* add training set transforms
* add DICE cross entropy loss
* convert pred and label to Tensor when calculating DICE score
* cleanups and allow train dataset batching
* fix DICE CE loss calculation
* jitted training step
* clean up DICE CE loss calculation
* initial support for sharding
* Revert "initial support for sharding"
This reverts commit e3670813b8a67469e7f694e09f2d15a8c40065da.
* minor updates
* cleanup imports
* add support for sharding
* apply temp patch to try to avoid OOM
* revert cstyle changes
* add gradient acc
* hotfix
* add FP16 support
* add ability to train on smaller image sizes
* add support for saving and loading checkpoints + cleanup some various modes
* fix issue with using smaller patch size + update W&B logging
* disable LR_WARMUP_EPOCHS
* updates
* minor cleanups
* cleanup
* update order of transformations
* more cleanups
* realize loss
* cleanup
* more cleanup
* some cleanups
* add RAM usage
* minor cleanups
* add support for gradient accumulation
* cleanup imports
* minor updates to not use GA_STEPS
* remove FP16 option since it's available now globally
* update multi-GPU setup
* add timing logs for training loop
* go back to using existing dataloader and add ability to preprocess data to save time
* clean up optimization and re-enable JIT and multi-GPU support for training and evaluation
* free train and eval steps memory
* cleanups and scale batch size based on the number of GPUs
* fix GlobalCounters import
* fix seed
* fix W&B setup
* update batch size default size
* add back metric divergence check
* put back JIT on UNet3d eval
* move dataset preprocessing inside training code
* add test for dice_loss
* add config logging support to W&B and other cleanups
* change how default float is getting retrieved
* remove TinyJit import duplicate
* update config logging to W&B and remove JIT on eval_step
* no need for caching preprocessed data anymore
* fix how evaluation is ran and how often
* add support for LR scaling
* fix issue with gaussian being moved to scipy.signal.windows
* remove DICE loss unit test
* fix issue where loss isn't compatible with multiGPU
* add individual BEAM control for train and eval steps
* fix ndimage scipy import
* add BENCHMARK
* cleanups on BENCHMARK + fix on rand_flip augmentation during training
* cleanup train and eval BEAM envs
* add checkpointing support after every eval
* cleanup model_eval
* disable grad during eval
* use new preprocessing dataset mechanism
* remove unused import
* use training and inference_mode contexts
* start eval after benchmarking
* add data fetching time
* cleanup decorators
* more cleanups on training script
* add message during benchmarking mode
* realize when reassigning LR on scheduler and update default number of epochs
* add JIT on eval step
* remove JIT on eval_step
* add train dataloader for unet3d
* move checkpointing to be done after every epoch
* revert removal of JIT on unet3d inference
* save checkpoint if metric is not successful
* Revert "add train dataloader for unet3d"
This reverts commit c166d129dfbe2e1c46d1937135a60b4ed25caa3d.
* Revert "Revert "add train dataloader for unet3d""
This reverts commit 36366c65d26f59ed1227acb670d5ce7b997606ae.
* hotfix: seed was defaulting to a value of 0
* fix SEED value
* remove the usage of context managers for setting BEAM and going from training to inference
* support new stack API for calculating eval loss and metric
* Revert "remove the usage of context managers for setting BEAM and going from training to inference"
This reverts commit 2c0ba8d322ec912bd8617cbe167c542e9ba229d9.
* check training and test preprocessed folders separately
* clean up imports and log FUSE_CONV_BW
* use train and val preprocessing constants
* add kits19 dataset setup script
* update to use the new test decorator for disabling grad
* update kits19 dataset setup script
* add docs on how to train the model
* set default value for BASEDIR
* add detailed instruction about BASEDIR usage
---------
Co-authored-by: chenyu <chenyu@fastmail.com>
* no UnaryOps.NEG in generated UOp patterns
removed pattern `x * (-1) -> -x` and `x != True`
* those are fine because NEG became CMPNE and True
* fix sd validation L2 norm
* move cifar into datasets
* support for pathlib Tensors, tar_extract, and fetch gunzip
* too early for Device.DEFAULT
* simpler hlb_cifar + .to(None) is default
* new compiler failure, start beautiful_cifar
* beautiful cifar runs but is broken
* jit train step
* cleaner
* std_mean, not mean_std
* more correct
* fast indexing
* don't print that
* torch load broken
* add eval
* nicer bar
* decoraters are the way to do this
* bounds check the load
* a few ops
* batchnorm bugfix, if track_running_stats is False, use online estimate
* full timing
* fix fusion
* unneeded realize
* master tensor
* most of the work from the uops2 branch
* schedule
* realize
* kernel
* lowerer
* search
* green
* merge uops with ops
* Revert "merge uops with ops"
This reverts commit 1408a59f12c97e3466679884266b247cf9df46bc.
* fix benchmark
* remove extra dedup
* tests if the linearizer is generating dumb code
* push consts to the end
* sort adds
* sorted add and mul
* this better
* simple expand/contract
* no math contract/expand
* mcts search
* mcts cleanups
* mcts cleanup
* random shuffle children order
* mcts in handcode_opt
* src and remove_node
* debug 3 to print ast
* print the type
* mcts in extra
* start writing openelm
* progress...hit bug
* repeat_interleave support
* gqa
* add rotary embedding
* spp
* i think it runs correctly
* broken
* output is good now
* cleanups
* no io_uring on android
* make buffer view optional with a flag [run_process_replay]
* do not view when sharding to save memory [run_process_replay]
* llama shard axis=0 sometimes
---------
Co-authored-by: George Hotz <72895+geohot@users.noreply.github.com>
Co-authored-by: chenyu <chenyu@fastmail.com>
replaced all dtype.np with _to_np_dtype defined in tensor.py.
after this, the only numpy usages are (1) Tensor(np.ndarray), (2) construct .numpy() output, (3) numpy random buffer
George Hotz
kormann
chenyu
Francis Lata
qazal
wozeparrot
Tobias Fischer
George Hotz
Elias Wahl
David Hou
reddyn12
nimlgen
Brennan Kinney
Nik