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### Description <!-- Describe your changes. --> Pre-packing is a feature, that allows kernels to re-arrange weights data to gain performance at interference time Currently, pre-packed blobs are shared when a cross-session weight sharing is enabled and only for those weights that are marked as shared by the user. Otherwise, data resides on the heap, the kernels own the data which may be duplicated. This change enables pre-packed data to be stored on disk alongside with the external initializers. The pre-packed blobs are memory mapped and are loaded into either the X-session shared container or a new container that shares pre-packed blobs within the session. With the new approach, pre-packed blobs are always owned by the shared container using the existing pre-pack mechanism for sharing. When X-session sharing is enabled, then the external container owns the data. A separate container owned by a root `SessionState` owns and shares the data when X-session sharing is not enabled. To facilitate this new approach, we introduce a new container that works in two modes. When an optimized model is being saved, and pre-packed weights saving is enabled, the new container will record pre-packed blobs and serialize them to disk using existing `ToGraphProtoWithExternalInitializers` function. To externalize the pre-packed weights, we introduce a new session option `kOrtSessionOptionsSavePrePackedConstantInitializers.` Note, that pre-packing should be enabled (default) for this to work. `ToGraphProtoWithExternalInitializers`function is modified to recurse into subgraphs to make sure we properly account for local initializer names. In the second mode, the container would simply hold the pre-packed weights memory-mapped from disk and share them with the kernels. ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> Reduce memory usage by pre-packed initializers and externalize them. |
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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 & Resources
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General Information: onnxruntime.ai
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Usage documentation and tutorials: onnxruntime.ai/docs
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YouTube video tutorials: youtube.com/@ONNXRuntime
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Companion sample repositories:
- ONNX Runtime Inferencing: microsoft/onnxruntime-inference-examples
- ONNX Runtime Training: microsoft/onnxruntime-training-examples
Builtin Pipeline Status
| System | Inference | Training |
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| Windows | ||
| Linux | ||
| Mac | ||
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| iOS | ||
| Web | ||
| Other |
This project is tested with BrowserStack.
Third-party Pipeline Status
| System | Inference | Training |
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| Linux |  |
Releases
The current release and past releases can be found here: https://github.com/microsoft/onnxruntime/releases.
For details on the upcoming release, including release dates, announcements, features, and guidance on submitting feature requests, please visit the release roadmap: https://onnxruntime.ai/roadmap.
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.