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### Description <!-- Describe your changes. --> We are introducing the FasterTransfomer model-level integration using ORT [custom op runtime wrapper](https://github.com/microsoft/onnxruntime/pull/13427). In order to make the FT wrapper/integration work, two things need to be done: - New API `KernelInfoGetConstantInput_tensor`. (Done in this PR) During custom op kernel initialization, it needs to get the model weights (saved as node's constant inputs) ready for FT's weights instantiation. What's why we need to add this new API to make kernel info capable of getting constant inputs. - Custom op and custom op kernel to wrap FT model. (Will provide in onnxruntime extensions or inference examples) During custom op kernel initialization, it can fetch attributes from kernel info to determine which kind of FT model instance create. During custom op kernel compute/inference, it can get input/output from kernel context and then assign input/output buffers for model instance to run. |
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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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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.