Fix repeated FlopsProfiler metric accumulation (#8246)
## Summary
- restore `F.scaled_dot_product_attention` after profiling
- restore `Tensor.__matmul__` after profiling
- restore `torch.bmm` from its correct saved implementation
- add a CPU regression test covering repeated profiling sessions and
operation restoration
- scope the existing FP16 skip to the test that actually requires FP16
## Problem
`FlopsProfiler` temporarily replaces PyTorch operations with
FLOP-counting wrappers. Its cleanup path did not restore
`F.scaled_dot_product_attention` or
`Tensor.__matmul__`, causing wrappers to accumulate across profiling
sessions. As a result, identical model executions reported progressively
increasing FLOPs and
MACs.
The `torch.bmm` cleanup was also incorrect:
```python
torch.bmm = old_functions[torch.matmul.__str__]
```
Because torch.matmul had already been restored, this rebound torch.bmm
to torch.matmul for the rest of the process. This changed normal PyTorch
behavior after
profiling: torch.bmm began accepting inputs supported by broadcasting
matmul but invalid for bmm.
## Fix
Restore every patched operation from its matching saved original
function. The patch and cleanup paths now cover the same set of
operations with matching PyTorch
version guards.
## Testing
A CPU regression test profiles the same scaled-dot-product-attention
operation three times and verifies:
- every session reports identical FLOP and MAC totals
- F.scaled_dot_product_attention, Tensor.__matmul__, and torch.bmm are
restored to their original function objects after every session
Observed totals:
Before:
[(65536, 32768), (131072, 65536), (196608, 98304)]
After:
[(65536, 32768), (65536, 32768), (65536, 32768)]
All pre-commit checks pass.
Fixes #7413
Signed-off-by: Vedant Chauhan <staranonymous1011@gmail.com>
