* file path as input and have parse be in OnnxRunner.__init__
* modelproto_to_onnxrunner -> modelproto_to_runner
* whoops, fix import
* oh flakiness again, is it because it's getting gc-ed?
* small changes
* CI flaky so just move compile4 fix in
* copy typing of onnx_load
* actually can just import onnx_load instead of onnx.load
* fix external_benchmark_openpilot
* fix onnx_runner test to use onnx_helper
* rerun CI
* try run_modelproto
* spam CI a few times
* revert run_modelproto since that's flaky also
* no external onnx_load usage except onnx.py
* cursor tab complete is evil. Snuck a darn sorted in. But does order change result? Why?
* model_benchmark 193s -> 80s, add OnnxRunner.to()...
* minimize diff and clean up
* device can be None, weird but eh
---------
Co-authored-by: chenyu <chenyu@fastmail.com>
* squash commits
* temp fix for const tensor
* actually realizing float16 can only happen in raw_data
* .float -> cast(float) to rerun CI
---------
Co-authored-by: chenyu <chenyu@fastmail.com>
* this
* clean up
* more clean ups and improve debug msg
* more correct training toggler
* remove manual training toggling
* change some variable names
* actually just add the training toggle for LIMIT envvar too
* more refinement
* __call__ and OnnxRunner
* fix half pylint, other half is importing from onnx while this file is onnx.py, figure out later
* ahhhh found another mistake
* remove limit from __call__
---------
Co-authored-by: chenyu <chenyu@fastmail.com>
exceptions can be raised from either model conversion or individual backend failed. openpilot on torch mps works, but does not work with torch cpu.
seperate the expcetion block so that the benchmark can inlcude torch mps for openpilot.
* 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>
* 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>
