pytorch

5efd1051 - [jit] speed up alias analysis (#36345)

[jit] speed up alias analysis (#36345) Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/36345 During compilation, we spend a huge amount of time in alias analyis. This PR does a few things to speed it up. 1. Separate the analysis into two phases: one where we build up the necessary data structures, and the other where we service aliasing queries. This allows us to defer building indices/maintaining index consistency until after the "buildup" phase is done. 2. Properly memoize/dynamic program the memory locations lookups. 3. Done naively, setting wildcards invalidates the above memoization, trigger costly recomputation. So I added a cache-aware `setWildcards`. Sadly that means you need alias analysis to reach into the guts of memorydag, but the speedup is worth it. Sadly, these changes are kind of coupled for correctness reasons, so they're all here at once. I used this model (thanks IlyaOvodov) as a provisional benchmark. You can get it here: https://www.dropbox.com/s/jlyygn6yygj1jkx/yolov3.zip. Unzip at run `python test_timing.py`. Baseline: (752.076s) right before 6bc8ffe82462c77ac4f9b27452046cb1f8f07d92 After optimizing before inlining: (699.593s) After deferring cache construction: (426.180s) After cache-aware `setWildcards`: (193.678s) So a nice 75% speedup to overall compilation. There's a lot more to do in other places of the compilation pipeline though. Followup to this PR specifically: Everything that fans out from the `analyze` call is the "buildup" phase of AliasDB construction. This should be factored into a separate analysis pass to statically distinguish the two phases (right now we just null out stuff to accomplish the same thing dynamically). Test Plan: Imported from OSS Differential Revision: D20952727 Pulled By: suo fbshipit-source-id: 099f797222d7e71e5c04991584adc2c7eab5a70f