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55d3a5def9* preallocate all realized buffers
* contiguous
* work
* comment that out
* move to schedule
* better
* correct fix
* just buffer
* disk bufs
* fixes disk tensor stuff
* fix symbolic stuff
* fix multi
* 162 failures
* bugfixes
* don't check that anymore
* fix schedule tests
* mnist should be contiguious
* type and buffer
* fix tests
* shrink axis correction
* mypy fixes
* tests skips
* same 37 failures
* dedup
* no shrink in the graph
* 29 failures
* skips
* fix custom kernel
* fix training
* those optimizations aren't supported currently
* simpler
* more correct
* tests
* 14 failures
* works
* fix that test
* broken
* 11 failures
* only kernel counts left
* fixes
* all tests pass
* remove tensor_map
* op test
* 200 -> 230
* test fixes
* fixes
* revert test_tiny thing
* guard
* revert that
* test tiny passes
* no contigs there
* base realize back
* Revert "no contigs there"
This reverts commit
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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 | ||
| optim.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