a6c5ba0185
### Description Add stable diffusion CUDA kernel optimizations. The following are included: (1) GroupNorm operator. This kernel is from TensorRT 8.5. (2) BiasSplitGelu operator. This kernel is modified from SplitGelu of TensorRT 8.5. We added bias to the SplitGelu. (3) NhwcConv operator. This adds support of NHWC format (ONNX Conv operator uses NCHW format). (3) Update MultiHeadAttention (packed kv and no bias) for cross attention. This could avoid transpose of kv for TRT fused cross attention kernel. (4) Optimization and benchmark script Not included: (1) Script to convert Conv to NhwcConv in onnx graph. (2) Update symbolic shape inference for NhwcConv. (3) Add SeqLen2Spatial operator (4) Documents Limitations: GroupNorm, BiasSplitGelu and NhwcConv kernels are implemented based on stable diffusion usage. They might not be applicable to any input size or dimensions. For example, BiasSplitGelu requires hidden size to be 2560 | 5120 | 10240, and NhwcConv assumes 4D input/weight. There is minor increasement of binary size. For SM=75 only, python package wheel size adds (33757K - 33640K) = 117 KB. It is possible to move NHWC from template parameter to constructor to reduce binary size (with slight cost of performance). Note: for RTX 4090/4080/4070 Ti, need build with CUDA 11.8 and latest cuDNN to get best performance. |
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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
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.