baaef59696
### Description Follow up of https://github.com/microsoft/onnxruntime/pull/20216 to add sparse attention kernel compiled by Triton for H100 (sm90). - [x] Refine sparse attention v1 kernel compilation (remove some combinations) - [x] compile kernels for v1 kernels - [x] compile kernels for H100 - [x] run performance tests ### Performane Test setting `batch_size=4, num_heads=32, max_seq_len=8192, head_size=128, sparse_block_size=64, local_blocks=16, vert_stride=8, num_layout=8` We compare sparse attention to corresponding GQA with local attention windows size 1024, or GQA with dense causal. Note that ORT-GQA-Dense has more computation than ORT-SparseAtt, while ORT-GQA-Local has less computation (no vertial strides) than ORT-SparseAtt. They are added for reference. It is not fair comparison, but could show the benefit of sparsity vs dense. Example results in Azure Standard_ND96isr_H100_v5 VM with NVIDIA H100-80GB-HBM3 GPU (sm=90): ``` prompt-sm90-batch4-head32-d128-local16-vert8-torch.float16: sequence_length TORCH-GQA ORT-GQA-Dense ORT-GQA-Local ORT-SparseAtt 0 16.0 0.079877 0.006362 0.006403 0.042758 1 32.0 0.086920 0.016404 0.016686 0.044183 2 64.0 0.090727 0.020429 0.020409 0.045343 3 128.0 0.128148 0.032009 0.031984 0.051516 4 256.0 0.323933 0.074110 0.073920 0.068308 5 512.0 1.021856 0.162167 0.161951 0.109226 6 1024.0 3.596002 0.452629 0.452780 0.231653 7 2048.0 13.865088 1.499534 1.195749 0.515488 8 4096.0 0.000000 5.454785 2.669682 1.163233 9 8192.0 0.000000 22.068159 6.018604 2.772873 token-sm90-batch4-head32-d128-local16-vert8-torch.float16: past_sequence_length TORCH-GQA ORT-GQA-Dense ORT-GQA-Local ORT-SparseAtt 0 16.0 0.104460 0.012652 0.012661 0.069549 1 32.0 0.113866 0.012776 0.012765 0.069024 2 64.0 0.124600 0.016791 0.012672 0.069397 3 128.0 0.108658 0.017900 0.018294 0.074844 4 256.0 0.115463 0.029409 0.029608 0.078911 5 512.0 0.149824 0.033968 0.033701 0.092998 6 1024.0 0.234050 0.042930 0.042951 0.116920 7 2048.0 0.390695 0.061462 0.043008 0.121555 8 4096.0 0.000000 0.097505 0.042948 0.134757 9 8191.0 0.000000 0.165861 0.043542 0.158796 ``` The following might be able to help performance on short sequence length. Need update operator spec: Fall back to flash attention when total_sequence length < local_blocks * block_size ### 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
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| Linux | ||
| Mac | ||
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Third-party Pipeline Status
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| Linux |  |
Data/Telemetry
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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.