diff --git a/test/dynamo/test_repros.py b/test/dynamo/test_repros.py
index d525ef54dec..5e47b5c1c26 100644
--- a/test/dynamo/test_repros.py
+++ b/test/dynamo/test_repros.py
@@ -3576,33 +3576,6 @@ class ReproTests(torch._dynamo.test_case.TestCase):
         compiled_fn(inp, vec1, vec2, alpha=alpha, beta=beta, out=compile_out)
         self.assertTrue(same(out, compile_out))
 
-    def test_setattr_requires_grad_graph_breaks(self):
-        def fn(x):
-            z = x + 4
-            x.requires_grad = True
-            y = x * z
-            return y
-
-        for backend in ["count", "eager", "aot_eager"]:
-            if backend == "count":
-                backend = CompileCounter()
-            opt_fn = torch.compile(fn, backend=backend)
-
-            eager = torch.zeros(5)
-            compiled = eager.clone()
-
-            out_eager = fn(eager)
-            out_opt = opt_fn(compiled)
-
-            self.assertEqual(out_eager, out_opt)
-
-            out_eager.sum().backward()
-            out_opt.sum().backward()
-
-            self.assertEqual(eager, compiled)
-            if isinstance(backend, CompileCounter):
-                self.assertEqual(backend.frame_count, 2)  # graph breaks
-
     def test_inductor_no_recursionerror_on_for_loops(self):
         def forward(x):
             for _ in range(1000):
diff --git a/torch/_dynamo/variables/builtin.py b/torch/_dynamo/variables/builtin.py
index 9892e3e1d2e..8d33360622a 100644
--- a/torch/_dynamo/variables/builtin.py
+++ b/torch/_dynamo/variables/builtin.py
@@ -1199,18 +1199,15 @@ class BuiltinVariable(VariableTracker):
         ):
             name = name_var.as_python_constant()
             if name == "data" and all(
-                isinstance(t, variables.TensorVariable) for t in [val, obj]
+                isinstance(t, variables.TensorVariable)
+                # and not (t.source is None or is_constant_source(t.source))
+                for t in [val, obj]
             ):
                 unimplemented(
                     ".data assignment to a tracked tensors can introduce aliasing, hence we "
                     "need to graph break to apply the aliasing (or track new aliased tensors) "
                     "to continue to trace the graph"
                 )
-            if name == "requires_grad" and isinstance(obj, variables.TensorVariable):
-                unimplemented(
-                    "mutating requires_grad can introduce a new leaf from non-leaf or vice versa in "
-                    "the middle of the graph, which aot_autograd does not currently know how to handle. "
-                )
             tx.output.side_effects.store_attr(obj, name, val)
             return val
         elif isinstance(obj, variables.UserDefinedObjectVariable):