1ebc5d3879
### Log ORTModule initialization overhead
When profiling some model for example
```
torchrun --nproc_per_node=1 examples/onnxruntime/training/language-modeling/run_mlm.py --model_name_or_path microsoft/deberta-v3-large --dataset_name wikitext --dataset_config_name wikitext-2-raw-v1 --num_train_epochs 10 --per_device_train_batch_size 1 --per_device_eval_batch_size 1 --do_train --overwrite_output_dir --output_dir ./outputs/ --seed 1137 --fp16 --report_to none --optim adamw_ort_fused --max_steps 200 --logging_steps 1 --use_module_with_loss
{'train_runtime': 303.8711, 'train_samples_per_second': 0.658, 'train_steps_per_second': 0.658, 'train_loss': 6.569518616199494, 'epoch': 0.09}
100%|200/200 [05:03<00:00, 1.52s/it]
***** train metrics *****
epoch = 0.09
train_loss = 6.5695
train_runtime = 0:05:03.87
train_samples = 2223
train_samples_per_second = 0.658
train_steps_per_second = 0.658
```
The end to end time is 303s (train_runtime=0:05:03.87), but the
ORTModule first step initialization (including export, graph build, etc)
takes about 255s, so when we compare the end to end time for a baseline
ORT with an improved version of ORT, there is no perf gains, since the
x% gains over (303-255) is diluted out among the overall 303s. This is
misleading!
So this PR outputs the ORTModule initialization overhead in the output,
then we can manually compute the real compte time and get the perf
gains.
If the log level is >= WARNING, then only the total end to end time +
export time is logged, otherwise, more details of break down is logged:
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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
-
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 |  |
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.