148495ebc5
ORT's default topo-order is a reversed DFS algorithm, while the priority-based topo-order is a forward BFS algorithm. It's likely that the default order is better than priority-based order on memory because tensor memory is more likely to be released right after it's consumed. Currently ORTModule uses priority-based order, for some models, it sorts lots of small Ops to the beginning, this introduces big CPU overhead at the beginning (see below screenshot), this PR is to use default order for training. The priority-based order is heavily used for some recompute optimization, so if there is recompute enabled, we will still use priority-based order. This PR also adds an optimization to the default order, which is to move all Shape/Size Ops to right after their parent nodes. This is to make sure the shape and size nodes are executed right after their parents so it's possible the input tensor memory can be released as soon as possible. This is especially important for non-CPU devices or for training case where some gradient graphs use only shape/size of tensors from forward. Profiling result: Before <img width="910" alt="截屏2023-11-13 12 09 02" src="https://github.com/microsoft/onnxruntime/assets/11661208/e54d5ead-274f-4725-923e-521bbcfce752"> After <img width="910" alt="截屏2023-11-13 12 10 44" src="https://github.com/microsoft/onnxruntime/assets/11661208/f50d196d-11ac-43a2-9493-517e4552ffab"> |
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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
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.