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### Description Since Cutlass can be built with CUDA 11.4 (The minimum CUDA version for onnxruntime CUDA build), there is no need to have a flag to disable cutlass. Changes: (1) Reverted https://github.com/microsoft/onnxruntime/pull/18761 (2) remove the condition to build cutlass. (3) Fix a few build errors or warnings during testing CUDA 11.4 build. Note that SM 89 and 90 (including fp8) requires CUDA 11.8 or later. Flash attention and cutlass fused multihead attention will not be built for CUDA < 11.6. It is recommended to use CUDA 11.8 or above to build if you want to support latest GPUs. It is better to include it in 1.17.0 (otherwise, the release branch might encounter build failure with CUDA 11.4). Tests: (1) Build with flash attention and efficient attention off: **passed** (2) Build with CUDA 11.4: **passed** Example build command used in Ubuntu 20.04: ``` export CUDA_HOME=/usr/local/cuda-11.4 export CUDNN_HOME=/usr/lib/x86_64-linux-gnu/ export CUDACXX=/usr/local/cuda-11.4/bin/nvcc sh build.sh --config Release --build_shared_lib --parallel --use_cuda --cuda_version 11.4 \ --cuda_home $CUDA_HOME --cudnn_home $CUDNN_HOME --build_wheel --skip_tests \ --cmake_extra_defines CMAKE_CUDA_ARCHITECTURES=80 \ --disable_types float8 ``` ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> |
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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.