Add option to skip functional passes in the pattern matcher's replacement graph (#134364)

The pattern matcher runs DCE and remove_noop_ops on the replacement
graph by default. Previously we had a switch for the DCE. This PR
changes that switch to also control if we run remove_noop_ops.

The context was that there is silent incorrectness with
auto_functionalized. We use the Pattern matcher to decompose
auto_functionalized into a mutable op + clones; remove_noop_ops were
deleting the clones.

Future: can try #134363

Test Plan:
- new test. I wasn't able to produce a silently incorrect example so I
  settled for asserting that clones still exist in the post-grad graph.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/134364
Approved by: https://github.com/eellison
ghstack dependencies: #133639

torch/_inductor/pattern_matcher.py

@@ -226,15 +226,25 @@ class Match:
         replacement_fn: ReplaceFn,
         args: Sequence[Any],
         trace_fn: Optional[TraceFn] = None,
-        run_dce: bool = True,
+        run_functional_passes: bool = True,
     ) -> None:
+        """Replace with a graph generated by tracing the replacement_fn.
+
+        Args:
+            run_functional_passes (bool). If we should run passes that
+                assume functional IR (like DCE, remove_noop_ops), on the
+                replacement graph.
+
+        """
         from torch._inductor.virtualized import V
 
         context = V.fake_mode if V.fake_mode is not None else contextlib.nullcontext
 
         with context:
             if trace_fn is None:
-                trace_fn = functools.partial(fwd_only, run_dce=run_dce)
+                trace_fn = functools.partial(
+                    fwd_only, run_functional_passes=run_functional_passes
+                )
             replacement = trace_fn(
                 replacement_fn, torch.fx.map_arg(args, lambda arg: arg.meta["val"])  # type: ignore[arg-type]
             )
@@ -1810,7 +1820,7 @@ def fwd_only(
     fn: Callable[..., Any],
     args: Sequence[Any],
     *,
-    run_dce: bool = True,
+    run_functional_passes: bool = True,
     get_decomp_fn: Optional[Callable[..., Any]] = None,
 ) -> torch.fx.GraphModule:
     """Build a normalized inference graph, for use with fx_to_pattern"""
@@ -1823,10 +1833,10 @@ def fwd_only(
 
     from .fx_passes.post_grad import remove_noop_ops
 
-    remove_noop_ops(gm.graph)
-
-    if run_dce:
+    if run_functional_passes:
+        remove_noop_ops(gm.graph)
         gm.graph.eliminate_dead_code()
+
     gm.recompile()
     return gm