a4bec3d374
### Description This PR modifies the run_dynamo_export function to ensure it mirrors the behavior of run_torchscript_merged_export rather than run_torchscript_separate_export. Additionally, I made adjustments to the main function to ensure that run_dynamo is correctly invoked. ### Motivation and Context The main motivation for this change is to enable successful export of LLaMA-2 and LLaMA-3 models using the Dynamo exporter to ONNX. Previously, the exporter was saving two copies of the weights, which is inefficient. The modified approach ensures that only one copy of the weights is saved, and the model can support both scenarios. These changes enhance the compatibility of the exporter with LLaMA models and subsequently other models and optimize the export process |
||
|---|---|---|
| .config | ||
| .devcontainer | ||
| .gdn | ||
| .github | ||
| .pipelines | ||
| .vscode | ||
| cgmanifests | ||
| cmake | ||
| csharp | ||
| dockerfiles | ||
| docs | ||
| include/onnxruntime/core | ||
| java | ||
| js | ||
| objectivec | ||
| onnxruntime | ||
| orttraining | ||
| rust | ||
| samples | ||
| tools | ||
| winml | ||
| .clang-format | ||
| .clang-tidy | ||
| .dockerignore | ||
| .gitattributes | ||
| .gitignore | ||
| .gitmodules | ||
| .lintrunner.toml | ||
| build.bat | ||
| build.sh | ||
| build_arm64x.bat | ||
| CITATION.cff | ||
| CODEOWNERS | ||
| CONTRIBUTING.md | ||
| lgtm.yml | ||
| LICENSE | ||
| NuGet.config | ||
| ort.wprp | ||
| ORT_icon_for_light_bg.png | ||
| packages.config | ||
| pyproject.toml | ||
| README.md | ||
| requirements-dev.txt | ||
| requirements-doc.txt | ||
| requirements-lintrunner.txt | ||
| requirements-training.txt | ||
| requirements.txt | ||
| 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 documentation 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.