[fx][const_fold] Refactor to use base split module to simplify, and correctly handle non-single-Tensor outputs #65933
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…orrectly handle non-single-Tensor outputs (pytorch#65933) Summary: Pull Request resolved: pytorch#65933 We use `split_module` to split the input model that we want to const fold into const and non-const subgraphs. Previously we were taking the non-const graph and trying to hack it back into the same signature as the input model. However this was complex/buggy. Instead, refactor to just keep using the base split module that contains both const and non-const graphs. This means we: - Inline the non-const graph into the split module - Remove the const graph from the module and replace it with a getattr that will be run to insert that attr when we `run_folding` Test Plan: Added test coverage to cover newly supported folding, and updated other tests for new strategy. Reviewed By: yinghai Differential Revision: D31293307 fbshipit-source-id: e1174337e1473c7c3a5f82f0227a6fd0ce42db8c
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🔗 Helpful links
💊 CI failures summary and remediationsAs of commit 447d55d (more details on the Dr. CI page):
🕵️ 1 new failure recognized by patternsThe following CI failures do not appear to be due to upstream breakages:
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Summary:
We use
split_moduleto split the input model that we want to const fold into const and non-const subgraphs. Previously we were taking the non-const graph and trying to hack it back into the same signature as the input model. However this was complex/buggy.Instead, refactor to just keep using the base split module that contains both const and non-const graphs. This means we:
run_foldingTest Plan: Added test coverage to cover newly supported folding, and updated other tests for new strategy.
Differential Revision: D31293307