Skip to main content

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 (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

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 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 wasm-tools)

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

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

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

p2w-0.2.2.tar.gz (196.4 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

p2w-0.2.2-py3-none-any.whl (232.7 kB view details)

Uploaded Python 3

File details

Details for the file p2w-0.2.2.tar.gz.

File metadata

  • Download URL: p2w-0.2.2.tar.gz
  • Upload date:
  • Size: 196.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: uv/0.11.1 {"installer":{"name":"uv","version":"0.11.1","subcommand":["publish"]},"python":null,"implementation":{"name":null,"version":null},"distro":{"name":"macOS","version":null,"id":null,"libc":null},"system":{"name":null,"release":null},"cpu":null,"openssl_version":null,"setuptools_version":null,"rustc_version":null,"ci":null}

File hashes

Hashes for p2w-0.2.2.tar.gz
Algorithm Hash digest
SHA256 7d0aaab59bcbfc4a6d441de6455391a777ee6c0c4ab017f07d525af63914dab5
MD5 d50ab2562c19ca408ad2b8524c166e94
BLAKE2b-256 2d86c7c0f65c46a5efe9e20b953a296b0b6ab2b64a1ae738e8d4f61ffbf99a84

See more details on using hashes here.

File details

Details for the file p2w-0.2.2-py3-none-any.whl.

File metadata

  • Download URL: p2w-0.2.2-py3-none-any.whl
  • Upload date:
  • Size: 232.7 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: uv/0.11.1 {"installer":{"name":"uv","version":"0.11.1","subcommand":["publish"]},"python":null,"implementation":{"name":null,"version":null},"distro":{"name":"macOS","version":null,"id":null,"libc":null},"system":{"name":null,"release":null},"cpu":null,"openssl_version":null,"setuptools_version":null,"rustc_version":null,"ci":null}

File hashes

Hashes for p2w-0.2.2-py3-none-any.whl
Algorithm Hash digest
SHA256 fb3bb471250a797023e7fd723780400980d6d8b0583143089c9e39120c7a0d96
MD5 763145cdba25e51fc1c67aef88d3fbb2
BLAKE2b-256 e19b1b63822a7868536f187b4d8f09385856f91f4d490eff8cba3fccebb9096d

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page