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### Description * Add a cuda provider option `sdpa_kernel` to choose which attention kernel to run for testing purpose. * Allow dump which attention kernel is used per node. * Reserve a flag for cudnn flash attention which will be added soon. #### CUDA provider option sdpa_kernel Instead of setting environment variable, we also support setting it in provider option. Note that the setting is global per session. That could help performance testing of each kernel. #### Attention Kernel Debug Info Set an environment variable `ORT_ENABLE_ATTENTION_KERNEL_DEBUG_INFO=1`, and ORT will print sdpa kernel used in each node: For example ``` ORT_ENABLE_ATTENTION_KERNEL_DEBUG_INFO=1 ./onnxruntime_test_all --gtest_filter=MultiHeadAttentionTest* ``` It will show debug information of kernel used in testing: ``` [ RUN ] MultiHeadAttentionTest.SelfAttention_Batch2_HeadSize32_NoBias_NoMask_PackedQKV AttentionKernelOptions: FLASH_ATTENTION=0 EFFICIENT_ATTENTION=0 TRT_FUSED_ATTENTION=1 CUDNN_FLASH_ATTENTION=0 TRT_FLASH_ATTENTION=1 TRT_CROSS_ATTENTION=0 TRT_CAUSAL_ATTENTION=0 MATH=1 Operator=MultiHeadAttention Node=node1 DataType=fp16 TRT_FUSED_ATTENTION=1 AttentionKernelOptions: FLASH_ATTENTION=0 EFFICIENT_ATTENTION=1 TRT_FUSED_ATTENTION=0 CUDNN_FLASH_ATTENTION=0 TRT_FLASH_ATTENTION=0 TRT_CROSS_ATTENTION=0 TRT_CAUSAL_ATTENTION=0 MATH=1 Operator=MultiHeadAttention Node=node1 DataType=fp16 EFFICIENT_ATTENTION=1 ``` In this test case, the debug info shows that one session uses trt fused attention and another session use efficient attention. |
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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.