14d7872ce9
### Description It always has been out of memory in training CUDA 12.2 packaging pipeline https://dev.azure.com/aiinfra/Lotus/_build?definitionId=1308&_a=summary since the PR #19910 I tried other CPU agents for example, D64as_v5(256G memory) and D32as_v4(128G memory and 256 G SSD temp storage), which are still out of memory like the below image  But it works on T4, though T4 only has 4 vCPUs, 28G memory and 180G temp storage, and it takes much more time. ### Motivation and Context Restore CUDA 12.2 training packaging pipeline first. More time is needed to investigate the root cause ### Other Clues. These 2 compilation steps take nearly 6 minutes with Cuda 12.2 on T4 And it runs out of memory on CPU machine. @ajindal1 cuda12.2 on T4 ``` 2024-03-14T05:39:08.7726865Z [ 90%] Building CUDA object CMakeFiles/onnxruntime_providers_cuda.dir/onnxruntime_src/onnxruntime/contrib_ops/cuda/bert/flash_attention/flash_fwd_split_hdim32_fp16_sm80.cu.o 2024-03-14T05:45:01.3223393Z [ 90%] Building CUDA object CMakeFiles/onnxruntime_providers_cuda.dir/onnxruntime_src/onnxruntime/contrib_ops/cuda/bert/flash_attention/flash_fwd_split_hdim64_bf16_sm80.cu.o 2024-03-14T05:46:07.9218003Z [ 90%] Building CUDA object CMakeFiles/onnxruntime_providers_cuda.dir/onnxruntime_src/onnxruntime/contrib_ops/cuda/bert/flash_attention/flash_fwd_split_hdim96_fp16_sm80.cu.o 2024-03-14T05:52:59.2387051Z [ 90%] Building CUDA object CMakeFiles/onnxruntime_providers_cuda.dir/onnxruntime_src/onnxruntime/contrib_ops/cuda/bert/group_query_attention_impl.cu.o ``` But they could be finished in about one minute with Cuda 11.8 on CPU ``` cuda11.8 on CPU 2024-04-09T11:34:35.0849836Z [ 90%] Building CUDA object CMakeFiles/onnxruntime_providers_cuda.dir/onnxruntime_src/onnxruntime/contrib_ops/cuda/bert/flash_attention/flash_fwd_split_hdim32_fp16_sm80.cu.o 2024-04-09T11:35:53.6648154Z [ 90%] Building CUDA object CMakeFiles/onnxruntime_providers_cuda.dir/onnxruntime_src/onnxruntime/contrib_ops/cuda/bert/flash_attention/flash_fwd_split_hdim64_bf16_sm80.cu.o cuda11.8 on GPU 024-03-13T12:16:33.4102477Z [ 90%] Building CUDA object CMakeFiles/onnxruntime_providers_cuda.dir/onnxruntime_src/onnxruntime/contrib_ops/cuda/bert/flash_attention/flash_fwd_split_hdim32_fp16_sm80.cu.o 2024-03-13T12:19:58.8268272Z [ 90%] Building CUDA object CMakeFiles/onnxruntime_providers_cuda.dir/onnxruntime_src/onnxruntime/contrib_ops/cuda/bert/flash_attention/flash_fwd_split_hdim64_bf16_sm80.cu.o ``` |
||
|---|---|---|
| .config | ||
| .devcontainer | ||
| .gdn | ||
| .github | ||
| .pipelines | ||
| .vscode | ||
| cgmanifests | ||
| cmake | ||
| csharp | ||
| dockerfiles | ||
| docs | ||
| include/onnxruntime/core | ||
| java | ||
| js | ||
| objectivec | ||
| onnxruntime | ||
| orttraining | ||
| rust | ||
| samples | ||
| tools | ||
| winml | ||
| .clang-format | ||
| .clang-tidy | ||
| .dockerignore | ||
| .gitattributes | ||
| .gitignore | ||
| .gitmodules | ||
| .lintrunner.toml | ||
| build.bat | ||
| build.sh | ||
| build_arm64x.bat | ||
| CITATION.cff | ||
| CODEOWNERS | ||
| CONTRIBUTING.md | ||
| lgtm.yml | ||
| LICENSE | ||
| NuGet.config | ||
| ort.wprp | ||
| ORT_icon_for_light_bg.png | ||
| 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 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.