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### Description <!-- Describe your changes. --> Rework some aspects of the transpose optimizer to ensure we have valid QDQ node units when it is done. Conceptually we need to let individual Transpose nodes move through the graph when optimizing. That can invalidate existing QDQ node units or require new ones. We can fix this after inserting new nodes, or when transpose optimization finishes moving Transpose nodes. Fix when inserting new node - TransposeInputs can add an Unsqueeze (to broadcast) and Transpose to a node's inputs - if there was a DQ node providing the input, add a Q -> DQ after inserting the Unsqueeze/Transpose to make a QDQ node unit for the new node. - Unsqueeze/Transpose don't change data, so we can copy the type/scale/zero point from the existing DQ Fixes when transpose optimization completes moving Transpose nodes - Remove empty DQ -> Q pairs if the type/scale/zero point match - Pushing a Transpose through may have resulted in an existing Transpose/Reshape being cancelled and removed leaving an empty QDQ node unit - the Transpose being moved may have started in a QDQ node unit - Transpose that got blocked inside existing QDQ node unit - e.g. if we hit a DQ -> MatMul -> Q node unit the Transpose gets blocked after the DQ - insert a Q -> DQ after the Transpose to put it in a QDQ node unit and repair the original QDQ node unit - Transpose moves past a DQ providing a graph output - insert a Q -> DQ so the Transpose is in a QDQ node unit This replaces the existing phase 2 logic which flipped a DQ -> Transpose to fix a broken QDQ node unit. The new approach should handle more scenarios and hopefully produce a better graph. Additionally the logic to handle updates to shared initializers that feed DQ nodes was simplified (i.e. largely removed). When we update the shared initializer a Squeeze (if broadcast) and Transpose is added between the initializer and the DQ for other usages of it. We only need to check for this pattern in EstimateTransposeValueCost by looking past a DQ node. We do not need to track the individual DQ nodes leading to an updated shared initializer. ### 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. --> Initially to fix QNN issue with non-const input being transpose and the QDQ node units being broken. |
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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 |
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| Windows | ||
| Linux | ||
| Mac | ||
| Android | ||
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Third-party Pipeline Status
| System | Inference | Training |
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| Linux |  |
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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.