* start
* tiny clean up
* whoops, didn't mean to accidentally fix this
* fix .to(device), kinda hacky and this fix makes it slower?
* merge properly
* FINALLY figured out slowness, also hack pylint for now
* add DEBUGONNX print for subgraph
* oops
* WOOOOOOOO SHAPE CACHE 50% SPEED INCREASE
* small fix, but maybe all deterministic Tensor creation in fp should be cached
* cache condition
* sliiiightly cleaner
* better abstraction?
* remove sam from model_benchmark
* remove shape cache speed up for now
* less lines
* isinstance fix
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Co-authored-by: chenyu <chenyu@fastmail.com>
* start
* add reference
* this is so much slower
* this makes sense but differs from official impl, but results are still correct..?
* add a comment
* Just keep it simple for now since I don't fully get it yet
* address comments
* correct
* teeny clean up
* another small comment improvement lol
* bump
* thou hast implement functions
* hacked in domain support
* some clean ups
* hack quantize_onnx_test too
* add helper lol, why onnx tests why
* better dispatcher, but need tests and better naming
* flaky ci
* change some names
* small clean ups
* make it easier to clean up tests once ORT supports 1.18.0
* nits
* fix bug of Softmax_1 being registered in onnx_ops
* need a default value
* resolve_const is better name
* fix OnnxRunner.to
* use proper domain names
* 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>
* why does max_unpool2d feel slower than out.gradient ...
* slightly cleaner
* what happened to ruff
* need to think about this some more
* slightly faster now?
* clean up, 1 more failing edge case
* ok good
* working TINY_BACKEND
* nit doc wording
* retry CI
* add DynamicDequantizeLinear and corresponding tests
* wow qlinearops are round away from zero
* this passes locally...
* again
* try
* try separate test
* round to even again
* also add QLinearMul
---------
Co-authored-by: chenyu <chenyu@fastmail.com>
* boom
* fix webgpu
* use exact variable names in test so that AI can read easier
* add tag for specific test name like test a specific dtype
* fix ruff
* astype everything
* dtype in array creation
* just arange
* is 67% considered fixed?
* move test up
* small cleanups
* share function
* add qgemm as well
* add qgemm too
* make sure qgemm comes out as int
* take out qgemm for now
* fixed test
* add correct qgemm
* addressing feedback here too, early naive fix for now
* simplify bias and c to be minimalistic enough to test correctness
* refactored qlinearops
* maybe these asserts aren't the best..
* fix test
* updated tests to cover new ops
* try to add to CI
* move test_onnx_ops into testextra/
* more attention tests
* qlinear_add atol=1
* attention still not fullllllly correct
* it is what it is
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Co-authored-by: chenyu <chenyu@fastmail.com>
