d9eeb48393
* Disable training code in DNNL LayerNorm code The capability code already does not claim the LayerNorm and SkipLayerNorm that require more than one output. However, building with training enabled was causing issues. The training specific code has been removed even when building with training enabled. Signed-off-by: George Nash <george.nash@intel.com> * Fix for DNNL FusedMatMul op. The bug was in the transpose code. Signed-off-by: Chethan Palangotu Keshava <chethan.palangotu.keshava@intel.com> * Use agreed upon memory format type when runnig Pooling Gradient in dnnl ep The dnnl ep does not currently have a way to pass memory_format information between the forward pooling primitive to the backward pooling primitive. This change explicitly sets the memory_format to use match that of Onnxruntime. For both the forward and backward pooling code. This will prevent using un-matched memory format that could result in an `unimplemented` error from dnnl ep. Signed-off-by: George Nash <george.nash@intel.com> * Update dnnl ep to use OneDNN v2.6 Do not run ReduceInfLogSum on the kDnnlExecutionProvider due to a calculation bug when doing Log or infinity valuse. The fix for this issue will be part of the next OneDNN release. Signed-off-by: George Nash <george.nash@intel.com> * Update PrintMemory function in dnnl ep This modification can be used to enable/disable memory printing for dnnl ep develpers. This is considered a developer only feature and is disabled by default. It must be enabled and code recompiled to use. Even if it is enabled it will not actually print any memory because the developer needs to take the extra step of spefifying the memory that will be printed to the screen. Signed-off-by: George Nash <george.nash@intel.com> * Update binary ops to run on intel GPU when using dnnl ep Binary ops (i.e. Add, Div, Mul, and Sub ) was updated to no longer call GetMemoryAndReshape in the past this would move the memory from CPU to the GPU. This extra call is no longer needed since it is taken care of by the GetMemoryInOrtFormat call. Removing the GetMemoryAndReshape prevented copying the memory to GPU twice. Signed-off-by: George Nash <george.nash@intel.com> Co-authored-by: Chethan Palangotu Keshava <chethan.palangotu.keshava@intel.com> |
||
|---|---|---|
| .config | ||
| .gdn | ||
| .github | ||
| .pipelines | ||
| cgmanifests | ||
| cmake | ||
| csharp | ||
| dockerfiles | ||
| docs | ||
| include/onnxruntime/core | ||
| java | ||
| js | ||
| objectivec | ||
| onnxruntime | ||
| orttraining | ||
| package/rpm | ||
| samples | ||
| server | ||
| tools | ||
| winml | ||
| .clang-format | ||
| .clang-tidy | ||
| .dockerignore | ||
| .flake8 | ||
| .gitattributes | ||
| .gitignore | ||
| .gitmodules | ||
| build.amd64.1411.bat | ||
| build.bat | ||
| build.sh | ||
| CITATION.cff | ||
| CODEOWNERS | ||
| CONTRIBUTING.md | ||
| LICENSE | ||
| NuGet.config | ||
| ort.wprp | ||
| ORT_icon_for_light_bg.png | ||
| packages.config | ||
| README.md | ||
| requirements-dev.txt | ||
| requirements-doc.txt | ||
| requirements-training.txt | ||
| requirements.txt.in | ||
| setup.py | ||
| ThirdPartyNotices.txt | ||
| VERSION_NUMBER | ||
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.