ONNX-MLIR: A Comprehensive Guide
ONNX-MLIR is an exciting project in the field of machine learning compilers. It's designed to transform ONNX graphs, which are used to represent machine learning models, into code that can be executed efficiently on various hardware platforms with minimal runtime support. Here's a detailed overview of what the project entails.
What is ONNX-MLIR?
ONNX-MLIR is a compiler technology that translates ONNX (Open Neural Network Exchange) graphs into executables. It adheres to the ONNX standard and leverages the powerful LLVM/MLIR compiler technology. The main goal is to enable efficient translation of machine learning models into formats that can be readily executed by computers.
Key Contributions of ONNX-MLIR
- ONNX Dialect: ONNX-MLIR introduces an ONNX Dialect that can be integrated into other projects, enhancing model compatibility.
- Compiler Interfaces: The project offers compiler interfaces that convert ONNX graphs into MLIR files, LLVM bytecodes, and C/Java libraries.
- onnx-mlir Driver: This driver facilitates the conversion of ONNX models into different formats.
- Runtime Environments: The project supports various runtime environments, including Python, C, C++, and Java.
Current Support Levels
ONNX-MLIR supports code generation for ONNX operations across various platforms, including generic CPUs and IBM’s Telum integrated AI accelerator. This ensures that models can be effectively deployed on different hardware.
Community Interaction
The project encourages community participation through discussions on the #onnx-mlir-discussion Slack channel. Additionally, GitHub Issues serve as a platform for comments, questions, and bug reports. Weekly informal meetings offer further opportunities for community engagement.
Getting Started with ONNX-MLIR
To use ONNX-MLIR, the recommended approach is through Docker Images and Containers, which simplify setup. Detailed instructions are available for those who prefer to set up ONNX-MLIR directly on Linux, OSX, or Windows systems.
Prerequisites
Some software prerequisites include:
- Python >= 3.8
- GCC >= 6.4
- Protocol Buffers >= 4.21.12
- CMake >= 3.13.4
An exhaustive list of dependencies is available in the requirements.txt file.
Building ONNX-MLIR
ONNX-MLIR can be built on various operating systems. After installation, the onnx-mlir executable is located in the build directory. For guidance on building, testing, and troubleshooting, refer to the relevant documentation.
Using ONNX-MLIR
Using ONNX-MLIR involves specifying options to guide the translation process of ONNX models. Users can choose targets for emission, optimization levels, and input formats. Environment variables can also be leveraged to streamline the compiling process.
Example Usage
A simple example involves converting an ONNX model into an ONNX Dialect, showcasing the transformation capabilities of ONNX-MLIR. More detailed examples are available to demonstrate real-world applications.
Contributing to ONNX-MLIR
Contributions from the community are welcome, and the project adheres to a Code of Conduct. Interested contributors are encouraged to follow the guidelines outlined in the CONTRIBUTING document.
Documentation
Comprehensive documentation is available in the docs sub-directory, providing a wealth of information for users and developers alike.
Related Projects
ONNX-MLIR is involved in related projects like onnx-mlir-serving, which implements a GRPC server to serve compiled models. This project highlights the flexibility and effectiveness of ONNX-MLIR in practical applications.
In summary, ONNX-MLIR is a pioneering project that bridges the gap between model development and deployment, enabling efficient execution of machine learning models across diverse platforms.