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### Description Previously, we only fused (DQ -> Q) into a QNN Convert if the quantization types differed (e.g., converting uint8 to uint16). This PR always fuses DQ -> Q regardless of the quantization type because a single QNN Convert op is faster than two separate ops. Example fusions: - [CURRENTLY SUPPORTED] Convert uint8 to uint16: - `uint8 -> DQ -> Q -> uint16` becomes `uint8 -> Convert -> uint16` - [CURRENTLY SUPPORTED] Convert uint16 to uint8: - `uint16 -> DQ -> Q -> uint8` becomes `uint16 -> Convert -> uint8` - [NEW] Convert uint8 (zp0, scale0) to uint8 (zp1, scale1): - `uint8(zp0/scale0) -> DQ -> Q -> uint8(zp1/scale1)` becomes `uint8(zp0/scale0) -> Convert -> uint8(zp1/scale1)` - [NEW] Convert uint16 (zp0, scale0) to uint16 (zp1, scale1): - `uint16(zp0/scale0) -> DQ -> Q -> uint16(zp1/scale1)` becomes `uint16(zp0/scale0) -> Convert -> uint16(zp1/scale1)` ### Motivation and Context The Transpose optimizer will normally remove empty DQ->Q sequences if the quantization params are equal. However, for cases in which the quantization params are not equal, QNN EP should convert DQ->Q to a single QNN Convert op for performance. This affects a customer model. |
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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.