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performance-oriented transpiler for Python

Project description

Human-oriented and high-performing transpiler for Python.

package version from PyPI build status from Travis CI build status from AppVeyor test coverage from Codecov license

The main aim of transpyle is to let everyone who can code well enough in Python, benefit from modern high-performing computer hardware without need to reimplement their application in one of traditional efficient languages such as C or Fortran.

Framework design

Framework consists of mainly the following kinds of modules:

  • parser
  • abstract syntax tree (AST) generalizer
  • unparser
  • compiler
  • binder

At least some of the modules are expected to be implemented for each language supported by the framework.

The modules are responsible for transforming the data between the following states:

  • language-specific code
  • language-specific AST
  • extended Python AST
  • compiled binary
  • Python interface for compiled binary

And thus:

  • parser transforms language-specific code into language-specific AST
  • AST generalizer transforms language-specific AST into extended Python AST
  • unparser transforms extended Python AST into language-specific code
  • compiler transforms language-specific code into compiled binary
  • binder transforms compiled binary into Python interface for compiled binary

The intermediate meeting point which effectively allows code to actually be transpiled between languages, is the extended Python AST.


Using Python AST as the intermediate representation, enables the AST to be directly manipulated, and certain performance-oriented transformations can be applied. Current transpiler implementation aims at:

  • inlining selected calls
  • decorating selected loops with compiler-extension pragmas

More optimizations will be introduced in the future.

Some (if not all) of the above optimizations may have very limited (if not no) performance impact in Python, however when C, C++ or Fortran code is generated, the performance gains can be much greater.

Command-line interface

The command-line interface (CLI) of transpyle allows one to translate source code files in supported languages.

API highlights

The API of transpyle allows using it to make your Python code faster.

The most notable part of the API is the transpile decorator, which in it’s most basic form is not very different from Numba’s jit decorator.

import transpyle

def my_function(a: int, b: int) -> int:
    return a + b

Additionally, you can use each of the modules of the transpiler individually, therefore transpyle can support any transformation sequence you are able to express:

import pathlib
import transpyle

path = pathlib.Path('')
code_reader = transpyle.CodeReader()
code = code_reader.read_file(path)

from_language = transpyle.Language.find('Python 3.6')
to_language = transpyle.Language.find('Fortran 95')
translator = transpyle.AutoTranslator(from_language, to_language)
fortran_code = translator.translate(code, path)

As transpyle is under heavy development, the API might change significantly between versions.

Language support

Transpyle intends to support selected subsets of: C, C++, Cython, Fortran, OpenCL and Python.

For each language pair and direction of translation, the set of supported features may differ.

C to Python AST

C-specific AST is created via pycparse, and some of elementary C syntax is transformed into Python AST.

Python AST to C

Not implemented yet.

C++ to Python AST

Parsing declarations, but not definitions (i.e. function signature, not body). And only selected subset of basic types and basic syntax is supported.

Python AST to C++

Only very basic syntax is supported currently.

Cython to Python AST

Not implemented yet.

Python AST to Cython

Not implemented yet.

Fortran to Python AST

Fortran-specific AST is created via Open Fortran Parser, then that AST is translated into Python AST.

Python AST to Fortran

Currently, the Fortran unparser uses special attribute fortran_metadata attached to selected Python AST nodes, and therefore unparsing raw Python AST created directly from ordinary Python file might not work as expected.

The above behaviour will change in the future.

OpenCL to Python AST

Not implemented yet.

Python AST to OpenCL

Not implemented yet.

Python to Python AST

Python 3.6 with whole-line comments outside expressions is fully supported. Presence of end-of-line comments or comments in expressions might result in errors.

Python AST to Python

Python 3.6 with whole-line comments outside expressions is fully supported. Presence of end-of-line comments or comments in expressions might result in errors.


Python 3.5 or later.

Python libraries as specified in requirements.txt.

Building and running tests additionally requires packages listed in dev_requirements.txt.

Support for transpilation from/to specific language requires additional Python packages specified in extras_requirements.json, which can be installed using the pip extras installation formula pip3 install transpyle[extras] where those extras can be one or more of the following:

  • All supported languages: all
  • C: c
  • C++: cpp
  • Cython: cython
  • Fortran: fortran
  • OpenCL: opencl

Therefore to enable support for all languages, execute pip3 install transpyle[all]. Alternatively, to enable support for C++ and Fortran only, execute pip3 install transpyle[cpp,fortran].

Additionally, full support for some languages requires the following software to be installed:

  • C++:
    • a modern C++ compiler – fully tested with GNU’s g++ versions 7 and 8 and partially tested with LLVM’s clang++ version 7
    • SWIG (Simplified Wrapper and Interface Generator) – tested with version 3
  • Fortran:
    • a modern Fortran compiler – fully tested with GNU’s gfortran versions 7 and 8 and partially tested with PGI’s pgfortran version 2018

The core functionality of transpyle is platform-independent. However, as support of some languages depends on presence of additional software, some functionality might be limited/unavailable on selected platforms.

Transpyle is fully tested on Linux, and partially tested on OS X and Windows.



pip3 install transpyle[all]

Docker image

There is a docker image prepared so that you can easily try the transpiler.

First, download and run the docker container (migth require sudo):

docker pull "mbdevpl/transpyle"
docker run -h transmachine -it "mbdevpl/transpyle"

By default, this will download latest more or less stable development build, if you wish to use a specific release, use "mbdevpl/transpyle:version" instead.

Then, in the container:

python3 -m jupyter notebook --ip="$(hostname -i)" --port=8080

Open the shown link in your host’s web browser, navigate to examples.ipynb, and start transpiling!

Project details

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