00b345eb7b
Using signed int, qgemm kernel avoids extending uint8 to int16 while computing matrix multiplication, achieving higher performance. We also find that by using only lower 64b of vector registers to load A and B matrix, we can get further performance improvements. We also experimented with using ldp to load two 64b in one shot, vs using two ldr to load one 64b at a time, in both Big and little cores, there is no noticeable differences. Submitting the LDP version. At this point we don't need to choose kernel based on micro-architecture. Inference time of resnet50, thread count 2 Big Core on Pixel 3a Current master: 292.947 ms First iteration S8S8: 188.239 ms LDP load two 64b reg: 178.715 ms LDR load one 64b reg: 179.536 ms Little Core Master: 546.317 ms S8S8: 513.332 ms LDP: 489.19 ms LDR: 497.865 ms Raspberry Pi 3B+ Master: 660.08 ms S8S8: 608.577 ms LDP: 603.675 ms LDR 602.075 ms |
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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 |
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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.