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### Description <!-- Describe your changes. --> - Add debug infrastructure to dump out model at various stages of transpose optimization. - Handle more scenarios where Transpose -> Reshape can be merged. - Run L1 optimizers after layout transform to constant fold initializers that had their layout changed. - Use cost check for Concat post layout transform as pushing a Transpose through it can potentially add Transpose nodes to multiple other inputs. - Update internal testing EP to support test where you want it to take all nodes, use NHWC layout, and to use dummy static kernels instead of compiling so the ops in the graph post-initialization can be counted. - Misc cleanup in InferenceSession to not unnecessarily pass args to TransposeGraph for class members. ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> Address perf issue seen with model where a Transpose gets blocked by a Reshape that could have been treated as a Transpose. --------- Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.com> |
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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 | Inference | Training |
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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.