47f136e2d3
### Description Add a greedy option to the initializer deduplication process in the Whisper export. Currently to detect shared initializers, ORT compares every initializer against every other initializer (n^2). In the comparison operator, if the two initializers have different data types (e.g. raw_data and int_64), both initializers are converted to a numpy array and the cast result is compared. This cast happens in every comparison, and exponentially affects the runtime of finding shared initializers. This cast operation is the bottleneck for the current Whisper export script. The conversion to the numpy array is useful for detecting equal initializer values across nodes of different data types (e.g. recognizing a bias value of 0.0 is the same as a slice index of 0) but isn't triggered when comparing initializers of the same data type (e.g. weight value of 0.6 == weight value of 0.6). The latter case is where the majority of utility is for Whisper, and so by eliminating our path for comparing numpy arrays for initializers we save a lot of time for minimal cost. In other words, this PR adds an option to remove the ability to detect shared initializers of different types (e.g. Slice Index and MatMul Constant) while retaining the ability to deduplicate weights. ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - Current time to export Whisper-large is prohibitive. --------- 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
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 |  |
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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.