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### Description #### Problem 1: Broken Transpose QDQ unit Layout transform's specialized cost function aggressively pushes down transposes with channel-first or channel-last perms. This can lead to a situation where a channel-fist/last Transpose gets stuck after being pushed through an Unsqueeze node that makes the Transpose's perm no longer channel-first/last. At this point, the specialized cost function defers to the default const function, which does not see a need to continue pushing this transpose node. This breaks the QDQ node units for both the Unsqueeze and the Transpose: DQ -> Unsqueeze -> Transpose -> Q. <img width="266" alt="image" src="https://github.com/microsoft/onnxruntime/assets/19691973/82f8432d-ca27-451b-8c36-c8d87b806e30"> The transpose optimizer should insert a Q -> DQ pair between the Unsqueeze and Transpose nodes to fix both QDQ node units: DQ -> Unsqueeze -> Q[new] -> DQ[new] -> Transpose -> Q <img width="198" alt="image" src="https://github.com/microsoft/onnxruntime/assets/19691973/5a584bdf-e5db-4622-b3bb-83c060e09261"> #### Problem 2: Inserted Squeeze/Transpose nodes should be constant folded when possible. The transpose optimizer inserts Squeeze (and Transpose) ops between an initializer and a DQ to counteract the effect of Unsqueezing that initializer if it is consumed by multiple nodes. This results in a graph where the inserted nodes are not in valid node units: Original graph where two Mul nodes share a common initializer input: <img width="456" alt="image" src="https://github.com/microsoft/onnxruntime/assets/19691973/4b9155ae-e32f-41fc-9136-f953b73e92e7"> Resulting graph after transpose optimization without constant folding: <img width="452" alt="image" src="https://github.com/microsoft/onnxruntime/assets/19691973/3c1bfef1-d45f-4d6e-aa19-1c2929eae3f5"> Here, the circled Transpose and Squeeze nodes operate on a quantized integer type but are not in valid QDQ node units. The solution is to run constant folding, which results in: <img width="405" alt="image" src="https://github.com/microsoft/onnxruntime/assets/19691973/aebdb91f-f38f-4583-adec-33e46126365f"> ### Motivation and Context Improve the layout transformation to allow more models to run on EPs that prefer the channel-last layout. --------- Co-authored-by: Scott McKay <skottmckay@gmail.com> |
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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 documentation 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 | ||
| iOS | ||
| Web | ||
| Other |
Third-party Pipeline Status
| System | Inference | Training |
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| 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.