Interfacing with some native libraries (for example written in C or C++)
can necessitate writing native callbacks to provide business logic to the
numba.cfunc() decorator creates a compiled function
callable from foreign C code, using the signature of your choice.
@cfunc decorator has a similar usage to
@jit, but with an
important difference: passing a single signature is mandatory.
It determines the visible signature of the C callback:
from numba import cfunc @cfunc("float64(float64, float64)") def add(x, y): return x + y
The C function object exposes the address of the compiled C callback as
address attribute, so that you can pass it to any
foreign C or C++ library. It also exposes a
object pointing to that callback; that object is also callable from
Python, making it easy to check the compiled code:
@cfunc("float64(float64, float64)") def add(x, y): return x + y print(add.ctypes(4.0, 5.0)) # prints "9.0"
In this example, we are going to be using the
function. That function accepts either a regular Python callback or
a C callback wrapped in a
ctypes callback object.
Let’s define a pure Python integrand and compile it as a C callback:
>>> import numpy as np >>> from numba import cfunc >>> def integrand(t): return np.exp(-t) / t**2 ...: >>> nb_integrand = cfunc("float64(float64)")(integrand)
We can pass the
ctypes callback to
scipy.integrate.quad and check that the results are the same as with
the pure Python function:
>>> import scipy.integrate as si >>> def do_integrate(func): """ Integrate the given function from 1.0 to +inf. """ return si.quad(func, 1, np.inf) ...: >>> do_integrate(integrand) (0.14849550677592208, 3.8736750296130505e-10) >>> do_integrate(nb_integrand.ctypes) (0.14849550677592208, 3.8736750296130505e-10)
Using the compiled callback, the integration function does not invoke the Python interpreter each time it evaluates the integrand. In our case, the integration is made 18 times faster:
>>> %timeit do_integrate(integrand) 1000 loops, best of 3: 242 µs per loop >>> %timeit do_integrate(nb_integrand.ctypes) 100000 loops, best of 3: 13.5 µs per loop
A less trivial use case of C callbacks involves doing operation on some array of data passed by the caller. As C doesn’t have a high-level abstraction similar to Numpy arrays, the C callback’s signature will pass low-level pointer and size arguments. Nevertheless, the Python code for the callback will expect to exploit the power and expressiveness of Numpy arrays.
In the following example, the C callback is expected to operate on 2-d arrays,
with the signature
void(double *input, double *output, int m, int n).
You can implement such a callback thusly:
from numba import cfunc, types, carray c_sig = types.void(types.CPointer(types.double), types.CPointer(types.double), types.intc, types.intc) @cfunc(c_sig) def my_callback(in_, out, m, n): in_array = carray(in_, (m, n)) out_array = carray(out, (m, n)) for i in range(m): for j in range(n): out_array[i, j] = 2 * in_array[i, j]
numba.carray() function takes as input a data pointer and a shape
and returns an array view of the given shape over that data. The data is
assumed to be laid out in C order. If the data is laid out in Fortran order,
numba.farray() should be used instead.
@cfunc signature can use any Numba types,
but only a subset of them make sense for a C callback. You should
generally limit yourself to scalar types (such as
or pointers to them (for example
A number of keyword-only arguments can be passed to the
cache. Their meaning is similar to those