d4a88cfe3f
* Implement Gemm op for DNNL execution provider Signed-off-by: George Nash <george.nash@intel.com> * Remove KernelRegistry and Gemm op for dnnl ep The KernelRegistry for the dnnl execution provider only registered a Gemm op that as best we can tell was never actually used and also was not using the dnnl library. We have implemented a Gemm op in the DNNL execution provider subgraph code and thus are removing the unused Gemm op that was in the dnnl KernelRegistry. Signed-off-by: George Nash <george.nash@intel.com> * Fix duplicated output and kernelshape inference fix getcapability to make sure subgraph outputs do not have duplicates fix kernelshape inference in pool Signed-off-by: Wang <zhaoyang.wang@intel.com> * Removed most dnnl specialized ifdefs from gradient_ops_test code Re-enable GlobalAveragePoolGrad test for dnnl ep The bugs that were exposed by the GlobalAveragePoolGrad test have been fixed and this test no longer needs to be disabled for DNNL. Removed the ReluGradDnnl test. We are getting the testing from the already existing ReluGrad test. MaxPoolGrad test no longer has specialized execution provider enabling for DNNL execution provider. It will now run without the extra enabling. ConvGrad is the only test that still has dnnl specialized ifdefs However, the ConvGrad code was not being executed by the code unless it was listed first in the list of execution providers. Signed-off-by: George Nash <george.nash@intel.com> * Fix transpose issue on Gemm On transposing square matrices, getmemoryandreshape will fail to reshape fix by adding a bool Signed-off-by: Wang <zhaoyang.wang@intel.com> * Save memory space by reusing internal tensor for output The intermediat matmul output tensor can be used as the output tensor for the binary calculation. Remove the unused IsAttributeSupported from the DnnlGemmNodeCapability class since we now support all of the Gemm attributes in our implementation. Signed-off-by: George Nash <george.nash@intel.com> Co-authored-by: Wang <zhaoyang.wang@intel.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 | |||
| 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.