wgpu-py
wonnx
wgpu-py | wonnx | |
---|---|---|
5 | 18 | |
371 | 1,493 | |
2.7% | 4.6% | |
8.6 | 6.3 | |
4 days ago | about 1 month ago | |
Python | Rust | |
BSD 2-clause "Simplified" License | GNU General Public License v3.0 or later |
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wgpu-py
- Pygfx/wgpu-py: Next generation GPU API for Python
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I'm working on techno audio-visuals using Ableton & Javascript
If you've a Python background, I might suggest checking out something like https://github.com/pygfx/wgpu-py
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Chrome Ships WebGPU
FYI you can already use webgpu directly in python, see https://github.com/pygfx/wgpu-py for webgpu wrappers and https://github.com/pygfx/pygfx for a more high level graphics library
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I've just started mixing shaders with Pygame and got some great results
This reminds me of another Python GPU project, bringing in WebGPU shaders.
https://github.com/pygfx/wgpu-py
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Unifying the CUDA Python Ecosystem
Somewhat related, I’ve built compute shaders using wgpu-py:
https://github.com/pygfx/wgpu-py
You can define any compute shader you like in Python, with the data types, and it compiles it to SPIRV and runs it.
wonnx
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Intel CEO: 'The entire industry is motivated to eliminate the CUDA market'
The two I know of are IREE and Kompute[1]. I'm not sure how much momentum the latter has, I don't see it referenced much. There's also a growing body of work that uses Vulkan indirectly through WebGPU. This is currently lagging in performance due to lack of subgroups and cooperative matrix mult, but I see that gap closing. There I think wonnx[2] has the most momentum, but I am aware of other efforts.
[1]: https://kompute.cc/
[2]: https://github.com/webonnx/wonnx
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VkFFT: Vulkan/CUDA/Hip/OpenCL/Level Zero/Metal Fast Fourier Transform Library
To a first approximation, Kompute[1] is that. It doesn't seem to be catching on, I'm seeing more buzz around WebGPU solutions, including wonnx[2] and more hand-rolled approaches, and IREE[3], the latter of which has a Vulkan back-end.
[1]: https://kompute.cc/
[2]: https://github.com/webonnx/wonnx
[3]: https://github.com/openxla/iree
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Onnx Runtime: “Cross-Platform Accelerated Machine Learning”
There's also a third-party WebGPU implementation: https://github.com/webonnx/wonnx
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Are there any ML crates that would compile to WASM?
By experimental I meant e.g. using WGPU to run compute shaders like wonnx, which is working fine but only on a very restricted set of devices and browsers.
- WebGPU ONNX inference runtime written in Rust
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PyTorch Primitives in WebGPU for the Browser
https://news.ycombinator.com/item?id=35696031 ... TIL about wonnx: https://github.com/webonnx/wonnx#in-the-browser-using-webgpu...
microsoft/onnxruntime: https://github.com/microsoft/onnxruntime
Apache/arrow has language-portable Tensors for cpp: https://arrow.apache.org/docs/cpp/api/tensor.html and rust: https://docs.rs/arrow/latest/arrow/tensor/struct.Tensor.html and Python: https://arrow.apache.org/docs/python/api/tables.html#tensors https://arrow.apache.org/docs/python/generated/pyarrow.Tenso...
Fwiw it looks like the llama.cpp Tensor is from ggml, for which there are CUDA and OpenCL implementations (but not yet ROCm, or a WebGPU shim for use with emscripten transpilation to WASM): https://github.com/ggerganov/llama.cpp/blob/master/ggml.h
Are the recommendable ways to cast e.g. arrow Tensors to pytorch/tensorflow?
FWIU, Rust has a better compilation to WASM; and that's probably faster than already-compiled-to-JS/ES TensorFlow + WebGPU.
What's a fair benchmark?
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rustformers/llm: Run inference for Large Language Models on CPU, with Rust 🦀🚀🦙
wonnx has done some fantastic work in this regard, so that's where we plan to start once we get there. In terms of general discussion of alternate backends, see this issue.
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I want to talk about WebGPU
> GPU in other ways, such as training ML models and then using them via an inference engine all powered by your local GPU?
Have a look at wonnix https://github.com/webonnx/wonnx
A WebGPU-accelerated ONNX inference run-time written 100% in Rust, ready for native and the web
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Chrome Ships WebGPU
Looking forward to your WebGPU ML runtime! Also, why not contribute back to WONNX? (https://github.com/webonnx/wonnx)
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OpenXLA Is Available Now
You can indeed perform inference using WebGPU (see e.g. [1] for GPU-accelerated inference of ONNX models on WebGPU; I am one of the authors).
The point made above is that WebGPU can only be used for GPU's and not really for other types of 'neural accelerators' (like e.g. the ANE on Apple devices).
[1] https://github.com/webonnx/wonnx
What are some alternatives?
CudaPy - CudaPy is a runtime library that lets Python programmers access NVIDIA's CUDA parallel computation API.
stablehlo - Backward compatible ML compute opset inspired by HLO/MHLO
cudf - cuDF - GPU DataFrame Library
onnx - Open standard for machine learning interoperability
SHA256-WebGPU - Implementation of sha256 in WGSL
tract - Tiny, no-nonsense, self-contained, Tensorflow and ONNX inference
grcuda - Polyglot CUDA integration for the GraalVM
iree - A retargetable MLIR-based machine learning compiler and runtime toolkit.
web-stable-diffusion - Bringing stable diffusion models to web browsers. Everything runs inside the browser with no server support.
burn - Burn is a new comprehensive dynamic Deep Learning Framework built using Rust with extreme flexibility, compute efficiency and portability as its primary goals.
copperhead - Data Parallel Python
blaze - A Rustified OpenCL Experience