76dfe8108b
* optimize some lstm gate computation. Remove no need string constructions. * change gcc optimization flags for computation bound logics in rnn_helpers * better qgemm for M=1 * Some improve on avx512 * add condition to limit GCC related marcros * Correct QGemm assembly for M=1 AVX2 optimization to pass mlas_test. * Fix rnn_helper build issue for wasm. * better asm code here according to feedbacks. * Remove customized vectorize and unroll option for GCC. Using restrict on some function to help GCC to correctly vectorize it. Rewrite clip_add_bias() to let GCC correctly vectorize it. * Better restrict semantic for merge_lstm_gates_to_memory() by adding in_place(). Add MSC __restrict for the clip_add_bias() mthod to vectorize correctly. * Force CI restart as it stucked by the onnxruntime-python-checks-ci-pipeline which can not restart. |
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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.