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### Description Since WebGPU supports only float32 and int32, having Gather, Reshape, Shape, Squeeze and Unsqueeze ops with other data types create additional MemCpy ops and slow down the overall execution as all other OPs with other tensor types will be done on CPU. Before this patch SD Unet had these numbers: Node(s) placed on [CPUExecutionProvider]. Number of nodes: 1141 Node(s) placed on [JsExecutionProvider]. Number of nodes: 4025 memcpy tokens: 2001 After patch: Node(s) placed on [CPUExecutionProvider]. Number of nodes: 1735 Node(s) placed on [JsExecutionProvider]. Number of nodes: 2243 memcpu tokens: 813 It also gives more than 5X performance benefit. From 12sec for one Unet step to 2.2sec on RTX 3090 Ti, so we are almost getting to native performance. UPD: with latest changes from main branch and multi-threading it went down to 1.6sec. Will try re-exporting my model to onnx with maximum optimizations, like using MultiHeadAttention to decrease node count. Maybe after implementing that it can go in less than 1 sec |
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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 |
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| 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.