of type PyObject, only pointer variables of type PyObject* can be declared. The sole exception are the type objects; since these must never be deallocated, they are typically static PyTypeObject objects.

所有 Python 对象（甚至 Python 整数）都有一个 type 和一个 reference count。对象的类型确定它是什么类型的对象（例如整数、列表或用户定义函数；还有更多，如 标准类型层级结构 中所述）。对于每个众所周知的类型，都有一个宏来检查对象是否属于该类型；例如，当（且仅当） a 所指的对象是 Python 列表时 PyList_Check(a) 为真。

引用计数

The reference count is important because today’s computers have a finite (and often severely limited) memory size; it counts how many different places there are that have a reference to an object. Such a place could be another object, or a global (or static) C variable, or a local variable in some C function. When an object’s reference count becomes zero, the object is deallocated. If it contains references to other objects, their reference count is decremented. Those other objects may be deallocated in turn, if this decrement makes their reference count become zero, and so on. (There’s an obvious problem with objects that reference each other here; for now, the solution is “don’t do that.”)

Reference counts are always manipulated explicitly. The normal way is to use the macro Py_INCREF() to increment an object’s reference count by one, and Py_DECREF() to decrement it by one. The Py_DECREF() macro is considerably more complex than the incref one, since it must check whether the reference count becomes zero and then cause the object’s deallocator to be called. The deallocator is a function pointer contained in the object’s type structure. The type-specific deallocator takes care of decrementing the reference counts for other objects contained in the object if this is a compound object type, such as a list, as well as performing any additional finalization that’s needed. There’s no chance that the reference count can overflow; at least as many bits are used to hold the reference count as there are distinct memory locations in virtual memory (assuming sizeof(Py_ssize_t) >= sizeof(void*)). Thus, the reference count increment is a simple operation.

It is not necessary to increment an object’s reference count for every local variable that contains a pointer to an object. In theory, the object’s reference count goes up by one when the variable is made to point to it and it goes down by one when the variable goes out of scope. However, these two cancel each other out, so at the end the reference count hasn’t changed. The only real reason to use the reference count is to prevent the object from being deallocated as long as our variable is pointing to it. If we know that there is at least one other reference to the object that lives at least as long as our variable, there is no need to increment the reference count temporarily. An important situation where this arises is in objects that are passed as arguments to C functions in an extension module that are called from Python; the call mechanism guarantees to hold a reference to every argument for the duration of the call.

However, a common pitfall is to extract an object from a list and hold on to it for a while without incrementing its reference count. Some other operation might conceivably remove the object from the list, decrementing its reference count and possibly deallocating it. The real danger is that innocent-looking operations may invoke arbitrary Python code which could do this; there is a code path which allows control to flow back to the user from a Py_DECREF(), so almost any operation is potentially dangerous.

A safe approach is to always use the generic operations (functions whose name begins with PyObject_, PyNumber_, PySequence_ or PyMapping_). These operations always increment the reference count of the object they return. This leaves the caller with the responsibility to call Py_DECREF() when they are done with the result; this soon becomes second nature.

Reference Count Details

The reference count behavior of functions in the Python/C API is best explained in terms of ownership of references. Ownership pertains to references, never to objects (objects are not owned: they are always shared). “Owning a reference” means being responsible for calling Py_DECREF on it when the reference is no longer needed. Ownership can also be transferred, meaning that the code that receives ownership of the reference then becomes responsible for eventually decref’ing it by calling Py_DECREF() or Py_XDECREF() when it’s no longer needed—-or passing on this responsibility (usually to its caller). When a function passes ownership of a reference on to its caller, the caller is said to receive a new reference. When no ownership is transferred, the caller is said to borrow the reference. Nothing needs to be done for a borrowed reference.

Conversely, when a calling function passes in a reference to an object, there are two possibilities: the function steals a reference to the object, or it does not. Stealing a reference means that when you pass a reference to a function, that function assumes that it now owns that reference, and you are not responsible for it any longer.

Few functions steal references; the two notable exceptions are PyList_SetItem() and PyTuple_SetItem(), which steal a reference to the item (but not to the tuple or list into which the item is put!). These functions were designed to steal a reference because of a common idiom for populating a tuple or list with newly created objects; for example, the code to create the tuple (1, 2, "three") could look like this (forgetting about error handling for the moment; a better way to code this is shown below):

  1. PyObject *t;
  3. t = PyTuple_New(3);
  4. PyTuple_SetItem(t, 0, PyLong_FromLong(1L));
  5. PyTuple_SetItem(t, 1, PyLong_FromLong(2L));
  6. PyTuple_SetItem(t, 2, PyUnicode_FromString("three"));

Here, PyLong_FromLong() returns a new reference which is immediately stolen by PyTuple_SetItem(). When you want to keep using an object although the reference to it will be stolen, use Py_INCREF() to grab another reference before calling the reference-stealing function.

Incidentally, PyTuple_SetItem() is the only way to set tuple items; PySequence_SetItem() and PyObject_SetItem() refuse to do this since tuples are an immutable data type. You should only use PyTuple_SetItem() for tuples that you are creating yourself.

Equivalent code for populating a list can be written using PyList_New() and PyList_SetItem().

However, in practice, you will rarely use these ways of creating and populating a tuple or list. There’s a generic function, Py_BuildValue(), that can create most common objects from C values, directed by a format string. For example, the above two blocks of code could be replaced by the following (which also takes care of the error checking):

  1. PyObject *tuple, *list;
  3. tuple = Py_BuildValue("(iis)", 1, 2, "three");
  4. list = Py_BuildValue("[iis]", 1, 2, "three");

It is much more common to use PyObject_SetItem() and friends with items whose references you are only borrowing, like arguments that were passed in to the function you are writing. In that case, their behaviour regarding reference counts is much saner, since you don’t have to increment a reference count so you can give a reference away (“have it be stolen”). For example, this function sets all items of a list (actually, any mutable sequence) to a given item:

  1. int
  2. set_all(PyObject *target, PyObject *item)
  3. {
  4.     Py_ssize_t i, n;
  6.     n = PyObject_Length(target);
  7.     if (n < 0)
  8.         return -1;
  9.     for (i = 0; i < n; i++) {
  10.         PyObject *index = PyLong_FromSsize_t(i);
  11.         if (!index)
  12.             return -1;
  13.         if (PyObject_SetItem(target, index, item) < 0) {
  14.             Py_DECREF(index);
  15.             return -1;
  16.         }
  17.         Py_DECREF(index);
  18.     }
  19.     return 0;
  20. }

The situation is slightly different for function return values. While passing a reference to most functions does not change your ownership responsibilities for that reference, many functions that return a reference to an object give you ownership of the reference. The reason is simple: in many cases, the returned object is created on the fly, and the reference you get is the only reference to the object. Therefore, the generic functions that return object references, like PyObject_GetItem() and PySequence_GetItem(), always return a new reference (the caller becomes the owner of the reference).

It is important to realize that whether you own a reference returned by a function depends on which function you call only —- the plumage (the type of the object passed as an argument to the function) doesn’t enter into it! Thus, if you extract an item from a list using PyList_GetItem(), you don’t own the reference —- but if you obtain the same item from the same list using PySequence_GetItem() (which happens to take exactly the same arguments), you do own a reference to the returned object.

Here is an example of how you could write a function that computes the sum of the items in a list of integers; once using PyList_GetItem(), and once using PySequence_GetItem().

  1. long
  2. sum_list(PyObject *list)
  3. {
  4.     Py_ssize_t i, n;
  5.     long total = 0, value;
  6.     PyObject *item;
  8.     n = PyList_Size(list);
  9.     if (n < 0)
  10.         return -1; /* Not a list */
  11.     for (i = 0; i < n; i++) {
  12.         item = PyList_GetItem(list, i); /* Can't fail */
  13.         if (!PyLong_Check(item)) continue; /* Skip non-integers */
  14.         value = PyLong_AsLong(item);
  15.         if (value == -1 && PyErr_Occurred())
  16.             /* Integer too big to fit in a C long, bail out */
  17.             return -1;
  18.         total += value;
  19.     }
  20.     return total;
  21. }
  1. long
  2. sum_sequence(PyObject *sequence)
  3. {
  4.     Py_ssize_t i, n;
  5.     long total = 0, value;
  6.     PyObject *item;
  7.     n = PySequence_Length(sequence);
  8.     if (n < 0)
  9.         return -1; /* Has no length */
  10.     for (i = 0; i < n; i++) {
  11.         item = PySequence_GetItem(sequence, i);
  12.         if (item == NULL)
  13.             return -1; /* Not a sequence, or other failure */
  14.         if (PyLong_Check(item)) {
  15.             value = PyLong_AsLong(item);
  16.             Py_DECREF(item);
  17.             if (value == -1 && PyErr_Occurred())
  18.                 /* Integer too big to fit in a C long, bail out */
  19.                 return -1;
  20.             total += value;
  21.         }
  22.         else {
  23.             Py_DECREF(item); /* Discard reference ownership */
  24.         }
  25.     }
  26.     return total;
  27. }

类型

There are few other data types that play a significant role in the Python/C API; most are simple C types such as int, long, double and char*. A few structure types are used to describe static tables used to list the functions exported by a module or the data attributes of a new object type, and another is used to describe the value of a complex number. These will be discussed together with the functions that use them.