2a11f29eaa
The PR optimizes BiasGelu/BiasGeluGrad CUDA kernel by 3 changes: - Use Erf instead of Normcdf for half compute - Change CUDA thread organization for BiasGelu kernel instead of using binary elementwise template - Add vectorized support Using BiasGelu(A[256, 128, 768] + B[768]) in V100 as example, the perf number below are in us Before change, FW: 246.37, BW: 292.77 Use Erf, FW: 152.86, BW: 238.98 All above changes, FW: 132.45, BW: 199.14 For Huggingface's bertweet-base model, with the changes, the step time (FW+BW) reduces from 324.71766 ms to 316.42552 ms, which is 1.026x faster. Using Erf is for half data only, evaluation shows that for float on CUDA, Normcdf is faster. I didn't check the perf for BFloat16 or on AMD, so keep them unchanged. |
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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 |
|---|---|---|
| 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.