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We introduce rulesets that eliminate QDQ nodes of unsupported types and for unsupported quantised operators for the NPU device. This leads to improved performance and accuracy on critical client AI models. Here's a summary of the changes: - Introduces the provider option `enable_qdq_optimizer` which when set to `True` enables stripping of QDQ nodes on the NPU device for models with `QuantizeLinear` and `DequantizeLinear` layers in them. `enable_qdq_optimizer` defaults to `False`. - Always strip out int16/uint16 QDQ layers as these types are not supported by the NPU compiler. - Only supported ops `Conv`, `MatMul`, and `Add` retain QDQ layers around them, specifically identified for optimal inference performance. OpenVINO EP achieves this by iterating through NodeUnits in the QDQ model, and reconstructing the graph only with the required layers. - Added provider APIs to manipulate node units from EP code by @adrianlizarraga - Added capability rule for the Pad operator when it takes DQ layers as input - Fixes from static code analysis tool --------- Co-authored-by: adrianlizarraga <adlizarraga@microsoft.com> Co-authored-by: Preetha Veeramalai <preetha.veeramalai@intel.com> Co-authored-by: sfatimar <sahar.fatima@intel.com> Co-authored-by: saurabhkale17 <saurabh1.kale@intel.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 |
|---|---|---|
| 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.