Show HN: Python at the Speed of Rust

Summary

Python is the most popular programming language in the world. It is an extremely simple and accessible language, making it the go-to choice for developers across numerous domains. It is used in everything from introduction to computer science classes; to powering the AI revolution we're all living through.However, Python's convenience comes with two significant drawbacks: First, running an interpreted language results in much slower execution compared to native languages like C or Rust. Second, it is incredibly difficult to embed Python-powered functions (e.g. Numpy, PyTorch) into cross-platform consumer applications (e.g. web apps, mobile).But what if we could compile Python into raw native code?Compiling a Toy FunctionArtificial Intelligence, and particularly Large Language Models (LLMs), rely heavily on matrix multiplications. These matrix operations, at their core, utilize a fundamental operation known as fused multiply-add (FMA):def fma (x, y, z): """ Perform a fused multiply-add. """ return x * y + zHardware vendors like Nvidia provide specialized instructions that perform the FMA in a single step, reducing computational overhead and improving numerical precision. Given that LLMs perform billions of these operations, even minor performance variations can significantly affect overall efficiency.result = fma(x=3, y=-1, z=2) print(result) # -1Let's explore how to compile the fma function, allowing it to run at native speed, cross-platform.Tracing the FunctionWe begin by capturing all operations performed within the function as a computation graph. We call this an Intermediate Representation (IR). This IR graph explicitly represents every operation—arithmetic operations, method calls, and data accesses—making it a powerful abstraction for compilation.To build this graph, we leverage CPython's frame evaluation API to perform Symbolic Tracing. This allows us to introspect Python bytecode execution, capturing each instruction's inputs, operations, and outputs dynamic...