47e7630378
CUDA's Transpose3DImpl is to transpose [batch, m, n] to [batch, n, m]. Currently it requires both m and n can be divided by 32 or 16. If it's not this case, the compute will fallback to general implementation, which is slow. This PR is to remove the limitation. Profiling in V100 using below size of tensors, got the cycles number from Nsight Compute: | Old | New -- | -- | -- [3072,64,512] | 760793 | 727140 [3072,16,2048] | 854303 | 851146 [3072,2048,12] | 986924 | 737884 [3072,1024,24] | 1212427 | 495117 It shows that even we added extra IF statements to the kernel implementation, it has nearly no impact to the old version (case 1 and 2). And for case 3 and 4 which will fallback to general implementation before, it's much faster. Above data was collected using FP16 tensors, similar results was observed for float tensors. This PR is to enhance the perf of ORT training of Huggingface's XLNet model which has[8,1024,1024,12].permute(0,3,1,2). |
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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
General Information: onnxruntime.ai
Usage documention and tutorials: onnxruntime.ai/docs
Companion sample repositories:
- ONNX Runtime Inferencing: microsoft/onnxruntime-inference-examples
- ONNX Runtime Training: microsoft/onnxruntime-training-examples
Build Pipeline Status
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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.