* mockgpu nv
* works
* comment that out
* fix merge
* setup gpuocelot
* install packages
* not run all of them
* passes
* fix ci
* almost
* should pass
* linter
* linter 2
* try this?
* ugn, not supported
* ci
* remove ticket from description
* better descs
* refactor to Program class
* switch to Program
* fix tests
* smaller diff
* self.p
* more tests
* fix metal test
* tests
* fix openpilot
* move that to linearizer
* p.launchdims
* subbuffer support
* diskbuffer offset
* cuda subbuffer works
* use subbuffer
* more subbuffer tests
* consecutive
* cast
* consec
* offset
* view is a better name
* offset is in nbytes
* fix view + memory planner
* delete unused DiskRunner
* reverse order
* no subbuffers on unrealized consts
* only enabled for disk
* don't reverse memory
* view supported devices
* pickle buffer view
* ring jit
* support extra view inputs in jit
* fix JIT=2 issue
* test copy jit
* p2p isn't an option anymore
* fix dep tracking issue
* fix mypy
* fix pickle
* from_nv is contents now
* don't call contiguous for unpadded const into multi tensor
fixed multi const folding for sharded const.
still wip, need to be careful that this does not break multi device cache somewhere
* ehh need a memory test for that
* simple sharded memory test
* 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>
* allow LB <- MLB assign, but don't reuse buffer
* update test
* update test
* assign assert axes are the same
* update tests to manually shard running stats
* unused import
* UnsyncedBatchNorm with synced trainable weights for hlb cifar
* multitensor reshape tests
* test mlb assign change axis
* E501
* argfix axis
* don't import batchnorm from hlb_cifar in test_multitensor
* pass num_devices to UnsyncedBatchNorm in test, allow UnsyncedBatchNorm to be used with LB
* add backprop test for UnsyncedBatchNorm
* break out MLB assign and reshape changes
* manually shard running mean and running var
* don't shard unless syncbn=0
* replace nn.BatchNorm2d with UnsyncedBatchNorm
* don't increment num_batches_tracked if not tracking running stats
* update tests
* oops
* Revert "oops"
This reverts commit 5e8a67a535.
* Revert "update tests"
This reverts commit 7ebf65d89a.
* Revert "don't increment num_batches_tracked if not tracking running stats"
This reverts commit 78de0ea9ee.
* Revert "replace nn.BatchNorm2d with UnsyncedBatchNorm"
This reverts commit d03da53da7.
* don't increment num_batched_tracked if not tracking running stats
* oops
* test_batchnorm_axis
* compare against torch
* types
---------
Co-authored-by: chenyu <chenyu@fastmail.com>
* shrink MLB on sharded axis
use onehot structure to store the real partition. goal is unsynced batchnorm2d that can be run on multigpu for training.
draft version in https://github.com/chenyuxyz/tinygrad/pull/109
* SYNCBN flag
* test unclean shrinks
* UnsyncedBatchNorm reuses BatchNorm
* more robust pad arg check
* better types
* more tests!
* 6 gpus in benchmark
* disable slow GPUS=6 benchmark
* move reduce over 0 len axis logic to lazy.py
this fixed uneven shard reduce case if the uneven one has length 0
* fix interpreted backends
* fix backwards for 0 shape tensors too
