benchmark

7b7276d2 - Inductor freezing bfloat16 conv folding needs high tolerance (#145623)

Inductor freezing bfloat16 conv folding needs high tolerance (#145623) Summary: Issue: https://github.com/pytorch/pytorch/issues/144888 Torchbench of timm lcnet_050 model fails on accuracy in case of `--frezing` `--inference` `--bfloat16` `res_error==0.12` If to turn off convolution inductor constant folding - `res_error==0.016` `float16 error ~ 0.00669` `float16 without conv folding ~ 0.0018` convolution folding results in increase of error almost at one order of magnitude. I think we should revisit and try to do something to improve the accuracy for conv folding. E.g. For example doing conv folding at compilation time with float64? At the moment I am adding counters to identify if convolution folding happened, and in case of bfloat16 and conv_folding - increase multiplier to the max level (10) to pass accuracy test. X-link: https://github.com/pytorch/pytorch/pull/145623 Approved by: https://github.com/eellison Reviewed By: ZainRizvi Differential Revision: D68897700 fbshipit-source-id: f407528b4b37eb45273a8c66f791c44e86c6632e