* add support for train/val datasets for kits19
* split dataset into train and val sets
* add tests for kits19 dataloader
* add MLPerf dataset tests to CI
* update unet3d model_eval script
* fix linting
* add nibabel
* fix how mock dataset gets created
* update ref implementation with permalink and no edits
* clean up test and update rand_flip implementation
* cleanups
we want to have different BEAM values for resnet train and eval. global JITBEAM cannot do this. added the flag to change beam behavior at cnt=0 (so it default behaves the same with or without TinyJit), and for cnt=1 it uses existing BEAM.value.
Also updated the context var BEAM in resnet to be outside of TinyJit. saves about 3 minutes compile time
* add DICE loss and metrics
* update dice to include reference implementation's link
* remove unused imports
* remove unnecessary test file and update pred + label for metrics and losses test
* add tests to CI + add exclusion of mlperf_unet3d
---------
Co-authored-by: chenyu <chenyu@fastmail.com>
* resnet individual layer benchmarks!
* small
* 1 and 2
* mem_used
* no ci
* better conv print
* defaults
* prints
* adjust
* adjust
* adjust
* benchmark only one layer example
* tensor.training, zero_grad, sum instead of mean, last mem, last kernel count
* default jitcnt=1
* scale flops/kernels with jitcnt
* add note about jitcnt memory
* touchup
* fp16 resnet
* cast running mean and var back to default float
* extra cast
* check symbolic no overflow
* add linearizer failure
* loss scaler after grad contig
* oops
* i think this works
* don't loss scale fp32
* remove overflow test case
* remove symbolic bounds check
* loss scaler should be float
* temporarily disable padto cuz bug
shruggie
* make running stats in batchnorm float32?
* calculate lars stuff in fp32?
* oops
* remove most changes
* move loss scaler out of optimizer
* no more FP16 var
* oops
---------
Co-authored-by: chenyu <chenyu@fastmail.com>
* this is a lot of stuff
TEST_TRAIN env for less data
don't diskcache get_train_files
debug message
no lr_scaler for fp32
comment, typo
type stuff
don't destructure proc
make batchnorm parameters float
make batchnorm parameters float
resnet18, checkpointing
hack up checkpointing to keep the names in there
oops
wandb_resume
lower lr
eval/ckpt use e+1
lars
report top_1_acc
some wandb stuff
split fw and bw steps to save memory
oops
save model when reach target
formatting
make sgd hparams consistent
just always write the cats tag...
pass X and Y into backward_step to trigger input replace
shuffle eval set to fix batchnorm eval
dataset is sorted by class, so the means and variances are all wrong
small cleanup
hack restore only one copy of each tensor
do bufs from lin after cache check (lru should handle it fine)
record epoch in wandb
more digits for topk in eval
more env vars
small cleanup
cleanup hack tricks
cleanup hack tricks
don't save ckpt for testeval
cleanup
diskcache train file glob
clean up a little
device_str
SCE into tensor
small
small
log_softmax out of resnet.py
oops
hack :(
comments
HeNormal, track gradient norm
oops
log SYNCBN to wandb
real truncnorm
less samples for truncated normal
custom init for Linear
log layer stats
small
Revert "small"
This reverts commit 988f4c1cf3.
Revert "log layer stats"
This reverts commit 9d98224585.
rename BNSYNC to SYNCBN to be consistent with cifar
optional TRACK_NORMS
fix label smoothing :/
lars skip list
only weight decay if not in skip list
comment
default 0 TRACK_NORMS
don't allocate beam scratch buffers if in cache
clean up data pipeline, unsplit train/test, put back a hack
remove print
run test_indexing on remu (#3404)
* emulated ops_hip infra
* add int4
* include test_indexing in remu
* Revert "Merge branch 'remu-dev-mac'"
This reverts commit 6870457e57, reversing
changes made to 3c4c8c9e16.
fix bad seeding
UnsyncBatchNorm2d but with synced trainable weights
label downsample batchnorm in Bottleneck
:/
:/
i mean... it runs... its hits the acc... its fast...
new unsyncbatchnorm for resnet
small fix
don't do assign buffer reuse for axis change
* remove changes
* remove changes
* move LARS out of tinygrad/
* rand_truncn rename
* whitespace
* stray whitespace
* no more gnorms
* delete some dataloading stuff
* remove comment
* clean up train script
* small comments
* move checkpointing stuff to mlperf helpers
* if WANDB
* small comments
* remove whitespace change
* new unsynced bn
* clean up prints / loop vars
* whitespace
* undo nn changes
* clean up loops
* rearrange getenvs
* cpu_count()
* PolynomialLR whitespace
* move he_normal out
* cap warmup in polylr
* rearrange wandb log
* realize both x and y in data_get
* use double quotes
* combine prints in ckpts resume
* take UBN from cifar
* running_var
* whitespace
* whitespace
* typo
* if instead of ternary for resnet downsample
* clean up dataloader cleanup a little?
* separate rng for shuffle
* clean up imports in model_train
* clean up imports
* don't realize copyin in data_get
* remove TESTEVAL (train dataloader didn't get freed every loop)
* adjust wandb_config entries a little
* clean up wandb config dict
* reduce lines
* whitespace
* shorter lines
* put shm unlink back, but it doesn't seem to do anything
* don't pass seed per task
* monkeypatch batchnorm
* the reseed was wrong
* add epoch number to desc
* don't unsyncedbatchnorm is syncbn=1
* put back downsample name
* eval every epoch
* Revert "the reseed was wrong"
This reverts commit 3440a07dff3f40e8a8d156ca3f1938558a59249f.
* cast lr in onecycle
* support fp16
* cut off kernel if expand after reduce
* test polynomial lr
* move polynomiallr to examples/mlperf
* working PolynomialDecayWithWarmup + tests.......
add lars_util.py, oops
* keep lars_util.py as intact as possible, simplify our interface
* no more half
* polylr and lars were merged
* undo search change
* override Linear init
* remove half stuff from model_train
* update scheduler init with new args
* don't divide by input mean
* mistake in resnet.py
* restore whitespace in resnet.py
* add test_data_parallel_resnet_train_step
* move initializers out of resnet.py
* unused imports
* log_softmax to model output in test to fix precision flakiness
* log_softmax to model output in test to fix precision flakiness
* oops, don't realize here
* is None
* realize initializations in order for determinism
* BENCHMARK flag for number of steps
* add resnet to bechmark.yml
* return instead of break
* missing return
* cpu_count, rearrange benchmark.yml
* unused variable
* disable tqdm if BENCHMARK
* getenv WARMUP_EPOCHS
* unlink disktensor shm file if exists
* terminate instead of join
* properly shut down queues
* use hip in benchmark for now
---------
Co-authored-by: George Hotz <72895+geohot@users.noreply.github.com>
* working PolynomialDecayWithWarmup + tests.......
add lars_util.py, oops
* keep lars_util.py as intact as possible, simplify our interface
* whitespace
* clean up
* clean up
* asserts
* test polylr for full resnet training run
* add comment
* rename
* fix do_optim
* don't cast lr
* info
* calculate from train_files
* skip it
* 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>
* initial multitensor jit support and tests
* Added graphs to multitensor jit and updated tests
* update unbind api
* fix set device, add TinyJit to resnet
* update_stats includes device
---------
Co-authored-by: ramenguy99 <ramenguy99@gmail.com>
* Rename in files
* Move files
* Moved to extra/datasets as suggested
* Changes to files
* Fixed stupid mistake
---------
Co-authored-by: terafo <terafo@protonmail.com>
