b3fc9b5a0e
### Description Upgrade cutlass to 3.5 to fix build errors using CUDA 12.4 or 12.5 in Windows - [x] Upgrade cutlass to 3.5.0. - [x] Fix flash attention build error with latest cutlass header files and APIs. This fix is provided by @wangyems. - [x] Update efficient attention to use new cutlass fmha interface. - [x] Patch cutlass to fix `hrsqrt` not found error for sm < 53. - [x] Disable TF32 Staged Accumulation to fix blkq4_fp16_gemm_sm80_test build error for cuda 11.8 to 12.3. - [x] Disable TRT 10 deprecate warnings. The following are not included in this PR: * TRT provider replaces the deprecated APIs. * Fix blkq4_fp16_gemm_sm80_test build error for cuda 12.4 or 12.5. This test is not built by default unless you add `--cmake_extra_defines onnxruntime_ENABLE_CUDA_EP_INTERNAL_TESTS=ON` in build command. To integrate to rel-1.18.1: Either bring in other changes (like onnx 1.16.1), or generate manifest and upload a new ONNX Runtime Build Time Deps artifact based on rel-1.18.1. ### Motivation and Context https://github.com/microsoft/onnxruntime/issues/19891 https://github.com/microsoft/onnxruntime/issues/20924 https://github.com/microsoft/onnxruntime/issues/20953 |
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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 |
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| Windows | ||
| Linux | ||
| Mac | ||
| Android | ||
| iOS | ||
| Web | ||
| Other |
Third-party Pipeline Status
| System | Inference | Training |
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| 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.