p2w 0.1.0

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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 (somewhat) working browser demos:

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

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

Command Line

# Compile Python to WAT
uv run p2w source.py > output.wat

# Convert WAT to WASM (requires wabt)
wat2wasm output.wat -o output.wasm

As a Library

from p2w import compile_to_wat

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

print(fib(10))
"""

wat_code = compile_to_wat(source)

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 straightforward compilation pipeline:

1. Parse: Python source -> AST (using Python's ast module)
2. Analyze: Scope analysis, type inference
3. Compile: AST -> WAT (WebAssembly Text format)
4. Assemble: WAT -> WASM (via external tools like wat2wasm)

The compiler generates WAT code that uses WASM 3.0 GC features for automatic memory management of Python objects.

Requirements

• Python 3.12+
• wabt (for wat2wasm)
• A WASM runtime with GC support (e.g., recent Chrome/Firefox, wasmtime with GC enabled)

License

MIT