wonnx
benchmark
wonnx | benchmark | |
---|---|---|
18 | 3 | |
1,493 | 782 | |
4.6% | 2.1% | |
6.3 | 9.8 | |
about 1 month ago | about 10 hours ago | |
Rust | Python | |
GNU General Public License v3.0 or later | BSD 3-clause "New" or "Revised" License |
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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
benchmark
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PyTorch Primitives in WebGPU for the Browser
>What's a fair benchmark?
the absolute golden benchmarks are https://github.com/pytorch/benchmark
- [R] AMD Instinct MI25 | Machine Learning Setup on the Cheap!
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[R] PyTorch | Budget GPU Benchmarking
TorchBench (https://github.com/pytorch/benchmark) is used by PyTorch core developers to test performance across a wide variety of models. I've never used it personally though.
What are some alternatives?
stablehlo - Backward compatible ML compute opset inspired by HLO/MHLO
llama.cpp - LLM inference in C/C++
onnx - Open standard for machine learning interoperability
potatogpt - Pure Typescript, dependency free, ridiculously slow implementation of GPT2 for educational purposes
tract - Tiny, no-nonsense, self-contained, Tensorflow and ONNX inference
opov - Operator Overloading for Typescript with Tagged Template Literals
iree - A retargetable MLIR-based machine learning compiler and runtime toolkit.
shumai - Fast Differentiable Tensor Library in JavaScript and TypeScript with Bun + Flashlight
burn - Burn is a new comprehensive dynamic Deep Learning Framework built using Rust with extreme flexibility, compute efficiency and portability as its primary goals.
requests-wasm-polyfill - Drop-in replacement for the requests library for wasm python
blaze - A Rustified OpenCL Experience
tfjs - A WebGL accelerated JavaScript library for training and deploying ML models.