321c1e5730
### Description
<!-- Describe your changes. -->
flatbuffers::String::c_str returns a pointer that may not be null
terminated.
This causes a warning when building on an A100 with gcc 11. Not clear
why other builds with gcc 11 (e.g. Ubuntu 22.04 WSL) don't generate a
warning. Either way it's safer to use str() as that constructs a
std::string with data() and size().
Unclear if this is an issue in reality as it's reading from the
flatbuffer and most likely didn't write out an empty string in order to
save space. There's no perf need to use c_str instead of str, and in
LOAD_STR_FROM_ORT_FORMAT we need to convert the return value to a
std::string anyway.
```c++
struct String : public Vector<char> {
const char *c_str() const { return reinterpret_cast<const char *>(Data()); }
std::string str() const { return std::string(c_str(), size()); }
```
```
inlined from ‘onnxruntime::common::Status onnxruntime::fbs::utils::LoadAttributeOrtFormat(const onnxruntime::fbs::Attribute&, onnx::AttributeProto&, std::unique_ptr<onnxruntime::Graph>&, onnxruntime::Graph&, onnxruntime::Node&, const onnxruntime::OrtFormatLoadOptions&, const onnxruntime::logging::Logger&)’ at /frdong_data/onnxruntime/onnxruntime/core/graph/graph_flatbuffers_utils.cc:385:3:
/usr/include/c++/11/bits/char_traits.h:399:32: error: ‘long unsigned int __builtin_strlen(const char*)’ reading 1 or more bytes from a region of size 0 [-Werror=stringop-overread]
```
### 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. -->
Fix build error on A100
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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
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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 |
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| Windows | ||
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Third-party Pipeline Status
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| Linux |  |
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For feature requests or bug reports, please file a GitHub Issue.
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License
This project is licensed under the MIT License.