dfac096501
### Fix segfault for multiple GPU run https://github.com/microsoft/onnxruntime/pull/15618 introduced `GetOrtDeviceByMemType`. The intention should be: handle CPU device differently in the if branch, while might by mistakenly passing the unique default non-cpu device id. ``` OrtDevice CUDAExecutionProvider::GetOrtDeviceByMemType(OrtMemType mem_type) const { if (mem_type == OrtMemTypeCPUInput || mem_type == OrtMemTypeCPUOutput) { return OrtDevice(OrtDevice::CPU, OrtDevice::MemType::CUDA_PINNED, default_device_.Id()); } return default_device_; } ``` We observed a segement fault thrown when running multiple GPU training ` CUDA_LAUNCH_BLOCKING=1 python -m torch.distributed.launch --nproc_per_node=2 examples/onnxruntime/training/language-modeling/run_mlm.py --model_name_or_path distilbert-base-uncased --dataset_name wikitext --dataset_config_name wikitext-2-raw-v1 --num_train_epochs 10 --per_device_train_batch_size 8 --per_device_eval_batch_size 8 --do_train --do_eval --overwrite_output_dir --output_dir ./outputs222/ --seed 1137 --fp16 --report_to none --optim adamw_ort_fused --max_steps 400 --logging_steps 1 ` It is found GPU0 works fine, GPU1 throw segement fault. Looking further, a Shape node trying to allocate it's output tensor, trying to fetch corresponding allocator with ORTDevice(Device:[DeviceType:0 MemoryType:1 DeviceId:1]), while CPU device did not have device id = 1, so a no allocator returned. When we try to call `AsStreamBasedAllocator` for the allocator, segement happens as no null check was done there. ### 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. --> |
||
|---|---|---|
| .config | ||
| .devcontainer | ||
| .gdn | ||
| .github | ||
| .pipelines | ||
| .vscode | ||
| cgmanifests | ||
| cmake | ||
| csharp | ||
| dockerfiles | ||
| docs | ||
| include/onnxruntime/core | ||
| java | ||
| js | ||
| objectivec | ||
| onnxruntime | ||
| orttraining | ||
| rust | ||
| samples | ||
| swift/OnnxRuntimeBindingsTests | ||
| tools | ||
| winml | ||
| .clang-format | ||
| .clang-tidy | ||
| .dockerignore | ||
| .gitattributes | ||
| .gitignore | ||
| .gitmodules | ||
| .lintrunner.toml | ||
| build.amd64.1411.bat | ||
| build.bat | ||
| build.sh | ||
| CITATION.cff | ||
| CODEOWNERS | ||
| CONTRIBUTING.md | ||
| lgtm.yml | ||
| LICENSE | ||
| NuGet.config | ||
| ort.wprp | ||
| ORT_icon_for_light_bg.png | ||
| Package.swift | ||
| packages.config | ||
| pyproject.toml | ||
| README.md | ||
| requirements-dev.txt | ||
| requirements-doc.txt | ||
| requirements-lintrunner.txt | ||
| requirements-training.txt | ||
| requirements.txt.in | ||
| SECURITY.md | ||
| setup.py | ||
| ThirdPartyNotices.txt | ||
| VERSION_NUMBER | ||
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 documention 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
Build Pipeline Status
| System | Inference | Training |
|---|---|---|
| Windows | ||
| Linux | ||
| Mac | ||
| Android | ||
| iOS | ||
| Web | ||
| Other |
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.