2.1. Types and signatures

2.1.1. Rationale

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.

2.1.2. Signatures

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 "f8(i4, i4)" (or the equivalent "float64(int32, int32)") which specifies a function taking two 32-bit integers and returning a double-precision float.

2.1.3. Basic types

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 numba.boolean). Many types are available both as a canonical name and a shorthand alias, following Numpy’s conventions.

2.1.3.1. Numbers

The following table contains the elementary numeric types currently defined by Numba and their aliases.

Type name(s) Shorthand Comments
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
intp pointer-sized 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

2.1.3.2. Arrays

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)

2.1.3.3. Functions

Warning

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 no_cfunc_wrapper=True decorator option.

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)

Here, cfunc compiled functions a and 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 funcs).

Currently, first-class function objects can be Numba cfunc compiled 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
Namespace scoping yes yes yes
Automatic overload yes no no

2.1.3.3.1. 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.

In addition, the WAP object may implement the __call__ method. This is necessary when calling WAP objects from Numba JIT compiled functions in object mode.

As an example, let us call the standard math library function cos within a Numba JIT compiled function. The memory address of cos can be established after loading the math library and using the ctypes package:

>>> 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)

2.1.3.4. Miscellaneous Types

There are some non-numerical types that do not fit into the other categories.

Type name(s) Comments
pyobject generic Python object
voidptr raw pointer, no operations can be performed on it

2.1.4. Advanced types

For more advanced declarations, you have to explicitly call helper functions or classes provided by Numba.

Warning

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.

2.1.4.1. Inference

numba.typeof(value)

Create a Numba type accurately describing the given Python value. ValueError is raised if the value isn’t supported in nopython mode.

>>> numba.typeof(np.empty(3))
array(float64, 1d, C)
>>> numba.typeof((1, 2.0))
(int64, float64)
>>> numba.typeof([0])
reflected list(int64)

2.1.4.2. Numpy scalars

Instead of using typeof(), non-trivial scalars such as structured types can also be constructed programmatically.

numba.from_dtype(dtype)

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)
class numba.types.NPDatetime(unit)

Create a Numba type for Numpy datetimes of the given unit. unit should be a string amongst the codes recognized by Numpy (e.g. Y, M, D, etc.).

class numba.types.NPTimedelta(unit)

Create a Numba type for Numpy timedeltas of the given unit. unit should be a string amongst the codes recognized by Numpy (e.g. Y, M, D, etc.).

See also

Numpy datetime units.

2.1.4.3. Arrays

class numba.types.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: A means any layout, C means C-contiguous and F means Fortran-contiguous.

2.1.4.4. Optional types

class numba.optional(typ)

Create an optional type based on the underlying Numba type typ. The optional type will allow any value of either typ or None.

>>> @jit((optional(intp),))
... def f(x):
...     return x is not None
...
>>> f(0)
True
>>> f(None)
False