2afce4830c
Adding code for symmetric quantized matrix multiplication. Used in quantized convolution, achieving significant perf gain. TODO, use Symmetric Quantized GEMM in other operators! TODO address activation buffer overread in custom allocators and tensors supplied by users. DOT kernel perf test: Pixel 5a: Cartoongan 513.539 ms 471.786 ms Efficient 57.5169 ms 56.4174 ms Edgetpu 14.6673 ms 13.5959 ms NEON kernel perf test Pixel 3a Cartoongan 1423.53 ms 1069.92 ms Efficient 114.086 ms 107.968 ms Edgetpu 39.2632 ms 36.9839 ms Co-authored-by: Chen Fu <fuchen@microsoft.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
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
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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.