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* [ROCm] Add FusedConv Op. * Enable ROCm for FusedConvTest * [ROCm] Implement FusedConv Op. with Fusion API The old code path was left as the fallback since some combinations are not supported (e.g., FusedConvTest.Conv2D_Bias_Z_Relu as of ROCM 5.1, where to bias layers are needed). * [ROCM] Suppress duplicated warnings in unsupported Fusion API usage. Know limitation for current MIOpen (verified with ROCM 5.2): Only one bias layer may present in the Fusion Plan. Adding the second bias operation to the Fusion plan will end up with miopenStatusUnsupportedOp. In this case the fallback code path will be taken to complete required FusedConv operation. However, previously this failure was not detected and cached, and applications that create multiple FusedConv Ops with both z and bias will keep printing error messages, which is annoying to end users while this message is mainly for developers. This commit will let it print the first error message as a reminder, and skip the Fusion API code path in following calls if both z and bias present. (Note: the skipping applies to all newly created FusedConv Ops). * [ROCM] Add cache mechanism for FusedConv Op. Now the operator with the same configuration will share the same Fusion Plan object, and the creation result will also be cached. Two benefits: 1. No duplicated Fusion plan creation, which is a presumably very costly process. 2. Failures due to MIOpen limitations (like z and b cannot present at the same time) will only be triggered once. Know limits: Due to the limitation of MIOpen Interface, the tensor order of the convolution operator can only be guessed. |
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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
General Information: onnxruntime.ai
Usage documention and tutorials: onnxruntime.ai/docs
Companion sample repositories:
- ONNX Runtime Inferencing: microsoft/onnxruntime-inference-examples
- ONNX Runtime Training: microsoft/onnxruntime-training-examples
Build Pipeline Status
| System | CPU | GPU | EPs |
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| Linux | |||
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| WebAssembly |
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.