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### Description This PR updates the Whisper export with beam search by adding the following. - Fixes a bug when running `DecoderMaskedMultiHeadAttention` in the Whisper with beam search model - Sets the default PyTorch attention implementation to `eager` to allow existing attention fusions to continue working - Re-uses the cache directory when loading the PyTorch model to reduce memory used on disk - Adds `--disable_auto_mixed_precision` to the example FP16 export command ### Motivation and Context - [This PR](https://github.com/microsoft/onnxruntime/pull/19112) added the `is_unidirectional` parameter to `CheckInputs`, but it was not provided when checking the inputs in `DecoderMaskedMultiHeadAttention`. - [This PR](https://github.com/microsoft/onnxruntime/pull/19200) explains the reasoning behind why `eager` is used to load the `WhisperAttention` class. - By re-using the cache directory for loading the PyTorch model, only one copy of the PyTorch model is saved on disk instead of two copies. - By providing this flag, there will be less Cast nodes in the Whisper with beam search model to switch between FP16 and FP32 precision. |
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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 documentation 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 | ||
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| Web | ||
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Third-party Pipeline Status
| System | Inference | Training |
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| Linux |  |
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.