b011f6fbf6
### Description Follow-up to: https://github.com/microsoft/onnxruntime/pull/21793 - Support looking past a per-axis DQ to do in-place Unsqueeze/Transpose of initializers - Support looking past a per-axis DQ to cancel a Transpose or Squeeze. ### Test models For all test models, the transpose optimizer pushes a Transpose through a Mul's input[0]. The Mul's input[1] is optionally unsqueezed and then transposed. ### I. Test in-place unsqueeze and transpose of per-axis quantized weight Original model has input[1] with shape (3,) <details><summary>click to expand model image</summary> <img src="https://github.com/user-attachments/assets/37b6f60c-77d2-4bd3-8ca2-58dc7c88a304" /> </details> Optimized model has input[1] with shape (1, 3, 1, 1). The initializer was unsqueezed and transposed in-place. <details><summary>click expand model image</summary> <img src="https://github.com/user-attachments/assets/adb72757-a164-400c-bfef-2a05f0e35825" /> </details> ### II. Test canceling existing Squeeze before per-axis DQ Original model has input[1] that is squeezed. <details><summary>click expand model image</summary> <img src="https://github.com/user-attachments/assets/f27e6742-b563-42a9-ad06-bb3178b0ceb8" /> </details> Optimized model unsqueezed and transposed input[1]. The original squeeze was removed due to the unsqueeze, leaving only the Transpose. <details><summary>click expand model image</summary> <img src="https://github.com/user-attachments/assets/e56261d4-eba6-4a9f-847b-dcd33548dd07" /> </details> ### III. Test canceling existing Transpose before per-axis DQ Original model has input[1] that is transposed. <details><summary>click expand model image</summary> <img src="https://github.com/user-attachments/assets/f157e04a-572a-479d-8e3b-cf57954df5c0" /> </details> Optimized model transposed input[1], thus canceling the existing transpose. <details><summary>click expand model image</summary> <img src="https://github.com/user-attachments/assets/63d742ce-3762-4ab2-bdb0-1b507886da9d" /> </details> ### IV. Test QDQ fix-up of Transpose/Unsqueeze for per-axis quantization Original model has input[1] that can be broadcasted. <details><summary>click expand model image</summary> <img src="https://github.com/user-attachments/assets/96c0092c-22ec-486d-882e-e2cb59ffe324" /> </details> The main transpose optimization loop inserts float32 Unsqueeze and Transpose after the DQ. The qdq fix-up pass inserts new per-axis Q/DQ ops after the inserted nodes. <details><summary>click expand model image</summary> <img src="https://github.com/user-attachments/assets/b6f89c11-974d-4b35-922f-11effdf06883" /> </details> ### Motivation and Context Enables the TransposeOptimizer to support more models with per-axis QDQ nodes. Per-axis quantization can improve model accuracy and is used by EPs like QNN. --------- Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.com> |
||
|---|---|---|
| .config | ||
| .devcontainer | ||
| .gdn | ||
| .github | ||
| .pipelines | ||
| .vscode | ||
| cgmanifests | ||
| cmake | ||
| csharp | ||
| dockerfiles | ||
| docs | ||
| include/onnxruntime/core | ||
| java | ||
| js | ||
| objectivec | ||
| onnxruntime | ||
| orttraining | ||
| rust | ||
| samples | ||
| tools | ||
| winml | ||
| .clang-format | ||
| .clang-tidy | ||
| .dockerignore | ||
| .gitattributes | ||
| .gitignore | ||
| .gitmodules | ||
| .lintrunner.toml | ||
| CITATION.cff | ||
| CODEOWNERS | ||
| CONTRIBUTING.md | ||
| LICENSE | ||
| NuGet.config | ||
| ORT_icon_for_light_bg.png | ||
| README.md | ||
| SECURITY.md | ||
| ThirdPartyNotices.txt | ||
| VERSION_NUMBER | ||
| build.bat | ||
| build.sh | ||
| build_arm64x.bat | ||
| lgtm.yml | ||
| ort.wprp | ||
| packages.config | ||
| pyproject.toml | ||
| requirements-dev.txt | ||
| requirements-doc.txt | ||
| requirements-lintrunner.txt | ||
| requirements-training.txt | ||
| requirements.txt | ||
| setup.py | ||
README.md
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
-
General Information: onnxruntime.ai
-
Usage documentation and tutorials: onnxruntime.ai/docs
-
YouTube video tutorials: youtube.com/@ONNXRuntime
-
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.