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onnxruntime/orttraining/tools/scripts
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Justin Chu c7c8757a1c
Use ruff as the formatter to replace black-isort (#23397) 
Use ruff as the code formatter in place of black and isort since it is
much faster, and as projects like PyTorch and ONNX have adopted ruff
format as well.

This PR include only auto-fixed changes in formatting.
2025-01-16 11:14:15 -08:00
..
experiment.py
gpt2_model_transform.py Bump ruff from 0.5.4 to 0.9.1 (#23328) 2025-01-15 11:11:17 -08:00
layer_norm_transform.py Fix typos according to reviewdog report. (#21335) 2024-07-22 13:37:32 -07:00
model_transform.py Bump ruff from 0.5.4 to 0.9.1 (#23328) 2025-01-15 11:11:17 -08:00
nv_run_pretraining.py Use ruff as the formatter to replace black-isort (#23397) 2025-01-16 11:14:15 -08:00
opset12_model_transform.py Bump ruff from 0.5.4 to 0.9.1 (#23328) 2025-01-15 11:11:17 -08:00
performance_investigation.py Disable PERF* rules in ruff to allow better readability (#16834) 2023-07-25 15:38:22 -07:00
pipeline_model_split.py Disable PERF* rules in ruff to allow better readability (#16834) 2023-07-25 15:38:22 -07:00
README.txt
single_node_perf.sh
sqldb_to_tensors.py
train.py
watch_experiment.py Bump ruff to 0.3.2 and black to 24 (#19878) 2024-03-13 10:00:32 -07:00

README.txt 

Procedure to export NV's pytorch model to ONNX.

1. cd into BERT in DeepLearningExamples and launch the docker.
2. run nv_run_pretraining.py using the same parameter you run run_pretraining.py. It will produce a model in your checkpoint directory.
3. Assume that the exported model's name is 'bert.onnx'. Run model_transform.py bert.onnx
4. Then run layer_norm_transform.py bert_optimized.onnx. The final model name would be bert_optimized_layer_norm.onnx
5. Now, you can run training with the newly created model.

Note that if you want to change model's configuration, you can edit bert_config.json in the BERT directory.

Example commands:
Step 2 (inside docker):
python3 /workspace/bert/nv_run_pretraining.py --input_dir=data/bookcorpus/hdf5_shards/ --output_dir=/results/checkpoints1 --config_file=bert_config.json --bert_model=bert-large-uncased --warmup_proportion=0 --num_steps_per_checkpoint=2000 --learning_rate=0.875e-4 --seed=42 --do_train --phase2 --max_seq_length=512 --max_predictions_per_seq=80 --max_steps=200 --train_batch_size=2 

Step 3 (inside onnxruntime/build/Linux/RelWithDeb):
sudo /data/anaconda/envs/py35/bin/python /bert_ort/wechi/DeepLearningExamples/PyTorch/LanguageModeling/BERT/model_transform.py /bert_ort/wechi/DeepLearningExamples/PyTorch/LanguageModeling/BERT/results/checkpoints1/bert_for_pretraining_without_loss_vocab_30528_hidden_1024_maxpos_512.onnx 

Step 4 (inside onnxruntime/build/Linux/RelWithDeb):
sudo /data/anaconda/envs/py35/bin/python /bert_ort/wechi/DeepLearningExamples/PyTorch/LanguageModeling/BERT/layer_norm_transform.py /bert_ort/wechi/DeepLearningExamples/PyTorch/LanguageModeling/BERT/results/checkpoints1/bert_for_pretraining_without_loss_vocab_30528_hidden_1024_maxpos_512_optimized.onnx

Step 5 (inside onnxruntime/build/Linux/RelWithDeb):
./onnxruntime_training_bert --num_of_perf_samples=100 --train_batch_size=1 --mode=perf --model_name /bert_ort/wechi/DeepLearningExamples/PyTorch/LanguageModeling/BERT/results/checkpoints1/bert_for_pretraining_without_loss_vocab_30528_hidden_1024_maxpos_512_optimized_layer_norm