PyLang -- lightweight implementation of a Python-like language in Javascript for use in node and the browser.
npm install pylangLightweight implementation of a Python-like language in Javascript for use in node and the browser. No dependence on CPython.
A few hundred killobytes compressed!
- GITHUB: https://github.com/sagemathinc/cowasm-python/tree/main/packages/pylang
- NPM: https://www.npmjs.com/package/pylang
Try out the REPL:
``sh`
$ npx pylang
Welcome to PyLang (12/6/2022, 11:53:14 PM) [Node.js v16.16.0 on arm64].
>>> 2 + 3
5
>>> sum(range(10**7))
49999995000000
>>> %time sum(range(10**7))
49999995000000
Wall time: 97ms
History: This is based on RapydScript\-ng, but with many months
of additional work, and very different design goals. RapydScript tried
to be a different language that was inspired by Python, whereas pylang
attempts to be as close to Python as possible.
Some goals:
- Very lightweight: this should build from source in a few seconds, rather than a few hours like pypy. The architecture of pylang is similar to pypy on some level \-\- there is a JIT \(coming from Javascript\), and pylang is an implementation of "the Python language" in Python. I put that in quotes since it's challenging to implement something 100% compatible with the official Python grammar, and of course there's no Python standard library, etc.
- Math friendly: option to create something that feels similar to SageMath with its preparser, i.e., support ^ for exponent, \[a..b\] for making range\(a,b\+1\), and arbitrary precision integer and floating point numerical literals. However, instead of an adhoc preparser, we built this extra functionality into the language parser/AST/generator itself, i.e., we do it properly. Also, each sage\-like piece of functionality can be enabled via an explicit import from __python__, similar to how official Python enables new functionality via imports.
- The main purpose of pylang is as an interactive REPL, Jupyter kernel \(eventually\), and language for writing small scripts and projects that run in the Javascript ecosystem, but use the Python language. Having a language other than Javascript is necessary because Javascript is an _not the best language_ for interactive mathematics computations, e.g., it doesn't have operator overloading, and only has single inheritance. I love Javascript, but only for what Javascript is good for.
- Since pylang will be used for mathematical computations, speed is important. Speed is, of course, also one of the big advantages of Javascript over Python.
The directory ../bench/ has a collection of microbenchmarks which all run in Python3, pypy3, and pylang, so they are useful for comparing the performance of different Python implementations. These range from pystones to tests from mypy, computer language shootout, etc. and many others I found or made. Here's what the numbers are as of Nov 2021. Nothing is run in parallel, and in each case this is result of running [pypy3|python3|pylang] all.py in the bench directory. The timings hardly change if you rerun the benchmarks. We do not make any attempt to compensate for the JIT (e.g., by running a benchmark multiple times and taking the best result) -- we just run all the benchmarks one by one and add up the times.
pypy3: 3474 ms
pylang: 6434 ms
python3.9: 11872 ms
python3.11: 9284 ms
python-wasm 3.11: 23109 ms
pypy3: 2902 ms
pylang: 2960 ms
python3.10: 6200ms
python3.11: 4491 ms
python-wasm 3.11: 12171 ms
It's interesting that pylang and pypy3 are exactly the same speed on M1 overall for these benchmarks. This suggests that pypy3 is less optimized for aarch64, since pypy3 is only about twice as fast as python3.10, but is usually advertised as 4x faster. Anyway, who knows.
Please don't take the above numbers too seriously. It's trivial with any collection of benchmarks to play around with parameters in such a way to skew them to tell a certain tail, which is why some people call this "benchmarketing". That's NOT our goal here! The only point is that we can use microbenchmarks to identify areas for improvement in our implementation.
pypy3: 1659ms
pylang: 4728 ms
python3.8: 6005 ms
Notice that pypy3 is _much_ faster under Linux than MacOS on the exact same hardware. Strangely, pylang is significantly slower under Linux than under MacOS. The node.js versions that are being used are identical, so this is kind of surprising.
The compiler can be modified with some more
mathematics friendly syntax. Right now only the notation [a..b] for ranges and caret for exponentiation \(and^^ for xor\) is implemented. I might implement more, though maybe that's enough.
You can get the same effect in a .py file as follows:
`python`a.py
from __python__ import exponent
print(2^3)
`bash``
$ npx pylang a.py
8
I noticed the Hacker news discussion of Codon, and people felt strongly that it should describe itself as "Python-like" rather than "Python". Their argument for doing so is compelling, so I've done the same here.