4c3e350a6a
### Description
<!-- Describe your changes. -->
### Error
```
RuntimeError: There was an error while exporting the PyTorch model to ONNX:-
Traceback (most recent call last):
File "/opt/conda/envs/ptca/lib/python3.8/site-packages/onnxruntime/training/ortmodule/_utils.py", line 254, in get_exception_as_string
raise exception
File "/opt/conda/envs/ptca/lib/python3.8/site-packages/onnxruntime/training/ortmodule/_graph_execution_manager.py", line 385, in _get_exported_model
torch.onnx.export(self._flattened_module,
File "/opt/conda/envs/ptca/lib/python3.8/site-packages/torch/onnx/__init__.py", line 305, in export
return utils.export(model, args, f, export_params, verbose, training,
File "/opt/conda/envs/ptca/lib/python3.8/site-packages/torch/onnx/utils.py", line 118, in export
_export(model, args, f, export_params, verbose, training, input_names, output_names,
File "/opt/conda/envs/ptca/lib/python3.8/site-packages/torch/onnx/utils.py", line 743, in _export
proto, export_map, val_use_external_data_format = graph._export_onnx(
RuntimeError: ONNX export failed: Couldn't export Python operator XDropout
```
The error leads to Out of Memory issue, because the log.txt file is **26
GB**.
### 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. -->
The root cause is that in each `_forward`
```
if log_level <= _logger.LogLevel.WARNING and not self._raised_ORTModuleONNXModelException:
warnings.warn(
(
f"Fallback to PyTorch due to exception {type(self._exception)} was triggered. "
"Report this issue with a minimal repro at https://www.github.com/microsoft/onnxruntime. "
f"See details below:\n\n{_utils.get_exception_as_string(self._exception)}"
),
UserWarning,
)
```
above code will be called and log the `exception` through
`get_exception_as_string`,
In my training case, this will lead to 40 k times of `Traceback` stdout
and 110 millions lines of `onnx graph` output and run into OOM.
### Validation
After above fixes, the log.txt file will only be **2.4 MB**.
---------
Co-authored-by: ruiren <ruiren@microsoft.com>
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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 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.