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`ScatterElements` in opset 18 has been around for a while. However, the highest opset supporting `ScatterElements` in ORT is 13. This PR implement this op in CUDA EP by replacing `assignment` in the current CDUA kernel with `atomic reduction` (e.g., atomic add, atomic max). A series of fundamental atomic functions (e.g., atomic max for int8_t and half) are implemented in `common.cuh`; the implementation is general enough to cover old CUDA and new CUDA versions. - The core changes are in `cuda/atomic/common.cuh` with very detailed documentation including `bit-wise operation's visualization`. They are also copied to `rocm/atomic/common.cuh` to support AMD GPU. - `/cuda/tensor/gather_elements_impl.cu` contains small changes to call the new atomic functions to support new `reduction` behavior in new `ScatterElements`. - New `ScatterElements` are defined in `rocm_execution_provider.cc` and `cuda_execution_provider.cc`. |
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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 |
|---|---|---|
| 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.