e8ba5145ce
* Transpose for DNNL EP Transpose reorders the memory to the right format but has the wrong dimentions and memory::format. So a new memory descriptor is created that points to the reordered memory. However, that memory is in a different location than the output expects. An extra parameter was added to the SetMemory to specify when memory must be copied if it is output from the subgraph. Signed-off-by: George Nash <george.nash@intel.com> * Implementation of Reshape op for dnnl ep Signed-off-by: George Nash <george.nash@intel.com> * Add Pow op to dnnl execution provider This Pow is limited; the exponent must be scaler or a one dimensional tensor e.g. a tensor with only a single element. The exponent must also be a constant initializer since it is only read when the primitive is created. OneDNN does not have any way to change the exponent after the primitive is created. The GraphViewer is now passed into the NodeCapability code since the GraphViewer is needed to find out if an input is a constant initializer. The unit tests for "Pow" did not make the exponent a constant initializer. To help verify the dnnl execution providers Pow function a version of the Pow unit tests was created for the DNNL execution provier that made the exponent a constant initializer. Signed-off-by: George Nash <george.nash@intel.com> * Add LeakyRelu to DNNL execution provider LeakyRelu was added to the dnnl elementwise ops. In the elementwise op the GetAlpha method was modified to take the default value for Alpha as a parameter instead of reading it from a member varable. This felt like it would be less likely to cause programer error. Signed-off-by: George Nash <george.nash@intel.com> * Switch dnnl_code_capability DataTypes from strings to enums Signed-off-by: George Nash <george.nash@intel.com> * Update DnnlSubgraphPrimitive.GetMemory function input This updates the GetMemory member function to take DnnlTensor instead of a string. This was done for two reasons. Every time the function was called it was always done using DnnlTensor.Name() this will reduce the code repition. We never called it using a saved string. This also makes the function inputs more closely match the GetMemoryAndReshape function. Making less differences between member functions. 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 |
|---|---|---|---|
| Windows | |||
| Linux | |||
| Mac | |||
| Android | |||
| iOS | |||
| 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.