JuliaCN / Py2Jl.jl

Python To Julia Transpiler

This project aims at providing a Py2Jl transpiler targeting the compatibility level 1 and 2 mentioned in the section #CPython Compatibility Level, while the methods to support libraries like numpy, scipy or pytorch will be given in the documentation.

This project is in its early stage, and now we have only partly achieved the level 1 compatibility. This means that you can already write performant Julia code using pure Python, but you might not succeed in transforming any existing Python codebase to Julia with this tool.

Usage

python -m Py2Jl input.py output.jl

To run the generated Julia code, you should add the package Py2JlRuntime to your environment (e.g., pkg> dev runtime-support/Py2JlRuntime). The package Py2JlRuntime is included in the runtime-support folder.

CPython Compatibility Level

1. Level 1: trivial transformation for seemingly similar code correspondence. With this level, Python code transpiled to Julia usually does not work the same.

2. Level 2: semantics-driven transformation that respects the Python languages semantics and behaviours. With this level, Pure Python code transpiled to Julia strictly works the same.

3. Level 3: the transpiler respects the original execution model of CPython. such as some unusual functions in the inspect module, sys._getframe. This level is usually forcing the transpiler to use the same or similar object models as the original CPython, which has negative performance implications.

4. Level 4: the transpiler respects the original object memory layout of CPython. This means the transpiler becomes a replication of the original CPython, which is too boring. This level also fully supports C-extensions, but it is not the only approach.