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### Description Extend GEMM autotuning by including algorithms exposed by a ROCBLAS extension API. ### Motivation and Context Based on our request, the ROCm team has implemented extension APIs in ROCBLAS that provides a list of application GEMM algorithms/implementations for a given input size, along with an API that actually performs the GEMM using the specified implementation/algorithm. We have observed that the ROCBLAS algorithm/implementation selection logic does not always pick the optimal. This PR uses the extension APIs to integrate the exposed ROCBLAS algorithms/implementations into the autotuning framework. The feature is disabled by default (the ROCBlas extension APIs are slated to be released with ROCm 5.5, and are not yet generally available). To enable: build with `--cmake-extra-defines USE_ROCBLAS_EXTENSION_API=1 CMAKE_HIP_FLAGS=-DUSE_ROCBLAS_EXTENSION_API` and then enable tuning in the provider options. Co-authored-by: Abhishek Udupa <abhishek.udupa@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.