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### Description The Java API currently only supports fp16 output tensors which it automatically casts to floats on the way out. This PR adds support for creating fp16 and bf16 tensors (from `java.nio.Buffer` objects or as the output of models, creation from Java short arrays is not supported), along with efficient methods for casting `FloatBuffer` into `ShortBuffer` filled with fp16 or bf16 values and vice versa. The fp16 conversions use a trick to pull in the efficient conversion methods added to Java 20, falling back to ports of the MLAS methods otherwise. The Java 20 methods can be special cased by the C2 JIT compiler to emit the single instruction on x86 and ARM which converts fp32<->fp16, or the vectorized versions thereof, so they should be quite a bit faster than the MLAS ported one. ### Motivation and Context fp16 and bf16 are increasingly popular formats and we've had several requests for this functionality. Fixes #7003. cc @yuslepukhin @cassiebreviu --------- Co-authored-by: Scott McKay <Scott.McKay@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 & Resources
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General Information: onnxruntime.ai
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Usage documention 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 |
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| Windows | ||
| Linux | ||
| Mac | ||
| Android | ||
| iOS | ||
| Web | ||
| Other |
Third-party Pipeline Status
| System | Inference | Training |
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| 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.