This extension API is exposed through the numba.extending
module.
To aid debugging extensions to Numba, it's recommended to set the following environment variable:
NUMBA_CAPTURED_ERRORS="new_style"
this makes it easy to differentiate between errors in implementation and acceptable errors that can take part in e.g. type inference. For more information see NUMBA_CAPTURED_ERRORS
.
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
@overload(len)
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.
The @overload_method
decorator similarly allows implementing a method on a type well-known to Numba.
numba.core.extending.overload_method
The @overload_classmethod
decorator similarly allows implementing a classmethod on a type well-known to Numba.
numba.core.extending.overload_classmethod
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 <low-level-extending>
.
The following example implements the ~numpy.ndarray.nbytes
attribute on Numpy arrays:
@overload_attribute(types.Array, 'nbytes')
def array_nbytes(arr):
def get(arr):
return arr.size * arr.itemsize
return get
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:
@njit
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.
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 ~numba.targets.imputils.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
@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
@njit('void(int64)')
def foo(x):
y = cast_int_to_byte_ptr(x)
foo.inspect_types()
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)
Warning
This is an experimental feature, the API may change without warning.
The numba.experimental.structref
module provides utilities for defining mutable pass-by-reference structures, a StructRef
. The following example demonstrates how to define a basic mutable structure:
../../../numba/tests/doc_examples/test_structref_usage.py
The following demonstrates using the above mutable struct definition:
../../../numba/tests/doc_examples/test_structref_usage.py
Methods and attributes can be attached using @overload_*
as shown in the previous sections.
The following demonstrates the use of @overload_method
to insert a method for instances of MyStructType
:
../../../numba/tests/doc_examples/test_structref_usage.py
numba.experimental.structref
The following function is provided for this purpose.
numba.extending.is_jitted