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### Description This adds support for partial RotaryEmbedding to DML. Essentially, partial RotaryEmbedding simply consists of doing the rotary embedding calculation on a subregion of the input tensor of as if its head size was `rotary_embedding_dim`, while leaving the second part of the tensor (i.e. `head_size - rotary_embedding_dim`) alone. To achieve this, all we need to do is follow the following steps: 1. Split the tensor into 2 parts 2. Run the rotary embedding algorithm on the first part, just like we were doing before on the entire tensor 3. Join the 2 parts back together Since we're leaving the middle part intact, the RotaryEmbedding fusion will still be done within DML. Also, the concat at the end is essentially free because DML optimizes it out and directly allocate the result of RotaryEmbedding at the right place. The only overhead here is the splitting of the tensor at the beginning, which we should eventually make part of the RotaryEmbedding fusion within DML. ### Motivation and Context This fix allows us to correctly run models that have a `partial_rotary_factor` setting in huggingface, including Nvidia's Nemotron: https://huggingface.co/nvidia/Nemotron-Mini-4B-Instruct |
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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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| Linux | ||
| Mac | ||
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| iOS | ||
| Web | ||
| Other |
This project is tested with BrowserStack.
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.