b77a6cda50
* Implementaiton of Squeeze op for dnnl ep Signed-off-by: George Nash <george.nash@intel.com> * Implementaiton of Unsqueeze op for dnnl ep Tests were added to the unsqueeze_op_test to test Unsqueeze op with a scalar input. The OneDNN (dnnl) ep automatically converts scalars to a one dimentional tensor. For most operations this causes no problems. However, for Unsqueeze the difference between a scalar vs. tensor couldn't be ignored. A IsScalar member function was added to the DnnlSubgrapPrimitive class that will return true if the ORT tensor was a scalar type. IsScalar() is then used inside the Unsqueeze code. updated the squeeze node capability to only accept ConstantInitializer inputs. All unsqueeze op tests that tested opset 13 now run with and without constant initializers. Signed-off-by: George Nash <george.nash@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
General Information: onnxruntime.ai
Usage documention and tutorials: onnxruntime.ai/docs
Companion sample repositories:
- ONNX Runtime Inferencing: microsoft/onnxruntime-inference-examples
- ONNX Runtime Training: microsoft/onnxruntime-training-examples
Build Pipeline Status
| System | CPU | GPU | EPs |
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| Linux | |||
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| WebAssembly |
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.