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Python-to-WebAssembly compiler using WASM GC

Project description

p2w - Python to WebAssembly Compiler

p2w compiles a substantial subset of Python to WebAssembly, leveraging WASM GC for automatic memory management.

Features

Supported Python Features

  • Data types: integers (arbitrary precision), floats, booleans, strings, bytes, lists, tuples, dicts, sets
  • Control flow: if/elif/else, for/while loops, break/continue, match statements
  • Functions: definitions, default arguments, *args/**kwargs, closures, lambdas, decorators
  • Classes: inheritance, properties, static/class methods, special methods (__init__, __str__, etc.)
  • Comprehensions: list, dict, set, generator expressions
  • Exception handling: try/except/finally, raise, exception chaining
  • Context managers: with statement
  • Generators: yield, generator functions
  • Other: f-strings, type annotations (ignored at runtime), walrus operator, unpacking

JavaScript Interop

p2w provides seamless JavaScript interoperability via the js module:

import js

canvas = js.document.getElementById("chart")
ctx = canvas.getContext("2d")
ctx.fillRect(0, 0, 100, 100)
js.console.log("Hello from Python!")

What Works

Browser Demos

The demos/ directory contains two browser demos:

  • data-dashboard: Bar chart visualization using Canvas API
  • simulation: Physics simulation with real-time rendering

NB: They are not working yet. They were ported from https://github.com/abilian/prescrypt/tree/main/demos but never finished.

Golden Programs

The programs/internal/ directory contains 104 test programs covering the supported Python subset. These serve as both regression tests and documentation of working features, including:

  • Classes with inheritance, properties, and special methods
  • Generators and comprehensions
  • Exception handling with chaining
  • Context managers
  • Match statements
  • F-strings and string operations
  • Collection types and methods

Benchmarks

Two benchmark suites validate correctness and measure performance:

programs/benchmarks/ - Classic benchmarks adapted for p2w:

  • fibonacci, primes, sieve, matmul
  • fannkuch, binarytrees, nbody
  • mandelbrot, spectralnorm, fasta
  • pystone

programs/benchmarks-alioth/ - Benchmarks from the Debian Benchmark Game with GCC baseline comparison:

  • binarytrees, nbody, spectralnorm, mandelbrot, fannkuchredux
  • Includes a runner script for automated comparison against GCC (-O3 -ffast-math)

Installation

# Clone the repository
git clone https://git.sr.ht/~sfermigier/p2w
cd p2w

# Install with uv
uv sync

Usage

Quick Start

Given a Python source file fib.py:

def fib(n):
    a, b = 0, 1
    for _ in range(n):
        a, b = b, a + b
    return a

print(fib(30))

The simplest way to compile and run it:

uv run p2w -r fib.py
# Output: 832040

Step by Step

If you want to inspect or keep the intermediate files:

# 1. Compile Python to WAT (WebAssembly Text format)
uv run p2w fib.py -o fib.wat

# 2. Convert WAT to WASM binary (requires wasm-tools)
wasm-tools parse fib.wat -o fib.wasm

The generated WASM cannot run standalone — it imports host functions for I/O (write_char, write_i32, etc.). The p2w -r flag handles this automatically by generating a Node.js loader that provides these imports and runs the module. For browser execution, see the demos in demos/.

CLI Options

p2w [-h] [-o OUTPUT] [-r] [-v] [-d] [-V] source

  -o, --output FILE   write WAT to file instead of stdout
  -r, --run           compile and run immediately (requires wasm-tools + Node.js)
  -v, --verbose       show compilation details on stderr
  -d, --debug         dump AST and debug info to stderr
  -V, --version       show version

As a Library

from p2w import compile_to_wat

wat_code = compile_to_wat('print("hello")')

Examples

Fibonacci

def fib(n: int) -> int:
    a, b = 0, 1
    for i in range(n):
        a, b = b, a + b
    return a

print(f"fib(30) = {fib(30)}")

Browser Demo

The demos/ directory contains browser examples:

  • data-dashboard: Interactive bar chart visualization
  • simulation: Physics simulation

To run a demo:

cd demos/data-dashboard
make  # Builds app.wasm
# Serve with any HTTP server and open index.html

Development

# Run tests
make test

# Run linting and type checking
make lint

# Format code
make format

# Run tests with coverage
make test-cov

Test Structure

  • tests/a_unit/ - Unit tests
  • tests/b_integration/ - Integration tests
  • tests/c_e2e/ - End-to-end tests

Architecture

p2w follows a 7-phase compilation pipeline:

  1. Parse: Python source → AST (using Python's ast module)
  2. Inline: Small functions inlined at call sites
  3. Analyze: Scope analysis, variable collection, generator detection
  4. Infer types: Forward-flow type inference, escape analysis, native type eligibility
  5. Generate code: AST → WAT via singledispatch visitor pattern
  6. Emit: WAT output combining user code with runtime library (types, builtins, helpers)
  7. Assemble: WAT → WASM (via wasm-tools)

The generated WASM uses GC extensions for automatic memory management. Execution requires a JavaScript host (Node.js or browser) that provides I/O and runtime imports.

See docs/architecture.md for full details including the type system, runtime architecture, calling convention, and JavaScript interop.

Requirements

  • Python 3.12+
  • wasm-tools (for WAT to WASM conversion)
  • Node.js 22+ (for running compiled WASM via p2w -r)
  • For browser execution: any browser with WASM GC support (recent Chrome/Firefox)

Prior Art and References

It's too long at this point to cite every influence and/or alternative. Here are a few that stand out:

License

MIT

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