jsource VS tinygrad

The one other example I know that morphs the language to that extent and to the detriment of readability by C programmers is the J interpreter[1,2]. But, once again, nobody (that I’ve read) claims it’s good or clear C. (Good C for those who speak J, maybe; I wouldn’t know.)

For a way to morph C syntax that does make things better, see libmill[3].

[1] https://code.jsoftware.com/wiki/Essays/Incunabulum

[2] https://github.com/jsoftware/jsource/tree/master/jsrc

[3] https://250bpm.com/blog:56/

Oh damn Whitney C representation.

I develop J btw

No, it was rhetorical, because it's obviously (to an APL-family programmer), not bad!

Your cultural prejudice is showing. There are good reasons APL is written the way it is, and this example is simply bringing those benefits to C by writing it in the dense APL style. There are other APL derivatives, like J[1] that are written the same way. These projects are well-maintained. They aren't collapsing under a load of technical debt. The style works. To them, it's clean code.

[1]: https://github.com/jsoftware/jsource

Recently, I wanted to write a simple piece of code in J, but immediately found a bug. I went ahead to fetch the source to see if I can fix it. But, hell no. I couldn't believe my eyes. Is this how someone programs, really? I just can't believe it didn't go through some kind of obfuscator.

Here are some samples, but almost anything in the repository is beyond me:

https://github.com/jsoftware/jsource/blob/master/jsrc/xo.c

You can view the code, but is not open source: https://github.com/jsoftware/jsource/blob/master/license.txt

This is the same dense style used in J.

All-in-all this example doesn't seem too bad. It's clear what happens and is easy to follow. If you wan't to see something remarkably terribly, check out Whitney style. It's used in APL/J/K family interpreters. Keep in mind, financial institutions run that code.

Not sure if I completely understand what "Ryzen AI" does, but Tinygrad for example has some limited support for RDNA3[0]. It isn't quite there yet in matters of performance though, as you can read in the comments of that file.

There's also a small tutorial by AMD on how to use the WMMA intrinsic[1] using AMD's hipcc[2] compiler. Documentation is sparse kinda sparse, but the instruction set is not huge. The RDNA3 ISA guide[3] might also be helpful (and only a fraction of the pages are relevant.)

0. https://github.com/tinygrad/tinygrad/blob/master/extra/gemm/...

1. https://gpuopen.com/learn/wmma_on_rdna3/

2. https://github.com/ROCm/HIPCC

3. https://www.amd.com/content/dam/amd/en/documents/radeon-tech...

This post describes how I added automatic differentiation to Tensorken. Tensorken is my attempt to build a fully featured yet easy-to-understand and hackable implementation of a deep learning library in Rust. It takes inspiration from the likes of PyTorch, Tinygrad, and JAX.

what do you think about tinygrad? I think its a good example of growing and well written, (partially) well documented library with many close to reference implementations

The idea of model architecture making fast hardware design easier is what makes https://github.com/tinygrad/tinygrad so interesting.

🌟 Support on GitHub Website: https://tinygrad.org/

Before we begin, make sure you have TinyGrad and the required dependencies installed. You can find the installation instructions here.

Try to get something like tinygrad[1] running locally, that way you can tweak things a bit run it again and see how it performs. While doing this you'll pick up most of the concepts and get a feeling of how things work. Also, take a look at projects like llama.cpp[2], you don't have to fully understand what's going on here, tho.

You may need some intermediate knowledge of linear algebra and this thing called "data science" nowadays, which is pretty much knowing how to mangle data and visualize it.

Try creating a small model on your own, it doesn't have to be super fancy just make sure it does something you want it to do. And then ... you'll probably could go on your own then.

1: https://github.com/tinygrad/tinygrad

2: https://github.com/ggerganov/llama.cpp