The load tail re-ran the already-filtered speed_optims tuple through
.items() as if it were still the raw applied dict from apply_speed_optims.
An empty tuple short-circuited to {} so CPU test runs passed, but on a real
GPU at least channels_last engages, the tuple is truthy, and every load
failed with 'tuple' object has no attribute 'items'. Store the filtered
tuple directly and add a regression test that forces one optimisation to
engage.
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||
|---|---|---|
| .. | ||
| data_recipe | ||
| export | ||
| inference | ||
| rag | ||
| training | ||
| __init__.py | ||
| _torchao_stub.py | ||
| import_guards.py | ||
| tool_healing.py | ||