Calling C functions from Python - part 3 - deep dive into ctypes implementation in CPython

Calling C functions from Python - part 3 - deep dive into ctypes implementation in CPython

Last time we’ve looked at using ctypes to call C API, and writing extension module using Python/C API. Now we can finally tie these two together - looking at how ctypes is actually implemented using mix of Python/C API and Python code.

  • You can find CPython source code here.
  • ctypes’ C implementation is here
  • ctypes’ python implementation is here.

Loading libraries

Recall that in ctypes we have cdll, windll, oledll object to help loading libraries. They are really LibraryLoader objects:

>>> print ctypes.cdll
<ctypes.LibraryLoader object at 0x000000000592F470>

And that type is just plain python code:

class LibraryLoader(object):
    def __init__(self, dlltype):
        self._dlltype = dlltype

    def __getattr__(self, name):
        if name[0] == '_':
            raise AttributeError(name)
        dll = self._dlltype(name)
        setattr(self, name, dll)
        return dll

    def __getitem__(self, name):
        return getattr(self, name)

    def LoadLibrary(self, name):
        return self._dlltype(name)

The __getattr__ is the magic that implements attribute-based library loading. Note that if the attribute is already there, CPython returns that attribute immediately without calling __getattr__. Otherwise you would end up with multiple copies of the same attribute, or keeping creating new library objects and discarding old ones - not very efficent.

dlltype is the type for each kind of DLL, such as CDLL, PyDll, WinDll, OleDll. __getattr__ creates new instances of these types as needed.

cdll, pydll, windll, oledll are simply instances of LibraryLoader class, which are created with corresponding dlltype.

cdll = LibraryLoader(CDLL)
pydll = LibraryLoader(PyDLL)

if == "nt":
    pythonapi = PyDLL("python dll", None, _sys.dllhandle)
elif _sys.platform == "cygwin":
    pythonapi = PyDLL("libpython%d.%d.dll" % _sys.version_info[:2])
    pythonapi = PyDLL(None)

if == "nt":
    windll = LibraryLoader(WinDLL)
    oledll = LibraryLoader(OleDLL)

Let’s look at CDLL first - its init does a dlopen to load the library:

class CDLL(object):
    def __init__(self, name, mode=DEFAULT_MODE, handle=None,
        if handle is None:
            self._handle = _dlopen(self._name, mode)
            self._handle = handle

The attribute access are defined in __getattr__ as well - it gets translated to __getitem__ call which creates a new _FuncPtr instance.

    def __getattr__(self, name):
        if name.startswith('__') and name.endswith('__'):
            raise AttributeError(name)
        func = self.__getitem__(name)
        setattr(self, name, func)
        return func
    def __getitem__(self, name_or_ordinal):
        func = self._FuncPtr((name_or_ordinal, self))
        if not isinstance(name_or_ordinal, int):
            func.__name__ = name_or_ordinal
        return func

We’ll look at _FuncPtr later - for now it’s good enough to know it represents function pointer.

The difference between OleDll and WinDll is simply the default settings:

For CDLL - the base class:

class CDLL(object):
    _func_flags_ = _FUNCFLAG_CDECL
    _func_restype_ = c_int

WinDll has StdCall as default calling convention, and deriving from CDLL:

    class WinDLL(CDLL):
        _func_flags_ = _FUNCFLAG_STDCALL

OleDll is like WinDll (in terms of calling convention), but the default return type is HRESULT.

    class OleDLL(CDLL):
        _func_flags_ = _FUNCFLAG_STDCALL
        _func_restype_ = HRESULT

Calling the function

In last section we discussed the how library are loaded and we didn’t talk about functions yet. Functions are presented as _FuncPtr which is basically a _CFuncPtr in _ctypes module:

        class _FuncPtr(_CFuncPtr):
            _flags_ = flags
            _restype_ = self._func_restype_

Now it’s type to put our Python/C API knowledge to good use - _CFuncPtr is implemented in C:

PyTypeObject PyCFuncPtr_Type = {
    PyVarObject_HEAD_INIT(NULL, 0)
    sizeof(PyCFuncPtrObject),                           /* tp_basicsize */
    0,                                          /* tp_itemsize */
    (destructor)PyCFuncPtr_dealloc,             /* tp_dealloc */
    0,                                          /* tp_print */
    0,                                          /* tp_getattr */
    0,                                          /* tp_setattr */
    0,                                          /* tp_reserved */
    (reprfunc)PyCFuncPtr_repr,                  /* tp_repr */
    &PyCFuncPtr_as_number,                      /* tp_as_number */
    0,                                          /* tp_as_sequence */
    0,                                          /* tp_as_mapping */
    0,                                          /* tp_hash */
    (ternaryfunc)PyCFuncPtr_call,               /* tp_call */
    0,                                          /* tp_str */
    0,                                          /* tp_getattro */
    0,                                          /* tp_setattro */
    &PyCData_as_buffer,                         /* tp_as_buffer */
    "Function Pointer",                         /* tp_doc */
    (traverseproc)PyCFuncPtr_traverse,          /* tp_traverse */
    (inquiry)PyCFuncPtr_clear,                  /* tp_clear */
    0,                                          /* tp_richcompare */
    0,                                          /* tp_weaklistoffset */
    0,                                          /* tp_iter */
    0,                                          /* tp_iternext */
    0,                                          /* tp_methods */
    0,                                          /* tp_members */
    PyCFuncPtr_getsets,                         /* tp_getset */
    0,                                          /* tp_base */
    0,                                          /* tp_dict */
    0,                                          /* tp_descr_get */
    0,                                          /* tp_descr_set */
    0,                                          /* tp_dictoffset */
    0,                                          /* tp_init */
    0,                                          /* tp_alloc */
    PyCFuncPtr_new,                             /* tp_new */
    0,                                          /* tp_free */

Let’s look at the tp_new function PyCFuncPtr_new first:

PyCFuncPtr_new(PyTypeObject *type, PyObject *args, PyObject *kwds)
    // ...

    if (1 <= PyTuple_GET_SIZE(args) && PyTuple_Check(PyTuple_GET_ITEM(args, 0)))
        return PyCFuncPtr_FromDll(type, args, kwds);

PyCFuncPtr_FromDll has quite a bit of code, but in the end these two lines are the most important:

static PyObject *
PyCFuncPtr_FromDll(PyTypeObject *type, PyObject *args, PyObject *kwds)
    // ...

#ifdef MS_WIN32
    address = FindAddress(handle, name, (PyObject *)type);
    // ...
    address = (PPROC)ctypes_dlsym(handle, name);
    // ...

In Windows it does a GetProcAddress and in linux/mac it does dlsym.

As far as calling function goes, calling the _CFuncPtr effectively calls tp_call field which is PyCFuncPtr_call:

static PyObject *
PyCFuncPtr_call(PyCFuncPtrObject *self, PyObject *inargs, PyObject *kwds)
    // ...
    callargs = _build_callargs(self, argtypes,
                               inargs, kwds,
                               &outmask, &inoutmask, &numretvals); 
    // ...
    result = _ctypes_callproc(pProc,
#ifdef MS_WIN32
    // ...
    return _build_result(result, callargs,
                         outmask, inoutmask, numretvals);

There are a lot of code in the function above, but it basically does 3 steps - preparing the arguments, making the call, and building the result and propagating the arguments back (for out/inout parameters).

Eventually it uses ffi_call from FFI to make the call.

    if (FFI_OK != ffi_prep_cif(&cif,
                               atypes)) {
                        "ffi_prep_cif failed");
        return -1;

    ffi_call(&cif, (void *)pProc, resmem, avalues);

FFI itself is quite complicated as it needs to understand all calling conventions and for different CPUs as well (for example, procedure calls in amd64 is drastically different in SPARC) - for now just think of it as a way of being able to say “I want to make a CDecl call to this function using these arguments”, without worrying about all the details in the ABI (Application Binary Interface) level.

Structs and metaclasses

Now that we’ve looked at library loading and function loading/calling, let’s take a look at how structure is implemented. Recall how you write a structure:

class VECTOR3(Structure):
    _fields_ = [("x", c_int), ("y", c_int), ("z", c_int)]

Somehow the VECTOR3 class gets the magic x, y, z attributes. How does this work?

The magic is in the PyCStructType metaclass.

Metaclass is a type used to create other types - it is an alternative way of doing subclassing / inheritance in Python, and a very powerful one too. If you understand metaclass you understand Python’s type system. If you are curious, see Primer on metaclasses on a excellent explanation on metaclasses and Understanding Python Metaclasses for a deeper dive. There is also a presentation version as well.

ctypes.Structure is implemented in _ctypes module as Struct_Type, and type of Struct_Type is PyCStructType (PyCStructType_Type object).

    Py_TYPE(&Struct_Type) = &PyCStructType_Type;
    Struct_Type.tp_base = &PyCData_Type;

This makes PyCStructType` a metaclass.

Whenever you are deriving from ctypes.Strucuture like following:

class VECTOR3(Structure):
    _fields_ = [("x", c_int), ("y", c_int), ("z", c_int)]

This effectively becomes:

VECTOR3 = PyCStructType('VECTOR3', (Structure), { 'fields' : [("x", c_int), ("y", c_int), ("z", c_int)]

Note that tp_new of PyCStructType_Type is PyCStructType_new:

PyTypeObject PyCStructType_Type = {
    PyVarObject_HEAD_INIT(NULL, 0)
    "_ctypes.PyCStructType",                            /* tp_name */
    PyCStructType_setattro,                     /* tp_setattro */
    CDataType_methods,                          /* tp_methods */
    PyCStructType_new,                                  /* tp_new */

So this ends up calling PyCStructType_new with those arguments, which retrieves the _fields_ from supplied dictionary, and assign it to _fields_ attribute, triggering PyCStructType_setattro:

static PyObject *
PyCStructType_new(PyTypeObject *type, PyObject *args, PyObject *kwds)
    return StructUnionType_new(type, args, kwds, 1);

static PyObject *
StructUnionType_new(PyTypeObject *type, PyObject *args, PyObject *kwds, int isStruct)
    PyTypeObject *result;
    PyObject *fields;
    StgDictObject *dict;

    result = (PyTypeObject *)PyType_Type.tp_new(type, args, kwds);
    PyDict_Update((PyObject *)dict, result->tp_dict));
    fields = PyDict_GetItemString((PyObject *)dict, "_fields_");
    fields = PyDict_GetItemString((PyObject *)dict, "_fields_");
    PyObject_SetAttrString((PyObject *)result, "_fields_", fields));

tp_setattro catch the _field_ access (from PyObject_SetAttrString call) and update the internal dictionary on the newly created VECTOR3 type:

static int
PyCStructType_setattro(PyObject *self, PyObject *key, PyObject *value)
    /* XXX Should we disallow deleting _fields_? */
    if (-1 == PyType_Type.tp_setattro(self, key, value))
        return -1;

    if (value && PyUnicode_Check(key) &&
        _PyUnicode_EqualToASCIIString(key, "_fields_"))
        return PyCStructUnionType_update_stgdict(self, value, 1);
    return 0;

PyCStructUnionType_update_stgdict mostly traverse the list of fields and create necessary PyCField instances as corresponding attributes. Interestingly, the attribute assignment also triggers setattro, which simply let it through as it only cares about _fields_ access (otherwise this would be an infinite loop). When you are accessing myVector3.x, you are setting/getting PyCField instance, which are descriptor classes that binds to the owner class, which is the structure itself.

PyTypeObject PyCField_Type = {
    PyVarObject_HEAD_INIT(NULL, 0)
    "_ctypes.CField",                                   /* tp_name */
    sizeof(CFieldObject),                       /* tp_basicsize */
    (reprfunc)PyCField_repr,                            /* tp_repr */
    "Structure/Union member",                   /* tp_doc */
    (descrgetfunc)PyCField_get,                 /* tp_descr_get */
    (descrsetfunc)PyCField_set,                 /* tp_descr_set */

PyCField_repr provides the nice output you see here:

>>> VECTOR3.x
<Field type=c_long, ofs=0, size=4>

While PyCField_get/PyCField_set provides access to the field on this structure (myVector3.x) through descriptor class and bindings to the structure instance:

static int
PyCField_set(CFieldObject *self, PyObject *inst, PyObject *value)
    CDataObject *dst;
    char *ptr;
    if (!CDataObject_Check(inst)) {
                        "not a ctype instance");
        return -1;
    dst = (CDataObject *)inst;
    ptr = dst->b_ptr + self->offset;
    if (value == NULL) {
                        "can't delete attribute");
        return -1;
    return PyCData_set(inst, self->proto, self->setfunc, value,
                     self->index, self->size, ptr);

In the above function, self is the PyCField instance, inst is VECTOR3 (or whatever structure you have), and value is the new value you are assigning with. Eventually it got set on the pointer to the structure + field offset, basically *(ptr + offset) = value.

But where is that ptr come from?

ctypes.Structure are essentially CDataObject*:

// Fields omitted for clarity 
static PyTypeObject Struct_Type = {
    PyVarObject_HEAD_INIT(NULL, 0)
    sizeof(CDataObject),                        /* tp_basicsize */
    GenericPyCData_new,                         /* tp_new */

A CDataObject looks like this:

struct tagCDataObject {
    char *b_ptr;                /* pointer to memory block */
    int  b_needsfree;           /* need _we_ free the memory? */
    CDataObject *b_base;        /* pointer to base object or NULL */
    Py_ssize_t b_size;          /* size of memory block in bytes */
    Py_ssize_t b_length;        /* number of references we need */
    Py_ssize_t b_index;         /* index of this object into base's
                               b_object list */
    PyObject *b_objects;        /* dictionary of references we need to keep, or Py_None */
    union value b_value;

Just think of it as a generic holder of any value - like VARIANT (if COM is your thing). In particular, b_value field holds the well known simple data values (it’s a union) and b_ptr points to the underlying data if it is a more complex type, like structures.

GenericPyCData_new is fairly straight-forward - it allocates enough memory as described by the internal stgdict dictionary, which you can treat it as physical layout information about its fields and total size, which is calculated when _fields_ get assigned.

static PyObject *
GenericPyCData_new(PyTypeObject *type, PyObject *args, PyObject *kwds)
    CDataObject *obj;
    StgDictObject *dict;

    dict = PyType_stgdict((PyObject *)type);
    if (!dict) {
                        "abstract class");
        return NULL;
    dict->flags |= DICTFLAG_FINAL;

    obj = (CDataObject *)type->tp_alloc(type, 0);
    if (!obj)
        return NULL;

    obj->b_base = NULL;
    obj->b_index = 0;
    obj->b_objects = NULL;
    obj->b_length = dict->length;

    if (-1 == PyCData_MallocBuffer(obj, dict)) {
        return NULL;
    return (PyObject *)obj;

PyCData_MallocBuffer handles two cases - if it is referring to a simple type (like c_int, etc), there is no need to allocate the int dynamically as it fits perfectly well in the b_value union field. Otherwise, it allocates the correct buffer size and assign to b_ptr.

static int PyCData_MallocBuffer(CDataObject *obj, StgDictObject *dict)
    if ((size_t)dict->size <= sizeof(obj->b_value)) {
        /* No need to call malloc, can use the default buffer */
        obj->b_ptr = (char *)&obj->b_value;
        /* The b_needsfree flag does not mean that we actually did
           call PyMem_Malloc to allocate the memory block; instead it
           means we are the *owner* of the memory and are responsible
           for freeing resources associated with the memory.  This is
           also the reason that b_needsfree is exposed to Python.
        obj->b_needsfree = 1;
    } else {
        /* In python 2.4, and ctypes 0.9.6, the malloc call took about
           33% of the creation time for c_int().
        obj->b_ptr = (char *)PyMem_Malloc(dict->size);
        if (obj->b_ptr == NULL) {
            return -1;
        obj->b_needsfree = 1;
        memset(obj->b_ptr, 0, dict->size);
    obj->b_size = dict->size;
    return 0;

You might already noticed that the buffer is 0 initialized, and gets freed when it gets finalized. The finalization happens in PyCData_dealloc which does a free if needed.

Next in the series

Originally I was planning to write 3 part series. But then I got interested in PyPy and decided to research into PyPy a bit more. In particular I suspect CFFI might have much better perf (at least in theory) than ctypes with a proper JIT implementation since the arguments “marshaling” can be pretty much “inlined”, but that also requires JIT to be aware of various calling conventions, which is also a pretty daunting task as well (essentially implementing FFI in the JIT).

I’ll update them with links once they become available:

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