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### Description This PR updates the LLaMA-2 attention fusions by adding the following. - Loading the PyTorch model from Hugging Face with the `LlamaAttention` class before exporting - Updating the attention mask pattern matching to support another case This PR also fixes [this issue](https://github.com/microsoft/onnxruntime/issues/19040). ### Motivation and Context Recent changes to Hugging Face's `transformers` library break the existing pattern matching. Since the attention fusions aim to change the graph from `LayerNorm Op --> Set of Attention Nodes --> LayerNorm Op` to `LayerNorm Op --> Attention Op --> LayerNorm Op` per layer, ultimately it does not matter what nodes comprise the `Set of Attention Nodes` because they will all be removed and replaced by the `Attention Op` in the end. Therefore, it does not matter whether the `LlamaAttention` class or a different attention class is used to load the PyTorch model before exporting because the expected graphs after the attention fusions will look identical no matter the attention class chosen. By loading the PyTorch model with the `LlamaAttention` class instead of other attention classes (e.g. `LlamaFlashAttention2` or `LlamaSdpaAttention`) and then exporting it to ONNX, the existing pattern matching will continue to work. |
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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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| iOS | ||
| 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.