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### Description Implements the STFT operator for the DirectML execution provider. This is implemented as a custom op, just like the DFT kernel, because it's implemented as a composite of two operators (DML Mul/Identity + DFT). As such, this inherits the same restrictions as the existing DFT kernel (requires power-of-two window sizes for now). This change also adds a native FP16 shader to DFT so that both DFT/STFT kernels support float16 tensors. There is no typed UAV fallback or emulation path, so the HW _needs_ to support native float16. It also appears the stockham shader was compiled with all optimizations disabled and debug symbols (tsk tsk, Sheil), and this has been fixed. This is passing all existing STFT tests (i.e. all of 1). I'm adding some additional collateral in the Windows AI conformance tests in parallel to check some extra cases. --------- Co-authored-by: Patrice Vignola <vignola.patrice@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
Build Pipeline Status
| System | Inference | Training |
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| Windows | ||
| Linux | ||
| Mac | ||
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| Web | ||
| Other |
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.