burn VS wonnx

I intend to grow this into an open-source project because deep inside, this is ideally how I would like ComfyUI to be. There's still a long journey ahead for building all the custom nodes, which is especially challenging given that the majority of code for AI workflows is written in Python. However, with my hands-on experience with Candle and Burn libraries, I may be able to get pretty close!

The need to build CubeCL came from the Burn deep learning framework (https://github.com/tracel-ai/burn), where we want to easily build algorithms like in CUDA with a real programming language, while also being able to integrate those algorithms inside a compiler at runtime to fuse dynamic graphs.

Since we don't want to rewrite everything multiple times, it also has to be multi-platform and optimal, so the feature set must be per-device, not per-language. I'm not aware of a tool that does that, especially in Rust (which Burn is written in).

Burn is an emerging deep learning framework written in pure Rust that aims to provide a flexible, efficient, and safe environment for building and training neural networks. With its modular design and strong type system, Burn represents a significant step forward in bringing deep learning to the Rust ecosystem.

7. Burn Burn is a dynamic deep-learning framework built with flexibility and efficiency in mind. If you're into AI or machine learning, this framework offers the ability to explore how Rust can power complex neural networks.

You can use libtorch directly via `tch-rs`, and at present I'm porting over to Burn (see https://burn.dev) which appears incredibly promising. My impression is it's in a good place, if of course not close to the ecosystem of Python/C++. At very least I've gotten my nn models training and running without too much difficulty. (I'm moving to Burn for the thread safety - their `Tensor` impl is `Sync` - libtorch doesn't have such a guarantee.)

Burn has Candle as one of its backends, which I understand is also quite popular.

[package] name = "resnet_burn" version = "0.1.0" edition = "2021" [dependencies] burn = { git = "https://github.com/tracel-ai/burn.git", rev = "75cb5b6d5633c1c6092cf5046419da75e7f74b11", features = ["ndarray"] } burn-import = { git = "https://github.com/tracel-ai/burn.git", rev = "75cb5b6d5633c1c6092cf5046419da75e7f74b11" } image = { version = "0.24.7", features = ["png", "jpeg"] }

Great!

For Burn project, we have WebGPU example and I was looking into how we could add automated tests in the browser. Now it seems possible.

Here is the image classification example if you'd like to check out:

https://github.com/tracel-ai/burn/tree/main/examples/image-c...

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

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

There's also a third-party WebGPU implementation: https://github.com/webonnx/wonnx

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.

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?

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.

> 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

Looking forward to your WebGPU ML runtime! Also, why not contribute back to WONNX? (https://github.com/webonnx/wonnx)

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