* fixes from chargpt for torch backend
* shrink support
* add stride support
* comment cleanup
* a few more
* work
* import the stream hack
* llvm multi auto
* rig up torch's testing framework [pr]
* support more movement ops
* dec on expand
* fix tests
* work
* fix tests
* a few more
* decomps + opt hook
* installed pytest
* put acc in front of the add chain
* handle the other case
* Make loop collapse more generic
* Remove mulacc_unrolled
* Actually remove it
---------
Co-authored-by: George Hotz <geohot@gmail.com>
Co-authored-by: chenyu <chenyu@fastmail.com>
`LLVM=1 BERT_SIZE="tiny" DEFAULT_FLOAT=HALF BENCHMARK=5 MODEL="bert" python3 examples/mlperf/model_train.py` runs for me with this. it should not failed with single device shard though
* move cast to before softmax in attention
saved some memory because exp (which is used for backward) are done in half. training bert seems fine and can fit BS=78 now (from 66)
* test
* 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 <chenyu@fastmail.com>
* fix edge cases in memsize_to_str()
Inputs <= 1 now return "0.00 B" for 0 and "1.00 B" for 1, avoiding an
IndexError. Also, memsize_to_str(1000) now returns "1.00 KB" instead of
"1000.00 B".
Replaced the list comprehension with a next(...) generator for conciseness
and efficiency.
* simplify code using idiomatic python
- Remove the unused `memsize_to_str()` function in helpers.
- Use a tuple for checking multiple string prefixes/suffixes.
- Avoid unnecessary list construction by using iterables directly.
- Check None in @diskcache to ensure proper caching of falsy values.
* revert generators back to list comprehension
Sometimes building list first could be faster. Keep it as is.
