8f738d8e9f
### Description
### Motivation and Context
The pipeline is green even Llama2 parity_check fails.
The PR should be merged after the below exception is solved.
'''
2024-06-25 03:49:43.621298481 [E:onnxruntime:,
sequential_executor.cc:514 ExecuteKernel] Non-zero status code returned
while running Expand node. Name:'/model/Expand' Status Message:
/model/Expand: left operand cannot broadcast on dim 3 LeftShape:
{1,1,9,9}, RightShape: {2,1,9,17}
An error occurred while verifying parity: Error in execution: Non-zero
status code returned while running Expand node. Name:'/model/Expand'
Status Message: /model/Expand: left operand cannot broadcast on dim 3
LeftShape: {1,1,9,9}, RightShape: {2,1,9,17}
Traceback (most recent call last):
File
"/workspace/onnxruntime/python/tools/transformers/models/llama/convert_to_onnx.py",
line 1043, in main
parity_check(parity_cmd)
File
"/workspace/onnxruntime/python/tools/transformers/models/llama/llama_parity.py",
line 298, in main
verify_parity(args, location, use_auth_token, kv_cache_ortvalues,
pytorch_model=llama, config=config)
File
"/workspace/onnxruntime/python/tools/transformers/models/llama/llama_parity.py",
line 137, in verify_parity
ort_model.run_with_iobinding(io_binding)
File
"/home/onnxruntimedev/.local/lib/python3.8/site-packages/onnxruntime/capi/onnxruntime_inference_collection.py",
line 331, in run_with_iobinding
self._sess.run_with_iobinding(iobinding._iobinding, run_options)
RuntimeError: Error in execution: Non-zero status code returned while
running Expand node. Name:'/model/Expand' Status Message: /model/Expand:
left operand cannot broadcast on dim 3 LeftShape: {1,1,9,9}, RightShape:
{2,1,9,17}
'''
The exception looks caused by #19832
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| CITATION.cff | ||
| CODEOWNERS | ||
| CONTRIBUTING.md | ||
| lgtm.yml | ||
| LICENSE | ||
| NuGet.config | ||
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| ORT_icon_for_light_bg.png | ||
| packages.config | ||
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| README.md | ||
| requirements-dev.txt | ||
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| requirements-training.txt | ||
| requirements.txt.in | ||
| SECURITY.md | ||
| setup.py | ||
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| 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
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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.