d0b08af37a
* Add QAttention to DNNL EP Add QAttention to DNNL EP (limited support and disable for gpu) update ONEDNN version to 2.4.4 bug fix in getcapability add memory debug print Signed-off-by: Wang <zhaoyang.wang@intel.com> * Address Code Review + MatMulInteger Fix clean up code and add comments fix matmulinteger and add fusion rule to enable initialized vector weight zero points of 0s update DNNL_TAG to v2.5 Signed-off-by: Wang <zhaoyang.wang@intel.com> * Linux Compile Fix + rollback ONEDNN to 2.4.4 Signed-off-by: Zhaoyang Wang <zhaoyang.wang@intel.com> * Fix QAttention Debug build Signed-off-by: Wang <zhaoyang.wang@intel.com> * Fix QAttention build if USE_DNNL not specified Signed-off-by: George Nash <george.nash@intel.com> Co-authored-by: Wang <zhaoyang.wang@intel.com> Co-authored-by: MTC <63478620+jeyblu@users.noreply.github.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
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 | |||
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| 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.