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**Description**: This PR including following works: 1. provide stream and related synchronization abstractions in onnxruntime. 2. enhance onnxruntime's execution planner / executor / memory arena to support execute multiple streams in parallel. 3. deprecate the parallel executor for cpu. 4. deprecate the Fence mechanism. 5. update the cuda / tensorrt EP to support the stream mechanism, support running different request in different cuda stream. **Motivation and Context** - Why is this change required? currently, the execution plan is just a linear list of those primitives, ort will execute them step by step. For any given graph, ORT will serialize it to a fixed execution order. This sequential execution design simplifies most scenarios, but it has the following limitations: 1. it is difficult to enable inter-node parallelization, we have a half-baked parallel executor but it is very difficult to make it work with GPU. 2. The fence mechanism can work with single gpu stream + cpu thread case, but when extend to multiple stream, it is difficult to manage the cross GPU stream synchronizations. 3. our cuda EP rely on the BFCArena to make the memory management work with the GPU async kernels, but current BFCArena is not aware of the streams, so it doesn't behavior correctly when run with multiple streams. This PR enhance our existing execution plan and executor to support multiple stream execution. we use an unified algorithm to mange both single stream and multiple stream scenarios. This PR mainly focus on the infrastructure support for multiple stream execution, that is said, given a valid stream assignment, onnxruntime can execute it correctly. How to generate a good stream assignment for a given model will be in the future PR. Co-authored-by: Cheng Tang <chenta@microsoft.com@orttrainingdev9.d32nl1ml4oruzj4qz3bqlggovf.px.internal.cloudapp.net> Co-authored-by: Cheng Tang <chenta@microsoft.com> Co-authored-by: RandySheriffH <48490400+RandySheriffH@users.noreply.github.com> Co-authored-by: Randy Shuai <rashuai@microsoft.com> Co-authored-by: cao lei <jslhcl@gmail.com> Co-authored-by: Lei Cao <leca@microsoft.com> |
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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
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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.