stablehlo
laskin.live
| stablehlo | laskin.live | |
|---|---|---|
| 5 | 2 | |
| 333 | 6 | |
| 4.2% | - | |
| 9.8 | 10.0 | |
| 4 days ago | almost 4 years ago | |
| MLIR | HTML | |
| Apache License 2.0 | MIT License |
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For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
stablehlo
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Nvidia H200 Tensor Core GPU
I am going to paste a cousin comment:
StableHLO[1] is an interesting project that might help AMD here:
> Our goal is to simplify and accelerate ML development by creating more interoperability between various ML frameworks (such as TensorFlow, JAX and PyTorch) and ML compilers (such as XLA and IREE).
From there, their goal would most likely be to work with XLA/OpenXLA teams on XLA[3] and IREE[2] to make RoCM a better backend.
[1] https://github.com/openxla/stablehlo
[2] https://github.com/openxla/iree
[3] https://www.tensorflow.org/xla
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Chrome Ships WebGPU
Also see the recently introduced StableHLO and its serialization format: https://github.com/openxla/stablehlo/blob/main/docs/bytecode...
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OpenXLA Is Available Now
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.
laskin.live
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Chrome Ships WebGPU
For anyone figuring out how to run webgpu on a remote computer (over webrtc) , see this: https://github.com/periferia-labs/laskin.live
Not sure if it works anymore (I made it 3 years ago), but will be interesting to see if there will be similar products for LLMs and so now.
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WebRTC.rs reached an important milestone in connectivity!
Shameless plug but a friend of mine and I combined both WebRTC and WebGPU some time ago: https://github.com/periferia-labs/laskin.live
What are some alternatives?
wonnx - A WebGPU-accelerated ONNX inference run-time written 100% in Rust, ready for native and the web
rivi-loader - Vulkan Compute program loader in Rust
SHA256-WebGPU - Implementation of sha256 in WGSL
wgpu-mm
iree - A retargetable MLIR-based machine learning compiler and runtime toolkit.
SHARK - SHARK - High Performance Machine Learning Distribution
glare-core - C++ code used in various Glare Tech Ltd products
mach - zig game engine & graphics toolkit
pygfx - A python render engine running on wgpu.
burn - Burn is a new comprehensive dynamic Deep Learning Framework built using Rust with extreme flexibility, compute efficiency and portability as its primary goals. [Moved to: https://github.com/Tracel-AI/burn]