StableHLO seems like a good candidate for an abstraction layer for a Web ML API. Has the web machine learning working group looked at that yet? I haven't been following what they've been doing for a while.
Also, OpenXLA is one of the external organizations in the Coordination section of the working group charter: https://w3c.github.io/machine-learning-charter/charter.html. We're looking forward to collaborating with WebML folks!
In IREE, we have prototypes targeting Wasm and WebGPU with ahead-of-time compilation, and we'd like to see more hardware exposed as compute devices via Vulkan/WebGPU (possibly leveraging extensions for computations like matrix multiplication).
Also worth pointing out IEEE intends to target wasm, vulkan/spir-v (webgpu's wgsl isn't entirely unrelated but would take work). So if you don't have ml on your system you still have good targetting options. If the web platform gets support, the browser could internally target vulkan as a baseline.
I'm curious how different the training vs inference needs are, and whether these tools can adequately serve both.
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).
Accelerators inside GPU (like Tensorcores) seems like a lot better deal as you can easy utilize it without 4 abstraction layers with only some unknown to us mortals operations support inside. (And my god i hope apple will allow to programmable run ANE or at least put this api inside Metal framework cause right now working with Coreml for anything new is a nightmare and even some old models are broken on new versions of coremltools)