6900109ee8
GetCpuPreferredNodes is a function to get CPU preferred nodes from a graph for target EP (such as CUDA). It starts from CPU outputs of target EP node and travel the graph and try to fallback tentative nodes from target EP to CPU EP. For example: Shape->Gather->Concat->Reshape, at the beginning, all these 4 nodes are all tentative nodes. Since output of Shape is CPU output, it starts from that output and travel the graph, and fallback Gather and Concat to CPU EP. Reshape cannot fallback because its another input is not CPU input. But for case: Shape->Gather->ReduceProd->Concat->Reshape, since ReduceProd doesn't have int64_t kernel in target EP (CUDA here), so it's not a tentative node. The travelling logic still starts from Shape's output, but with current logic, it will stop when reaching ReduceProd, so that Concat will not fallback at the end and is assigned with target EP, at the end, Memcpy nodes are added before and after the Concat node because both of its input and output are CPU tensors. This PR is to fix this issue. For above case, since ReduceProd is not a tentative node, it means either is already have EP assigned, or there is no kernel found of target EP for it, so we can still continue the graph travelling and make it a CPU node and all its outputs CPU outputs. |
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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
| System | CPU | GPU | EPs |
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| WebAssembly |
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.