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* embedding is slow * failing * float is fine * null * it fails * simplify embedding with broadcasting * ATOMIC_ADD incoming * min change * simpler test * better test * fix test * real test * simpler * cleanups * types and names * _zero_kernel * grad multi * hack * none * multi unshard * more for call * don't tag in call * good * call_multi * call_multi wow claude is useless * embedding backward mutli test * test passes * fix as_param * shape_to_shape_arg * add clip * before cast * fix spec=2, use atomics
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