tinygrad
stable-diffusion.cpp
| tinygrad | stable-diffusion.cpp | |
|---|---|---|
| 17 | 10 | |
| 24,018 | 2,587 | |
| 3.3% | - | |
| 10.0 | 8.8 | |
| 6 days ago | 7 days ago | |
| Python | C++ | |
| MIT License | MIT License |
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tinygrad
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AMD Unveils Ryzen 8000G Series Processors: Zen 4 APUs for Desktop with Ryzen AI
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...
- Tinygrad 0.8.0 Release
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Beyond Backpropagation - Higher Order, Forward and Reverse-mode Automatic Differentiation for Tensorken
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.
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[D] What is a good way to maintain code readability and code quality while scaling up complexity in libraries like Hugging Face?
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
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AMD MI300 Performance – Faster Than H100, but How Much?
The idea of model architecture making fast hardware design easier is what makes https://github.com/tinygrad/tinygrad so interesting.
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💻 7 Open-Source DevTools That Save Time You Didn't Know to Exist ⌛🚀
🌟 Support on GitHub Website: https://tinygrad.org/
- Tinygrad
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How to train an Iris dataset classifier with Tinygrad
Before we begin, make sure you have TinyGrad and the required dependencies installed. You can find the installation instructions here.
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Decomposing Language Models into Understandable Components
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
- Tinygrad 0.7.0
stable-diffusion.cpp
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I've open sourced my Flutter plugin to run on-device LLMs on any platform. TestFlight builds available now.
I did start with integrating SD with this repo: https://github.com/leejet/stable-diffusion.cpp
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Stable Diffusion in pure C/C++
Interesting. It still doesn't seem to be very quick: https://github.com/leejet/stable-diffusion.cpp/issues/6
But don't get me wrong, I look forward to playing with ggml SD and its development.
- StableDiffusion CPP
- Stable-Diffusion.cpp
What are some alternatives?
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
ggml - Tensor library for machine learning
jax - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
stable-diffusion-webui - Stable Diffusion web UI
llama.cpp - LLM inference in C/C++
root - The official repository for ROOT: analyzing, storing and visualizing big data, scientifically
llama - Inference code for Llama models
seed-alchemy - Frontend UI and Backend Server for Stable Diffusion models
openpilot - openpilot is an open source driver assistance system. openpilot performs the functions of Automated Lane Centering and Adaptive Cruise Control for 250+ supported car makes and models.
vit.cpp - Inference Vision Transformer (ViT) in plain C/C++ with ggml
tensorflow_macos - TensorFlow for macOS 11.0+ accelerated using Apple's ML Compute framework.
Vrmac - Vrmac Graphics, a cross-platform graphics library for .NET. Supports 3D, 2D, and accelerated video playback. Works on Windows 10 and Raspberry Pi4.