d8792f8040
Fix memory leak issue which comes from TRT EP's allocator object not
being released upon destruction.
Following is the log from valgrind:
```
==1911860== 100,272 (56 direct, 100,216 indirect) bytes in 1 blocks are definitely lost in loss record 1,751 of 1,832
==1911860== at 0x483CFA3: operator new(unsigned long) (vg_replace_malloc.c:472)
==1911860== by 0x315DC2: std::_MakeUniq<onnxruntime::OrtAllocatorImplWrappingIAllocator>::__single_object std::make_unique<onnxruntime::OrtAllocatorImplWrappingIAllocator, std::shared_ptr<onnxruntime::IAllocator> >(std::shared_ptr<onnxruntime::IAllocator>&&) (unique_ptr.h:857)
==1911860== by 0x30EE7B: OrtApis::KernelContext_GetAllocator(OrtKernelContext const*, OrtMemoryInfo const*, OrtAllocator**) (custom_ops.cc:121)
==1911860== by 0x660D115: onnxruntime::TensorrtExecutionProvider::Compile(std::vector<onnxruntime::IExecutionProvider::FusedNodeAndGraph, std::allocator<onnxruntime::IExecutionProvider::FusedNodeAndGraph> > const&, std::vector<onnxruntime::NodeComputeInfo, std::allocator<onnxruntime::NodeComputeInfo> >&)::{lambda(void*, OrtApi const*, OrtKernelContext*)#3}::operator()(void*, OrtApi const*, OrtKernelContext*) const (tensorrt_execution_provider.cc:2223)
```
This issue happens after this [EP allocator
refactor](https://github.com/microsoft/onnxruntime/pull/15833)
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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
-
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 |  |
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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.