A short tour of Transonic public API

Transonic supports both ahead-of-time and just-in-time compilations. When using the API for AOT compilation, the files need to be “compiled” to get speedup.

Decorator boost and command # transonic def

import h5py
import mpi4py

from transonic import boost

# transonic def myfunc(int, float)

def myfunc(a, b):
    return a * b


Most of this code looks familiar to Pythran users. The differences:

  • One can use (for example) h5py and mpi4py (of course not in the Pythran functions).

  • # transonic def instead of # pythran export.

  • A tiny bit of Python… The decorator @boost replaces the Python function by the compiled function if Transonic has been used to produced the associated Pythran/Cython/Numba file.

With type annotations

The previous example can be rewritten without # transonic def. It is the recommended syntaxes for ahead-of-time compilation:

import numpy as np
import h5py

from transonic import boost

def myfunc(a: float, d: int):
    return a * np.ones(d * [10])


Nice (shorter and clearer than with the Pythran command) but very limited (only simple types and only one signature)… So one can also elegantly define many signatures using Transonic types and/or Pythran types in strings (see these examples and our API to define types (and fused types) in transonic.typing).

Moreover, it is possible to add more signatures with # transonic def commands.

Targetting Cython

Cython needs to know the types of local variables to really speedup the computations. Transonic is able to write fast Cython from such code:

from transonic import boost

@boost(boundscheck=False, wraparound=False)
def mysum(arr: "float[:]"):
    i: int
    n: int = arr.shape[0]
    result: float = 0.0
    for i in range(n):
        result += arr[i]
    return result


When targetting Cython, don’t use multi-signatures and prefer fused types. Cython itself does not support multi-signatures. Since these 2 mechanisms are so different, our Cython backend does not even try to support multi-signatures. You’ll get a warning if you use the Cython backend with multi-signatures.

Just-In-Time compilation

With Transonic, one can use the Ahead-Of-Time compilers Pythran and Cython in a Just-In-Time mode. It is really the easiest way to speedup a function with Pythran, just by adding a decorator! And it also works in notebooks!

import numpy as np

from transonic import jit

def func0(a, b):
    return a + b

def func1(a, b):
    return np.exp(a) * b * func0(a, b)

Note that the @jit decorator takes into account type hints (see the example in the documentation).

Implementation details for just-in-time compilation: A Pythran file is produced for each “JITed” function (function decorated with @jit). The file is compiled at the first call of the function and the compiled version is used as soon as it is ready. The warmup can be quite long but the compiled version is saved and can be reused (without warmup!) by another process.

Define accelerated blocks

Transonic blocks can be used with classes and more generally in functions with lines that cannot be compiled by Pythran.

from transonic import Transonic

ts = Transonic()

class MyClass:


    def func(self, n):
        a, b = self.something_that_cannot_be_pythranized()

        if ts.is_transpiled:
            result = ts.use_block("name_block")
            # transonic block (
            #     float a, b;
            #     int n
            # )

            # transonic block (
            #     complex a, b;
            #     int n
            # )

            result = a**n + b**n

        return self.another_func_that_cannot_be_pythranized(result)

For blocks, we need a little bit more of Python.

  • At import time, we have ts = Transonic(), which detects which Pythran module should be used and imports it. This is done at import time since we want to be very fast at run time.

  • In the function, we define a block with three lines of Python and special Pythran annotations (# transonic block). The 3 lines of Python are used (i) at run time to choose between the two branches (is_transpiled or not) and (ii) at compile time to detect the blocks.

Note that the annotations in the command # transonic block are different (and somehow easier to write) than in the standard command # pythran export.

Blocks can also be defined with type hints!


I’m not satisfied by the syntax for blocks so I (PA) proposed an alternative syntax in issue #6.

Python classes: @boost and @jit for methods

For simple methods only using attributes, we can write:

import numpy as np

from transonic import boost

A = "float[:]"

class MyClass:

    arr0: A
    arr1: A

    def __init__(self, n):
        self.arr0 = np.zeros(n)
        self.arr1 = np.zeros(n)

    def compute(self, alpha: float):
        return (self.arr0 + self.arr1).mean() ** alpha


Calling another method in a boosted method is not yet supported!

More examples on how to use Transonic for Object Oriented Programing are given here.

Make the Pythran/Cython/Numba files and compile the extensions

With transonic command

There is a command-line tool transonic which makes the associated Pythran/Cython/Numba files from a Python file. For example one can run:

# Pythran is the default backend
transonic myfile.py -af "-march=native -DUSE_XSIMD -Ofast"
# Now using Cython
transonic myfile.py -b cython

By default and if the Python compiler is available, the produced files are compiled.

With the Meson Build system

Transonic is compatible with the Meson Build system and there is a --meson option to be used in the meson.build files as shown in the example packages and in Fluidsim).

With setuptools

There is also a function make_backend_files that can be used in a setup.py like this:

from pathlib import Path

from transonic.dist import make_backend_files

here = Path(__file__).parent.absolute()

paths = ["fluidsim/base/time_stepping/pseudo_spect.py"]
make_backend_files([here / path for path in paths])

Note that make_backend_files does not compile the backend files. The compilation has to be done after the call of this function (see for example how it is done in the example packages).