Revert "[dynamo] Log guard latency (pytorch#145132)"

This reverts commit 0a310d7.

Reverted pytorch#145132 on behalf of https://github.com/anijain2305 due to CI failures observed after PR was merged ([comment](pytorch#145132 (comment)))

Author: pytorchmergebot

test/dynamo/test_utils.py

@@ -212,8 +212,6 @@ def test_dynamo_timed(self, mock_time, mock_time_ns):
         # much easier.
         raw = dataclasses.asdict(compilation_events[0])
         del raw["feature_usage"]
-        # guard_latency_us is not deterministic
-        del raw["guard_latency_us"]
         self.assertExpectedInline(
             pprint.pformat(raw),
             """\
@@ -294,7 +292,6 @@ def test_dynamo_timed(self, mock_time, mock_time_ns):
         # Second event is for the backward
         raw = dataclasses.asdict(compilation_events[1])
         del raw["feature_usage"]
-        del raw["guard_latency_us"]
         self.assertExpectedInline(
             pprint.pformat(raw),
             """\

torch/_dynamo/guards.py

@@ -48,7 +48,6 @@
     TensorProperty,
     TensorPropertySource,
 )
-from torch._dynamo.utils import CompileEventLogger
 from torch._guards import (
     CompileContext,
     CompileId,
@@ -2481,7 +2480,6 @@ def cleanup_builder(weak_b):
                 self.guard_manager.root, output_graph.local_scope, 50
             )
             guards_log.debug("Guard eval latency = %s us", f"{latency:.2f}")
-            CompileEventLogger.compilation_metric(guard_latency_us=latency)
 
         # NB - We have to very careful of cleaning up here. Because of the
         # invalidate function, we can create a weakref finalizer that keeps

torch/_dynamo/testing.py

@@ -181,32 +181,30 @@ def insert_nops(instructions: list[Any], code_options: Any) -> None:
         instructions.insert(0, create_instruction("NOP"))
         instructions.insert(0, create_instruction("NOP"))
 
-    metrics_context = torch._dynamo.utils.get_metrics_context()
-    with torch._dynamo.utils.dynamo_timed("debug_insert_nops"), metrics_context:
-        if is_generator(frame.f_code):
-            return None
-
-        debug_checks(frame.f_code)
-        code = transform_code_object(frame.f_code, insert_nops)
-        graph = OutputGraph(
-            code_options={},
-            compiler_fn=None,
-            root_tx=None,
-            export=False,
-            export_constraints=None,
-            frame_state={"_id": 0},
-            # TODO: shouldn't this be f_locals/f_globals from frame?
-            local_scope=locals(),
-            global_scope=globals(),
-            f_code=frame.f_code,
-            torch_function_mode_stack=[],
-        )
+    if is_generator(frame.f_code):
+        return None
 
-        return GuardedCode(
-            code,
-            CheckFunctionManager(frame.f_code, graph).guard_manager,  # type: ignore[arg-type]
-            CompileId(frame_id=0, frame_compile_id=0),
-        )
+    debug_checks(frame.f_code)
+    code = transform_code_object(frame.f_code, insert_nops)
+    graph = OutputGraph(
+        code_options={},
+        compiler_fn=None,
+        root_tx=None,
+        export=False,
+        export_constraints=None,
+        frame_state={"_id": 0},
+        # TODO: shouldn't this be f_locals/f_globals from frame?
+        local_scope=locals(),
+        global_scope=globals(),
+        f_code=frame.f_code,
+        torch_function_mode_stack=[],
+    )
+
+    return GuardedCode(
+        code,
+        CheckFunctionManager(frame.f_code, graph).guard_manager,  # type: ignore[arg-type]
+        CompileId(frame_id=0, frame_compile_id=0),
+    )
 
 
 class CompileCounter:

torch/_dynamo/utils.py

@@ -1186,7 +1186,6 @@ class CompilationMetrics:
     tensorify_float_attempt: Optional[bool] = None
     tensorify_float_success: Optional[bool] = None
     tensorify_float_failure: Optional[set[str]] = None
-    guard_latency_us: Optional[float] = None
 
     @classmethod
     def create(cls, metrics: dict[str, Any]):