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StableHLO has a serialization format which is based on MLIR bytecode. https://github.com/openxla/stablehlo/blob/main/docs/bytecode... goes into details of reading/writing portable artifacts for StableHLO programs and associated compatibility guarantees.
I'd also like to comment on our (StableHLO's) relationship with related work. StableHLO was a natural choice for the OpenXLA project, because a very similar operation set called HLO powers many of its key components. However, I would also like to give a shout out to related opsets in the ML community, including MIL, ONNX, TFLite, TOSA and WebNN.
Bootstrapping from HLO made a lot of sense to get things going, but that's just a starting point. There are many great ideas out there, and we're looking to evolve StableHLO beyond its roots. For example, we want to provide functionality to represent dynamism, quantization and sparsity, and there's so much to learn from related work.
We'd love to collaborate, and from the StableHLO side we can offer production-grade lowerings from TensorFlow, JAX and PyTorch, as well as compatibility with OpenXLA. Some of these connections in the ML ecosystem have already started growing organically, and we're super excited about that.
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wonnx
A WebGPU-accelerated ONNX inference run-time written 100% in Rust, ready for native and the web
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).
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That's a very good question and definitely something of interest. Note, that the compiler is only part of this story (as Mika also mentioned here). With OpenXLA we want to be able to take advantage of the best of what each platform can provide and opsets like StableHLO are meant to provide a portability layer while being expressive enough that targeting specialized hardware efficiently is possible. If you look inside the openxla/iree repo (as well as iree/iree-samples and iree/iree-jax repos, paper Scott cited or sers of IREE like Shark (https://github.com/nod-ai/SHARK#quick-start-for-shark-stable...)) you'll see some example.
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If you mean StableHLO, then it has an MLIR dialect: https://github.com/openxla/stablehlo/blob/main/stablehlo/dia....
In the StableHLO spec, we are talking about this in more abstract terms - "StableHLO opset" - to be able to unambiguously reason about the semantics of StableHLO programs. However, in practice the StableHLO dialect is the primary implementation of the opset at the moment.
I wrote "primary implementation" because e.g. there is also ongoing work on adding StableHLO support to the TFLite flatbuffer schema: https://github.com/tensorflow/tensorflow/blob/master/tensorf.... Having an abstract notion of the StableHLO opset enables us to have a source of truth that all the implementations correspond to.
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