* Slice to unbind symbolic
* use vmax for now
* assert shape in reshape is valid
* update test_symbolic_ops to use shrink instead of reshape
* remove infer_with_bound_values for npw
* symbolic output doesnt have symbolic strides
* symbolic jit tests use shrink to unregister symbolic
* update test
* update more tests
* wrap vmax in int()
* only create a new st if the store is not an assigne
* unwrap st
* comments
* var_vals is str,int
* remove imports
* remove print
* fix test
* change var_vals in hcq
* update test_hcq
* fix multitensor _device_num var
* fix syminfer test
* shorten line
* p.vars stays list[Variable]
* shorten line
* vars is back to tuple[Variable, ...]
* change var_vals in extra
* change var_vals from shapetracker
* var_vals is str:int
* fix signature
* Modify tests and start work towards removing symbolic reshape
* Refactor symbolic reshape
* fix small error
* much cleaner + fix more tests
* Can remove this now
* Update test_symbolic_ops and test_tiny
* Couple more tests
* Unused import
* More tests and add EXPAND to Tensor.empty
* Fix test beam search
* all int
* Fix rangeify by adding shrink
* Remove OOB check and so fix test_symbolic_jit
* test_symbolic_jit doesn't need OOB Context anymore either
* Should remove that test now
* Cleanups part 1
* fix linters
* Final cleanups
* Don't reassign inside for loop
---------
Co-authored-by: chenyu <chenyu@fastmail.com>
* kernel.py no longer permutes reduce axis [pr]
* delete tests that handcode uops
* regen of sops is broken...
* put import back
* just remove that
* disable those tests
* fix extract_dataset + tests
* add CI
* sops.gz itself is same as master
* yml + gzip -c + ge
* don't commit that
* bump limit to 1000
* axis=7
* test_tiny
* don't run linearize().uop tests in get_action_space test
this part takes 2 minutes in CI and has nothing to do with action space. also not sure if the "for some reason" comment is still relevant
* -n=auto test/models
* Kernel.apply_opts [pr]
updated all `for opt in`. also updated a few test_liinearizer tests to not implcitly depend on hand_coded_optimization
* not you yet
* assign early folding [pr]
* move to to_si
* -
* fix generate_dataset
* diff too big
* no recreation, no diff
* gzip
* new sops from tiny10
* final try
* generate new kernel dataset
pre req to remove NumNode
```
extra/optimization/generate_dataset.sh
gzip -k /tmp/sops
mv /tmp/sops.gz extra/datasets/
```
* fix var range in fuzz_linearizer
