[Dynamo] Fix torch.is_tensor and torch.overrides.is_tensor_like (#88704)

Fixes error from 7k github models: https://github.com/jansel/pytorch-jit-paritybench/blob/master/generated/test_arashwan_matrixnet.py

Error:
```
AssertionError: torch.* op returned non-Tensor bool call_function <function is_tensor at 0x7fca94d0faf0>

from user code:
   File "/scratch/ybliang/work/repos/pytorch-jit-paritybench/generated/test_arashwan_matrixnet.py", line 749, in scatter
      return scatter_map(inputs)
   File "/scratch/ybliang/work/repos/pytorch-jit-paritybench/generated/test_arashwan_matrixnet.py", line 741, in scatter_map
      assert not torch.is_tensor(obj), 'Tensors not supported in scatter.'
```

Pull Request resolved: https://github.com/pytorch/pytorch/pull/88704
Approved by: https://github.com/jansel

test/dynamo/test_misc.py

@@ -400,6 +400,23 @@ class MiscTests(torch._dynamo.test_case.TestCase):
 
         return torch._dynamo.testing.standard_test(self, fn=fn, nargs=2, expected_ops=3)
 
+    def test_is_tensor2(self):
+        def fn(x):
+            if torch.is_tensor(x):
+                return x + 1
+            else:
+                return torch.ones([2, 3])
+
+        x1 = {"input": torch.rand(2, 3)}
+        x2 = torch.rand(2, 3)
+        ref1 = fn(x1)
+        ref2 = fn(x2)
+        opt_fn = torch._dynamo.optimize("eager")(fn)
+        res1 = opt_fn(x1)
+        res2 = opt_fn(x2)
+        self.assertEqual(ref1, res1)
+        self.assertEqual(ref2, res2)
+
     def test_numel(self):
         def fn(a):
             return a + a.numel() + torch.numel(a)
@@ -1244,6 +1261,32 @@ class MiscTests(torch._dynamo.test_case.TestCase):
         self.assertTrue(same(ref0, res0))
         self.assertTrue(same(ref1, res1))
 
+    def test_is_tensor_like2(self):
+        class MyTensor(object):
+            @classmethod
+            def __torch_function__(cls, func, types, args=(), kwargs=None):
+                if kwargs is None:
+                    kwargs = {}
+
+                if func is torch.max:
+                    return torch.tensor(123)
+                return func(*args, **kwargs)
+
+        def fn(x):
+            if torch.overrides.is_tensor_like(x):
+                return torch.max(x)
+            else:
+                return torch.zeros(1)
+
+        x = MyTensor()
+        ref0 = fn(x)
+        ref1 = fn(4)
+        opt_fn = torch._dynamo.optimize("eager")(fn)
+        res0 = opt_fn(x)
+        res1 = opt_fn(4)
+        self.assertTrue(same(ref0, res0))
+        self.assertTrue(same(ref1, res1))
+
     def test_version_ci(self):
         # temporary test to check that the ci torch version is set correctly
         self.assertTrue(hasattr(torch, "_subclasses"))

torch/_dynamo/variables/torch.py

@@ -163,8 +163,6 @@ class TorchVariable(VariableTracker):
             torch.finfo,
             torch.iinfo,
             torch.is_floating_point,
-            torch.is_tensor,
-            torch.overrides.is_tensor_like,
         ):
             return True
         return getattr(self.value, "__module__", None) == "math"
@@ -177,9 +175,9 @@ class TorchVariable(VariableTracker):
             DynamicShapeVariable,
             GradModeVariable,
             TensorVariable,
+            UserDefinedObjectVariable,
         )
 
-        # print("CALLING ON TORCH", self.value)
         from .builder import wrap_fx_proxy
 
         constant_args = check_constant_args(args, kwargs)
@@ -206,21 +204,26 @@ class TorchVariable(VariableTracker):
                 return self._call_cross_entropy_loss(tx, args, kwargs, options)
             else:
                 unimplemented(f"construct nn.Module: {self.value.__name__}")
+        elif self.value in (torch.is_tensor, torch.overrides.is_tensor_like):
+            assert len(args) == 1
+            if isinstance(args[0], TensorVariable) or (
+                self.value is torch.overrides.is_tensor_like
+                and isinstance(args[0], UserDefinedObjectVariable)
+                and hasattr(args[0].value, "__torch_function__")
+            ):
+                return ConstantVariable(True, **options)
+            else:
+                return ConstantVariable(False, **options)
         elif (
             self.value
             in (
-                torch.is_tensor,
                 torch.is_floating_point,
                 torch.is_complex,
-                torch.overrides.is_tensor_like,
-                torch.is_complex,
             )
             and isinstance(args[0], TensorVariable)
             and args[0].dtype is not None
         ):
-            if self.value in (torch.is_tensor, torch.overrides.is_tensor_like):
+            if self.value is torch.is_floating_point:
-                return ConstantVariable(True, **options)
-            elif self.value is torch.is_floating_point:
                 return ConstantVariable(args[0].dtype.is_floating_point, **options)
             elif self.value is torch.is_complex:
                 return ConstantVariable(args[0].dtype.is_complex, **options)