2cb5781b43
### Description
In some environments the test code has undefined behavior. To prove it, save the following code as
test.cpp
```c++
#include <iostream>
#include <stdio.h>
int main(){
char buf[1024];
int ret = snprintf(buf, sizeof(buf), "%ls","abc");
if(ret <0){
std::cout<< ret<< std::endl;
} else{
std::cout<< "OK: ret="<<ret<< std::endl;
}
return 0;
}
```
Then compile it as
```
g++ -DNDEBUG -std=gnu++17 test.cpp -o /tmp/t
```
Or
```
g++ -O2 -DNDEBUG -std=gnu++17 test.cpp -o /tmp/t
```
The first command is without optimization. The second one turns on
optimization. Then the outputs are different.
When optimization is enabled, the output might be:
```
OK: ret=-1
```
You cannot explain why it would go to this branch when ret is "-1". It
might be a bug of a specific version of GCC. However, at this moment we
cannot change the version. It was found in GCC version 8.5.0 20210514
(Red Hat 8.5.0-18) (GCC) that is provided by UBI8. RHEL9 doesn't have
the problem. snprintf is a builtin function of GCC. So the problem was
not related to glibc.
### 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. -->
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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
-
General Information: onnxruntime.ai
-
Usage documentation and tutorials: onnxruntime.ai/docs
-
YouTube video tutorials: youtube.com/@ONNXRuntime
-
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.