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Update on "Fused attention patterns"
Patterns based on #94729 mainly as a forcing function for implementing joint graph replacements. Up until now, we had two places to do pattern matching 1) Pre-grad has janky infra (graph not normalized or functional), but is desirable for many types of passes where you want your change to affect grad formulas. 2) Post-grad has good infra, but cant change grad formulas. This PR adds a third place to do pattern matching: the joint forward+backwards graph. The idea is to take the patterns and lower them to a joint graph and replace both the forwards+backwards before we partition them. This allows us to do something similar to pre-grad transforms, but run after normalization and functionalization. Note that we don't seem to have kernels for all of these patterns, some get decomposed in the dispatcher. cc soumith voznesenskym penguinwu anijain2305 EikanWang jgong5 Guobing-Chen XiaobingSuper zhuhaozhe blzheng Xia-Weiwen wenzhe-nrv jiayisunx peterbell10 desertfire [ghstack-poisoned]
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