* 1
* 83 failed
* learning how git works
* lol idk
* zero shape aaaa
* space lol
* aaa
* test check
* haha
* fixed gather
* 73 failing
* 71 failing
* 68 failing
* added some debug
* fking resize
* lol
* 62 failing
* 58 failling fucking did nearest resize hell yeah
* clean up
* 56 failing
* janitor duty
* lol
* 53 failing
* hi mom
* 50 failing
* added linear interp, but coord_trans is wrong
* did lin interpolation woohoo
* 43 failing
* 40 failing
* temporary Gather fix
* 39 failing
* fixed slice onnxver<10
* 37 failing
* 35 failing
* excluded tests that use float64
* 32 failing with hacks
* added _batchnorm() for 3D 5D batchnorm, 29 failing
* changed ALLOWED_KERNEL_COUNT from 199 to 207
* added improved Gather op, reverted ALLOWED_KERNEL_COUNT commit
* support Round op
* added storage_order/indices maxpool, 27 failing
* support maxunpool, 25 failures
* support Gradient, 23 failures
* merged new where
* added Adam
* cleanups
* added Momentum and Nesterov Momentum
* added Adagrad
* support sequence_type, 20 failing
* ugh git
* I give up on cubic interp :D, 9 failing
* sexy 1 liner gather, much improved, wow
* polished gather to make it shine bright like a diamond
* clean 1 liner for gather
* improved readability of gather
* uhh
* clean up
* more clean up
* WHITEspace
* implemented SoftmaxCrossEntropyLoss op
* added comments and cleaned up if statements
* update
* thank based wozeparrot for pow and new GatherElements
* CPU and TORCH all pass | cast float64 -> float32 for all fromCPU()
* _nearest_gather() failing on yolo
* reverted ops_cpu change and added assert in Resize
* added comments for resize for multiple channels
* oops
* merge
* test
* switched np.pad to Tensor.pad for constant padding
* gah
* gah2
* sexy reflect pad with movementops -> add
* delete commented out lines
* edge mode pad sexy as well
* trying out model_benchmark
* revert gitignore change lol
* init
* Revert "init"
This reverts commit 682bf2073a.
* wrote cast workaround for CPU, CPU and TORCH all pass
* wrote cast workaround for CPU, CPU and TORCH all pass
* skipped tests w/ 0 shape for METAL and GPU
* excluded tests for CLANG, CPU, TORCH, CLANG pass
* fixed hacky ConvTranspose
* gotta figure out autopad
* UOps.STORE support cast bool -> float
* small fix for fast gather
* reverted 0 shape skipped tests
* oops missed a file
* added comment
* fixed slice op hack
* First commit to pr
* More trig ops
* More trig ops
* format
* isinf support
* More ops
* changed onnx_ops to use our new gather :D
* Det op bug fix
* rebase
* fixed some tests
* det broken and slow
* fixed compress to use new gather
* implemented argmax argmin
* support variable types in type_proto
* support Upsample and Identity sequence
* we support float64 now and tinygrad support automatic broadcasting
* added EyeLike op
* resize does support multiple channels now actually
* yolov8 onnx runs successfully
* added batch size 1
* oops
* finally fixed type_proto I think
* fixed some llvm bugs
* del whitespaces
* added ZenginU Format PR
* test
* oops
* added float64 exclude tests back
* more skipped tests
* try
* ok openpilot pass
* flake8 pass
* woooooohooo
* revert external_model_benchmark changes
* perf tested gather
* removed promote types from ops_cpu
* numerical errors from 1681 is fixed
---------
Co-authored-by: ZenginU <umutzengin00@gmail.com>
* Symbolic Shape JIT
update tests
2 variables symbolic ops, adding more tests
test passing
cleanup
* more test cases
* single flag
* review update
* jit attention one piece
* realize
* symbolic_jit test for cuda
* old artifact
* works with cuda gpu but failed ci
* CUDACPU
* feat: train cifar using multigpu
* feat: split eval batch across 5
* feat: cleaner allreduce
* feat: 93.88%
* feat: cleaner batch chunking from bert
* feat: cleaner grad sync
* feat: tinygrad argmax
* feat: make it work with different gpu counts
* feat: move some stuff into the normal __init__
* feat: autodetect gpu count
* feat: move import inside
* move assembly, assembly_ptx
* successful but broken rendering of ptx asm
* clear ins before render asm
* slightly less broken :')
* we needed thread syncs
* fix float16 loading, rounding modifiers and other casting stuff, passing casts_from_half
* Fix runtime_args for gpuocelot
* our casts were flipped on both ends
* more casting
* add ternary where op
* dealing with storing/loading bool
* add test for casting to bool from negative
* Fix args.valid on ConstOp
* add to CI, TODO: fix runtime_args for test_uops
* fix placement of runtime_args to work with lazy.Device
* undo ci changes so I can push
* fix lints
* start cleanup and fix things we broke fixing lints
* add checks for PTX specifc asm instructions
* revert added test -- doesn't pass on llvm
* skip tests for underflow,overflow
* another fix for how we're setting runtime args
* Less broken cleanup
* add to CI
* add more env variables for ci test
* fix ci to install pycuda for ptx
* ci: copy cuda test command
* cleanup
* assert to make sure we're actually running ptx in ci
* remove test assert
* move is_ptx arg
* move assembly, assembly_ptx back to extras
* fix imports
* initial merge fixes
* clear registers, fix UOps.LOAD with invalid value
* draft merge fixes
* remove prints
* quick lint and merge fixes
* cleanup
* remove PTXProgram wrapper
* final cleanup
* temp change for ci rerun
* ci rerun
* rollback ISA version
* try to run commavq
* fix 0 dim, start implementing new ops
- Implement EmbedLayerNormalization
- Implement Attention
* SkipLayerNormalization and FastGelu
* use original torch model, cast inputs
* fix some ops:
- properly do Cast
- Attention: bi- and unidirectional
- FastGelu: add bias before gelu
* cleanup onnx_ops.py
* add validation option to benchmark
* cleanup imports
* add checks incase onnx2torch implements ops in future
* run onnx instead of original torch
* just skip gpu on m1
* reactivate the other models
* check for strange params & squash whitespace
* cleanup
* fix causal mask Attention
* Range doesn't need int cast
* embedding vocab_counter same dtype as input
* no need to cast
* always validate, fix PosixPath ort
---------
Co-authored-by: George Hotz <george@comma.ai>
* testing new memops
* better debugging
* testing padded conv
* branching with load
* refactoring a bit
* first try
* fixing bugs
* fixing some
* eq
* eq2
* do not use x's
* working
* fixing imm
* getting things working
* refactor
* pow not working
* working except one
* refactor: one store mem
* refactor: global load
* refactor: imm
* refactor: cleaning
* fixing big offsets
* refactor with ci
* try ci
* typo
* another typo
* ubuntu default
* forgot git
* do i need git?
* missing packages
* adding python-dev
* with cache?
* buildx action
* buildx name issue?
* maybe now?
* python3
* newline warning
* maybe now
* i actually need this
* ci should work now
* improved caching
* fixing cache
* maybe now it will cache
* this
* testing cache
* trying again
* load
* missing platform
* caching gha
* testing cache
* full testing
* typo
* now?
* why
* adding checkout back
* bad formatting
* fixing convention issues
* supporting python
* adding CI flag
* testing all
* better comments
* adding debugging
* takes 12x longer
* does it output progress now?
* ignore models for speed
* fixing merge
* excluding conv_transpose2d
* only 2 test cuz is to slow
* another approach
* let's see
* faster duh
* my bad
* T_T
* typo
* sup
* with output?
* comment test
* comment test
* comment test
* :?
* no comment
* with cache
* back to normal
* testing that ci works
* back to passing
* trying again
* does it create another entry
* does it create another entry?
* build local
* hey
* Revert "excluding conv_transpose2d"
This reverts commit cc7348de03.
* does it cache if done before?
* does it cache?
* done
* adding test ops
* bad formatting
* no need for this
* working static mem
* sum 1d
* add ndim
* better reg import
* fix stack
* back to np
* working except for softmax
* 5 failing
* no pogress
* remove keystone
* remove keystone
* testops passing
* cleanups
* more cleanup
* typo
* ci
* ci2
* cond import
* ci3
* ci4
* ci4
* ci5
* ci5
* ci6
* aligment
* test all
* correct test
* err read_unmapped
* passing test
* ignore for speed
* ignore for speed
* ci7
* cleanup
* remove docker
* fixing merge
* fixing bugs
* add skipload for const ops
* comments
* First merge to master: Renderer
* fix emulation
* passing all tests arm64
* cleaning
* fix handcoded binary
* cleaning
* fix errs
* fix runtime arg binary
* clean git diff
* fix and clean
* fixing metal test
* cleaning
* fix metal test
* ci ~8 min
* fix pylint and clang
* cache the files in ops_clang
---------
Co-authored-by: George Hotz <72895+geohot@users.noreply.github.com>
* feat: world
* feat: tests
* feat: no more backwards
* feat: recv into
* feat: whoops
* feat: test in ci
* feat: some debug logging
* feat: workflow naming
* feat: need to set pythonpath
* feat: just send to same device
* feat: allreduce
* feat: test
* feat: need contiguous
* feat: test in ci
* feat: exit with correct code
* feat: don't need that
* feat: opencl wait_for just doesn't work
* feat: synchronize on out
* feat: try?
* feat: try again?
* feat: add extra realizes
* feat: print
* feat: seed
* feat: tol
* feat: test ones and zeros
* feat: remove print
* feat: are you just flaky
* feat: seperate scatter and gather?
* feat: just try synchronizing
* feat: remove print again
* feat: bring back difference
* feat: no sync
* feat: revert that
* feat: back to wait_for
* fix: typo
* feat: world
* feat: tests
* feat: no more backwards
* feat: recv into
* feat: whoops
* feat: test in ci
* feat: some debug logging
* feat: workflow naming
* feat: need to set pythonpath
* feat: just send to same device
* add disk_tensor
* fix jit
* new baseline before whitening
* whitening through torch
* whiting done currently at 91.65%
* 91.99%
* clean up mixup and 92.3%
* clean up 92.30%
* 92.49% before searching for new hyper-parameters
* fix CI
* fix white space
* add whitening init in test
* refactor, update hyperpara, 92.72%
* converting whiting to tinygrad operation
* update CI kernels count for CIFAR
* add pad reflect
* add random crop 92.53%
* update hyperpara 93%
* 93.15% on docker container, need to refactor the assignment for hyper param
* print out weights and bias to be separated
* bias/non-bias params separated
* fix whitespace
* clean up
* refactor hyper-param with dict
* refactor lr schedular params
* fix whitespace
* fix cross entropy loss
* fix whitespace
* move opt hyp to hyp dict
* minor fixup
* adjust model, loss scaling
* 92.74% while using half of compute as before
* update hyp for cutmix
* random shuffle during batches
* clean up
* updating the model
* update ConvGroup
* disable gradients for batchnorm layer weights
* whitespace
* 93.92%
* clean up
* finally 94%git add .!
* rewrite whitening to remove dependency on torch
* whitespace
* remove dependency on torch, 93.91%
* back to 94.03%
* clean up
* update test_real_world
* Rename in files
* Move files
* Moved to extra/datasets as suggested
* Changes to files
* Fixed stupid mistake
---------
Co-authored-by: terafo <terafo@protonmail.com>
* Fixes + improved test coverage for helpers.py
- added exception handling in `proc`, if an exception was thrown, the thread would hang
- made `_early_exec_process` catch any Exception, before if an exception was thrown before the process was started, it would hand the thread
* Made `_early_exec_process` catch any Exception
Otherwise, if an exception was thrown before the process was started, it would hang the thread. For example a type error for an argument passed to `subprocess.check_output`
* Fixed `from tinygrad.helpers import Timing` import
oops, for some reason my IDE cleaned that import from extra/helpers.
* Fixed import in llama.py
Another one that I skipped by accident, mybad
* Extracted a class for tests of early exec
* Normalize line endings, windows uses /r/n
* Made `cross_process` not a daemon
* fixed division by zero for fast operations
* made et closer to 0
* replace POW llop with SQRT
* updated mlops to swap SQRT and POW llops
* updated hlops to swap POW and SQRT
* added sqrt llop to cpu runtime
* added sqrt llop to cstyle codegen
* added POW llop to llvm ir codegen
* added SQRT llop to torch runtime
* moved pow from mlops to hlops
* found a better way to do reverse pow
* fixed indentation
* added SQRT llop to triton
* update docs to match new llops
* removed POW operator from assembly codegen
* added sqrt and rsqrt to pow hlop
* rewrote pow function in tensor.py
* Adjust tolerance
* Adjust for adamw
* Reduce for Adam too
* removed accidental leftover code
* removed all of accidental code
* added rsqrt test
* removed pow from mlops again
it was added back when resolving merge conflicts
---------
Co-authored-by: Jacky Lee <jla524@sfu.ca>
