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### Description <!-- Describe your changes. --> 1. Add a new test lib `onnxruntime_providers_cuda_ut` which is similar to `onnxruntime_providers_cuda` but `onnxruntime_providers_cuda_ut` is only built if `onnxruntime_BUILD_UNIT_TESTS` is set. We can call all CUDA UTs through this ut lib without affecting production lib `onnxruntime_providers_cuda`. 2. Move all test cases from `core/providers/cuda/test/` to `test/providers/cuda/`. These test cases are built into lib `onnxruntime_providers_cuda_ut` and run by `./onnxruntime_test_all --gtest_filter="*CUDA_EP_Unittest*"`. Since the lib is only for test, we can use gtest macros in the test cases. Previous implementation do not support using gtest lib in the CUDA UT cases. 3. The cmake code in `cmake/onnxruntime_providers.cmake` is refactored a bit. A new function `onnxruntime_add_object_library` is to build a object target. The 2 libs `onnxruntime_providers_cuda_ut` & `onnxruntime_providers_cuda` share most of the code, so the object files can be used in both libs, which helps reduce build time. Another function `config_cuda_provider_shared_module` is used to configure all 3 similar targets(onnxruntime_providers_cuda_obj/onnxruntime_providers_cuda/onnxruntime_providers_cuda_ut). 4. Refactored the test to call `testing::InitGoogleTest` & `RUN_ALL_TESTS` in `libonnxruntime_providers_cuda_ut.so`'s `TestAll`. After this change, we can see all the cases running in `CUDA_EP_Unittest.All`:  ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> After https://github.com/microsoft/onnxruntime/pull/13016, there are still test files in test/providers/cuda/ that are not moved to core/providers/cuda/test/ and the test cases are disabled. This PR helps to clean the unfinished TODOs. Even through onnxruntime_shared_lib_test covers some test for CUDA provider. onnxruntime_shared_lib_test works like a coarse grain end-to-end test for CUDA provider. If CUDA unittest can run cases for a single component, this wound be helpful for CUDA developers. --------- Co-authored-by: Yuhong Guo <yuhong.gyh@antgroup.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 |
|---|---|---|
| Windows | ||
| Linux | ||
| Mac | ||
| Android | ||
| iOS | ||
| Web | ||
| Other |
Third-party Pipeline Status
| System | Inference | Training |
|---|---|---|
| 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.