diff --git a/test/distributed/tensor/debug/test_debug_mode.py b/test/distributed/tensor/debug/test_debug_mode.py
index 20da99f52eb0..aab91ddebe94 100644
--- a/test/distributed/tensor/debug/test_debug_mode.py
+++ b/test/distributed/tensor/debug/test_debug_mode.py
@@ -330,46 +330,6 @@ class TestDTensorDebugMode(TestCase):
             f(x)
         self.assertEqual(len(debug_mode.debug_string()), 0)
 
-    def test_nn_module(self):
-        class Foo(torch.nn.Module):
-            def __init__(self):
-                super().__init__()
-                self.l1 = torch.nn.Linear(4, 4)
-                self.l2 = torch.nn.Linear(4, 4)
-
-            def forward(self, x):
-                return self.l2(self.l1(x))
-
-        class Bar(torch.nn.Module):
-            def __init__(self):
-                super().__init__()
-                self.abc = Foo()
-                self.xyz = torch.nn.Linear(4, 4)
-
-            def forward(self, x):
-                return self.xyz(self.abc(x))
-
-        mod = Bar()
-        inp = torch.randn(4, 4)
-        with DebugMode(record_nn_module=True) as debug_mode:
-            _ = mod(inp)
-
-        self.assertExpectedInline(
-            debug_mode.debug_string(),
-            """\
-    [nn.Mod] Bar
-      [nn.Mod] Bar.abc
-        [nn.Mod] Bar.abc.l1
-          aten::t(t: f32[4, 4])
-          aten::addmm(t: f32[4], t: f32[4, 4], t: f32[4, 4])
-        [nn.Mod] Bar.abc.l2
-          aten::t(t: f32[4, 4])
-          aten::addmm(t: f32[4], t: f32[4, 4], t: f32[4, 4])
-      [nn.Mod] Bar.xyz
-        aten::t(t: f32[4, 4])
-        aten::addmm(t: f32[4], t: f32[4, 4], t: f32[4, 4])""",
-        )
-
 
 instantiate_parametrized_tests(TestDTensorDebugMode)
 
diff --git a/torch/utils/_debug_mode.py b/torch/utils/_debug_mode.py
index 1986828c519b..7f7de2b7334f 100644
--- a/torch/utils/_debug_mode.py
+++ b/torch/utils/_debug_mode.py
@@ -1,6 +1,6 @@
 # mypy: allow-untyped-defs
 import contextlib
-from typing import Optional, TYPE_CHECKING
+from typing import Optional
 
 import torch
 from torch._subclasses.fake_tensor import FakeTensor, FakeTensorMode
@@ -13,10 +13,6 @@ from torch.utils._python_dispatch import (
 from torch.utils._pytree import tree_map
 
 
-if TYPE_CHECKING:
-    from torch.distributed._tools.mod_tracker import ModTracker
-
-
 __all__ = ["DebugMode", "get_active_debug_mode"]
 
 REDISTRIBUTE_FUNC = "redistribute_input"
@@ -110,17 +106,6 @@ def _op_to_str(op, attributes, *args, **kwargs) -> str:
     return f"{op_name}({args_str}{kwargs_str})"
 
 
-class _NNModuleCall(_DebugCall):
-    """Designates entering an nn.Module's forward method"""
-
-    def __init__(self, module_name: str, call_depth: int):
-        super().__init__(call_depth)
-        self.module_name = module_name
-
-    def render(self, attributes: list[str]) -> str:
-        return f"[nn.Mod] {self.module_name}"
-
-
 class DebugMode(TorchDispatchMode):
     def __init__(
         self,
@@ -129,7 +114,6 @@ class DebugMode(TorchDispatchMode):
         record_faketensor=False,
         record_realtensor=True,
         record_tensor_attributes=None,
-        record_nn_module=False,
     ):
         super().__init__()
         import torch.distributed.tensor  # noqa: F401
@@ -140,12 +124,6 @@ class DebugMode(TorchDispatchMode):
         self.record_realtensor = record_realtensor
         self.record_tensor_attributes = record_tensor_attributes or []
 
-        self.record_nn_module = record_nn_module
-
-        self.module_tracker: Optional[ModTracker] = None
-        if self.record_nn_module:
-            self.module_tracker_setup()
-
         self.operators = []
         self.call_depth = 0
 
@@ -198,35 +176,14 @@ class DebugMode(TorchDispatchMode):
             torch._C._push_on_torch_function_stack(self)
 
         super().__enter__()
-        if self.record_nn_module:
-            self.module_tracker.__enter__()  # type: ignore[attribute, union-attr]
         return self
 
     # pyrefly: ignore  # bad-override
     def __exit__(self, *args):
         super().__exit__(*args)
-        if self.record_nn_module:
-            self.module_tracker.__exit__()  # type: ignore[attribute, union-attr]
         if self.record_torchfunction:
             torch._C._pop_torch_function_stack()
 
-    def module_tracker_setup(self):
-        from torch.distributed._tools.mod_tracker import ModTracker
-
-        self.module_tracker = ModTracker()
-
-        # module pre-fw hook: record module call
-        def pre_fw_hook(module, input):
-            fqn = self.module_tracker._get_mod_name(module)  # type: ignore[attribute, union-attr]
-            self.operators.append(_NNModuleCall(fqn, self.call_depth + 1))
-            self.call_depth += 1
-
-        # module post-fw hook: decrement call depth
-        def post_fw_hook(module, input, output):
-            self.call_depth -= 1
-
-        self.module_tracker.register_user_hooks(pre_fw_hook, post_fw_hook)
-
     @contextlib.contextmanager
     def record_redistribute_calls(
         self,