micrograd VS dylint

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.

There is also https://github.com/trailofbits/dylint for writing custom lints.

Apart from clippy (which uses rustc-internal APIs), there are two other projects which can be used to implement lints: rust-analyzer can be extended with more diagnostics, and dylint provides an interface to run custom lints for Rust.

RE: Program is a model

There are some more advanced refactoring tools now available. These tools enable you to write code to detect bad code patterns and even automatically fix them. You can use them to write one-off transformations of code too. Rust has Dylint [1] and C# has Roslyn Analyzers [2]. Facebook has tooling [3] that helps writing CodeMods, enabling authors to generate changes for thousands of files at a time.

The thing I really would like to see is a smarter CI system. Caching of build outputs, so you don't have to rebuild the world from scratch every time. Distributed execution of tests and compilation, so you are not bottle-necked by one machine. Something that keeps track of which tests are flaky and which are broken on master, so you don't have to diagnose spurious build failures. Something that only runs the test that transitively depend on the code you change. Automatic bisecting of errors to the offending commit.

[1] https://github.com/trailofbits/dylint

[2] https://docs.microsoft.com/visualstudio/code-quality/roslyn-...

[3] one example: https://github.com/facebook/jscodeshift

If you're looking for something like clippy but with custom lints, there's also dylint -- it is clippy, but with support for running dynamically loaded lints across multiple versions of Rust.

You might find dylint useful! It's exactly that: a tool to run custom clippy lints.

Dylint. A tool for running Rust lints from dynamic libraries.