Major speedup for model lowering: Add should_run() + fast-copy infrastructure with targeted_ops annotations (#18497)

[apullin]

Summary:

Adds infrastructure for skipping and fast-copying unchanged nodes during
ExportPass execution, then annotates ~60 ARM backend passes to use it.

Changes

1. should_run() hook on ExportPass / ArmPass

Subclasses that declare a targeted_ops class attribute (a set of op
overloads) can be skipped entirely when the graph contains none of their
target ops. ArmPass provides a default implementation via inheritance.

2. Fast-copy for cold nodes

When a pass declares targeted_ops, nodes whose ops are NOT in the set
are copied into the new graph via graph.node_copy() instead of full
FakeTensor dispatch. Per-node cost drops from ~0.4 ms to ~0.02 ms (~20x).

Includes a safety guard: nodes without val metadata (e.g. nodes
inserted by call() overrides before super().call()) fall back to
full dispatch instead of propagating None.

3. FakeTensor cache extension

Context manager _extend_faketensor_cache_builtins() temporarily extends
the FakeTensor dispatch cache to cover ExecuTorch op namespaces
(quantized_decomposed, tosa, dim_order_ops, cortex_m). Avoids redundant
re-dispatches for non-builtin ops across 50+ passes.

4. init_subclass auto-discovery on ArmPass

Subclasses with existing _TARGET_OPS, _supported_ops, or
_EDGE_OPS/_ATEN_OPS attributes get targeted_ops populated
automatically at class definition time — no manual annotation needed.

5. targeted_ops annotations on ~60 ARM passes

Each annotation is a one-liner declaring the ops the pass checks in
call_operator(). Combined with should_run() and fast-copy, this
achieves the measured speedup below.

Benchmark

Model: small CNN feature extractor (~50K params, 9 conv layers with
LayerNorm, targeting Ethos-U55 via the ARM/TOSA lowering pipeline).
Graph: ~1200 nodes, 146 ExportPass invocations.

lower() before: 186 s
lower() after: 100 s
Passes skipped: 53 of 146
Delta: -86 s (-46 %)
Adds should_run() hook to ExportPass that subclasses can override to skip
execution when a pass has no work to do. ArmPass implements a default that
checks a targeted_ops class attribute against the graph's call_function nodes.

Also adds:

• _fast_copy_node path in ExportInterpreter.run_node that uses graph.node_copy
  instead of full FakeTensor dispatch for cold nodes in passes that declare
  targeted_ops. Per-node cost drops from ~0.4ms to ~0.02ms.
• _extend_faketensor_cache_builtins context manager that extends FakeTensor
  dispatch cache to cover ExecuTorch ops (quantized_decomposed, tosa, etc.)
• init_subclass on ArmPass for auto-discovery of targeted_ops from
  existing _TARGET_OPS, _supported_ops, _EDGE_OPS/_ATEN_OPS attributes
• targeted_ops annotations on ~60 ARM pass subclasses

Measured on SleepNet featurizer (U55 lowering):
lower(): 185s -> 96s = -89s (-48%)

Differential Revision: D97528110

[pytorch-bot]

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⚠️ 11 Awaiting Approval

As of commit 9dd58e8 with merge base d050015 ():

AWAITING APPROVAL - The following workflows need approval before CI can run:

• Build Cadence (gh)
• Build documentation (gh)
• Build Presets (gh)
• Lint (gh)
• periodic (gh)
• pull (gh)
• Test ARM Backend (gh)
• Test CoreML Backend (gh)
• Test QNN Backend (gh)
• Test Vulkan Backend (gh)
• Test XNNPACK Backend (gh)

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