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### Description <!-- Describe your changes. --> Split out the more basic changes from #15552 for easier review. Re-organize to clarify the structure - Separate out generic base functionality from ORT specific components - pass in handlers for internal ORT ops to Optimize - Split out layout transformation from transpose optimization - Separate out level 1 transpose optimizer - Cleanup some naming to try and clarify things like an optimizer vs. general optimization code Most of the changes are from this movement of code. Two implementation changes: - the extended handlers are queried first in GetHandler - allows the extended handlers to override the default behaviour for an ONNX operator - simplify the Optimize function to remove OptimizerMode. - `can_modify_node` is used instead of `mode` and `ignore_assigned_nodes` and a long description of the current usage is added. I don't _think_ that changes the current behavior and hopefully clarifies what happens and when, and makes the base transpose optimizer implementation more generic. ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> Create a cleaner separation to support adding EP specific logic next to cleanly handle where an EP has additional layout sensitive behaviour required (e.g. it's Resize implementation only handles one layout). |
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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 & Resources
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General Information: onnxruntime.ai
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Usage documention and tutorials: onnxruntime.ai/docs
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YouTube video tutorials: youtube.com/@ONNXRuntime
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Companion sample repositories:
- ONNX Runtime Inferencing: microsoft/onnxruntime-inference-examples
- ONNX Runtime Training: microsoft/onnxruntime-training-examples
Builtin Pipeline Status
| System | Inference | Training |
|---|---|---|
| Windows | ||
| Linux | ||
| Mac | ||
| Android | ||
| iOS | ||
| Web | ||
| Other |
Third-party Pipeline Status
| System | Inference | Training |
|---|---|---|
| Linux |  |
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.