1.5. Compiling python classes with @jitclass

Note

This is a early version of jitclass support. Not all compiling features are exposed or implemented, yet.

Numba supports code generation for classes via the numba.jitclass() decorator. A class can be marked for optimization using this decorator along with a specification of the types of each field. We call the resulting class object a jitclass. All methods of a jitclass is compiled into nopython functions. The data of a jitclass instance is allocated on the heap as a C-compatible structure so that any compiled functions can have direct access to the underlying data, bypassing the interpreter.

1.5.1. Basic usage

Here’s an example of a jitclass:

import numpy as np
from numba import jitclass          # import the decorator
from numba import int32, float32    # import the types

spec = [
    ('value', int32),               # a simple scalar field
    ('array', float32[:]),          # an array field
]

@jitclass(spec)
class Bag(object):
    def __init__(self, value):
        self.value = value
        self.array = np.zeros(value, dtype=np.float32)

    @property
    def size(self):
        return self.array.size

    def increment(self, val):
        for i in range(self.size):
            self.array[i] = val
        return self.array

(see full example at examples/jitclass.py from the source tree)

In the above example, a spec is provided as a list of 2-tuples. The tuples contain the name of the field and the numba type of the field. Alternatively, user can use a dictionary (an OrderedDict preferrably for stable field ordering), which maps field names to types.

The definition of the class requires at least a __init__ method for initializing each defined fields. Uninitialized fields contains garbage data. Methods and properties (getters and setters only) can be defined. They will be automatically compiled.

1.5.2. Support operations

The following operations of jitclasses work in both the interpreter and numba compiled functions:

• calling the jitclass class object to construct a new instance (e.g. mybag = Bag(123));
• read/write access to attributes and properties (e.g. mybag.value);
• calling methods (e.g. mybag.increment(3));

Using jitclasses in numba compiled function is more efficient. Short methods can be inlined (at the discretion of LLVM inliner). Attributes access are simply reading from a C structure. Using jitclasses from the intpreter has the same overhead of calling any numba compiled function from the interpreter. Arguments and return values must be unboxed or boxed between python objects and native representation. Values encapsulated by a jitclass does not get boxed into python object when the jitclass instance is handed to the interpreter. It is during attribute access to the field values that they are boxed.

1.5.3. Limitations

• A jitclass class object is treated as a function (the constructor) inside a numba compiled function.
• isinstance() only works in the interpreter.
• Manipulating jitclass instances in the interpreter is not optimized, yet.
• Support for jitclasses are available on CPU only. (Note: Support for GPU devices is planned for a future release.)

1.5.4. The decorator: @jitclass

numba.jitclass(spec)

A decorator for creating a jitclass.

arguments:

• spec:

  Specifies the types of each field on this class. Must be a dictionary or a sequence. With a dictionary, use collections.OrderedDict for stable ordering. With a sequence, it must contain 2-tuples of (fieldname, fieldtype).

returns:

A callable that takes a class object, which will be compiled.