* assert jitted times in openpilot
* better error
* better error
* add ASSERT_MIN_STEP_TIME to more models
* t is step_times
* update benchmark times
* update times
* clean up unused imports in examples
* enable unused import checking in examples
* lint
* ignore F541 and F841 - focus on unused imports only
* clean up
* restore tinygrad.frontend.torch for TINY_BACKEND
* tiny change
* Don't use numpy inside hlb_cifar10 training loop
* Lint it
* jit it
* Drop the last half-batch
* Use gather for random_crop and reuse perms
* Wrap train_cifar in FUSE_ARANGE context
* No need to pass FUSE_ARANGE=1 to hlb_cifar10.py
* Add cutmix to jittable augmentations
* Remove .contiguous() from fetch_batches
* Fix indexing boundary
---------
Co-authored-by: Irwin1138 <irwin1139@gmail.com>
* MultiLazyBuffer is UOp [pr]
* this is new mlb
* this is the idea
* progress
* multitensor works
* more movement ops
* this
* MultiLazyBuffer is UOp
* cleanups
* multi axis
* fix more tests
* work
* not that
* add multi grad and move shard to ops
* mops not views
* no double contig
* sweet, all mt tests passing
* port old logic
* remove lbs
* fix realized
* whitespace
* assign tweak
* test_assign_kv_cache_multi passes
* fix is_realized
* fix JIT for multi
* just a few more lines i'll pay them back soon i swear please bro just a few more
* no split reduceop for multi
* 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>
* env var to change default float to fp16 or bf16
looking for standard names for these. we have FLOAT16 that does something to IMAGE and HALF to convert weights.
working on default bf16 too.
```
RuntimeError: compile failed: <null>(6): error: identifier "__bf16" is undefined
__bf16 cast0 = (nv_bfloat16)(val0);
```
remove that in cifar
* DEFAULT_FLOAT
* default of default
* unit test
* don't check default
* tests work on linux
* training cifar with BF16 on CUDA
memory usage is between float and half due to numpy calls on dataset preprocessing, which converts into float.
* simpler bf16 functions
* bf16 cifar works for HSA too just very slow
* simpler bf16 functions, we love cuda
prepared bfloat16 change. added float() and cast(default_float) in whiteing, explicitly set dtype in various places that convert between numpy and Tensor
* 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
* cpu tests pass
* torch works
* works
* metal works
* fix ops_disk
* metal jit works
* fix openpilot
* llvm and clang work
* fix webgpu
* docs are rly broken
* LRU works on metal
* delete comment
* revert name to ._buf. LRU only on Compiled
* changes
* allocator
* allocator, getting closer
* lru alloc
* LRUAllocator
* all pass
* metal
* cuda
* test examples
* linearizer
* test fixes
* fix custom + clean realize
* fix hip
* skip tests
* fix tests
* fix size=0
* fix MOCKHIP
* fix thneed
* copy better
* simple
* old style metal copy
* fix thneed
* np reshape
* give cuda a device
