1.7. Creating C callbacks with @cfunc
¶
Interfacing with some native libraries (for example written in C or C++)
can necessitate writing native callbacks to provide business logic to the
library. The numba.cfunc()
decorator creates a compiled function
callable from foreign C code, using the signature of your choice.
1.7.1. Basic usage¶
The @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
the address
attribute, so that you can pass it to any
foreign C or C++ library. It also exposes a ctypes
callback
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"
1.7.2. Example¶
In this example, we are going to be using the scipy.integrate.quad
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 nb_integrand
object’s 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
1.7.3. Dealing with pointers and array memory¶
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]
The 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.
1.7.4. Signature specification¶
The explicit @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 int8
or float64
)
or pointers to them (for example types.CPointer(types.int8)
).
1.7.5. Compilation options¶
A number of keyword-only arguments can be passed to the @cfunc
decorator: nopython
and cache
. Their meaning is similar to those
in the @jit
decorator.