micrograd VS llama2.c

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.

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

A bit off topic but ML-guru Mr Karpathy has implemented a state-of-art Llama2 model in a plain C with no dependencies on 3rd party/freeware libraries. See repo.

Related. I built karpathy’s llama2.c (https://github.com/karpathy/llama2.c) without modifications to WASM and run it in the browser. It was a fun exercise to directly compare native vs. Web perf. Getting 80% of native performance on my M1 Macbook Air and haven’t spent anytime optimizing the WASM side.

Demo: https://diegomarcos.com/llama2.c-web/

Code:

Would be cool for there to be a `llama2.f`, similar to https://github.com/karpathy/llama2.c, to demo it's capabilities

This is a fork of https://github.com/karpathy/llama2.c

karpathy's llama2.c is like llama.cpp but it is written in C and the python training code is available in that same repo. llama2.c's goal is to be a elegant single file C implementation of the inference and an elegant python implementation for training.

His goal is for people to understand how llama 2 and LLM's work, so he keeps it simple and sweet. As the project progresses, so will features and performance improvements added.

Currently it can infer baby (small) Story models trained by Karpathy at a fast pace. It can also infer Meta LLAMA 2 7b models, but at a very slow rate such as 1 token per second.

So currently this can be used for learning or as a tech preview.

Our friendly fork tries to make it portable, performant and more usable (bells and whistles) over time. Since we mirror upstream closely, the inference capabilities of our fork is similar but slightly faster if compiled with acceleration. What we try to do different is that we try to make this bootable (not there yet) and portable. Right now you can get binary portablity - use the same run.com on any x86_64 machine running on any OS, it will work (possible due to cosmopolitan toolchain). The other part that works is unikernels - boot this as unikernel in VM's (possible due unikraft unikernel & toolchain).

See our fork currently as a release early and release often toy tech demo. We plan to build it out into a useful product.

In my latest release of ggml.js, I've added support for Karapathy's llama2.c model.

I really enjoyed Anrej Kaparthy's llama2.c project (https://github.com/karpathy/llama2.c), which runs through creating and running a miniature Llama2 architecture model from scratch.

- https://github.com/karpathy/llama2.c

I think there may be some applications in this limited space that are worth looking into. You won’t replicate GPT-anything but it may be possible to solve some nice problems very much more efficiently that one would expect at first.

It does now: https://github.com/karpathy/llama2.c#metas-llama-2-models