e9057d2e49
* Add FBGEMM submodule * Add fbgemm based per-channel quantization * Add missing logic for pre-layernorm transformer model fusion * add support for structured pruning architecture -fastformers * Fix windows build * Add a default behavior when head_size is not present for the backward compatibility * Remove FBGEMM and default to tensor-wise quantization, column-wise quantization will be enabled later * Fixed some unit test errors * Fix windows compile error and unit test errors * delete the option removed from the upstream * Addresses review comments and fixes a merge error * Remove commented out code * add non-zero zp support * support A and B scale with any dimensions * fix build breaks * fix warning in MSVC * Fix bug for not checking original float value names when treat it as not existing. * Clean up head size * Clean up python tools * Enable per column quantization * fix quant weight cleanup bug * A few code clean up * Some code clean-up * Some code clean-up * Change option name * update default value * Rename option and parameter names * Missing argument name change * Add tests for quantization options for attention and matmul Co-authored-by: Yufeng Li <liyufeng1987@gmail.com> Co-authored-by: Lei Zhang <zhang.huanning@hotmail.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
Build Pipeline Status
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|---|---|---|---|
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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.