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### Slice op upstream refactor A refactor work for https://github.com/microsoft/onnxruntime/pull/13672. ### Motivation and Context There is a similar optimization opportunity for other operator upstreaming, to reduce compute flops. So refactor the existing code base for making it easier to support other ops. The changes in this PR are mainly about renaming and moving. - Move common logic (from compute_optimizer.h/cc) into upstream_transformer_base.h/cc and shared_utils.h/cc. - For upstream common logic, they are moved into upstream_transformer_base.h/cc - For shared utilities, they are moved to shared_utils.h/cc. - After the move, compute_optimizer.h/cc mainly for upstreaming gather implementation (inheriting upstream_transformer_base.h/cc). Ideally it should be renamed, but for easier review this time, I keep its name. |
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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 documention 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
Build Pipeline Status
| System | Inference | Training |
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| Windows | ||
| Linux | ||
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| Other |
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.