7c98f45928
### Fix layernorm and softmax axis after upstream
For Gather (the slicing is a scalar), the output rank is small than its
inputs.
When we upstream this kind of Gather before softmax or layernorm, we
should also update the axis attribute.
Otherwise, the axis might be out-of-date and incorrect for the updated
rank.
```
File "/opt/conda/envs/ptca/lib/python3.8/site-packages/onnxruntime/training/ortmodule/_fallback.py", line 157, in handle_exception
raise exception
File "/opt/conda/envs/ptca/lib/python3.8/site-packages/onnxruntime/training/ortmodule/_training_manager.py", line 280, in forward
self._build_graph(graph_transformer_config)
File "/opt/conda/envs/ptca/lib/python3.8/site-packages/onnxruntime/training/ortmodule/_logger.py", line 158, in wrapper
result = func(graph_execution_manager, *args, **kwargs)
File "/opt/conda/envs/ptca/lib/python3.8/site-packages/onnxruntime/training/ortmodule/_logger.py", line 273, in wrapper
result = func(graph_execution_manager, *args, **kwargs)
File "/opt/conda/envs/ptca/lib/python3.8/site-packages/onnxruntime/training/ortmodule/_training_manager.py", line 361, in _build_graph
super()._build_graph(graph_transformer_config)
File "/opt/conda/envs/ptca/lib/python3.8/site-packages/onnxruntime/training/ortmodule/_graph_execution_manager.py", line 184, in _build_graph
self._graph_builder.build(config)
RuntimeError: /onnxruntime/orttraining/orttraining/python/orttraining_pybind_state.cc:823 onnxruntime::python::addObjectMethodsForTraining(pybind11::module&, onnxruntime::python::ExecutionProviderRegistrationFn)::<lambda(onnxruntime::training::OrtModuleGraphBuilder*, const onnxruntime::training::TrainingGraphTransformerConfiguration&)> [ONNXRuntimeError] : 1 : FAIL : Node (Softmax_2904) Op (Softmax) [ShapeInferenceError] 'axis' must be in [-3 , 2]. Its actual value is: 3
```
|
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|---|---|---|
| .config | ||
| .devcontainer | ||
| .gdn | ||
| .github | ||
| .pipelines | ||
| .vscode | ||
| cgmanifests | ||
| cmake | ||
| csharp | ||
| dockerfiles | ||
| docs | ||
| include/onnxruntime/core | ||
| java | ||
| js | ||
| objectivec | ||
| onnxruntime | ||
| orttraining | ||
| rust | ||
| samples | ||
| swift/OnnxRuntimeBindingsTests | ||
| tools | ||
| winml | ||
| .clang-format | ||
| .clang-tidy | ||
| .dockerignore | ||
| .gitattributes | ||
| .gitignore | ||
| .gitmodules | ||
| .lintrunner.toml | ||
| build.bat | ||
| build.sh | ||
| CITATION.cff | ||
| CODEOWNERS | ||
| CONTRIBUTING.md | ||
| lgtm.yml | ||
| LICENSE | ||
| NuGet.config | ||
| ort.wprp | ||
| ORT_icon_for_light_bg.png | ||
| Package.swift | ||
| packages.config | ||
| pyproject.toml | ||
| README.md | ||
| requirements-dev.txt | ||
| requirements-doc.txt | ||
| requirements-lintrunner.txt | ||
| requirements-training.txt | ||
| requirements.txt.in | ||
| SECURITY.md | ||
| 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 & Resources
-
General Information: onnxruntime.ai
-
Usage documention and tutorials: onnxruntime.ai/docs
-
YouTube video tutorials: youtube.com/@ONNXRuntime
-
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.