[Numpy-discussion] Getting C-function pointers from Python to C

[Nathaniel Smith]

...isn't this an operation that will be performed once per compiled
function? Is the overhead of the easy, robust method (calling ctypes.cast)
actually measurable as compared to, you know, running an optimizing
compiler?

I mean, I doubt there'd be any real problem with adding this extra API to
numpy, but it does seem like there might be higher priority targets :-)
On Apr 10, 2012 1:12 AM, "Travis Oliphant" <teoliphant at gmail.com> wrote:

> Hi all,
>
> Some of you are aware of Numba.   Numba allows you to create the
> equivalent of C-function's dynamically from Python.   One purpose of this
> system is to allow NumPy to take these functions and use them in operations
> like ufuncs, generalized ufuncs, file-reading, fancy-indexing, and so
> forth.  There are actually many use-cases that one can imagine for such
> things.
>
> One question is how do you pass this function pointer to the C-side.    On
> the Python side, Numba allows you to get the raw integer address of the
> equivalent C-function pointer that it just created out of the Python code.
>    One can think of this as a 32- or 64-bit integer that you can cast to a
> C-function pointer.
>
> Now, how should this C-function pointer be passed from Python to NumPy?
> One approach is just to pass it as an integer --- in other words have an
> API in C that accepts an integer as the first argument that the internal
> function interprets as a C-function pointer.
>
> This is essentially what ctypes does when creating a ctypes function
> pointer out of:
>
>  func = ctypes.CFUNCTYPE(restype, *argtypes)(integer)
>
> Of course the problem with this is that you can easily hand it integers
> which don't make sense and which will cause a segfault when control is
> passed to this "function"
>
> We could also piggy-back on-top of Ctypes and assume that a ctypes
> function-pointer object is passed in.   This allows some error-checking at
> least and also has the benefit that one could use ctypes to access a
> c-function library where these functions were defined. I'm leaning towards
> this approach.
>
> Now, the issue is how to get the C-function pointer (that npy_intp
> integer) back and hand it off internally.   Unfortunately, ctypes does not
> make it very easy to get this address (that I can see).    There is no
> ctypes C-API, for example.    There are two potential options:
>
>        1) Create an API for such Ctypes function pointers in NumPy and use
> the ctypes object structure.  If ctypes were to ever change it's object
> structure we would have to adapt this API.
>
>        Something like this is what is envisioned here:
>
>             typedef struct {
>                        PyObject_HEAD
>                        char *b_ptr;
>             } _cfuncptr_object;
>
>        then the function pointer is:
>
>            (*((void **)(((_sp_cfuncptr_object *)(obj))->b_ptr)))
>
>        which could be wrapped-up into a nice little NumPy C-API call like
>
>        void * Npy_ctypes_funcptr(obj)
>
>
>        2) Use the Python API of ctypes to do the same thing.   This has
> the advantage of not needing to mirror the simple _cfuncptr_object
> structure in NumPy but it is *much* slower to get the address.   It
> basically does the equivalent of
>
>        ctypes.cast(obj, ctypes.c_void_p).value
>
>
>        There is working code for this in the ctypes_callback branch of my
> scipy fork on github.
>
>
> I would like to propose two things:
>
>        * creating a Npy_ctypes_funcptr(obj) function in the C-API of NumPy
> and
>        * implement it with the simple pointer dereference above (option #1)
>
>
> Thoughts?
>
> -Travis
>
>
>
>
>
>
>
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