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* Changes to fuse embed layer for gpt2, kernal changes pending * verified add output and regular add match * Test added for additional output embedlayernorm, working on CUDA * Test passing on CPU * updated convert_to_onnx toll to check parity correctly * removed some debugs * couple of TODO left as in optimizer.py * removed changes to optimizer.py * fixing build * fixing build * updated order of initilization * added a test case for float16 * updating the docs * updating tests failing due to embed layer fusion * update unit tests * updating CUDA documentation in operatorkernels.md * addressing comments * OperatorKernels.md updated with CUDA * adding TODO to qembed_layer * minor edit * updated docs * addressing comments * adding position ids to embed layer gpt2 * updating fused gpt2 model * added extra test * remove comments * addressing comments * contrib_defs.cc updated * all tests passing * fixing a typo * minor edit * trigger build * qembedlayernorm checkinputs updated * fixing build error * fixing build error * fixing build error |
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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
General Information: onnxruntime.ai
Usage documention and tutorials: onnxruntime.ai/docs
Companion sample repositories:
- ONNX Runtime Inferencing: microsoft/onnxruntime-inference-examples
- ONNX Runtime Training: microsoft/onnxruntime-training-examples
Build Pipeline Status
| System | CPU | GPU | EPs |
|---|---|---|---|
| Windows | |||
| Linux | |||
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| WebAssembly |
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.