Types and signatures¶
As an optimizing compiler, Numba needs to decide on the type of each variable to generate efficient machine code. Python’s standard types are not precise enough for that, so we had to develop our own fine-grained type system.
You will encounter Numba types mainly when trying to inspect the results of Numba’s type inference, for debugging or educational purposes. However, you need to use types explicitly if compiling code ahead-of-time.
A signature specifies the type of a function. Exactly which kind of signature is allowed depends on the context (AOT or JIT compilation), but signatures always involve some representation of Numba types to specify the concrete types for the function’s arguments and, if required, the function’s return type.
An example function signature would be the string
(or the equivalent
"float64(int32, int32)") which specifies a
function taking two 32-bit integers and returning a double-precision float.
The most basic types can be expressed through simple expressions. The
symbols below refer to attributes of the main
numba module (so if
you read “boolean”, it means that symbol can be accessed as
Many types are available both as a canonical name and a shorthand alias,
following Numpy’s conventions.
The following table contains the elementary numeric types currently defined by Numba and their aliases.
|boolean||b1||represented as a byte|
|uint8, byte||u1||8-bit unsigned byte|
|uint16||u2||16-bit unsigned integer|
|uint32||u4||32-bit unsigned integer|
|uint64||u8||64-bit unsigned integer|
|int8, char||i1||8-bit signed byte|
|int16||i2||16-bit signed integer|
|int32||i4||32-bit signed integer|
|int64||i8||64-bit signed integer|
|intc||–||C int-sized integer|
|uintc||–||C int-sized unsigned integer|
|uintp||–||pointer-sized unsigned integer|
|float32||f4||single-precision floating-point number|
|float64, double||f8||double-precision floating-point number|
|complex64||c8||single-precision complex number|
|complex128||c16||double-precision complex number|
The easy way to declare array types is to subscript an elementary type according to the number of dimensions. For example a 1-dimension single-precision array:
>>> numba.float32[:] array(float32, 1d, A)
or a 3-dimension array of the same underlying type:
>>> numba.float32[:, :, :] array(float32, 3d, A)
This syntax defines array types with no particular layout (producing code
that accepts both non-contiguous and contiguous arrays), but you can
specify a particular contiguity by using the
::1 index either at
the beginning or the end of the index specification:
>>> numba.float32[::1] array(float32, 1d, C) >>> numba.float32[:, :, ::1] array(float32, 3d, C) >>> numba.float32[::1, :, :] array(float32, 3d, F)
The feature of considering functions as first-class type objects is under development.
Functions are often considered as certain transformations of input arguments to output values. Within Numba JIT compiled functions, the functions can also be considered as objects, that is, functions can be passed around as arguments or return values, or used as items in sequences, in addition to being callable.
First-class function support is enabled for all Numba JIT
compiled functions and Numba
cfunc compiled functions except when:
- using a non-CPU compiler,
- the compiled function is a Python generator,
- the compiled function has Omitted arguments,
- or the compiled function returns Optional value.
To disable first-class function support, use
For instance, consider an example where the Numba JIT compiled function applies user-specified functions as a composition to an input argument:
>>> @numba.njit ... def composition(funcs, x): ... r = x ... for f in funcs[::-1]: ... r = f(r) ... return r ... >>> @numba.cfunc("double(double)") ... def a(x): ... return x + 1.0 ... >>> @numba.njit ... def b(x): ... return x * x ... >>> composition((a, b), 0.5), 0.5 ** 2 + 1 (1.25, 1.25) >>> composition((b, a, b, b, a), 0.5), b(a(b(b(a(0.5))))) (36.75390625, 36.75390625)
cfunc compiled functions
b are considered as
first-class function objects because these are passed in to the Numba
JIT compiled function
composition as arguments, that is, the
composition is JIT compiled independently from its argument function
objects (that are collected in the input argument
Currently, first-class function objects can be Numba
functions, JIT compiled functions, and objects that implement the
Wrapper Address Protocol (WAP, see below) with the following restrictions:
|Context||JIT compiled||cfunc compiled||WAP objects|
|Can be used as arguments||yes||yes||yes|
|Can be called||yes||yes||yes|
|Can be used as items||yes*||yes||yes|
|Can be returned||yes||yes||yes|
* at least one of the items in a sequence of first-class function objects must have a precise type.
Wrapper Address Protocol - WAP¶
Wrapper Address Protocol provides an API for making any Python object a first-class function for Numba JIT compiled functions. This assumes that the Python object represents a compiled function that can be called via its memory address (function pointer value) from Numba JIT compiled functions. The so-called WAP objects must define the following two methods:
__wrapper_address__(self) → int¶
Return the memory address of a first-class function. This method is used when a Numba JIT compiled function tries to call the given WAP instance.
signature(self) → numba.typing.Signature¶
Return the signature of the given first-class function. This method is used when passing in the given WAP instance to a Numba JIT compiled function.
As an example, let us call the standard math library function
within a Numba JIT compiled function. The memory address of
be established after loading the math library and using the
>>> import numba, ctypes, ctypes.util, math >>> libm = ctypes.cdll.LoadLibrary(ctypes.util.find_library('m')) >>> class LibMCos(numba.types.WrapperAddressProtocol): ... def __wrapper_address__(self): ... return ctypes.cast(libm.cos, ctypes.c_voidp).value ... def signature(self): ... return numba.float64(numba.float64) ... >>> @numba.njit ... def foo(f, x): ... return f(x) ... >>> foo(LibMCos(), 0.0) 1.0 >>> foo(LibMCos(), 0.5), math.cos(0.5) (0.8775825618903728, 0.8775825618903728)
There are some non-numerical types that do not fit into the other categories.
|pyobject||generic Python object|
|voidptr||raw pointer, no operations can be performed on it|
For more advanced declarations, you have to explicitly call helper functions or classes provided by Numba.
The APIs documented here are not guaranteed to be stable. Unless necessary, it is recommended to let Numba infer argument types by using the signature-less variant of @jit.
Instead of using
typeof(), non-trivial scalars such as
structured types can also be constructed programmatically.
Create a Numba type corresponding to the given Numpy dtype:
>>> struct_dtype = np.dtype([('row', np.float64), ('col', np.float64)]) >>> ty = numba.from_dtype(struct_dtype) >>> ty Record([('row', '<f8'), ('col', '<f8')]) >>> ty[:, :] unaligned array(Record([('row', '<f8'), ('col', '<f8')]), 2d, A)
Create a Numba type for Numpy datetimes of the given unit. unit should be a string amongst the codes recognized by Numpy (e.g.
Array(dtype, ndim, layout)¶
Create an array type. dtype should be a Numba type. ndim is the number of dimensions of the array (a positive integer). layout is a string giving the layout of the array:
Ameans any layout,
Cmeans C-contiguous and
Create an optional type based on the underlying Numba type typ. The optional type will allow any value of either typ or
>>> @jit((optional(intp),)) ... def f(x): ... return x is not None ... >>> f(0) True >>> f(None) False
Create a Numba type corresponding to the given Python type annotation.
TypingErroris raised if the type annotation can’t be mapped to a Numba type. This function is meant to be used at statically compile time to evaluate Python type annotations. For runtime checking of Python objects see
For any numba type,
as_numba_type(nb_type) == nb_type.
>>> numba.extending.as_numba_type(int) int64 >>> import typing # the Python library, not the Numba one >>> numba.extending.as_numba_type(typing.List[float]) ListType[float64] >>> numba.extending.as_numba_type(numba.int32) int32
as_numba_typeis automatically updated to include any
>>> @jitclass ... class Counter: ... x: int ... ... def __init__(self): ... self.x = 0 ... ... def inc(self): ... old_val = self.x ... self.x += 1 ... return old_val ... >>> numba.extending.as_numba_type(Counter) instance.jitclass.Counter#11bad4278<x:int64>
as_numba_typeis only used to infer fields for