Translating Cython to Mojo, a first attempt

Summary

Ever since I heard about Mojo I (and presumably most other people) thought it would be a good language to speed up functions to be called from Python. Everyone knows that vanilla Python can be slow, but one of the reasons that Python programs can be reasonably fast in practice is because Python often leans on libraries written in more performant languages, predominantly C/C++, but increasingly also Rust. Until recently, there has been no real way to call Mojo code from Python, but about a month ago (in Max release 25.4) the ability to call Mojo from Python was added as a beta feature. It’s not fully cooked yet, and it will likely still change a lot, but I wanted to give it a look just to get an idea of where things are heading. One specific idea that I had when I heard about Mojo was that Mojo might be a good replacement for Cython and apparently I was not the only one to have had this thought: Convergent thinking on HN The comments are from the HackerNews discussion on Vincent Warmerdam’s blog post titled “Python can run Mojo now” which made it to the front page of HN a while ago. So where can I find a lot of Cython code? Scikit-learn Scikit-learn implements a bunch of machine learning algorithms and related utilities, and makes heavy use of Cython. How hard would it be to translate some of the Cython code in scikit-learn to Mojo? I wanted a piece of code that was relatively simple, both just as I didn’t want to jump into the deep end, but also because there are some restrictions on Mojo functions being called from Python, namely (from the known limitations section of the Mojo/Python interop): Functions taking more than 3 arguments. Currently PyTypeBuilder.add_function() and related function bindings only support Mojo functions that take up to 3 PythonObject arguments: fn(PythonObject, PythonObject, PythonObject). A simple case: dbscan_inner An example I found that satisfies this criteria is the inner loop of DBSCAN that assigns points to clusters. It’s relatively ...