instead of running test with 3.10, add onnx to mypy which would have caught StrEnum regression. Several type annotation failed mypy now that does not affect running the code and were skipped for now
* start
* remove onnx.load from compile4 and move np to dropout
* clean up and enable test
* clean up
* move WebGPU ONNX test into MacOS (WebGPU)
* leave test in ONNX (CPU)
* fix raw_data init None, and simplify onnx_runner test a little?
* THESE TESTS ARE SO UGLY UGHH
* need to really think about how to structure the test
* wow LLMs are quite something
* not always on disk now
* also add external data loading test
* cleaner tests
* minimize diff and add const folding tests
* add external data loading too
* whoops add webgpu back.. but why was it not needed in the first place?
* better comment
* move webgpu test to macos(webgpu)?
* llm english so much better than me wow
* trigger CI to check flakiness
---------
Co-authored-by: chenyu <[email protected]>
* 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 <[email protected]>
* 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 <[email protected]>
* fix
* add early verbose demo test
* is this how to write tests :s
* is definition drift even a thing? gemini says it is
* clean up
* better
* even better
* try add to CI
* doesn't work quite yet
* much more work to be done
* whoops
* partition the test heh
* skipif
* some nits for better names
* add webgpu test for onnxrunner
* fix reference links
* flush for now
* start
* still need the float16 workaround in
* tiny nit for correctness
* idk hacks, I need to understand this device stuff better
* no-op?
* remove that assert for true nooooooop
* add fallback_context
* 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
* wow argmax is so good
* 1 less line
* clean up and better variable names
* is this torch thing right...?
* add more tests
* slap a TODO on it
* clean ups
* prettier looking code and fix ceil mode test
* add return types and some docs
* ok that was a bad example since indices == value, just no example
* terrible but somewhat working impl
* linux behaves differently than macos?
* slightly better impl
* small clean up; haven't figured this out yet
* better
* torch has different behavior on linux and macos for duplicated values
* add sum docs
* fix test
* add torch return_type test
* add an exception test
* wrap_fxn instead, and move op lower in order
* better repeated values test
* rerun 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 <[email protected]>
* 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
---------
Co-authored-by: chenyu <[email protected]>
* pytorch scatter -> scatter_reduce
* WIP scatter_reduce implementation
* _pre_scatter return type hint
* split out src, mask to satisfy linter
* Add src cast back in
* dict of lambdas instead of ifs
* sum and prod reduction ops with include_self
* add reduce arg error message
* add amax and amin reduction ops
* Fix include_self for higher dims
* Simplify
* Simplify amax and amin too
* Pull include_self logic out into _inv_mask function
* reduce arg cannot be None for scatter_reduce
* Fix self-mask issue
* Add mean reduce op
* Add tests
* any() not needed here
* remove comment
* End support for Tensor src with reduce arg in tinygrad scatter
* Process index, dim inside actual functions
* Add scatter_reduce to onnx
* Add excluded onnx ScatterElements reduction tests back in
* Save 2 lines on the mask helpers
* Update docs
* Add include_self=False tests
* cleanup
* Remove unneeded helper function
---------
Co-authored-by: chenyu <[email protected]>
* 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 <[email protected]>
* validate variable dims and fix buffer_parse to not use numpy
* fix var_dim parsing
* gah float16
* revert buffer_parse stuff
* revert that revert
* correct some err msges
* add some more debug msgs I find helpful
* tensor init noop
* add an assert just for the sake of it.
---------
Co-authored-by: chenyu <[email protected]>
it's a python style mod. possibily can be cleaner with a floor div
relaxed the vmin for MOD slightly for cstyle negatives mod, it's more correct and might fix other bugs
* move to_python_const out
* move more over
* try deleting alternative gather implementation
* Revert "try deleting alternative gather implementation"
This reverts commit d46b30b717.
* add types to onnx ops
* better debug msg
* improve some com.microsoft too
---------
Co-authored-by: chenyu <[email protected]>
