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### Description Fixes the issue with IRFFT output dimension calculation as described in #13236 ### Motivation and Context Please refer to #13236 for detailed description. Specifically, [this code](https://github.com/microsoft/onnxruntime/blob/main/onnxruntime/contrib_ops/cuda/math/fft_ops.cc#L103) computes the output dimension as: ``` out_dim = in_dim * 2 - 1 ``` while it should be this instead: ``` out_dim = 2 * (in_dim - 1) ``` (assuming the original signal has even number of samples, of course). For example, if the original signal has 4 samples, then the round trip should look something like: ``` 4 -> (one-sided RFFT) -> 3 (complex) -> (one-sided IRFFT) -> 4 ``` with the current code the output will be a signal with 5 points. --------- Co-authored-by: Alexey Kamenev <akamenev@nvidia.com> Co-authored-by: Nick Geneva <nicholasgeneva@gmail.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
Builtin Pipeline Status
| System | Inference | Training |
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| Windows | ||
| Linux | ||
| Mac | ||
| Android | ||
| iOS | ||
| Web | ||
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Third-party Pipeline Status
| System | Inference | Training |
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| Linux |  |
Data/Telemetry
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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.