87076553b0
### Description Follow up of #20216 to add kernel for sm=75 (GPU like T4, Geforce RTX 2080, GeForce GTX 1650 Ti, NVIDIA TITAN RTX, RTX 4000 etc) - [x] Add kernel for sm=75 - [x] Update dispatch code to use sm to call different kernel. - [x] Update compile script to use num_stages=2 instead of 3 for sm=75 - [x] Refactor test script and add tests for bfloat16. - [x] Fix performance test of token generation (previously we did not concatenate past_key) - [x] Fix debug build - [x] Run performance test and update numbers. For sm=70, the v1 kernel can be compiled but there is error in compiling v2 kernel. So it is skipped in this pull request. Performance Test on T4 GPU (using Standard_NC4as_T4_v3 Azure VM) with `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 dense causal. Note that GQA with dense need more computation since no sparsity is used. The TORCH-GQA use naive implementation (using cuSPARSE Block-SpMM could be faster). ``` prompt-sm75-batch4-head32-d128-local16-vert8-torch.float16: sequence_length TORCH-GQA ORT-GQA-Dense ORT-SparseAtt 1 32.0 0.184173 2.994347 0.089064 2 64.0 0.303300 3.023986 0.107418 3 128.0 0.887795 3.073728 0.174213 4 256.0 2.797654 3.246899 0.357869 5 512.0 10.055048 3.814039 0.893903 6 1024.0 37.849937 5.818439 2.658720 7 2048.0 148.641785 13.638480 7.202690 8 4096.0 OOM 43.556847 17.680954 9 8192.0 OOM 161.628540 44.336670 token-sm75-batch4-head32-d128-local16-vert8-torch.float16: past_sequence_length TORCH-GQA ORT-GQA-Dense ORT-SparseAtt 1 32.0 0.110353 2.996305 0.137509 2 64.0 0.145088 3.006860 0.165424 3 128.0 0.219500 3.036448 0.192001 4 256.0 0.347496 3.071341 0.249125 5 512.0 0.595842 3.135225 0.398726 6 1024.0 1.081216 3.261110 0.612744 7 2048.0 2.060307 3.515578 0.685670 8 4096.0 OOM 4.022986 0.819707 9 8191.0 OOM 5.024528 1.072912 ``` ### Motivation and Context To inference Phi-3-small in T4 GPU |
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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.