luajit-remake VS mypy

It's really cool that Haoran Xu copy-and-patch technique is catching on, I remember discovering it through his blog posts on applying these techniques to his own LuaJIT remake project[0][1] (and I probably found those through a post here). I highly recommend them if you're into that sort of thing, BTW. They're incredible deep dives, but he uses the details-element to keep the metaphorical descents into Mariana Trench optional so it doesn't get too overwhelming.

I even had the privilege of congratulating him the 1000th star of the GH repo[2], where he reassured me and others that he's still working on it despite the long pause after the last blog post, and that this mainly has to do with behind-the-scenes rewrites that make no sense to publish in part.

[0] https://sillycross.github.io/2022/11/22/2022-11-22/

[1] https://sillycross.github.io/2023/05/12/2023-05-12/

[2] https://github.com/luajit-remake/luajit-remake/issues/11

This seems like an awesome way of writing faster interpreters – i.e. not in assembly, but in C++ snippets you stitch together with a tool.

I did peek at the deegen tool a bit, and it seems quite large? https://github.com/luajit-remake/luajit-remake/tree/master/d...

I would be interested in an overview of all the analysis it has to do, which as I understand is basically “automated Mike Pall”

FWIW I think this is the hand-written equivalent with LuaJIT’s dynasm tool: https://github.com/LuaJIT/LuaJIT/blob/v2.1/src/vm_x64.dasc (just under 5000 lines)

Also there are several of these files with no apparent sharing, as you would get with deegen:

https://github.com/LuaJIT/LuaJIT/blob/v2.1/src/vm_x86.dasc

https://github.com/LuaJIT/LuaJIT/blob/v2.1/src/vm_ppc.dasc

It's got type annotations and mypy has a discussion about it here as well: https://github.com/python/mypy/issues/1282

There is already an AOT compiler for Python: Nuitka[0]. But I don't think it's much faster.

And then there is mypyc[1] which uses mypy's static type annotations but is only slightly faster.

And various other compilers like Numba and Cython that work with specialized dialects of Python to achieve better results, but then it's not quite Python anymore.

[0] https://nuitka.net/

[1] https://github.com/python/mypy/tree/master/mypyc

In Python there is MyPy that can help with this. https://www.mypy-lang.org/

It's funny you should say this.

Reading this article prompted me to future-proof a program I maintain for fun that deals with time; it had one use of utcnow, which I fixed.

And then I tripped over a runtime type problem in an unrelated area of the code, despite the code being green under "mypy --strict". (and "100% coverage" from tests, except this particular exception only occured in a "# pragma: no-cover" codepath so it wasn't actually covered)

It turns out that because of some core decisions about how datetime objects work, `datetime.date.today() < datetime.datetime.now()` type-checks but gives a TypeError at runtime. Oops. (cause discussed at length in https://github.com/python/mypy/issues/9015 but without action for 3 years)

One solution is apparently to use `datetype` for type annotations (while continuing to use `datetime` objects at runtime): https://github.com/glyph/DateType

PEP 695 is great. I've been using mypy every day at work in last couple years or so with very strict parameters (no any type etc) and I have experience writing real life programs with Rust, Agda, and some Haskell before, so I'm familiar with strict type systems. I'm sure many will disagree with me but these are my very honest opinions as a professional who uses Python types every day:

* Some types are better than no types. I love Python types, and I consider them required. Even if they're not type-checked they're better than no types. If they're type-checked it's even better. If things are typed properly (no any etc) and type-checked that's even better. And so on...

* Having said this, Python's type system as checked by mypy feels like a toy type system. It's very easy to fool it, and you need to be careful so that type-checking actually fails badly formed programs.

* The biggest issue I face are exceptions. Community discussed this many times [1] [2] and the overall consensus is to not check exceptions. I personally disagree as if you have a Python program that's meticulously typed and type-checked exceptions still cause bad states and since Python code uses exceptions liberally, it's pretty easy to accidentally go to a bad state. E.g. in the linked github issue JukkaL (developer) claims checking things like "KeyError" will create too many false positives, I strongly disagree. If a function can realistically raise a "KeyError" the program should be properly written to accept this at some level otherwise something that returns type T but 0.01% of the time raises "KeyError" should actually be typed "Raises[T, KeyError]".

* PEP 695 will help because typing things particularly is very helpful. Often you want to pass bunch of Ts around but since this is impractical some devs resort to passing "dict[str, Any]"s around and thus things type-check but you still get "KeyError" left and right. It's better to have "SomeStructure[T]" types with "T" as your custom data type (whether dataclass, or pydantic, or traditional class) so that type system has more opportunities to reject bad programs.

* Overall, I'm personally very optimistic about the future of types in Python!

[1] https://github.com/python/mypy/issues/1773

# is fixed: https://github.com/python/mypy/issues/12987.

You probably already know but you can add type hints and then check for consistency with https://github.com/python/mypy in python.

Modern Python with things like https://learnpython.com/blog/python-match-case-statement/ + mypy + Ruff for linting https://github.com/astral-sh/ruff can get pretty good results.

I found typed dataclasses (https://docs.python.org/3/library/dataclasses.html) in python using mypy to give me really high confidence when building data representations.