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### Description For fp16 Atype, the fallback operation is convert the data to fp32 and calculate. Added neon intrinsics version to speed up the conversion. Store address alignment and loop unrolling have insignificant impact on latency so they are omitted. |Benchmark | Time | CPU | |--------------|---------------------------------------------|--------------------| |M_ConvertF16ToF32/baseline/real_time | 1076961 ns | 1083398 ns | |M_ConvertF16ToF32/aligned:0/real_time | 46785 ns | 46516 ns | |M_ConvertF16ToF32/aligned:1/real_time | 46631 ns | 46391 ns | |M_ConvertF16ToF32_unroll2/aligned:0/real_time | 44074 ns | 44392 ns | |M_ConvertF16ToF32_unroll2/aligned:1/real_time | 44726 ns | 45226 ns | |M_ConvertF32ToF16/baseline/real_time | 520109 ns | 527329 ns | |M_ConvertF32ToF16/aligned:0/real_time | 73610 ns | 74015 ns | |M_ConvertF32ToF16/aligned:1/real_time | 71557 ns | 71525 ns | |M_ConvertF32ToF16_unroll2/aligned:0/real_time | 64227 ns | 63374 ns | |M_ConvertF32ToF16_unroll2/aligned:1/real_time | 67428 ns | 67989 ns | ### Motivation and Context speed up fallback implementation of Fp16 MatMulNBits |
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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 documentation 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
Builtin Pipeline Status
| System | Inference | Training |
|---|---|---|
| Windows | ||
| Linux | ||
| Mac | ||
| Android | ||
| iOS | ||
| Web | ||
| Other |
This project is tested with BrowserStack.
Third-party Pipeline Status
| System | Inference | Training |
|---|---|---|
| Linux |  |
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.