* 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>
* 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
* lazy rewrite, try 2
* min fix tests
* pass contig test
* put broken pads back
* move that to realize
* no contig child fixes array packing
* so wrong
* now that's correct
* base children
* fix bind issues
* disable to_image_idx
* fix tests
* that failure shouldn't break other tests
* more fixes
* fix torch
* skip failing tests in CI
* 1e-7
* half is broken
* 1e-6 margin of error
* validate stable diffusion for seed 0
the closest false positive i can get is with the setup and one less step. dist = 0.0036
same setup with fp16 has dist=5e-6.
so setting validation threshold to 1e-4 should be good
* run with --seed 0
* `global_load` and `global_store` using buffer dtype
* `UOps.PHI` in all dtypes
* `UOps.ALU` in all dtypes
* `UOps.CONST` & `UOps.DEFINE_ACC` in all dtypes
* -- endof implementation --
+tiny lint changes
* these tests require the fp16 extention
you can run them locally to confirm they're green: (GPT2 test is broken in master for mac, see [this](https://discord.com/channels/1068976834382925865/1069001075828469790/1177993277958533261)
`GPU=1 python3 -m pytest test/external/external_test_onnx_backend.py::OnnxBackendNodeModelTest::test_dequantizelinear_e4m3fn_float16_cpu test/external/external_test_onnx_backend.py::OnnxBackendNodeModelTest::test_max_float16_cpu test/external/external_test_onnx_backend.py::OnnxBackendNodeModelTest::test_min_float16_cpu test/models/test_real_world.py::TestRealWorld::test_llama test/models/test_real_world.py::TestRealWorld::test_gpt2 test/models/test_whisper.py test/test_specific_conv.py::TestSpecific::test_big_vec_mul`
skip the new test_linearizer_failures in CI GPU because of the fp16 extention
This passes on a real GPU since the extention is available:
`GPU=1 python3 -m pytest test/test_linearizer_failures.py::TestLinearizerFailures::test_failure_8`
see CI logs [here](https://github.com/tinygrad/tinygrad/actions/runs/6996590597/job/19032641427#step:14:644)
* these tests fail in CI due to segfaults and CPU crashes
To confirm they're green locally, you can run the following commands:
1. For the tests skipped in test_ops.py (note: CLANG is very slow)
`for var in GPU CUDA CLANG; do export $var=1; for test in test/test_ops.py::TestOps::test_slice_fancy_indexing_no_dim_collapse test/test_ops.py::TestOps::test_slice_fancy_indexing_dim_collapse_int test/test_ops.py::TestOps::test_slice_fancy_indexing_dim_inject_none test/test_ops.py::TestOps::test_slice_fancy_indexing_dim_inject_and_collapse; do python3 -m pytest $test; done; unset $var; done`
2. For the ONNX tests skipped in CLANG:
```
CLANG=1 python3 -m pytest test/external/external_test_onnx_backend.py::OnnxBackendNodeModelTest::test_ai_onnx_ml_array_feature_extractor_cpu \
test/external/external_test_onnx_backend.py::OnnxBackendNodeModelTest::test_gather_elements_0_cpu \
test/external/external_test_onnx_backend.py::OnnxBackendNodeModelTest::test_nllloss_NCd1_expanded_cpu \
test/external/external_test_onnx_backend.py::OnnxBackendNodeModelTest::test_sce_mean_weight_ii_3d_cpu \
test/external/external_test_onnx_backend.py::OnnxBackendNodeModelTest::test_gather_elements_1_cpu \
test/external/external_test_onnx_backend.py::OnnxBackendNodeModelTest::test_sce_NCd1_mean_weight_negative_ii_cpu \
test/external/external_test_onnx_backend.py::OnnxBackendNodeModelTest::test_nllloss_NCd1_weight_expanded_cpu \
test/external/external_test_onnx_backend.py::OnnxBackendNodeModelTest::test_nllloss_NCd1d2d3_none_no_weight_negative_ii_expanded_cpu \
test/external/external_test_onnx_backend.py::OnnxBackendNodeModelTest::test_nllloss_NCd1_ii_expanded_cpu \
test/external/external_test_onnx_backend.py::OnnxBackendNodeModelTest::test_sce_mean_weight_ii_4d_cpu \
test/external/external_test_onnx_backend.py::OnnxBackendNodeModelTest::test_sce_mean_weight_ii_3d_log_prob_cpu \
test/external/external_test_onnx_backend.py::OnnxBackendNodeModelTest::test_gather_elements_negative_indices_cpu \
test/external/external_test_onnx_backend.py::OnnxBackendNodeModelTest::test_sce_NCd1d2d3d4d5_mean_weight_log_prob_cpu \
test/external/external_test_onnx_backend.py::OnnxBackendNodeModelTest::test_sce_NCd1_mean_weight_negative_ii_log_prob_cpu \
test/external/external_test_onnx_backend.py::OnnxBackendNodeModelTest::test_nllloss_NCd1d2_no_weight_reduction_mean_ii_expanded_cpu \
test/external/external_test_onnx_backend.py::OnnxBackendNodeModelTest::test_sce_NCd1d2d3d4d5_mean_weight_cpu \
test/external/external_test_onnx_backend.py::OnnxBackendNodeModelTest::test_nllloss_NCd1d2d3d4d5_mean_weight_expanded_cpu \
test/external/external_test_onnx_backend.py::OnnxBackendNodeModelTest::test_nllloss_NCd1_mean_weight_negative_ii_expanded_cpu \
test/external/external_test_onnx_backend.py::OnnxBackendNodeModelTest::test_sce_mean_weight_ii_4d_log_prob_cpu \
test/external/external_test_onnx_backend.py::OnnxBackendNodeModelTest::test_nllloss_NCd1d2_with_weight_reduction_mean_expanded_cpu \
test/external/external_test_onnx_backend.py::OnnxBackendNodeModelTest::test_nllloss_NCd1_weight_ii_expanded_cpu \
test/external/external_test_onnx_backend.py::OnnxBackendNodeModelTest::test_nllloss_NCd1d2_with_weight_reduction_sum_ii_expanded_cpu \
test/external/external_test_onnx_backend.py::OnnxBackendNodeModelTest::test_nllloss_NCd1d2_with_weight_reduction_sum_expanded_cpu \
test/external/external_test_onnx_backend.py::OnnxBackendNodeModelTest::test_nllloss_NCd1d2_expanded_cpu \
test/external/external_test_onnx_backend.py::OnnxBackendNodeModelTest::test_nllloss_NCd1d2_reduction_sum_expanded_cpu \
test/external/external_test_onnx_backend.py::OnnxBackendNodeModelTest::test_nllloss_NCd1d2d3d4d5_none_no_weight_expanded_cpu \
test/external/external_test_onnx_backend.py::OnnxBackendNodeModelTest::test_nllloss_NCd1d2d3_sum_weight_high_ii_expanded_cpu \
test/external/external_test_onnx_backend.py::OnnxBackendNodeModelTest::test_nllloss_NCd1d2_reduction_mean_expanded_cpu \
test/external/external_test_onnx_backend.py::OnnxBackendNodeModelTest::test_nllloss_NCd1d2_with_weight_expanded_cpu
```
3. The LLVM test I skipped here is already [skipped in master for all backends](https://github.com/tinygrad/tinygrad/blob/master/test/external/external_test_onnx_backend.py#L186), I just made it more specific
`LLVM=1 python3 -m pytest test/external/external_test_onnx_backend.py::OnnxBackendNodeModelTest::test_dequantizelinear_e4m3fn_float16_cpu`
* Revert "these tests fail in CI due to segfaults and CPU crashes"
This reverts commit 15db570143.
* merge with cleanup-vectorized-hip-renders
* barely working HIP P1, ALU ops need a refactor?
* manage the fact that in HIP [half2 is actually an unsigned int vec](f921880387/hip/include/hip/amd_detail/amd_hip_fp16.h (L59)) and half is a totally different __half that [has an unsigned int element in it](f921880387/hip/include/hip/amd_detail/amd_hip_fp16.h (L50)) but can't be accessed [because it's private](f921880387/hip/include/hip/amd_detail/amd_hip_fp16.h (L86)). If you just do this:
```
half2 val0 = // ...
half val1 = // ...
```
then you can't do:
```
val0.x + val1 // error: use of overloaded operator '+' is ambiguous (with operand types 'unsigned short' and 'half' (aka '__half'))
```
* update the sign definition to avoid division by zero in all dtypes
* diff cleanup p1: why were these in the diff anyways
* less hacky HIP, enable CIFAR fp16 benchmark, test ops for HIP in CI!
add ALU ops overloads for HIP
this will make HIP max work
handle mod
Revert "handle mod"
This reverts commit 370fd4b3fbe99b6ae8cc293d005b106628205933.
update max to use hmax
add HIP GEP render logic
enable CIFAR fp16 benchmark
test ops for HIP
back to store as float because this only works for float4 grouping right now
test_ops for hip!!
always sign
* back to the sign we had before because we cant do a backward pass on a Less node
* remove old hacks
HIP compiling test_ops in CI takes ~9 mins, not doing it for now
new HIP ALUs
* reduce accs done right
* refactor to function
* no device hacks
hacks p2
the other way
* LLVM ALU ops
half, float and double are all float
update max
* update test_uops, cmplt is always a bool in the real linearizer. assertAlmostEqual is wrong when ret is bool
* cleanup LLVM wrong code
* dummy change for the CUDA install glitch
---------
Co-authored-by: George Hotz <72895+geohot@users.noreply.github.com>
* add name support
* use fetch in gpt2
* remove requests from main lib, networkx also optional
* umm, keep that assert
* updates to fetch
* i love the walrus so much
* stop bundling mnist with tinygrad
* err, https
* download cache names
* add DOWNLOAD_CACHE_VERSION
* need env.
* ugh, wrong path
* replace get_child
