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* lars optimizer + tests * fix skip list! * use id to compare in skip list * go back to using set * Tensor(bool) * Tensor(bool) is and * don't lint external/mlperf_resnet * whitespace * add external_test_optim to opencl tests * give mlperf task a name * mlperf under onnx * remove track_gnorm * contiguous instead of realize * assert momentum and weight decay positive --------- Co-authored-by: chenyu <chenyu@fastmail.com>
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