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Add support for PyTorch `resize_` operation. The PyTorch API method is documented here: https://pytorch.org/docs/stable/generated/torch.Tensor.resize_.html Implementation notes: There are some implementation details that might deviate from expectations: - As the Onnxruntime::tensor does not support resize operation, this functionality is supported on the TensorImpl by swapping out the backing tensor if the size changes. - In the ORT model the shape of the TensorImpl is defined by the backing onnxruntime::tensor, so it is not supported to have a TensorImpl with a different shape / size than the backing onnxruntime::tensor. This means when resizing to a smaller TensorImpl, other implementations might keep the same backing storage, ORT will re-allocate a new onnxruntime::tensor and copy over as many of the existing elements that fit. Functionally, you will end up with same output, but the underlying buffer will be re-allocated. A future change could be to allow ORTTensorImpl to have a different size / shape than the onnxrutime::tensor backing it, and then we could improve this behavior. The canonical CPU / CUDA implementations in PyTorch repository: CPU: aten/src/ATen/native/Resize.cpp CUDA: aten/src/ATen/native/cuda/Resize.cpp |
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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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| 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.