[cond] add a set_ and data mutation expected failure test (pytorch#12…

…8457)

A follow up of the discussion in pytorch#126936.

Cond errors out early because of a graph break triggered by DelayGraphBreakVariable, which is created due to `aten.set_` [here](https://github.com/pytorch/pytorch/blob/main/torch/_dynamo/variables/tensor.py#L366-L376).

We might need to see what happened to this test if we allow graph break in higher order op.

Pull Request resolved: pytorch#128457
Approved by: https://github.com/zou3519

test/functorch/test_control_flow.py

@@ -2599,6 +2599,74 @@ def wrapper(x):
         res = torch.vmap(wrapper)(a)
         self.assertEqual(res, a + 1)
 
+    def test_cond_trace_set__and_mutate_input(self):
+        def f(a, tmp):
+            a_view = a.view(-1)
+            with torch.no_grad():
+                a.set_(tmp)
+                a_view.mul_(2)
+            return a + tmp
+
+        inp = torch.ones(3, 3, requires_grad=True)
+        tmp = torch.ones(3, 3, requires_grad=True)
+        # graph break: torch._dynamo.exc.Unsupported: call_function DelayGraphBreakVariable() [TensorVariable()] {}
+        # due to set_
+        with self.assertRaisesRegex(
+            torch._dynamo.exc.UncapturedHigherOrderOpError,
+            "Cond doesn't work unless it is captured completely with torch.compile",
+        ):
+            torch.cond(inp.sum() > 0, f, f, (inp, tmp))
+
+    def test_cond_trace_set__and_mutate_intermediate(self):
+        def f(a, tmp):
+            a = a.clone()
+            a_view = a.view(-1)
+            tmp = tmp.clone()
+            with torch.no_grad():
+                a.set_(tmp)
+                a_view.mul_(2)
+            return a + tmp
+
+        inp = torch.ones(3, 3, requires_grad=True)
+        tmp = torch.ones(3, 3, requires_grad=True)
+
+        class Mod(torch.nn.Module):
+            def forward(self, inp: torch.Tensor, tmp: torch.Tensor) -> torch.Tensor:
+                return torch.cond(inp.sum() > 0, f, f, (inp, tmp))
+
+        with self.assertRaisesRegex(
+            RuntimeError, "cannot mutate tensors with frozen storage"
+        ):
+            out = torch.compile(Mod(), backend="aot_eager")(inp, tmp)
+
+        with self.assertRaisesRegex(
+            RuntimeError, "cannot mutate tensors with frozen storage"
+        ):
+            out = torch.compile(Mod(), backend="inductor")(inp, tmp)
+
+        from torch._dynamo.testing import EagerAndRecordGraphs
+
+        backend = EagerAndRecordGraphs()
+        out = torch.compile(Mod(), backend=backend)(inp, tmp)
+        self.assertExpectedInline(
+            backend.graphs[0].cond_true_0.code.strip("\n"),
+            """\
+def forward(self, l_inp_, l_tmp_):
+    l_inp__1 = l_inp_
+    l_tmp__1 = l_tmp_
+    clone = l_inp__1.clone();  l_inp__1 = None
+    view = clone.view(-1)
+    clone_1 = l_tmp__1.clone();  l_tmp__1 = None
+    _set_grad_enabled = torch._C._set_grad_enabled(False)
+    set_ = clone.set_(clone_1)
+    mul_ = view.mul_(2);  view = None
+    _set_grad_enabled_1 = torch._C._set_grad_enabled(True)
+    add = clone + clone_1;  clone = clone_1 = None
+    return (add,)
+    """,
+        )
+        self.assertEqual(out, f(inp, tmp))
+
 
 instantiate_parametrized_tests(TestControlFlowTraced)