Add hasattr for tensor variable

Enables tests that are now running sucessfully under dynamo

ghstack-source-id: 3fcdb33d957c30b3e740e1d83c148c1572279360
Pull Request resolved: https://github.com/pytorch/pytorch/pull/131008

test/dynamo/test_misc.py

@@ -1364,6 +1364,20 @@ utils_device.CURRENT_DEVICE == None""".split(
         r2 = opt_fn(i)
         self.assertEqual(r1, r2)
 
+    def test_tensor_hasattr(self):
+        @torch.compile(fullgraph=True)
+        def fn(x):
+            if hasattr(x, "test"):
+                return x + 2
+            else:
+                return x + 1
+
+        self.assertEqual(torch.ones(2, 2) + 1, fn(torch.ones(2, 2)))
+
+        inp = torch.ones(2, 2)
+        inp.test = None
+        self.assertEqual(torch.ones(2, 2) + 2, fn(inp))
+
     def test_shape_unpack(self):
         def fn(x):
             a, b = x.size()

torch/_dynamo/variables/tensor.py

@@ -333,6 +333,27 @@ class TensorVariable(VariableTracker):
             tx, [self], {}
         )
 
+    def call_hasattr(self, tx, name):
+        from . import GetAttrVariable
+        from .builtin import BuiltinVariable
+
+        try:
+            var = BuiltinVariable(getattr).call_function(
+                tx, [self, ConstantVariable(name)], {}
+            )
+            # in the event that TensorVariable returns NotImplemented
+            # BuiltinVariable.call_getattr returns GetAttrVariable
+            ret_val = not isinstance(var, GetAttrVariable)
+        except AttributeError:
+            ret_val = False
+
+        if self.source:
+            install_guard(
+                AttrSource(self.source, name).make_guard(GuardBuilder.HASATTR)
+            )
+
+        return ConstantVariable(ret_val)
+
     def var_getattr(self, tx, name):
         from . import UserDefinedClassVariable
 
@@ -1171,8 +1192,8 @@ class NumpyNdarrayVariable(TensorVariable):
         if name in ["__len__", "size", "tolist"]:
             # delegate back to TensorVariable
             return super().call_method(tx, name, args, kwargs)
-        if name == "tobytes":
-            unimplemented("tobytes is not modelled in torch._numpy")
+        if name in ("tostring", "tobytes"):
+            unimplemented(f"{name} is not modelled in torch._numpy")
         proxy = tx.output.create_proxy(
             "call_function",
             numpy_method_wrapper(name),