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### Description Added Group Query Attention op, supporting integer multiple number of heads for Q / KV. As of now, this op can only use FlashAttention kernel, meaning it only supports sm>=80 on Linux. Results from onnxruntime/test/python/transformers/benchmark_gqa.py show an on-average ~37% speed-up over Decoder Masked Multi-Head Attention, with even greater improvements for long past sequence lengths. ``` op batch s_kv heads h_dim ms TFLOPS gqa 16 2048 8 32 0.34 0.10 dmmha 16 2048 8 32 0.39 0.09 --------- gqa 16 2048 8 64 0.45 0.15 dmmha 16 2048 8 64 0.61 0.11 --------- gqa 16 2048 8 128 0.54 0.25 dmmha 16 2048 8 128 0.83 0.16 --------- gqa 16 2048 16 32 0.45 0.15 dmmha 16 2048 16 32 0.69 0.10 --------- gqa 16 2048 16 64 0.69 0.19 dmmha 16 2048 16 64 0.83 0.16 --------- gqa 16 2048 16 128 0.71 0.38 dmmha 16 2048 16 128 1.28 0.21 --------- gqa 16 2048 32 32 0.58 0.23 dmmha 16 2048 32 32 0.77 0.17 --------- gqa 16 2048 32 64 0.58 0.46 dmmha 16 2048 32 64 1.25 0.21 --------- gqa 16 2048 32 128 0.76 0.71 dmmha 16 2048 32 128 2.15 0.25 --------- gqa 16 2048 64 32 0.68 0.39 dmmha 16 2048 64 32 1.23 0.22 --------- gqa 16 2048 64 64 0.77 0.70 dmmha 16 2048 64 64 2.11 0.25 --------- gqa 16 2048 64 128 1.10 0.97 dmmha 16 2048 64 128 4.06 0.26 --------- gqa 16 2048 128 32 1.00 0.54 dmmha 16 2048 128 32 2.09 0.26 --------- gqa 16 2048 128 64 1.10 0.97 dmmha 16 2048 128 64 4.08 0.26 ``` ### Motivation and Context As of now, this op is targeted for use on LLama models, as it supports kv-caching and different number of heads for Q and KV (Grouped Query Attention). We plan to add support for more platforms, input formats, etc. in the future. --------- Co-authored-by: Tianlei Wu <tlwu@microsoft.com> Co-authored-by: tlwu@microsoft.com <tlwu@a100.crj0ad2y1kku1j4yxl4sj10o4e.gx.internal.cloudapp.net> |
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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
Builtin Pipeline Status
| System | Inference | Training |
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| Windows | ||
| Linux | ||
| Mac | ||
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| Web | ||
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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.