thinc VS tinygrad

Agree, though I wouldn’t call PyTorch a drop-in for NumPy either. CuPy is the drop-in. Excepting some corner cases, you can use the same code for both. Thinc’s ops work with both NumPy and CuPy:

https://github.com/explosion/thinc/blob/master/thinc/backend...

I love those tiny DNN frameworks, some examples that I studied in the past (I still use PyTorch for work related projects) :

thinc.by the creators of spaCy https://github.com/explosion/thinc

thinc - defining neural nets in functional way jax, a new deep learning framework puts emphasis on functions rather than tensors, I've tested it for a couple of applications and it's really cool, you can write stuff like you'd write math expressions in papers using numpy. That speeds up development significantly, and makes code much more readable

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