07e6648e12
Last week I fixed error #16484 found when trying to build onnxruntime with the icpx compiler. Another thing I found out is that icpx uses -ffast-math flag by default. You can check it by running the compiler with -v flag like following: ```bash # Setup the environment . /opt/intel/oneapi/setvars.sh # Compile any file to see all the implicit flags icpx -v main.cpp ``` This leads to a bunch of warnings during the build like: ```bash In file included from /mnt/f/wsl_home/onnxruntime/onnxruntime/test/providers/cpu/tensor/upsample_op_test.cc:5: In file included from /mnt/f/wsl_home/onnxruntime/onnxruntime/test/providers/provider_test_utils.h:6: In file included from /mnt/f/wsl_home/onnxruntime/onnxruntime/test/providers/checkers.h:10: In file included from /mnt/f/wsl_home/onnxruntime/onnxruntime/core/util/math_cpuonly.h:68: In file included from /mnt/f/wsl_home/onnxruntime/build/Linux/RelWithDebInfo/_deps/eigen-src/Eigen/Core:172: /mnt/f/wsl_home/onnxruntime/build/Linux/RelWithDebInfo/_deps/eigen-src/Eigen/src/Core/MathFunctions.h:1019:12: warning: comparison with NaN always evaluates to false in fast floating point modes [-Wtautological-constant-compare] return isnan EIGEN_NOT_A_MACRO (x); ^~~~~~~~~~~~~~~~~~~~~~~~~~~ ``` And some tests are failing as well, usually with infinities involved. To list a few: ```bash # ... 1: [ FAILED ] IsInfTest.test_isinf_float 1: [ FAILED ] IsInfTest.test_isinf_double 1: [ FAILED ] IsInfTest.test_isinf_positive_float 1: [ FAILED ] IsInfTest.test_isinf_positive_double 1: [ FAILED ] IsInfTest.test_isinf_negative_float 1: [ FAILED ] IsInfTest.test_isinf_negative_double 1: [ FAILED ] IsNaNOpTest.IsNaNFloat 1: [ FAILED ] IsNaNOpTest.IsNaNDouble # ... ``` This PR adds a quick global check for the IntelLLVM compiler, as in the way its name is reported by CMake and then, depending on the compiler driver, sets either MSVC-like or GCC-like switch to disable fast-maths. Probably a bit cleaner solution would be to use ```target_compile_options(${TARGET} PRIVATE MEOW)``` instead of a global-wide ```set(CMAKE_CXX_FLAGS MEOW)```, but then we'd be required to add it to all the individual targets and execution providers and this will lead to a lot of code duplication. |
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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.