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High-level extension API

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

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.

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

Implementing attributes

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

Importing Cython Functions

The function get_cython_function_address obtains the address of a C function in a Cython extension module. The address can be used to access the C function via a ctypes.CFUNCTYPE() callback, thus allowing use of the C function inside a Numba jitted function. For example, suppose that you have the file foo.pyx:

from libc.math cimport exp

cdef api double myexp(double x):
    return exp(x)

You can access myexp from Numba in the following way:

import ctypes
from numba.extending import get_cython_function_address

addr = get_cython_function_address("foo", "myexp")
functype = ctypes.CFUNCTYPE(ctypes.c_double, ctypes.c_double)
myexp = functype(addr)

The function myexp can now be used inside jitted functions, for example:

def double_myexp(x):
    return 2*myexp(x)

One caveat is that if your function uses Cython’s fused types, then the function’s name will be mangled. To find out the mangled name of your function you can check the extension module’s __pyx_capi__ attribute.

Implementing intrinsics

The @intrinsic decorator is used for marking a function func as typing and implementing the function in nopython mode using the llvmlite IRBuilder API. This is an escape hatch for expert users to build custom LLVM IR that will be inlined into the caller, there is no safety net!

The first argument to func is the typing context. The rest of the arguments corresponds to the type of arguments of the decorated function. These arguments are also used as the formal argument of the decorated function. If func has the signature foo(typing_context, arg0, arg1), the decorated function will have the signature foo(arg0, arg1).

The return values of func should be a 2-tuple of expected type signature, and a code-generation function that will passed to lower_builtin(). For an unsupported operation, return None.

Here is an example that cast any integer to a byte pointer:

from numba import types
from numba.extending import intrinsic

def cast_int_to_byte_ptr(typingctx, src):
    # check for accepted types
    if isinstance(src, types.Integer):
        # create the expected type signature
        result_type = types.CPointer(types.uint8)
        sig = result_type(types.uintp)
        # defines the custom code generation
        def codegen(context, builder, signature, args):
            # llvm IRBuilder code here
            [src] = args
            rtype = signature.return_type
            llrtype = context.get_value_type(rtype)
            return builder.inttoptr(src, llrtype)
        return sig, codegen

it may be used as follows:

from numba import njit

def foo(x):
    y = cast_int_to_byte_ptr(x)


and the output of .inspect_types() demonstrates the cast (note the uint8*):

def foo(x):

    #   x = arg(0, name=x)  :: int64
    #   $0.1 = global(cast_int_to_byte_ptr: <intrinsic cast_int_to_byte_ptr>)  :: Function(<intrinsic cast_int_to_byte_ptr>)
    #   $0.3 = call $0.1(x, func=$0.1, args=[Var(x, check_intrin.py (24))], kws=(), vararg=None)  :: (uint64,) -> uint8*
    #   del x
    #   del $0.1
    #   y = $0.3  :: uint8*
    #   del y
    #   del $0.3
    #   $const0.4 = const(NoneType, None)  :: none
    #   $0.5 = cast(value=$const0.4)  :: none
    #   del $const0.4
    #   return $0.5

    y = cast_int_to_byte_ptr(x)