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9e32a1f5cd
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/37106 Recomputing the aliasdb on every fusion iteration + in every subblock is hugely expensive. Instead, update it in-place when doing fusion. The graph fuser pass operates by pushing nodes into a fusion group. So we start with ``` x, y = f(a, b, c) ``` and end with: ``` x_out, y_out = prim::fusionGroup(a, b, c) x_in, y_in = f(a_in, b_in, c_in) -> x_in, y_in ``` We destroy the `x` and `y` `Value*`s in the process. This operation is easy to express as an update to the aliasDb--`x_out` just takes on all the aliasing information `x` used to have. In particular, since we know `f` and `prim::fusionGroup` are purely functional, we don't have to mess with any write information. This PR is the bare minimum to get this working, in the interest of unscrewing the compilation times ASAP. Followups I want to do: - We don't have a way of expressing deletion of values in AliasDb. In `graph_fuser.cpp` we sometimes construct nodes that we end up throwing away, and we are littering `MemoryDAG` with references to dangling pointers. Because of the way the pass works, it's fine, but this is fragile so I want to fix it. - We should decouple alias analysis from write tracking, to simplify the job of keeping the write caches consistent as we mutate the aliasing information. - the tensorexpr fuser doesn't do this and thus is incorrect today, we need to update it to work. Test Plan: Imported from OSS Differential Revision: D21219179 Pulled By: suo fbshipit-source-id: 8ae5397b3a0ad90edec2fbc555647091f1ad5284 |
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| tensorexpr | ||
| __init__.py | ||