6.1. High-level extension API

This extension API is exposed through the numba.extending module.

6.1.1. Implementing functions

The @overload decorator allows you to implement arbitrary functions for use in nopython mode functions. The function decorated with @overload is called at compile-time with the types of the function’s runtime arguments. It should return a callable representing the implementation of the function for the given types. The returned implementation is compiled by Numba as if it were a normal function decorated with @jit. Additional options to @jit can be passed as dictionary using the jit_options argument.

For example, let’s pretend Numba doesn’t support the len() function on tuples yet. Here is how to implement it using @overload:

from numba import types
from numba.extending import overload

def tuple_len(seq):
   if isinstance(seq, types.BaseTuple):
       n = len(seq)
       def len_impl(seq):
           return n
       return len_impl

You might wonder, what happens if len() is called with something else than a tuple? If a function decorated with @overload doesn’t return anything (i.e. returns None), other definitions are tried until one succeeds. Therefore, multiple libraries may overload len() for different types without conflicting with each other.

6.1.2. Implementing methods

The @overload_method decorator similarly allows implementing a method on a type well-known to Numba. The following example implements the take() method on Numpy arrays:

@overload_method(types.Array, 'take')
def array_take(arr, indices):
   if isinstance(indices, types.Array):
       def take_impl(arr, indices):
           n = indices.shape[0]
           res = np.empty(n, arr.dtype)
           for i in range(n):
               res[i] = arr[indices[i]]
           return res
       return take_impl

6.1.3. Implementing attributes

Finally, the @overload_attribute decorator allows implementing a data attribute (or property) on a type. Only reading the attribute is possible; writable attributes are only supported through the low-level API.

The following example implements the nbytes attribute on Numpy arrays:

@overload_attribute(types.Array, 'nbytes')
def array_nbytes(arr):
   def get(arr):
       return arr.size * arr.itemsize
   return get