diff --git a/test/dynamo/test_activation_checkpointing.py b/test/dynamo/test_activation_checkpointing.py
index 5dfaa14067d3..9c168a8e04ae 100644
--- a/test/dynamo/test_activation_checkpointing.py
+++ b/test/dynamo/test_activation_checkpointing.py
@@ -1647,6 +1647,29 @@ Non-primal fwd outputs from model w/o backward hook: {mod_no_hook_fwd_outputs_no
 
         self.assertEqual(opt_fn(x), fn(x))
 
+    @torch._dynamo.config.patch(skip_fwd_side_effects_in_bwd_under_checkpoint=True)
+    def test_nonlocal_mutation(self):
+        counter = 0
+
+        def gn(x):
+            nonlocal counter
+            counter += 1
+            return torch.sin(x)
+
+        def fn(x):
+            return torch.utils.checkpoint.checkpoint(gn, x, use_reentrant=True)
+
+        x = torch.randn(4, 4, requires_grad=True)
+        fn(x).sum().backward()
+        # The mutation is reapplied in the backward as well
+        self.assertEqual(counter, 2)
+        counter = 0
+
+        opt_fn = torch.compile(fn, backend="eager", fullgraph=True)
+        opt_fn(x).sum().backward()
+        # The mutation is not reapplied in the backward because the flag was on.
+        self.assertEqual(counter, 1)
+
 
 devices = ["cuda", "hpu"]
 instantiate_device_type_tests(
diff --git a/torch/_dynamo/config.py b/torch/_dynamo/config.py
index d62dd086f055..d35ba10ef1af 100644
--- a/torch/_dynamo/config.py
+++ b/torch/_dynamo/config.py
@@ -633,6 +633,14 @@ compiled_autograd = False
 # See https://github.com/pytorch/pytorch/issues/157452 for more context
 graph_break_on_nn_param_ctor = True
 
+# Eager AC/SAC reapplies the mutations (like global dict mutations) in the
+# backward during the recomputation of forward. torch.compile has no easy way to
+# reapply python mutations in the backward. But many users might be ok to skip
+# reapplication of side effects in the backward. They can set this config flag
+# to accept this eager and compile divergence.
+skip_fwd_side_effects_in_bwd_under_checkpoint = False
+
+
 # Overrides torch.compile() kwargs for Compiled Autograd:
 compiled_autograd_kwargs_override: dict[str, Any] = {}
 """Overrides torch.compile() kwargs for Compiled Autograd.
diff --git a/torch/_dynamo/side_effects.py b/torch/_dynamo/side_effects.py
index 4e45dc7446d2..47912dadb941 100644
--- a/torch/_dynamo/side_effects.py
+++ b/torch/_dynamo/side_effects.py
@@ -218,7 +218,10 @@ class SideEffects:
         return bool(
             output_graph
             and output_graph.current_tx.output.current_tracer.under_activation_checkpoint
-            and output_graph.current_tx.output.current_tracer.allow_side_effects_under_checkpoint
+            and (
+                output_graph.current_tx.output.current_tracer.allow_side_effects_under_checkpoint
+                or torch._dynamo.config.skip_fwd_side_effects_in_bwd_under_checkpoint
+            )
         )
 
     def should_allow_externally_visible_side_effects_in_subtracer(self) -> bool:
diff --git a/torch/_dynamo/variables/higher_order_ops.py b/torch/_dynamo/variables/higher_order_ops.py
index 8c08a68e3b27..956eb4676018 100644
--- a/torch/_dynamo/variables/higher_order_ops.py
+++ b/torch/_dynamo/variables/higher_order_ops.py
@@ -2145,6 +2145,9 @@ class ReparametrizeModuleCallVariable(FunctorchHigherOrderVariable):
 class WrapHigherOrderVariable(TorchHigherOrderOperatorVariable):
     supports_input_mutation = True
     supports_aliasing = True
+    # TODO - Go through all subclasses of WrapHigherOrderVariable to see if
+    # restore_side_effects can be ignored. For now, this is conservative.
+    restore_side_effects = True
 
     def install_subgraph_in_output_graph(
         self, tx, fn_vt, fn_args_vt, kwargs, body_gmod, attr_name="wrap_body"
@@ -2178,6 +2181,7 @@ class WrapHigherOrderVariable(TorchHigherOrderOperatorVariable):
             kwargs,
             description,
             source_target=self.value,
+            restore_side_effects=self.restore_side_effects,
             should_flatten_outputs=True,
             under_activation_checkpoint=under_activation_checkpoint,
             supports_input_mutation=self.supports_input_mutation,
@@ -2565,6 +2569,14 @@ class StrictModeHigherOrderVariable(TorchHigherOrderOperatorVariable):
 
 
 class CheckpointHigherOrderVariable(WrapHigherOrderVariable):
+    def __init__(self, *args, **kwargs) -> None:
+        super().__init__(*args, **kwargs)
+        # If side effects are allowed under checkpoint, we should not restore
+        # the side effects after speculate subgraph.
+        self.restore_side_effects = (
+            not torch._dynamo.config.skip_fwd_side_effects_in_bwd_under_checkpoint
+        )
+
     def _call_function(
         self,
         tx: "InstructionTranslator",