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fix inference mode / PyDispatcher / Functionalize interaction #103275
Commits on Jun 8, 2023
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fix inference mode / PyDispatcher / Functionalize interaction
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Commits on Jun 9, 2023
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Update on "fix inference mode / PyDispatcher / Functionalize interact…
…ion" Fixes #103132 This is kind of annoying: Functionalization (and also vmap, I think?) manually figures out which ops have C++ CompositeImplicit decomps, and directly registers them to the Functionalize key. This is a problem for the PyDispatcher: We normally want the PyDispatcher to take precedence over the regular dispatcher. But in this case, we have a python decomp registered to `CompositeImplicitAutograd`, and a C++ decomp registered *directly* to the `Functionalize` key, so the C++ decomp gets precedence over the python decomp. The way this showed up was that a model was running `matmul()` under inference mode, so we never hit the autograd dispatch key, and go straight to the functionalize dispatch key. Matmul has both a python decomp and a c++ decomp, but we were running the C++ decomp. That C++ decomp isn't meant to be used with dynamic shapes, so we were failing with the "tried to call `.sizes()` on a tensor with dynamic shapes" error. For now, I had the PyDispatcher mimic the behavior of functionalization codegen: when you register a python decomp to the `CompositeImplicitAutograd` key, this PR just automatically registers that decomp to the `Functionalize` key at the same time. I'm trying to remember now why we didn't just add `Functionalize` (and all of the other functorch transform keys) directly to the `CompositeImplicitAutograd` alias keyset, but I couldn't remember (zou3519 any chance you remember?). cc voznesenskym penguinwu anijain2305 EikanWang jgong5 Guobing-Chen XiaobingSuper zhuhaozhe blzheng Xia-Weiwen wenzhe-nrv jiayisunx ipiszy aakhundov [ghstack-poisoned]
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Update on "fix inference mode / PyDispatcher / Functionalize interact…
…ion" Fixes #103132 This is kind of annoying: Functionalization (and also vmap, I think?) manually figures out which ops have C++ CompositeImplicit decomps, and directly registers them to the Functionalize key. This is a problem for the PyDispatcher: We normally want the PyDispatcher to take precedence over the regular dispatcher. But in this case, we have a python decomp registered to `CompositeImplicitAutograd`, and a C++ decomp registered *directly* to the `Functionalize` key, so the C++ decomp gets precedence over the python decomp. The way this showed up was that a model was running `matmul()` under inference mode, so we never hit the autograd dispatch key, and go straight to the functionalize dispatch key. Matmul has both a python decomp and a c++ decomp, but we were running the C++ decomp. That C++ decomp isn't meant to be used with dynamic shapes, so we were failing with the "tried to call `.sizes()` on a tensor with dynamic shapes" error. For now, I had the PyDispatcher mimic the behavior of functionalization codegen: when you register a python decomp to the `CompositeImplicitAutograd` key, this PR just automatically registers that decomp to the `Functionalize` key at the same time. I'm trying to remember now why we didn't just add `Functionalize` (and all of the other functorch transform keys) directly to the `CompositeImplicitAutograd` alias keyset, but I couldn't remember (zou3519 any chance you remember?). cc voznesenskym penguinwu anijain2305 EikanWang jgong5 Guobing-Chen XiaobingSuper zhuhaozhe blzheng Xia-Weiwen wenzhe-nrv jiayisunx ipiszy aakhundov [ghstack-poisoned]
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Commits on Jun 20, 2023
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Update on "fix inference mode / PyDispatcher / Functionalize interact…
…ion" Fixes #103132 This is kind of annoying: Functionalization (and also vmap, I think?) manually figures out which ops have C++ CompositeImplicit decomps, and directly registers them to the Functionalize key. This is a problem for the PyDispatcher: We normally want the PyDispatcher to take precedence over the regular dispatcher. But in this case, we have a python decomp registered to `CompositeImplicitAutograd`, and a C++ decomp registered *directly* to the `Functionalize` key, so the C++ decomp gets precedence over the python decomp. The way this showed up was that a model was running `matmul()` under inference mode, so we never hit the autograd dispatch key, and go straight to the functionalize dispatch key. Matmul has both a python decomp and a c++ decomp, but we were running the C++ decomp. That C++ decomp isn't meant to be used with dynamic shapes, so we were failing with the "tried to call `.sizes()` on a tensor with dynamic shapes" error. For now, I had the PyDispatcher mimic the behavior of functionalization codegen: when you register a python decomp to the `CompositeImplicitAutograd` key, this PR just automatically registers that decomp to the `Functionalize` key at the same time. I'm trying to remember now why we didn't just add `Functionalize` (and all of the other functorch transform keys) directly to the `CompositeImplicitAutograd` alias keyset, but I couldn't remember (zou3519 any chance you remember?). cc voznesenskym penguinwu anijain2305 EikanWang jgong5 Guobing-Chen XiaobingSuper zhuhaozhe blzheng Xia-Weiwen wenzhe-nrv jiayisunx ipiszy chenyang78 aakhundov [ghstack-poisoned]
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