Add HierarchicalCSEPass infrastructure for nested graph optimization

[hsharma35]

Summary:
Adds HierarchicalCSEPass class for recursively processing submodules with CSE,
enabling better optimization of map operations and nested graphs.

Differential Revision: D94365159

[pytorch-bot]

🔗 Helpful Links

🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/17737

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❌ 3 New Failures, 2 Unrelated Failures

As of commit 6840ede with merge base 5a9b280 ():

NEW FAILURES - The following jobs have failed:

• pull / android / run-emulator (gh)
  The process '/usr/bin/sh' failed with exit code 133
• pull / test-samsung-models-linux / linux-job (gh)
  RuntimeError: Command docker exec -t 509aac306ff395357c97b98321f0eaba0165113ad828ab14374143c2b90a4f80 /exec failed with exit code 1
• pull / test-samsung-quantmodels-linux / linux-job (gh)
  RuntimeError: Command docker exec -t 2a4718c573df558b76f2a4e40ebb98033af5c9234d5bd00facbacbc8adc71c94 /exec failed with exit code 1

FLAKY - The following jobs failed but were likely due to flakiness present on trunk:

• Test CUDA Builds / test-models-cuda (add) / linux-job (gh) (detected as infra flaky with no log or failing log classifier)
• Test CUDA Builds / test-models-cuda (conv1d) / linux-job (gh) (detected as infra flaky with no log or failing log classifier)

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[meta-codesync]

@hsharma35 has exported this pull request. If you are a Meta employee, you can view the originating Diff in D94365159.

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