* initial commit
* 81 passing
* 105 passing tests
* 148 passing
* CI tests
* install dep on ci
* try opencl pkgs
* try using vulkan
* down to only 6 failing
* refactor
* cleaning up
* another test skipped due to buffer limit
* linter
* segfault
* indent fix
* another segfault found
* small touchups
* Fix max and maxpool tests
* Add constant folding
* Add javascript export script
* better asserts in codegen
* manual upcasting
* reverted token type change
* skip safetensor test due to unsupported type
* FIx efficientnet and all other model tests
* Remove np copy
* fixed indent and missing import
* manually destroy the buffer
* revert back to length
* linter errors
* removed extra val
* skip broken tests
* skipping more tests
* Make the page pretty
* Save model weights as safetensor
* Fix imagenet to c test
* Fix second imagenet to c bug
* Async and paralel kernel compilation
* workgroup support
* reversed local size
* fixed non local bug
* correct local groups
* ci experiment
* removed typo
* Fix define local by using shared memory
* Refactor
* try running on mac
* match metal tests
* add more workers
* scope down tests
* trying windows runner
* fixed windows env
* see how many it can do
* merged master
* refactor
* missed refactor
* increase test suite coverage
* missing import
* whitespace in test_efficientnet.py
* getting there
* fixed reset
* fixed bufs
* switched to cstyle
* cleanup
* min/max rename
* one more linter issue
* fixed demo
* linter
* testing ci chrome
* add unsafe webgpu arg
* add build step
* remove WEBGPU from cmd line
* use module
* try forcing directx
* trying forced metal backend
* temp disable conv2d for CI
* disable conv_trasnpose2d
---------
Co-authored-by: 0x4d - Martin Loretz <20306567+martinloretzzz@users.noreply.github.com>
Co-authored-by: George Hotz <72895+geohot@users.noreply.github.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>
* Add ResNet inference test and cannon
* Test with ResNet50
* test_car works with resnet fix
* Add KiTS19 dataset
* KiTS19: Implement iterate
* No batch load for this dataset
* Save results on iterate
* Implement dice score
* Add data prep and eval functions
* Resolve shape issue
* Conversion works but wrong values
* Segfaults when load_from_pretrained is called
* Fix segfault and assign properly
* Final result generated, though very slow
* Store and load final result to save time
* Fix typo in finalize
* Score computes
* More bug fixes, dice score is very low
* Working broken code
* Assign output values to result
* Getting a much higher score now
* Fix dataset preprocessing
* Mean DICE score of 88.5
* Ugh, typo
* Attempt to reimplement model
* Rename layers
* Tiny model works, kinda
* Accuracy? gone
* Implement InstanceNorm and match torch
* Test instance norm 2d and 3d
* Combined input block with downsample block
* Tiny model works, support strided convtranspose
* Commands to download dataset
* Clean up a bit
* unet3d_v2 -> unet3d
* Remove duplicated code
* Oops, put tests back
* feat: add mlperf bert model
* feat: switch to nn.Embedding
* clean+fix: fix formatting
* feat: add simple downloader
* feat: metrics
* feat: don't actually need exact match
* feat: doing a run
* feat: set eps on the layernorms
* clean+fix: cleaner impl + hopefully fixed
* feat: move dataset initialization into iterate
* feat: move tokenizer out of iterate
* clean+fix: cleaner + working
* clean: cleanup
* fix: fix metrics
* feat: need to use original bert gelu + download vocab
* feat: make directory if it doesn't exist yet
* feat: jit go brrr
* feat: initial rnn-t
* feat: working with BS>1
* feat: add lstm test
* feat: test passing hidden
* clean: cleanup
* feat: specify start
* feat: way faster lstm & model
* fix: default batch size
* feat: optimization
* fix: fix metrics
* fix: fix feature splicing
* feat: cleaner stacktime
* clean: remove unused import
* clean: remove extra prints
* fix: fix tests and happy llvm
* feat: have the librispeech dataset in its own dir
* clean: unused variable
* feat: no longer need numpy for the embedding + slightly more memory efficient lstm
* fix: forgot to remove something that broke tests
* feat: use relative paths
* feat: even faster
* feat: remove pointless transposes in StackTime
* fix: correct forward
* feat: switch to soundfile for loading and fix some leaks
* feat: add comment about initial dataset setup
* feat: jit more things
* feat: default batch size back to 1
larger than 1 is broken again :(
and even in the reference implementation it gives worse results
* start clang backend
* mostly working
* no group for reduce w clang
* it compiles
* compiles
* a11y
* minor fixups
* formatting
* add a test
* rename test
* mypy fun
* things are just faster
* running fast
* mypy is fast
* compile.sh
* no gpu hack
* refactor ops_cpu and ops_torch to not subclass
* make weak buffer work
* tensor works
* fix test failing
* cpu/torch cleanups
* no or operator on dict in python 3.8
* that was junk
* fix warnings
* comment and touchup
* Some progress on yolov3
* Removed some debugging comments… Also, the forward pass eats all RAM for some reason
* forward pass almost runs
* forward pass runs almost
* forward pass runs, now we gotta load the weights
* loading weights works
* fetches config and weights
* everything kind of works, postprocessing of output still needs to be implemented, temp_process_results kind of works, but its kind of terrible, and not how things should be done
* some changes
* fixed some bugs in the forward pass and load_weights function, now outputs more correct values, however some values are still loaded incorrectly
* Something is wrong with the forward pass, Conv2d tests added
* forward pass almost outputs correct values, gotta fix one more thign
* yolo works
* some final changes
* reverting changes
* removed dataloader
* fixed some indentation
* comment out failing test, somehow it fails CI even though it passes on my computer…
* fixed wrong probabilities
* added webcam option to YOLO, now just need to add bounding boxes and speed it up
* some progress towards adding bounding boxes
* trying to speed up yolo layer on GPU, still faster on CPU but with 30GB ram usage
* Faster inference times, bounding boxes added correctly, webcam works, but is slow, and there is a memory leak when running on CPU... Also added tinygrads output on the classic dog image
* removed some debugging print statements
* updated result image
* something weird is going on, mean op on GPU tensor randomly faults, copying a tensor from GPU->CPU takes 10+ seconds…
* ane query is success
* cite and build instructions
* low level access, need to disable AMFI
* coreml_ane works
* coreml fun
* more work
* compiled example
* progress
* compiler works
* model flow
* TODOs in the readme
* put some real weights in
* we are learning objc
* much progress i think
* signed model still doesn't work
* working example
* there are float16
* clean up: part 1
* h11ane header, more cleanup
* cleanup DeviceController creation
* remove the stupid sleep
* notes
* start a hwx parser
* no tabs
* compare stuff
* hmm, why don't inputs work
* cache doesn't seem to fix it
* hmm, the issue was the compiler
* fix the compiler, guess i didn't put in weights
* logging for compiler
* uselessness in plist
* remove hwx before compile, weights are converted to float16
* better compare
* better compare
* last line in comparE
* opcodes from compiler
* notes
Diogo
terafo
George Hotz
George Hotz
Jacky Lee
wozeparrot
Joqsan
Marcello Fuschi
George Hotz
Skosh
Rene Delgado
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