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Extending Python with C or C++

2018-03-01 10:28 609 查看
It is quite easy to add new built-in modules to Python, if you know how to program in C. Such extension modules can do two things that can’t be done directly in Python: they can implement new built-in object types, and they can call C library functions and system calls.
To support extensions, the Python API (Application Programmers Interface) defines a set of functions, macros and variables that provide access to most aspects of the Python run-time system. The Python API is incorporated in a C source file by including the header 
"Python.h"
.
The compilation of an extension module depends on its intended use as well as on your system setup; details are given in later chapters.
Note The C extension interface is specific to CPython, and extension modules do not work on other Python implementations. In many cases, it is possible to avoid writing C extensions and preserve portability to other implementations. For example, if your use case is calling C library functions or system calls, you should consider using the 
ctypes
 module or the cffi library rather than writing custom C code. These modules let you write Python code to interface with C code and are more portable between implementations of Python than writing and compiling a C extension module.

1.1. A Simple Example

Let’s create an extension module called 
spam
(the favorite food of Monty Python fans…) and let’s say we want to create a Python interface to the C library function 
system()
[1]. This function takes a null-terminated character string as argument and returns an integer. We want this function to be callable from Python as follows:
>>> import spam
>>> status = spam.system("ls -l")
Begin by creating a file 
spammodule.c
. (Historically, if a module is called 
spam
, the C file containing its implementation is called 
spammodule.c
; if the module name is very long, like 
spammify
, the module name can be just 
spammify.c
.)
The first line of our file can be:
#include <Python.h>
which pulls in the Python API (you can add a comment describing the purpose of the module and a copyright notice if you like).
Note Since Python may define some pre-processor definitions which affect the standard headers on some systems, you must include 
Python.h
before any standard headers are included. All user-visible symbols defined by 
Python.h
have a prefix of 
Py
or 
PY
, except those defined in standard header files. For convenience, and since they are used extensively by the Python interpreter, 
"Python.h"
includes a few standard header files: 
<stdio.h>
, 
<string.h>
, 
<errno.h>
, and 
<stdlib.h>
. If the latter header file does not exist on your system, it declares the functions 
malloc()
, 
free()
and 
realloc()
directly.
The next thing we add to our module file is the C function that will be called when the Python expression 
spam.system(string)
is evaluated (we’ll see shortly how it ends up being called):
static PyObject *
spam_system(PyObject *self, PyObject *args)
{
const char *command;
int sts;

if (!PyArg_ParseTuple(args, "s", &command))
return NULL;
sts = system(command);
return Py_BuildValue("i", sts);
}
There is a straightforward translation from the argument list in Python (for example, the single expression 
"ls -l"
) to the arguments passed to the C function. The C function always has two arguments, conventionally named self and args.
For module functions, the self argument is NULL or a pointer selected while initializing the module (see 
Py_InitModule4()
). For a method, it would point to the object instance.
The args argument will be a pointer to a Python tuple object containing the arguments. Each item of the tuple corresponds to an argument in the call’s argument list. The arguments are Python objects — in order to do anything with them in our C function we have to convert them to C values. The function 
PyArg_ParseTuple()
 in the Python API checks the argument types and converts them to C values. It uses a template string to determine the required types of the arguments as well as the types of the C variables into which to store the converted values. More about this later.
PyArg_ParseTuple()
 returns true (nonzero) if all arguments have the right type and its components have been stored in the variables whose addresses are passed. It returns false (zero) if an invalid argument list was passed. In the latter case it also raises an appropriate exception so the calling function can return NULL immediately (as we saw in the example).

1.2. Intermezzo: Errors and Exceptions

An important convention throughout the Python interpreter is the following: when a function fails, it should set an exception condition and return an error value (usually a NULL pointer). Exceptions are stored in a static global variable inside the interpreter; if this variable is NULL no exception has occurred. A second global variable stores the “associated value” of the exception (the second argument to 
raise
). A third variable contains the stack traceback in case the error originated in Python code. These three variables are the C equivalents of the Python variables 
sys.exc_type
, 
sys.exc_value
and 
sys.exc_traceback
(see the section on module 
sys
 in the Python Library Reference). It is important to know about them to understand how errors are passed around.
The Python API defines a number of functions to set various types of exceptions.
The most common one is 
PyErr_SetString()
. Its arguments are an exception object and a C string. The exception object is usually a predefined object like 
PyExc_ZeroDivisionError
. The C string indicates the cause of the error and is converted to a Python string object and stored as the “associated value” of the exception.
Another useful function is 
PyErr_SetFromErrno()
, which only takes an exception argument and constructs the associated value by inspection of the global variable 
errno
. The most general function is 
PyErr_SetObject()
, which takes two object arguments, the exception and its associated value. You don’t need to 
Py_INCREF()
 the objects passed to any of these functions.
You can test non-destructively whether an exception has been set with 
PyErr_Occurred()
. This returns the current exception object, or NULL if no exception has occurred. You normally don’t need to call 
PyErr_Occurred()
 to see whether an error occurred in a function call, since you should be able to tell from the return value.
When a function f that calls another function g detects that the latter fails, f should itself return an error value (usually NULL or 
-1
). It should not call one of the 
PyErr_*()
functions — one has already been called by g. f’s caller is then supposed to also return an error indication to its caller, again without calling 
PyErr_*()
, and so on — the most detailed cause of the error was already reported by the function that first detected it. Once the error reaches the Python interpreter’s main loop, this aborts the currently executing Python code and tries to find an exception handler specified by the Python programmer.
(There are situations where a module can actually give a more detailed error message by calling another 
PyErr_*()
function, and in such cases it is fine to do so. As a general rule, however, this is not necessary, and can cause information about the cause of the error to be lost: most operations can fail for a variety of reasons.)
To ignore an exception set by a function call that failed, the exception condition must be cleared explicitly by calling 
PyErr_Clear()
. The only time C code should call 
PyErr_Clear()
 is if it doesn’t want to pass the error on to the interpreter but wants to handle it completely by itself (possibly by trying something else, or pretending nothing went wrong).
Every failing 
malloc()
call must be turned into an exception — the direct caller of 
malloc()
(or 
realloc()
) must call 
PyErr_NoMemory()
 and return a failure indicator itself. All the object-creating functions (for example, 
PyInt_FromLong()
) already do this, so this note is only relevant to those who call 
malloc()
directly.
Also note that, with the important exception of 
PyArg_ParseTuple()
 and friends, functions that return an integer status usually return a positive value or zero for success and 
-1
for failure, like Unix system calls.
Finally, be careful to clean up garbage (by making 
Py_XDECREF()
 or 
Py_DECREF()
 calls for objects you have already created) when you return an error indicator!
The choice of which exception to raise is entirely yours. There are predeclared C objects corresponding to all built-in Python exceptions, such as 
PyExc_ZeroDivisionError
, which you can use directly. Of course, you should choose exceptions wisely — don’t use 
PyExc_TypeError
to mean that a file couldn’t be opened (that should probably be 
PyExc_IOError
). If something’s wrong with the argument list, the 
PyArg_ParseTuple()
 function usually raises 
PyExc_TypeError
. If you have an argument whose value must be in a particular range or must satisfy other conditions, 
PyExc_ValueError
is appropriate.
You can also define a new exception that is unique to your module. For this, you usually declare a static object variable at the beginning of your file:
static PyObject *SpamError;
and initialize it in your module’s initialization function (
initspam()
) with an exception object (leaving out the error checking for now):
PyMODINIT_FUNC
initspam(void)
{
PyObject *m;

m = Py_InitModule("spam", SpamMethods);
if (m == NULL)
return;

SpamError = PyErr_NewException("spam.error", NULL, NULL);
Py_INCREF(SpamError);
PyModule_AddObject(m, "error", SpamError);
}
Note that the Python name for the exception object is 
spam.error
. The 
PyErr_NewException()
 function may create a class with the base class being 
Exception
 (unless another class is passed in instead of NULL), described in Built-in Exceptions.
Note also that the 
SpamError
variable retains a reference to the newly created exception class; this is intentional! Since the exception could be removed from the module by external code, an owned reference to the class is needed to ensure that it will not be discarded, causing 
SpamError
to become a dangling pointer. Should it become a dangling pointer, C code which raises the exception could cause a core dump or other unintended side effects.
We discuss the use of 
PyMODINIT_FUNC
as a function return type later in this sample.
The 
spam.error
exception can be raised in your extension module using a call to 
PyErr_SetString()
 as shown below:
static PyObject *
spam_system(PyObject *self, PyObject *args)
{
const char *command;
int sts;

if (!PyArg_ParseTuple(args, "s", &command))
return NULL;
sts = system(command);
if (sts < 0) {
PyErr_SetString(SpamError, "System command failed");
return NULL;
}
return PyLong_FromLong(sts);
}

1.3. Back to the Example

Going back to our example function, you should now be able to understand this statement:
if (!PyArg_ParseTuple(args, "s", &command))
return NULL;
It returns NULL (the error indicator for functions returning object pointers) if an error is detected in the argument list, relying on the exception set by 
PyArg_ParseTuple()
. Otherwise the string value of the argument has been copied to the local variable 
command
. This is a pointer assignment and you are not supposed to modify the string to which it points (so in Standard C, the variable 
command
should properly be declared as 
const char *command
).
The next statement is a call to the Unix function 
system()
, passing it the string we just got from 
PyArg_ParseTuple()
:
sts = system(command);
Our 
spam.system()
function must return the value of 
sts
as a Python object. This is done using the function 
Py_BuildValue()
, which is something like the inverse of 
PyArg_ParseTuple()
: it takes a format string and an arbitrary number of C values, and returns a new Python object. More info on 
Py_BuildValue()
 is given later.
return Py_BuildValue("i", sts);
In this case, it will return an integer object. (Yes, even integers are objects on the heap in Python!)
If you have a C function that returns no useful argument (a function returning 
void
), the corresponding Python function must return 
None
. You need this idiom to do so (which is implemented by the 
Py_RETURN_NONE
 macro):
Py_INCREF(Py_None);
return Py_None;
Py_None
 is the C name for the special Python object 
None
. It is a genuine Python object rather than a NULL pointer, which means “error” in most contexts, as we have seen.

1.4. The Module’s Method Table and Initialization Function

I promised to show how 
spam_system()
is called from Python programs. First, we need to list its name and address in a “method table”:
static PyMethodDef SpamMethods[] = {
...
{"system",  spam_system, METH_VARARGS,
"Execute a shell command."},
...
{NULL, NULL, 0, NULL}        /* Sentinel */
};
Note the third entry (
METH_VARARGS
). This is a flag telling the interpreter the calling convention to be used for the C function. It should normally always be 
METH_VARARGS
or 
METH_VARARGS | METH_KEYWORDS
; a value of 
0
means that an obsolete variant of 
PyArg_ParseTuple()
 is used.
When using only 
METH_VARARGS
, the function should expect the Python-level parameters to be passed in as a tuple acceptable for parsing via 
PyArg_ParseTuple()
; more information on this function is provided below.
The 
METH_KEYWORDS
 bit may be set in the third field if keyword arguments should be passed to the function. In this case, the C function should accept a third 
PyObject *
parameter which will be a dictionary of keywords. Use 
PyArg_ParseTupleAndKeywords()
 to parse the arguments to such a function.
The method table must be passed to the interpreter in the module’s initialization function. The initialization function must be named 
initname()
, where name is the name of the module, and should be the only non-
static
item defined in the module file:
PyMODINIT_FUNC
initspam(void)
{
(void) Py_InitModule("spam", SpamMethods);
}
Note that PyMODINIT_FUNC declares the function as 
void
return type, declares any special linkage declarations required by the platform, and for C++ declares the function as 
extern "C"
.
When the Python program imports module 
spam
for the first time, 
initspam()
is called. (See below for comments about embedding Python.) It calls 
Py_InitModule()
, which creates a “module object” (which is inserted in the dictionary 
sys.modules
under the key 
"spam"
), and inserts built-in function objects into the newly created module based upon the table (an array of 
PyMethodDef
 structures) that was passed as its second argument. 
Py_InitModule()
 returns a pointer to the module object that it creates (which is unused here). It may abort with a fatal error for certain errors, or return NULL if the module could not be initialized satisfactorily.
When embedding Python, the 
initspam()
function is not called automatically unless there’s an entry in the 
_PyImport_Inittab
table. The easiest way to handle this is to statically initialize your statically-linked modules by directly calling 
initspam()
after the call to 
Py_Initialize()
:
int
main(int argc, char *argv[])
{
/* Pass argv[0] to the Python interpreter */
Py_SetProgramName(argv[0]);

/* Initialize the Python interpreter.  Required. */
Py_Initialize();

/* Add a static module */
initspam();

...
An example may be found in the file 
Demo/embed/demo.c
in the Python source distribution.
Note Removing entries from 
sys.modules
or importing compiled modules into multiple interpreters within a process (or following a 
fork()
without an intervening 
exec()
) can create problems for some extension modules. Extension module authors should exercise caution when initializing internal data structures. Note also that the 
reload()
 function can be used with extension modules, and will call the module initialization function (
initspam()
in the example), but will not load the module again if it was loaded from a dynamically loadable object file (
.so
on Unix, 
.dll
on Windows). A more substantial example module is included in the Python source distribution as 
Modules/xxmodule.c
. This file may be used as a template or simply read as an example.

1.5. Compilation and Linkage

There are two more things to do before you can use your new extension: compiling and linking it with the Python system. If you use dynamic loading, the details may depend on the style of dynamic loading your system uses; see the chapters about building extension modules (chapter Building C and C++ Extensions with distutils) and additional information that pertains only to building on Windows (chapter Building C and C++ Extensions on Windows) for more information about this.
If you can’t use dynamic loading, or if you want to make your module a permanent part of the Python interpreter, you will have to change the configuration setup and rebuild the interpreter. Luckily, this is very simple on Unix: just place your file (
spammodule.c
for example) in the 
Modules/
directory of an unpacked source distribution, add a line to the file 
Modules/Setup.local
describing your file:
spam spammodule.o
and rebuild the interpreter by running make in the toplevel directory. You can also run make in the 
Modules/
subdirectory, but then you must first rebuild 
Makefile
there by running ‘make Makefile’. (This is necessary each time you change the 
Setup
file.)
If your module requires additional libraries to link with, these can be listed on the line in the configuration file as well, for instance:
spam spammodule.o -lX11

1.6. Calling Python Functions from C

So far we have concentrated on making C functions callable from Python. The reverse is also useful: calling Python functions from C. This is especially the case for libraries that support so-called “callback” functions. If a C interface makes use of callbacks, the equivalent Python often needs to provide a callback mechanism to the Python programmer; the implementation will require calling the Python callback functions from a C callback. Other uses are also imaginable.
Fortunately, the Python interpreter is easily called recursively, and there is a standard interface to call a Python function. (I won’t dwell on how to call the Python parser with a particular string as input — if you’re interested, have a look at the implementation of the 
-c
 command line option in 
Modules/main.c
from the Python source code.)
Calling a Python function is easy. First, the Python program must somehow pass you the Python function object. You should provide a function (or some other interface) to do this. When this function is called, save a pointer to the Python function object (be careful to 
Py_INCREF()
 it!) in a global variable — or wherever you see fit. For example, the following function might be part of a module definition:
static PyObject *my_callback = NULL;

static PyObject *
my_set_callback(PyObject *dummy, PyObject *args)
{
PyObject *result = NULL;
PyObject *temp;

if (PyArg_ParseTuple(args, "O:set_callback", &temp)) {
if (!PyCallable_Check(temp)) {
PyErr_SetString(PyExc_TypeError, "parameter must be callable");
return NULL;
}
Py_XINCREF(temp);         /* Add a reference to new callback */
Py_XDECREF(my_callback);  /* Dispose of previous callback */
my_callback = temp;       /* Remember new callback */
/* Boilerplate to return "None" */
Py_INCREF(Py_None);
result = Py_None;
}
return result;
}
This function must be registered with the interpreter using the 
METH_VARARGS
 flag; this is described in section The Module’s Method Table and Initialization Function. The 
PyArg_ParseTuple()
 function and its arguments are documented in section Extracting Parameters in Extension Functions.
The macros 
Py_XINCREF()
 and 
Py_XDECREF()
 increment/decrement the reference count of an object and are safe in the presence of NULL pointers (but note that temp will not be NULL in this context). More info on them in section Reference Counts.
Later, when it is time to call the function, you call the C function 
PyObject_CallObject()
. This function has two arguments, both pointers to arbitrary Python objects: the Python function, and the argument list. The argument list must always be a tuple object, whose length is the number of arguments. To call the Python function with no arguments, pass in NULL, or an empty tuple; to call it with one argument, pass a singleton tuple. 
Py_BuildValue()
 returns a tuple when its format string consists of zero or more format codes between parentheses. For example:
int arg;
PyObject *arglist;
PyObject *result;
...
arg = 123;
...
/* Time to call the callback */
arglist = Py_BuildValue("(i)", arg);
result = PyObject_CallObject(my_callback, arglist);
Py_DECREF(arglist);
PyObject_CallObject()
 returns a Python object pointer: this is the return value of the Python function. 
PyObject_CallObject()
 is “reference-count-neutral” with respect to its arguments. In the example a new tuple was created to serve as the argument list, which is 
Py_DECREF()
-ed immediately after the 
PyObject_CallObject()
 call.
The return value of 
PyObject_CallObject()
 is “new”: either it is a brand new object, or it is an existing object whose reference count has been incremented. So, unless you want to save it in a global variable, you should somehow 
Py_DECREF()
 the result, even (especially!) if you are not interested in its value.
Before you do this, however, it is important to check that the return value isn’t NULL. If it is, the Python function terminated by raising an exception. If the C code that called 
PyObject_CallObject()
 is called from Python, it should now return an error indication to its Python caller, so the interpreter can print a stack trace, or the calling Python code can handle the exception. If this is not possible or desirable, the exception should be cleared by calling 
PyErr_Clear()
. For example:
if (result == NULL)
return NULL; /* Pass error back */
...use result...
Py_DECREF(result);
Depending on the desired interface to the Python callback function, you may also have to provide an argument list to 
PyObject_CallObject()
. In some cases the argument list is also provided by the Python program, through the same interface that specified the callback function. It can then be saved and used in the same manner as the function object. In other cases, you may have to construct a new tuple to pass as the argument list. The simplest way to do this is to call 
Py_BuildValue()
. For example, if you want to pass an integral event code, you might use the following code:
PyObject *arglist;
...
arglist = Py_BuildValue("(l)", eventcode);
result = PyObject_CallObject(my_callback, arglist);
Py_DECREF(arglist);
if (result == NULL)
return NULL; /* Pass error back */
/* Here maybe use the result */
Py_DECREF(result);
Note the placement of 
Py_DECREF(arglist)
immediately after the call, before the error check! Also note that strictly speaking this code is not complete: 
Py_BuildValue()
 may run out of memory, and this should be checked.
You may also call a function with keyword arguments by using 
PyObject_Call()
, which supports arguments and keyword arguments. As in the above example, we use 
Py_BuildValue()
 to construct the dictionary.
PyObject *dict;
...
dict = Py_BuildValue("{s:i}", "name", val);
result = PyObject_Call(my_callback, NULL, dict);
Py_DECREF(dict);
if (result == NULL)
return NULL; /* Pass error back */
/* Here maybe use the result */
Py_DECREF(result);

1.7. Extracting Parameters in Extension Functions

The 
PyArg_ParseTuple()
 function is declared as follows:
int PyArg_ParseTuple(PyObject *arg, char *format, ...);
The arg argument must be a tuple object containing an argument list passed from Python to a C function. The format argument must be a format string, whose syntax is explained in Parsing arguments and building values in the Python/C API Reference Manual. The remaining arguments must be addresses of variables whose type is determined by the format string.
Note that while 
PyArg_ParseTuple()
 checks that the Python arguments have the required types, it cannot check the validity of the addresses of C variables passed to the call: if you make mistakes there, your code will probably crash or at least overwrite random bits in memory. So be careful!
Note that any Python object references which are provided to the caller are borrowed references; do not decrement their reference count!
Some example calls:
int ok;
int i, j;
long k, l;
const char *s;
int size;

ok = PyArg_ParseTuple(args, ""); /* No arguments */
/* Python call: f() */
ok = PyArg_ParseTuple(args, "s", &s); /* A string */
/* Possible Python call: f('whoops!') */
ok = PyArg_ParseTuple(args, "lls", &k, &l, &s); /* Two longs and a string */
/* Possible Python call: f(1, 2, 'three') */
ok = PyArg_ParseTuple(args, "(ii)s#", &i, &j, &s, &size);
/* A pair of ints and a string, whose size is also returned */
/* Possible Python call: f((1, 2), 'three') */
{
const char *file;
const char *mode = "r";
int bufsize = 0;
ok = PyArg_ParseTuple(args, "s|si", &file, &mode, &bufsize);
/* A string, and optionally another string and an integer */
/* Possible Python calls:
f('spam')
f('spam', 'w')
f('spam', 'wb', 100000) */
}
{
int left, top, right, bottom, h, v;
ok = PyArg_ParseTuple(args, "((ii)(ii))(ii)",
&left, &top, &right, &bottom, &h, &v);
/* A rectangle and a point */
/* Possible Python call:
f(((0, 0), (400, 300)), (10, 10)) */
}
{
Py_complex c;
ok = PyArg_ParseTuple(args, "D:myfunction", &c);
/* a complex, also providing a function name for errors */
/* Possible Python call: myfunction(1+2j) */
}

1.8. Keyword Parameters for Extension Functions

The 
PyArg_ParseTupleAndKeywords()
 function is declared as follows:
int PyArg_ParseTupleAndKeywords(PyObject *arg, PyObject *kwdict,
char *format, char *kwlist[], ...);
The arg and format parameters are identical to those of the 
PyArg_ParseTuple()
 function. The kwdict parameter is the dictionary of keywords received as the third parameter from the Python runtime. The kwlist parameter is a NULL-terminated list of strings which identify the parameters; the names are matched with the type information from format from left to right. On success, 
PyArg_ParseTupleAndKeywords()
 returns true, otherwise it returns false and raises an appropriate exception.
Note Nested tuples cannot be parsed when using keyword arguments! Keyword parameters passed in which are not present in the kwlist will cause 
TypeError
 to be raised. Here is an example module which uses keywords, based on an example by Geoff Philbrick (philbrick@hks.com):
#include "Python.h"

static PyObject *
keywdarg_parrot(PyObject *self, PyObject *args, PyObject *keywds)
{
int voltage;
char *state = "a stiff";
char *action = "voom";
char *type = "Norwegian Blue";

static char *kwlist[] = {"voltage", "state", "action", "type", NULL};

if (!PyArg_ParseTupleAndKeywords(args, keywds, "i|sss", kwlist,
&voltage, &state, &action, &type))
return NULL;

printf("-- This parrot wouldn't %s if you put %i Volts through it.\n",
action, voltage);
printf("-- Lovely plumage, the %s -- It's %s!\n", type, state);

Py_INCREF(Py_None);

return Py_None;
}

static PyMethodDef keywdarg_methods[] = {
/* The cast of the function is necessary since PyCFunction values
* only take two PyObject* parameters, and keywdarg_parrot() takes
* three.
*/
{"parrot", (PyCFunction)keywdarg_parrot, METH_VARARGS | METH_KEYWORDS,
"Print a lovely skit to standard output."},
{NULL, NULL, 0, NULL}   /* sentinel */
};
void
initkeywdarg(void)
{
/* Create the module and add the functions */
Py_InitModule("keywdarg", keywdarg_methods);
}

1.9. Building Arbitrary Values

This function is the counterpart to 
PyArg_ParseTuple()
. It is declared as follows:
PyObject *Py_BuildValue(char *format, ...);
It recognizes a set of format units similar to the ones recognized by 
PyArg_ParseTuple()
, but the arguments (which are input to the function, not output) must not be pointers, just values. It returns a new Python object, suitable for returning from a C function called from Python.
One difference with 
PyArg_ParseTuple()
: while the latter requires its first argument to be a tuple (since Python argument lists are always represented as tuples internally), 
Py_BuildValue()
 does not always build a tuple. It builds a tuple only if its format string contains two or more format units. If the format string is empty, it returns 
None
; if it contains exactly one format unit, it returns whatever object is described by that format unit. To force it to return a tuple of size 0 or one, parenthesize the format string.
Examples (to the left the call, to the right the resulting Python value):
Py_BuildValue("")                        None
Py_BuildValue("i", 123)                  123
Py_BuildValue("iii", 123, 456, 789)      (123, 456, 789)
Py_BuildValue("s", "hello")              'hello'
Py_BuildValue("ss", "hello", "world")    ('hello', 'world')
Py_BuildValue("s#", "hello", 4)          'hell'
Py_BuildValue("()")                      ()
Py_BuildValue("(i)", 123)                (123,)
Py_BuildValue("(ii)", 123, 456)          (123, 456)
Py_BuildValue("(i,i)", 123, 456)         (123, 456)
Py_BuildValue("[i,i]", 123, 456)         [123, 456]
Py_BuildValue("{s:i,s:i}",
"abc", 123, "def", 456)    {'abc': 123, 'def': 456}
Py_BuildValue("((ii)(ii)) (ii)",
1, 2, 3, 4, 5, 6)          (((1, 2), (3, 4)), (5, 6))

1.10. Reference Counts

In languages like C or C++, the programmer is responsible for dynamic allocation and deallocation of memory on the heap. In C, this is done using the functions 
malloc()
and 
free()
. In C++, the operators 
new
and 
delete
are used with essentially the same meaning and we’ll restrict the following discussion to the C case.
Every block of memory allocated with 
malloc()
should eventually be returned to the pool of available memory by exactly one call to 
free()
. It is important to call 
free()
at the right time. If a block’s address is forgotten but 
free()
is not called for it, the memory it occupies cannot be reused until the program terminates. This is called a memory leak. On the other hand, if a program calls 
free()
for a block and then continues to use the block, it creates a conflict with re-use of the block through another 
malloc()
call. This is called using freed memory. It has the same bad consequences as referencing uninitialized data — core dumps, wrong results, mysterious crashes.
Common causes of memory leaks are unusual paths through the code. For instance, a function may allocate a block of memory, do some calculation, and then free the block again. Now a change in the requirements for the function may add a test to the calculation that detects an error condition and can return prematurely from the function. It’s easy to forget to free the allocated memory block when taking this premature exit, especially when it is added later to the code. Such leaks, once introduced, often go undetected for a long time: the error exit is taken only in a small fraction of all calls, and most modern machines have plenty of virtual memory, so the leak only becomes apparent in a long-running process that uses the leaking function frequently. Therefore, it’s important to prevent leaks from happening by having a coding convention or strategy that minimizes this kind of errors.
Since Python makes heavy use of 
malloc()
and 
free()
, it needs a strategy to avoid memory leaks as well as the use of freed memory. The chosen method is called reference counting. The principle is simple: every object contains a counter, which is incremented when a reference to the object is stored somewhere, and which is decremented when a reference to it is deleted. When the counter reaches zero, the last reference to the object has been deleted and the object is freed.
An alternative strategy is called automatic garbage collection. (Sometimes, reference counting is also referred to as a garbage collection strategy, hence my use of “automatic” to distinguish the two.) The big advantage of automatic garbage collection is that the user doesn’t need to call 
free()
explicitly. (Another claimed advantage is an improvement in speed or memory usage — this is no hard fact however.) The disadvantage is that for C, there is no truly portable automatic garbage collector, while reference counting can be implemented portably (as long as the functions 
malloc()
and 
free()
are available — which the C Standard guarantees). Maybe some day a sufficiently portable automatic garbage collector will be available for C. Until then, we’ll have to live with reference counts.
While Python uses the traditional reference counting implementation, it also offers a cycle detector that works to detect reference cycles. This allows applications to not worry about creating direct or indirect circular references; these are the weakness of garbage collection implemented using only reference counting. Reference cycles consist of objects which contain (possibly indirect) references to themselves, so that each object in the cycle has a reference count which is non-zero. Typical reference counting implementations are not able to reclaim the memory belonging to any objects in a reference cycle, or referenced from the objects in the cycle, even though there are no further references to the cycle itself.
The cycle detector is able to detect garbage cycles and can reclaim them so long as there are no finalizers implemented in Python (
__del__()
 methods). When there are such finalizers, the detector exposes the cycles through the 
gc
 module (specifically, the 
garbage
 variable in that module). The 
gc
 module also exposes a way to run the detector (the 
collect()
 function), as well as configuration interfaces and the ability to disable the detector at runtime. The cycle detector is considered an optional component; though it is included by default, it can be disabled at build time using the 
--without-cycle-gc
option to the configure script on Unix platforms (including Mac OS X) or by removing the definition of 
WITH_CYCLE_GC
in the 
pyconfig.h
header on other platforms. If the cycle detector is disabled in this way, the 
gc
 module will not be available.

1.10.1. Reference Counting in Python

There are two macros, 
Py_INCREF(x)
and 
Py_DECREF(x)
, which handle the incrementing and decrementing of the reference count. 
Py_DECREF()
 also frees the object when the count reaches zero. For flexibility, it doesn’t call 
free()
directly — rather, it makes a call through a function pointer in the object’s type object. For this purpose (and others), every object also contains a pointer to its type object.
The big question now remains: when to use 
Py_INCREF(x)
and 
Py_DECREF(x)
? Let’s first introduce some terms. Nobody “owns” an object; however, you can own a reference to an object. An object’s reference count is now defined as the number of owned references to it. The owner of a reference is responsible for calling 
Py_DECREF()
 when the reference is no longer needed. Ownership of a reference can be transferred. There are three ways to dispose of an owned reference: pass it on, store it, or call 
Py_DECREF()
. Forgetting to dispose of an owned reference creates a memory leak.
It is also possible to borrow [2] a reference to an object. The borrower of a reference should not call 
Py_DECREF()
. The borrower must not hold on to the object longer than the owner from which it was borrowed. Using a borrowed reference after the owner has disposed of it risks using freed memory and should be avoided completely [3].
The advantage of borrowing over owning a reference is that you don’t need to take care of disposing of the reference on all possible paths through the code — in other words, with a borrowed reference you don’t run the risk of leaking when a premature exit is taken. The disadvantage of borrowing over owning is that there are some subtle situations where in seemingly correct code a borrowed reference can be used after the owner from which it was borrowed has in fact disposed of it.
A borrowed reference can be changed into an owned reference by calling 
Py_INCREF()
. This does not affect the status of the owner from which the reference was borrowed — it creates a new owned reference, and gives full owner responsibilities (the new owner must dispose of the reference properly, as well as the previous owner).

1.10.2. Ownership Rules

Whenever an object reference is passed into or out of a function, it is part of the function’s interface specification whether ownership is transferred with the reference or not.
Most functions that return a reference to an object pass on ownership with the reference. In particular, all functions whose function it is to create a new object, such as 
PyInt_FromLong()
 and 
Py_BuildValue()
, pass ownership to the receiver. Even if the object is not actually new, you still receive ownership of a new reference to that object. For instance, 
PyInt_FromLong()
 maintains a cache of popular values and can return a reference to a cached item.
Many functions that extract objects from other objects also transfer ownership with the reference, for instance 
PyObject_GetAttrString()
. The picture is less clear, here, however, since a few common routines are exceptions: 
PyTuple_GetItem()
, 
PyList_GetItem()
, 
PyDict_GetItem()
, and 
PyDict_GetItemString()
 all return references that you borrow from the tuple, list or dictionary.
The function 
PyImport_AddModule()
 also returns a borrowed reference, even though it may actually create the object it returns: this is possible because an owned reference to the object is stored in 
sys.modules
.
When you pass an object reference into another function, in general, the function borrows the reference from you — if it needs to store it, it will use 
Py_INCREF()
 to become an independent owner. There are exactly two important exceptions to this rule: 
PyTuple_SetItem()
 and 
PyList_SetItem()
. These functions take over ownership of the item passed to them — even if they fail! (Note that 
PyDict_SetItem()
 and friends don’t take over ownership — they are “normal.”)
When a C function is called from Python, it borrows references to its arguments from the caller. The caller owns a reference to the object, so the borrowed reference’s lifetime is guaranteed until the function returns. Only when such a borrowed reference must be stored or passed on, it must be turned into an owned reference by calling 
Py_INCREF()
.
The object reference returned from a C function that is called from Python must be an owned reference — ownership is transferred from the function to its caller.

1.10.3. Thin Ice

There are a few situations where seemingly harmless use of a borrowed reference can lead to problems. These all have to do with implicit invocations of the interpreter, which can cause the owner of a reference to dispose of it.
The first and most important case to know about is using 
Py_DECREF()
 on an unrelated object while borrowing a reference to a list item. For instance:
void
bug(PyObject *list)
{
PyObject *item = PyList_GetItem(list, 0);

PyList_SetItem(list, 1, PyInt_FromLong(0L));
PyObject_Print(item, stdout, 0); /* BUG! */
}
This function first borrows a reference to 
list[0]
, then replaces 
list[1]
with the value 
0
, and finally prints the borrowed reference. Looks harmless, right? But it’s not!
Let’s follow the control flow into 
PyList_SetItem()
. The list owns references to all its items, so when item 1 is replaced, it has to dispose of the original item 1. Now let’s suppose the original item 1 was an instance of a user-defined class, and let’s further suppose that the class defined a 
__del__()
 method. If this class instance has a reference count of 1, disposing of it will call its 
__del__()
 method.
Since it is written in Python, the 
__del__()
 method can execute arbitrary Python code. Could it perhaps do something to invalidate the reference to 
item
in 
bug()
? You bet! Assuming that the list passed into 
bug()
is accessible to the 
__del__()
 method, it could execute a statement to the effect of 
del list[0]
, and assuming this was the last reference to that object, it would free the memory associated with it, thereby invalidating 
item
.
The solution, once you know the source of the problem, is easy: temporarily increment the reference count. The correct version of the function reads:
void
no_bug(PyObject *list)
{
PyObject *item = PyList_GetItem(list, 0);

Py_INCREF(item);
PyList_SetItem(list, 1, PyInt_FromLong(0L));
PyObject_Print(item, stdout, 0);
Py_DECREF(item);
}
This is a true story. An older version of Python contained variants of this bug and someone spent a considerable amount of time in a C debugger to figure out why his 
__del__()
 methods would fail…
The second case of problems with a borrowed reference is a variant involving threads. Normally, multiple threads in the Python interpreter can’t get in each other’s way, because there is a global lock protecting Python’s entire object space. However, it is possible to temporarily release this lock using the macro 
Py_BEGIN_ALLOW_THREADS
, and to re-acquire it using 
Py_END_ALLOW_THREADS
. This is common around blocking I/O calls, to let other threads use the processor while waiting for the I/O to complete. Obviously, the following function has the same problem as the previous one:
void
bug(PyObject *list)
{
PyObject *item = PyList_GetItem(list, 0);
Py_BEGIN_ALLOW_THREADS
...some blocking I/O call...
Py_END_ALLOW_THREADS
PyObject_Print(item, stdout, 0); /* BUG! */
}

1.10.4. NULL Pointers

In general, functions that take object references as arguments do not expect you to pass them NULL pointers, and will dump core (or cause later core dumps) if you do so. Functions that return object references generally return NULL only to indicate that an exception occurred. The reason for not testing for NULL arguments is that functions often pass the objects they receive on to other function — if each function were to test for NULL, there would be a lot of redundant tests and the code would run more slowly.
It is better to test for NULL only at the “source:” when a pointer that may be NULL is received, for example, from 
malloc()
or from a function that may raise an exception.
The macros 
Py_INCREF()
 and 
Py_DECREF()
 do not check for NULL pointers — however, their variants 
Py_XINCREF()
 and 
Py_XDECREF()
 do.
The macros for checking for a particular object type (
Pytype_Check()
) don’t check for NULL pointers — again, there is much code that calls several of these in a row to test an object against various different expected types, and this would generate redundant tests. There are no variants with NULL checking.
The C function calling mechanism guarantees that the argument list passed to C functions (
args
in the examples) is never NULL — in fact it guarantees that it is always a tuple [4].
It is a severe error to ever let a NULL pointer “escape” to the Python user.

1.11. Writing Extensions in C++

It is possible to write extension modules in C++. Some restrictions apply. If the main program (the Python interpreter) is compiled and linked by the C compiler, global or static objects with constructors cannot be used. This is not a problem if the main program is linked by the C++ compiler. Functions that will be called by the Python interpreter (in particular, module initialization functions) have to be declared using 
extern "C"
. It is unnecessary to enclose the Python header files in 
extern "C" {...}
— they use this form already if the symbol 
__cplusplus
is defined (all recent C++ compilers define this symbol).

1.12. Providing a C API for an Extension Module

Many extension modules just provide new functions and types to be used from Python, but sometimes the code in an extension module can be useful for other extension modules. For example, an extension module could implement a type “collection” which works like lists without order. Just like the standard Python list type has a C API which permits extension modules to create and manipulate lists, this new collection type should have a set of C functions for direct manipulation from other extension modules.
At first sight this seems easy: just write the functions (without declaring them 
static
, of course), provide an appropriate header file, and document the C API. And in fact this would work if all extension modules were always linked statically with the Python interpreter. When modules are used as shared libraries, however, the symbols defined in one module may not be visible to another module. The details of visibility depend on the operating system; some systems use one global namespace for the Python interpreter and all extension modules (Windows, for example), whereas others require an explicit list of imported symbols at module link time (AIX is one example), or offer a choice of different strategies (most Unices). And even if symbols are globally visible, the module whose functions one wishes to call might not have been loaded yet!
Portability therefore requires not to make any assumptions about symbol visibility. This means that all symbols in extension modules should be declared 
static
, except for the module’s initialization function, in order to avoid name clashes with other extension modules (as discussed in section The Module’s Method Table and Initialization Function). And it means that symbols that should be accessible from other extension modules must be exported in a different way.
Python provides a special mechanism to pass C-level information (pointers) from one extension module to another one: Capsules. A Capsule is a Python data type which stores a pointer (
void *
). Capsules can only be created and accessed via their C API, but they can be passed around like any other Python object. In particular, they can be assigned to a name in an extension module’s namespace. Other extension modules can then import this module, retrieve the value of this name, and then retrieve the pointer from the Capsule.
There are many ways in which Capsules can be used to export the C API of an extension module. Each function could get its own Capsule, or all C API pointers could be stored in an array whose address is published in a Capsule. And the various tasks of storing and retrieving the pointers can be distributed in different ways between the module providing the code and the client modules.
Whichever method you choose, it’s important to name your Capsules properly. The function 
PyCapsule_New()
 takes a name parameter (
const char *
); you’re permitted to pass in a NULL name, but we strongly encourage you to specify a name. Properly named Capsules provide a degree of runtime type-safety; there is no feasible way to tell one unnamed Capsule from another.
In particular, Capsules used to expose C APIs should be given a name following this convention:
modulename.attributename
The convenience function 
PyCapsule_Import()
 makes it easy to load a C API provided via a Capsule, but only if the Capsule’s name matches this convention. This behavior gives C API users a high degree of certainty that the Capsule they load contains the correct C API.
The following example demonstrates an approach that puts most of the burden on the writer of the exporting module, which is appropriate for commonly used library modules. It stores all C API pointers (just one in the example!) in an array of 
void
pointers which becomes the value of a Capsule. The header file corresponding to the module provides a macro that takes care of importing the module and retrieving its C API pointers; client modules only have to call this macro before accessing the C API.
The exporting module is a modification of the 
spam
module from section A Simple Example. The function 
spam.system()
does not call the C library function 
system()
directly, but a function 
PySpam_System()
, which would of course do something more complicated in reality (such as adding “spam” to every command). This function 
PySpam_System()
is also exported to other extension modules.
The function 
PySpam_System()
is a plain C function, declared 
static
like everything else:
static int
PySpam_System(const char *command)
{
return system(command);
}
The function 
spam_system()
is modified in a trivial way:
static PyObject *
spam_system(PyObject *self, PyObject *args)
{
const char *command;
int sts;

if (!PyArg_ParseTuple(args, "s", &command))
return NULL;sts = PySpam_System(command);
return Py_BuildValue("i", sts);}
In the beginning of the module, right after the line
#include "Python.h"
two more lines must be added:
#define SPAM_MODULE
#include "spammodule.h"
The 
#define
is used to tell the header file that it is being included in the exporting module, not a client module. Finally, the module’s initialization function must take care of initializing the C API pointer array:
PyMODINIT_FUNC
initspam(void)
{
PyObject *m;
static void *PySpam_API[PySpam_API_pointers];
PyObject *c_api_object;

m = Py_InitModule("spam", SpamMethods);
if (m == NULL)
return;

/* Initialize the C API pointer array */
PySpam_API[PySpam_System_NUM] = (void *)PySpam_System;

/* Create a Capsule containing the API pointer array's address */
c_api_object = PyCapsule_New((void *)PySpam_API, "spam._C_API", NULL);

if (c_api_object != NULL)
PyModule_AddObject(m, "_C_API", c_api_object);
}
Note that 
PySpam_API
is declared 
static
; otherwise the pointer array would disappear when 
initspam()
terminates!
The bulk of the work is in the header file 
spammodule.h
, which looks like this:
#ifndef Py_SPAMMODULE_H
#define Py_SPAMMODULE_H
#ifdef __cplusplus
extern "C" {
#endif

/* Header file for spammodule */

/* C API functions */
#define PySpam_System_NUM 0
#define PySpam_System_RETURN int
#define PySpam_System_PROTO (const char *command)

/* Total number of C API pointers */
#define PySpam_API_pointers 1

#ifdef SPAM_MODULE
/* This section is used when compiling spammodule.c */

static PySpam_System_RETURN PySpam_System PySpam_System_PROTO;

#else
/* This section is used in modules that use spammodule's API */

static void **PySpam_API;

#define PySpam_System \
(*(PySpam_System_RETURN (*)PySpam_System_PROTO) PySpam_API[PySpam_System_NUM])

/* Return -1 on error, 0 on success.
* PyCapsule_Import will set an exception if there's an error.
*/
static int
import_spam(void)
{
PySpam_API = (void **)PyCapsule_Import("spam._C_API", 0);
return (PySpam_API != NULL) ? 0 : -1;
}

#endif

#ifdef __cplusplus
}
#endif

#endif /* !defined(Py_SPAMMODULE_H) */
All that a client module must do in order to have access to the function 
PySpam_System()
is to call the function (or rather macro) 
import_spam()
in its initialization function:
PyMODINIT_FUNC
initclient(void)
{
PyObject *m;

m = Py_InitModule("client", ClientMethods);
if (m == NULL)
return;
if (import_spam() < 0)
return;
/* additional initialization can happen here */
}
The main disadvantage of this approach is that the file 
spammodule.h
is rather complicated. However, the basic structure is the same for each function that is exported, so it has to be learned only once.
Finally it should be mentioned that Capsules offer additional functionality, which is especially useful for memory allocation and deallocation of the pointer stored in a Capsule. The details are described in the Python/C API Reference Manual in the section Capsules and in the implementation of Capsules (files 
Include/pycapsule.h
and 
Objects/pycapsule.c
in the Python source code distribution).Footnotes
[1]An interface for this function already exists in the standard module 
os
 — it was chosen as a simple and straightforward example.
[2]The metaphor of “borrowing” a reference is not completely correct: the owner still has a copy of the reference.
[3]Checking that the reference count is at least 1 does not work — the reference count itself could be in freed memory and may thus be reused for another object!
[4]These guarantees don’t hold when you use the “old” style calling convention — this is still found in much existing code.
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