MAmathisfun123·May 9, 2026
https://github.com/triton-lang/triton
https://github.com/tenstorrent/tt-mlir
https://github.com/onnx/onnx-mlir
https://github.com/openxla/stablehlo
plenty more - just google
In Mojo 1.0 Betaopenxla
stablehlo
MLIR2023–2026
openxla / stablehlo
stablehlo
Backward compatible ML compute opset inspired by HLO/MHLO
5Distinct people
7Mentions
0Explicit recommenders
724GitHub stars
What to know before exploring
What it is
Backward compatible ML compute opset inspired by HLO/MHLO
GitHub
Where it is used
No documented product use has been verified yet.
What people are saying
No recommendation phrase was found in the indexed comments.
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Also in these discussions
- iree-org/iree · linked in 2 of the same discussions
Mentioned in the same Hacker News threads. That is not proof they are substitutes.
This week on Hacker News
Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 3:56:07 PM UTC.
No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.
Mentions over time
HN mentions by year7 dated mentions
Observed mentions: 2023-03-09 17:18:31 UTC to 2026-05-09 06:32:51 UTC.
* An incomplete calendar period is marked with an outlined bar. Its count is not directly comparable with a full period.
Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.
View mention counts and date ranges
| Period | Start (UTC) | End (exclusive, UTC) | Mentions | Submissions | Comments |
|---|---|---|---|---|---|
| 2023 | 2023-01-01 00:00:00 UTC | 2024-01-01 00:00:00 UTC | 5 | 0 | 5 |
| 2024 | 2024-01-01 00:00:00 UTC | 2025-01-01 00:00:00 UTC | 0 | 0 | 0 |
| 2025 | 2025-01-01 00:00:00 UTC | 2026-01-01 00:00:00 UTC | 1 | 0 | 1 |
| 2026 (partial) | 2026-01-01 00:00:00 UTC | 2027-01-01 00:00:00 UTC | 1 | 0 | 1 |
What Hacker News says
7 sources> not things like multiplication
in order to perform that kind of canonicalization/folding/cse you effectively need to embed a whole-ass tensor ops interpreter into your compiler. note this isn't so far-fetched
https://github.com/openxla/stablehlo/blob/main/docs/interpre...
the problem is it needs to be performant as well. so what some people do is jit these canonicalizations using the compiler (yo dawg i heard you like compiling...).
In Tensor evolution: A framework for fast tensor computations using recurrencesStableHLO[1] and IREE[2] are interesting projects that might help AMD here, from [1]:
> 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
In Nvidia H200 Tensor Core GPUStableHLO[1] and IREE[2] are interesting projects that might help AMD here, from [1]:
> 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
In Nvidia H200 Tensor Core GPUAlso see the recently introduced StableHLO and its serialization format: https://github.com/openxla/stablehlo/blob/main/docs/bytecode...
In Chrome ships WebGPUIf 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.
In OpenXLA Is Available NowStableHLO 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 represe
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nt 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.