Benchmark: Model benchmark - deterministic training support #731
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- Add _enable_deterministic_training() method to set all necessary seeds - Add --deterministic and --random_seed command line arguments - Integrate deterministic training in _create_model() and _generate_dataset() - Add comprehensive unit tests for deterministic functionality - Tests validate parameter parsing, functionality, and regression scenarios - All tests pass and integrate with existing SuperBench test suite
…pass check_frequency to _is_finished in train/infer; add test capturing checksum log; stabilize fp32 loss path and small-dims determinism tests
…oss BERT/GPT2/CNN/LSTM/Mixtral; per-step fp32 loss logging; checksum logs; tests updated to strict/soft determinism pattern; add strict determinism CI guidance
…rings; fix GPT-2 params; soft vs strict checks stabilized
…sum tests with BERT pattern, improve docstrings and skip logic.
…BERT, GPT-2, LSTM, CNN, LLaMA examples
… models; update tests
…/CNN/BERT/Mixtral with periodic fingerprints, per-step loss capture, TF32 off, SDPA math kernel; add model_log_utils; update examples and tests, add env gating for cuBLAS.
…ted example file, remove redundant code
… unnecessary code
…idual model classes
… reduce redundant code
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…nd inference steps
Co-authored-by: Copilot <[email protected]>
Co-authored-by: Copilot <[email protected]>
Co-authored-by: Copilot <[email protected]>
…etadata overriding
…es not need to be set explicitly before running the benchmarks
Co-authored-by: Copilot <[email protected]>
Co-authored-by: Copilot <[email protected]>
Co-authored-by: Copilot <[email protected]>
…b.com/microsoft/superbenchmark into aishwaryatonpe/deterministic-training
…tcases pass on local
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Support for deterministic training and reproducible logging to all PyTorch model benchmarks in SuperBench (BERT, GPT2, LLaMA, LSTM, CNN, Mixtral).
Deterministic mode: Makes sure model runs are consistent every time by fixing random seeds, turning off TF32, and using stable math operations.
Log generation: Saves key info like loss and activation stats during training.
Log comparison: Lets you compare a new run with a previous one to check if they match.
New command-line options:
--enable-determinism
--generate-log {boolean flag which when enabled, stores the metrics (loss and activation mean) to the results file}
--compare-log {log path of the json file against which you want to compare the results of the current run}
--check-frequency
Changes -
Updated pytorch_base.py to handle deterministic settings, logging, and comparisons.
Added a new example script: pytorch_deterministic_example.py
Added a test file: test_pytorch_determinism_all.py to verify everything works as expected.
Usage -
Run with --enable-determinism --generate-log to create a reference log.
Run again with --compare-log to check if the new run matches the reference.
Make sure all parameters stay the same between runs.