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* Using vectorized loads (float2) for fp16 to improve performance * Fix a few warnings from cpplint * Fix a few warnings from cpplint * Use __float2half2_rn and fix some cpplint warnings * Move some computaions to LaunchFastGeluKernel * Fix some Lint C++ warning * Using vectorized loads (float4) for fp16 to improve performance * Switch whether to optimize FastGelu with float4 vectorization * Switch to float4 memory access based on input_length in FastGelu * Comment how to set the threshold of float2 and float4 vectorized kernels * Add FastGelu fp16 unit tests for bias_length = 2 and 8 * Make vectorized kernels generic with aligned_vector * Unify the vectorized kernels with/without bias * Refactor the code to suppress cpplint warnings * Solve formatting issues * Remove cudaDeviceProp from FastGeluKernel and LaunchFastGeluKernel * Move fast_gelu_impl.h to rocm/bert * Fix some Lint C++ warnings and code alignment |
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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 |
|---|---|---|---|
| Windows | |||
| Linux | |||
| Mac | |||
| Android | |||
| iOS | |||
| 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.