* models matrix
* fix typo and install gpu deps
* install llvm deps if needed
* fix
* testops with cuda
* remove pip cache since not work
* cuda env
* install cuda deps
* maybe it will work now
* i can't read
* all tests in matrix
* trim down more
* opencl stuff in matrix
* opencl pip cache
* test split
* change cuda test exclusion
* test
* fix cuda maybe
* add models
* add more n=auto
* third thing
* fix bug
* cache pip more
* change name
* update tests
* try again cause why not
* balance
* try again...
* try apt cache for cuda
* try on gpu:
* try cuda again
* update packages step
* replace libz-dev with zlib1g-dev
* only cache cuda
* why error
* fix gpuocelot bug
* apt cache err
* apt cache to slow?
* opt and image in single runner
* add a couple n=autos
* remove test matrix
* try cuda apt cache again
* libz-dev -> zlib1g-dev
* remove -s since not supported by xdist
* the cache takes too long and doesn't work
* combine webgpu and metal tests
* combine imagenet to c and cpu tests
* torch tests with linters
* torch back by itself
* small windows clang test with torch tests
* fix a goofy windows bug
* im dumb
* bro
* clang with linters
* fix pylint error
* linter not work on windows
* try with clang again
* clang and imagenet?
* install deps
* fix
* fix quote
* clang by itself (windows too slow)
* env vars for imagenet
* cache pip for metal and webgpu tests
* try torch with metal and webgpu
* doesn't work, too long
* remove -v
* try -n=logical
* don't use logical
* revert accidental thing
* remove some prints unless CI
* fix print unless CI
* ignore speed tests for slow tests
* clang windows in matrix (ubuntu being tested in imagenet->c test)
* try manual pip cache
* fix windows pip cache path
* all manual pip cache
* fix pip cache dir for macos
* print_ci function in helpers
* CI as variable, no print_ci
* missed one
* cuda tests with docker image
* remove setup-python action for cuda
* python->python3?
* remove -s -v
* try fix pip cache
* maybe fix
* try to fix pip cache
* is this the path?
* maybe cache pip
* try again
* create wheels dir
* ?
* cuda pip deps in dockerfile
* disable pip cache for clang
* image from ghcr instead of docker hub
* why is clang like this
* fast deps
* try use different caches
* remove the fast thing
* try with lighter image
* remove setup python for cuda
* small docker and cuda fast deps
* ignore a few more tests
* cool docker thing (maybe)
* oops
* quotes
* fix docker command
* fix bug
* ignore train efficientnet test
* remove dockerfile (docker stuff takes too long)
* remove docker stuff and normal cuda
* oops
* ignore the tests for cuda
* does this work
* ignore test_train on slow backends
* add space
* llvm ignore same tests as cuda
* nvm
* ignore lr scheduler tests
* get some stats
* fix ignore bug
* remove extra '
* remove and
* ignore test for llvm
* change ignored tests and durationon all backends
* fix
* and -> or
* ignore some more cuda tests
* finally?
* does this fix it
* remove durations=0
* add some more tests to llvm
* make last pytest more readable
* fix
* don't train efficientnet on cpu
* try w/out pip cache
* pip cache seems to be generally better
* pytest file markers
* try apt fast for cuda
* use quick install for apt-fast
* apt-fast not worth
* apt-get to apt
* fix typo
* suppress warnings
* register markers
* disable debug on fuzz tests
* change marker names
* apt update and apt install in one command
* update marker names in test.yml
* webgpu pytest marker
* 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>
* matrix strategy
* push env to GITHUB_ENV
* use printf instead of echo
* use temp helper function for cross os paths
* use path join
* switched to using temp helper function
* skip test on windows due to memory limit
* small fix
* removed semi
* touchups
* clean up
* seperate tests
* test changes to test_utils on windows
* small refactor
* more cleanups
* undo helpers change
* only skip if in CI and WINDOWS
* added metal int64 and some simple tests
* removed bool return type def
* typo in test
* also missing in clang and gpu runtimes
* switched order for opencl
* increased atol and removed new line in kernel prefix
* 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: promote Embedding to nn
* fix: fix failing test
* feat: add test with jit
* feat: rewrite embedding to no longer need stacked for loops
* clean+fix: don't know how that happened
* conv2d is an hlop
* shorter conv
* KOPT=-1
* alt imp
* MULACC
* smarter mulacc
* pop conv
* 7x7 -> 5x5
* didn't fix, that's not going to work
* this is faster and matches old behavior
* oh, non lazy just won't work with mulacc
* mulacc in torch
* bool types were creeping in
* optimizer is actually better with hlop conv
* fix pushing permutes issue
* refactor einsum_mulacc
* fix up readme
* update readme
* _image_conv2d
* fix bias addition location
* pushing permutes gets back to 200 kernels
* conv cleanup
* disable hlop conv
* don't hide that in helpers
* Rename Normalize and move to nn
* Fix comparison to None error
* Add test for GroupNorm
* Rename test case
* Flip parameters to match PyTorch
* Increase error tolerance
* Fix elementwise_affine on channels
* Match arguments with PyTorch
* Initialize weight and bias only when affine is true
* Is this it?
* A bit cleaner
* Handle case where weight or bias is None
* added resnets
* fix minor
* fix minor
* resnet in models
* added resnet test
* added resnet train test
* added linear, conv2d nn tests
* fix minor in extra/training
* resnet in models
* fix minor
* fix tolerance for linear in nn test
* fix eval, this causes cpu and gpu UT failing
* revert transformer test
* fix minor for CPU test
* improved model get_params for sequential layer
* fix minor for params counting
* commented broken ops tests
* improved train for resnet
* 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…
* Split tests
Split tests into "Test CPU" and "Test GPU".
Add test flag "TEST_DEVICES" which is a comma separated list of devices:
CPU,GPU,ANE
* Run tests based on provided TEST_DEVICES flag
By default will run all "CPU,GPU,ANE"
* fix bad quote
* Revert changes and use GPU=1
This is done through setting the default Tensor Device to Device.CPU of
GPU=1 is set.
Run GPU tests: GPU=1 pytest -s -v
* Update all devices to be tested
ANE, CPU and OCL all now support all tests.
However tests are not currently passing on GPU and I cannot test on CPU.
Failing GPU test are not an issue caused by this update. Tests have not
been passing due to a missing "six" required installation.
OpenCL Tests have not been run since commit: 1a1c63a08b
devices have 3 types and are handle by a new DeviceTypes enum. (The goal
is to revert to Tensor.<type>, but this current setup allows for keyword
argument defaults: `device=DeviceType.CPU`)
All references to Tensor.GPU/CPU/ANE as been converted to the
corresponding `DeviceTypes` enum.
Refactor of the conversion code to allow for any device to any device
conversion.
* Add six dependency in requirements.txt
* Resolve failure to run tests
Move six into gpu required installs. Remove six from standard
installation.
* Remove repeated data conversion
* Refactor method names
Also reduce code with .to and .to_
* Dynamic device handlers
* Refactor DeviceTypes -> Device
* Add mem copy profiling back
* test_backward_pass_diamond_model passing
* Resolve Sum issue on GPU
* Revert batchnorm2d tests
* Update README with upadated API
* ANE testing with
* Last minute line gains
