benchmark

d36d4778 - Fix misleadingly high AOT Inductor dashboard performance (#153060)

Fix misleadingly high AOT Inductor dashboard performance (#153060) Summary: Fixes misleadingly high AOTInductor performance benchmark numbers in scenarios where a model updates internal parameters during `torch.export.export`. Since `FakeTensorMode` is enabled during export, all such parameters become `FakeTensor`s, slowing down future eager-mode runs using that model substantively. This, in turn, causes misleading performance stats, where the slowness of eager-mode makes `AOTInductor` look _very_ good. An [example benchmark](https://hud.pytorch.org/benchmark/timm_models/inductor_aot_inductor?dashboard=torchinductor&startTime=Wed%2C%2030%20Apr%202025%2015%3A54%3A04%20GMT&stopTime=Wed%2C%2007%20May%202025%2015%3A54%3A04%20GMT&granularity=hour&mode=inference&dtype=bfloat16&deviceName=cuda%20(h100)&lBranch=main&lCommit=1dd36ad2d440a4f3faf724b3a8e13925e3180c24&rBranch=main&rCommit=cc7346bf19c019255dcb4484694a75850ed74d5a&model=convit_base) with this issue. The equivalent `cpp_wrapper` benchmark run shows a 2x performance gain, not 20x. Only two benchmarks we regularly run are affected by this, both in the TIMM set. X-link: https://github.com/pytorch/pytorch/pull/153060 Approved by: https://github.com/desertfire Reviewed By: jeanschmidt Differential Revision: D74729281 fbshipit-source-id: bf25cd22933d9670018d935747b0604dec4178aa