e5189330d5
### Description Remove attention_mask from unnecessary code paths in the whisper export process. ### Motivation and Context Current export script frequently hits OOM error when export whisper-large. Memory profiling shows that this is a result of generating dummy inputs for the `encoder_attention_mask` input for a model pass during exporting - in whisper-large, this dummy tensor can be around 20GB in size. `encoder_attention_mask` is ultimately a dummy input - it's just there to satisfy certain BeamSearch requirements. Thus, we're currently creating a 20GB tensor and passing it to the model, which then discards the input anyways. By removing the code path to generate a dummy encoder_mask tensor, we can reduce the memory requirements to export whisper substantially, while keeping the BeamSearch checks satisfied. --------- Co-authored-by: Peter McAughan <petermca@microsoft.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 | ||
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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.