Fix dead code elimination in onnx export #22476
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Differential Revision: D16100172 Differential Version: 85987667
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oncall: jit
This was referenced Jul 2, 2019
Differential Revision: D16100172 Differential Version: 85991639
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This pull request has been merged in 17cc798. |
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Jul 5, 2019
Summary: Pull Request resolved: pytorch#22476 Dead code elimination assumes a valid jit graph because it checks if operators have side effects. The onnx export path destroys the jit graph right before calling dead code elimination, but it actually doesn't care about side effects. We can just call dead code elimination and disable side effect lookup and things should work. Reviewed By: houseroad Differential Revision: D16100172 fbshipit-source-id: 8c790055e0d76c4227394cafa93b07d1310f2cea
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Hi @houseroad, @smessmer, next time can you loop in a JIT team member to review a change to a core optimization pass? I have concerns because this essentially adds a flag that says "make this pass produce incorrect results"—we should try to quarantine such special-case behavior to the ONNX level instead of adding it to the JIT itself. |
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Sure, I agree |
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Can we try to unmerge & find a different approach ? |
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Stack:
:white_circle: #22239 Open up AliasAnalysisKind for any ops 💛
:white_circle: #22175 AliasAnalysisKind::CONSERVATIVE/FROM_SCHEMA 💛
:black_circle: #22476 Fix dead code elimination in onnx export 💛
:white_circle: #22461 EraseNumberTypes cleans itself up 💚
Dead code elimination assumes a valid jit graph because it checks if operators have side effects.
The onnx export path destroys the jit graph right before calling dead code elimination, but it actually doesn't care about side effects.
We can just call dead code elimination and disable side effect lookup and things should work.
Differential Revision: D16100172