micrograd VS schism

I recently decided to turbo-teach myself basic cuda with a proper project. I really enjoyed Karpathy’s micrograd (https://github.com/karpathy/micrograd), so I extended it with cuda kernels and 2D tensor logic. It’s a bit longer than the original project, but it’s still very readable for anyone wanting to quickly learn about gpu acceleration in practice.

FOr inference, less than 1KLOC of pure, dependency-free C is enough (if you include the tokenizer and command line parsing)[1]. This was a non-obvious fact for me, in principle, you could run a modern LLM 20 years ago with just 1000 lines of code, assuming you're fine with things potentially taking days to run of course.

Training wouldn't be that much harder, Micrograd[2] is 200LOC of pure Python, 1000 lines would probably be enough for training an (extremely slow) LLM. By "extremely slow", I mean that a training run that normally takes hours could probably take dozens of years, but the results would, in principle, be the same.

If you were writing in C instead of Python and used something like Llama CPP's optimization tricks, you could probably get somewhat acceptable training performance in 2 or 3 KLOC. You'd still be off by one or two orders of magnitude when compared to a GPU cluster, but a lot better than naive, loopy Python.

[1] https://github.com/karpathy/llama2.c

[2] https://github.com/karpathy/micrograd

Perhaps they were thinking of https://github.com/karpathy/micrograd

I was (for fun) trying to recreate micrograd in Haskell. The ideia is simple:

Check out this repo I found a few years back when I was looking into understanding pytorch better. It's basically a super tiny autodiff library that only works on scalars. The whole repo is under 200 lines of code, so you can pull up pycharm or whatever and step through the code and see how it all comes together. Or... you know. Just read it, it's not super complicated.

I'm doing an ML apprenticeship [1] these weeks and Karpathy's videos are part of it. We've been deep down into them. I found them excellent. All concepts he illustrates are crystal clear in his mind (even though they are complicated concepts themselves) and that shows in his explanations.

Also, the way he builds up everything is magnificent. Starting from basic python classes, to derivatives and gradient descent, to micrograd [2] and then from a bigram counting model [3] to makemore [4] and nanoGPT [5]

[1]: https://www.foundersandcoders.com/ml

[2]: https://github.com/karpathy/micrograd

[3]: https://github.com/karpathy/randomfun/blob/master/lectures/m...

[4]: https://github.com/karpathy/makemore

[5]: https://github.com/karpathy/nanoGPT

Just published my first crate, rustygrad, a Rust implementation of Andrej Karpathy's micrograd!

I've been trying to reimplement Karpathy's micrograd library in rust as a fun side project.

I don't know why you've been downvoted, I've given you an upvote for linking to an interesting project (even if it's linked in some way to Google). I'd also like to link to the updated GH link: <https://github.com/schism-lang/schism>.

Looks like Schism (https://github.com/schism-lang/schism) got part of the way there, but it unfortunately seems to be dead.

It would be interesting to reboot something like Lush but using Wasm and Scheme with https://github.com/schism-lang/schism then you could use code generation internally be emitting wasm from your schism code and then reloading the entire environment.

I love E! Or at least the problems it is trying to solve. As you know Wasm also has a capabilities model. And it is fairly trivial to persist the Wasm heap, it just an array of bytes. I think Wasm aligns nicely.

Chez is a great Scheme, but it doesn't have a Wasm backend. I find https://github.com/schism-lang/schism very interesting.

As for C programs going crazy, well yeah. I did a thing where I would copy of the body of functions around in memory, it worked on some version of Linux and GCC, but only by accident. I would be much less comfortable doing this kind of circuit bending than modifying Python stack frames. If I were to achieve a similar goal in the future, I'd use TCC, generate C code and compile directly into memory.

Framehacks aren't going to do the same thing, and one should have tests for it regardless. Framehacks get you tail calls, stack scope and a bunch of other nice properties.

Happy Hacking!

There is a WIP unofficial project from developers at Google called Schism [1].

[1] https://github.com/schism-lang/schism