ONNX replacement?
Much of this code is not "new" in the sense that much of the OpenXLA effort has been extracting the existing XLA representations and compiler from the TensorFlow codebase so it can be more modularly used by the ecosystem (including PyTorch).
A better frame is TensorFlow exporting its stable representation that many vendors have already built around, more than a "new" standard.
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.
Lots of innovation here. Time for a proper DL compiler.