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# Motivation Currently, ORT minimal builds use kernel def hashes to map from nodes to kernels to execute when loading the model. As the kernel def hashes must be known ahead of time, this works for statically registered kernels. This works well for the CPU EP. For this approach to work, the kernel def hashes must also be known at ORT format model conversion time, which means the EP with statically registered kernels must also be enabled then. This is not an issue for the always-available CPU EP. However, we do not want to require that any EP which statically registers kernels is always available too. Consequently, we explore another approach to match nodes to kernels that does not rely on kernel def hashes. An added benefit of this is the possibility of moving away from kernel def hashes completely, which would eliminate the maintenance burden of keeping the hashes stable. # Approach In a full build, ORT uses some information from the ONNX op schema to match a node to a kernel. We want to avoid including the ONNX op schema in a minimal build to reduce binary size. Essentially, we take the necessary information from the ONNX op schema and make it available in a minimal build. We decouple the ONNX op schema from the kernel matching logic. The kernel matching logic instead relies on per-op information which can either be obtained from the ONNX op schema or another source. This per-op information must be available in a minimal build when there are no ONNX op schemas. We put it in the ORT format model. Existing uses of kernel def hashes to look up kernels are replaced with the updated kernel matching logic. We no longer store kernel def hashes in the ORT format model’s session state and runtime optimization representations. We no longer keep the logic to generate and ensure stability of kernel def hashes. |
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ONNX Runtime is a cross-platform inference and training machine-learning accelerator.
ONNX Runtime inference can enable faster customer experiences and lower costs, supporting models from deep learning frameworks such as PyTorch and TensorFlow/Keras as well as classical machine learning libraries such as scikit-learn, LightGBM, XGBoost, etc. ONNX Runtime is compatible with different hardware, drivers, and operating systems, and provides optimal performance by leveraging hardware accelerators where applicable alongside graph optimizations and transforms. Learn more →
ONNX Runtime training can accelerate the model training time on multi-node NVIDIA GPUs for transformer models with a one-line addition for existing PyTorch training scripts. Learn more →
Get Started
General Information: onnxruntime.ai
Usage documention and tutorials: onnxruntime.ai/docs
Companion sample repositories:
- ONNX Runtime Inferencing: microsoft/onnxruntime-inference-examples
- ONNX Runtime Training: microsoft/onnxruntime-training-examples
Build Pipeline Status
| System | CPU | GPU | EPs |
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| Windows | |||
| Linux | |||
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| WebAssembly |
Data/Telemetry
Windows distributions of this project may collect usage data and send it to Microsoft to help improve our products and services. See the privacy statement for more details.
Contributions and Feedback
We welcome contributions! Please see the contribution guidelines.
For feature requests or bug reports, please file a GitHub Issue.
For general discussion or questions, please use GitHub Discussions.
Code of Conduct
This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.
License
This project is licensed under the MIT License.