* 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
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Co-authored-by: chenyu <chenyu@fastmail.com>
* cast after sum
* comment out skipif
* minor fix
* only test IMAGE
* IMAGE is supported now
* simpler
* simplerr
* only cast if dtype is None
* dont need to change base_imaeg_type
* only cast when dtype is half
* add explicit test
* actually no, workflow seems better
* actually, keep both
* move test
* fix indent
---------
Co-authored-by: Utkarsh Gill <engelbart@Utkarshs-MacBook-Pro.local>
* 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>
* start LLM app, tons of clean up required. target is 200 line ollama
* kind of works
* simpler
* add k/v cache
* with SYM=1, it loops
* no rope cache
* simpler
* more cleanups
* cleanups
* works
* argparse and comments
* from gguf
* generate is a function
* no copy from cpu
* fix max context pass in
* test
* improve test
* ai2_arc
* fix 8B, use less ram
* 136 lines
* merge view infinite loop test
* adjust condition in `x//d -> x//(-d)*-1`
* Fix division by zero in add views
* adjust offset end
* fix typo in comment
* add target to test_merge_views_variable
* fix view incorrectly being masked
* ssimplify strides and offset of the new view to canonicalize
* remove print in test
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Co-authored-by: qazal <qazal.software@gmail.com>
