How to tidy/fix PyCXX's creation of new-style Python extension-class?

Question

I\'ve nearly finished rewriting a C++ Python wrapper (PyCXX).

The original allows old and new style extension classes, but also allows one to derive from the new-style c

Answer 1

PyCXX is not convoluted. It does have two bugs, but they can be easily fixed without requiring significant changes to the code.

When creating a C++ wrapper for the Python API, one encounters a problem. The C++ object model and the Python new-style object model are very different. One fundamental difference is that C++ has a single constructor that both creates and initializes the object. While Python has two stages; tp_new creates the object and performs minimal intialization (or just returns an existing object) and tp_init performs the rest of the initialization.

PEP 253, which you should probably read in its entirety, says:

The difference in responsibilities between the tp_new() slot and the tp_init() slot lies in the invariants they ensure. The tp_new() slot should ensure only the most essential invariants, without which the C code that implements the objects would break. The tp_init() slot should be used for overridable user-specific initializations. Take for example the dictionary type. The implementation has an internal pointer to a hash table which should never be NULL. This invariant is taken care of by the tp_new() slot for dictionaries. The dictionary tp_init() slot, on the other hand, could be used to give the dictionary an initial set of keys and values based on the arguments passed in.

...

You may wonder why the tp_new() slot shouldn't call the tp_init() slot itself. The reason is that in certain circumstances (like support for persistent objects), it is important to be able to create an object of a particular type without initializing it any further than necessary. This may conveniently be done by calling the tp_new() slot without calling tp_init(). It is also possible hat tp_init() is not called, or called more than once -- its operation should be robust even in these anomalous cases.

The entire point of a C++ wrapper is to enable you to write nice C++ code. Say for example that you want your object to have a data member that can only be initialized during its construction. If you create the object during tp_new, then you cannot reinitialize that data member during tp_init. This will probably force you to hold that data member via some kind of a smart pointer and create it during tp_new. This makes the code ugly.

The approach PyCXX takes is to separate object construction into two:

• tp_new creates a dummy object with just a pointer to the C++ object which is created tp_init. This pointer is initially null.

• tp_init allocates and constructs the actual C++ object, then updates the pointer in the dummy object created in tp_new to point to it. If tp_init is called more than once it raises a Python exception.

I personally think that the overhead of this approach for my own applications is too high, but it's a legitimate approach. I have my own C++ wrapper around the Python C/API that does all the initialization in tp_new, which is also flawed. There doesn't appear to be a good solution for that.

Answer 2

Here is a small C example that shows how Python allocates memory for object of classes derived from C types:

typedef struct
{
    PyObject_HEAD
    int dummy[100];
} xxx_obj;

It also needs a type object:

static PyTypeObject xxx_type = 
{
    PyObject_HEAD_INIT(NULL)
};

And a module initialization function that initializes this type:

extern "C"
void init_xxx(void)
{
    PyObject* m;

    xxx_type.tp_name = "_xxx.xxx";
    xxx_type.tp_flags = Py_TPFLAGS_DEFAULT | Py_TPFLAGS_BASETYPE;

    xxx_type.tp_new = tp_new; // IMPORTANT
    xxx_type.tp_basicsize = sizeof(xxx_obj); // IMPORTANT

    if (PyType_Ready(&xxx_type) < 0)
        return;

    m = Py_InitModule3("_xxx", NULL, "");

    Py_INCREF(&xxx_type);
    PyModule_AddObject(m, "xxx", (PyObject *)&xxx_type);
}

What is missing is the implementation of tp_new: The Python docs require that:

The tp_new function should call subtype->tp_alloc(subtype, nitems) to allocate space for the object

So lets do that and add a few printouts.

static
PyObject *tp_new(PyTypeObject *subtype, PyObject *args, PyObject *kwds)
{
    printf("xxx.tp_new():\n\n");

    printf("\t subtype=%s\n", subtype->tp_name);
    printf("\t subtype->tp_base=%s\n", subtype->tp_base->tp_name);
    printf("\t subtype->tp_base->tp_base=%s\n", subtype->tp_base->tp_base->tp_name);

    printf("\n");

    printf("\t subtype->tp_basicsize=%ld\n", subtype->tp_basicsize);
    printf("\t subtype->tp_base->tp_basicsize=%ld\n", subtype->tp_base->tp_basicsize);
    printf("\t subtype->tp_base->tp_base->tp_basicsize=%ld\n", subtype->tp_base->tp_base->tp_basicsize);

    return subtype->tp_alloc(subtype, 0); // IMPORTANT: memory allocation is done here!
}

Now run a very simple Python program to test it. This program creates a new class derived from xxx, and then creates an object of type derived.

import _xxx

class derived(_xxx.xxx):
    def __init__(self):
        super(derived, self).__init__()

d = derived()

To create an object of type derived, Python will call its tp_new, which in turn will call its base class' (xxx) tp_new. This call generates the following output (exact numbers depends on the machine architecture):

xxx.tp_new():

    subtype=derived
    subtype->tp_base=_xxx.xxx
    subtype->tp_base->tp_base=object

    subtype->tp_basicsize=432
    subtype->tp_base->tp_basicsize=416
    subtype->tp_base->tp_base->tp_basicsize=16

The subtype argument to tp_new is the type of the object being created (derived), it derives from our C type (_xxx.xxx), which in turns derives from object. The base object is of size 16, which is just PyObject_HEAD, the xxx type has an additional 400 bytes for its dummy member for a total of 416 bytes and the derived Python class adds additional 16 bytes.

Because subtype->tp_basicsize accounts for the sizes of all three levels of the hierarchy (object, xxx and derived) for a total of 432 bytes, the right amount of memory is being allocated.