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0fbc551622* fp8 train * clean * lint * test fix from #13439 * skip first/last layer * rm __init__, restore unroll <=32 check * tests * clean test, remove unused * multi-gpu test, clean quantize_to_fp8 * remove bert contiguous * run script * test: better check * run script search * add seed in bert data shuffle * move script to mi350x folder --------- Co-authored-by: chenyu <chenyu@fastmail.com> |
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| .. | ||
| scripts | ||
| training_submission_v4.0/tinycorp | ||
| training_submission_v4.1/tinycorp | ||
| training_submission_v5.0/tinycorp | ||
| training_submission_v5.1/tinycorp | ||
| training_submission_v6.0/tinycorp | ||
| dataloader.py | ||
| helpers.py | ||
| initializers.py | ||
| losses.py | ||
| lr_schedulers.py | ||
| metrics.py | ||
| model_eval.py | ||
| model_spec.py | ||
| model_train.py | ||
| README | ||
Each model should be a clean single file. They are imported from the top level `models` directory It should be capable of loading weights from the reference imp. We will focus on these 5 models: # Resnet50-v1.5 (classic) -- 8.2 GOPS/input # Retinanet # 3D UNET (upconvs) # RNNT # BERT-large (transformer) They are used in both the training and inference benchmark: https://mlcommons.org/en/training-normal-21/ https://mlcommons.org/en/inference-edge-30/ And we will submit to both. NOTE: we are Edge since we don't have ECC RAM