680fac64ed
### Description [QNN EP] Support non-quantized Op on HTP 1. Remove the limitation in GetCapability that always require QDQ node unit group to partition the node on NPU backend. So that we can support non-quantized Slice op with int32 data input on HTP. 2. Enable Where QDQ node unit 3. Separate out the flag is_npu_backend & is_quantized_node to make it clear 4. Separate output QuantizeLinear, DequantizeLinear to QdqOpBuilder to better identify quantized/un-quantized input/output tensor 5. Separate out a TransposeOpBuilder to make it simple for Transpose node processing. Especially for Single Transpose node in QDQ model, for case like Q->Tranpose->DQ, Transpose is not QDQ node unit group, it's single node. But we should treat it as quantized node. Output should has same data type and quantization parameter with input. Another case is to support non-quantized data for Transpose in QDQ model. 6. Remove is_npu_backend flag from OpBuilder interface. Set the backend type in QnnBackendManager, QnnMOdel & QnnModelWrapper, so that OpBuilders can always get it from QnnModelWrapper. 7. Add unit tests for quantized/non-quantized Transpose (int32, float32) on HTP backend |
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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
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| Linux |  |
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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.
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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.