onnx-mlir
ONNX model compiler technology lowering ONNX graphs to MLIR and LLVM bytecodes
GraphCanon updated 3w · GitHub synced 3w
Decision brief
ONNX-MLIR is optimised for compiling ONNX models to MLIR and LLVM bytecodes, offering cross-platform support and multiple runtime environments.
Good fit when
- For users needing compile-time optimization of ONNX models to improve inference performance in a variety of language runtimes such as C++, Java, and Python
- If the target system is Linux, macOS, or Windows and benefits from an LLVM-based backend for deployment flexibility
Avoid when
- When quick setup and environment management are desired without using prebuilt containers, as setting up prerequisites manually may be challenging
- For teams primarily focused on real-time inference serving with dedicated AI hardware that requires specialized frameworks not covered by ONNX-MLIR's support matrix
Observed Jul 16, 2026 · Source: enrich:decision_facts
Verify the decision
Maintenance and security
Full trust report- Maintenance
- Very active (3d since push)
- As of 3w
- Provenance
- Not a fork · Organization account
- As of 3w
- Security (OSV)
- 3 low (3 low)
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
git clone https://github.com/onnx/onnx-mlirSimilar tools
Same-category neighbours. No typed graph edges are catalogued for this tool yet.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
Provides ONNX Dialect integration, compilers for transforming ONNX models into code using the underlying LLVM/MLIR infrastructure, with support for C++, Java runtime environments, cross-platform compatibility
Capability facts
- Languages
- c++, python
Source: github.language+pyproject.toml · Aug 4, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Aug 4, 2026)
* and a python/C/C++/Java runtime environment.Source link
Tags
README
ONNX-MLIR
This project (https://onnx.ai/onnx-mlir/) provides compiler technology to transform a valid Open Neural Network Exchange (ONNX) graph into code that implements the graph with minimum runtime support. It implements the ONNX standard and is based on the underlying LLVM/MLIR compiler technology.
| System | Build Status | Model Zoo Status |
|---|---|---|
| s390x-Linux | ||
| amd64-Linux | ||
| amd64-Windows | ||
| amd64-macOS | ||
This project contributes:
- an ONNX Dialect that can be integrated in other projects,
- a compiler interfaces that lower ONNX graphs into MLIR files/LLVM bytecodes/C & Java libraries,
- an
onnx-mlirdriver to perform these lowering, - and a python/C/C++/Java runtime environment.
Current levels of support for the code generation of ONNX operations are listed here for a generic CPU and IBM's Telum integrated AI accelerator.
Interacting with the community.
For ongoing discussions, we use an #onnx-mlir-discussion slack channel established under the Linux Foundation AI and Data Workspace.
Join this workspace using this link.
We use GitHub Issues for request for comments, questions, or bug reports. Security-related issues are reported using the channels listed in the SECURITY page.
We hold informal weekly meetings on Tuesdays where we discuss current issues and progress. Meeting agenda, notes, and links (to participate) are found here. Please email alexe@us.ibm.com to request a 15-30 min time slot to discuss a specific topic of interest.
Setting up ONNX-MLIR using Prebuilt Containers
The preferred approach to using and developing ONNX-MLIR is to use Docker Images and Containers, as getting the proper code dependences may be tricky on some systems. Our instructions on using ONNX-MLIR with Dockers are here.
If you intend to develop code, you should look at our workflow document which help you setup your Docker environment in a way that let you contribute code easily.
Setting up ONNX-MLIR directly
ONNX-MLIR runs natively on Linux, OSX, and Windows. Detailed instructions are provided below.
Prerequisites
python >= 3.11
clang >= 18.1.3
protobuf >= 33.5
cmake >= 3.26.0
make >= 4.3 or ninja >= 1.10.2
java >= 21 (optional)
All the PyPi package dependencies and their appropriate versions are captured in requirements.txt.
Look here for help to set up the prerequisite software.
At any point in time, ONNX-MLIR depends on a specific commit of the LLVM project that has been shown to work with the project. Periodically the maintainers need to move to a more recent LLVM level. Among other things, this requires to update the LLVM commit string in clone-mlir.sh. When updating ONNX-MLIR, it is good practice to check that the commit string of the MLIR/LLVM is the same as the one listed in that file. See instructions here when third-party ONNX also need to be updated.
Build
Directions to install MLIR and ONNX-MLIR are dependent on your OS.
- Linux or OSX.
- Windows.
After installation, an onnx-mlir executable should appear in the build/Debug/bin or build/Release/bin directory.
If you have difficulties building, rebuilding, or testing onnx-mlir, check this page for helpful hints.
Using ONNX-MLIR
The usage of onnx-mlir is as such:
OVERV
For agents
This page has a .md twin and JSON over the API.