Correctly mark unannotated NamedTuple field to be inferred TensorType

test/test_jit.py

@@ -82,7 +82,7 @@
 from itertools import product
 import itertools
 from textwrap import dedent
-from typing import List, Dict, Optional, Tuple, Union
+from typing import List, Dict, NamedTuple, Optional, Tuple, Union
 import inspect
 import math
 import functools
@@ -13796,6 +13796,23 @@ def test_non_primitive_types(x):
         out = test_non_primitive_types(_MyNamedTuple(value=torch.tensor(5.0)))
         self.assertEqual(out, torch.tensor(6.0))
 
+    def test_namedtuple_type_inference(self):
+        _AnnotatedNamedTuple = NamedTuple('_NamedTupleAnnotated', [('value', int)])
+        _UnannotatedNamedTuple = namedtuple('_NamedTupleUnAnnotated', ['value'])
+
+        def test_check_named_tuple_value():
+            named_tuple = _AnnotatedNamedTuple(1)
+            return named_tuple.value
+
+        self.checkScript(test_check_named_tuple_value, ())
+
+        def test_error():
+            return _UnannotatedNamedTuple(1)
+
+        with self.assertRaisesRegex(RuntimeError, r"Expected a value of type \'Tensor \(inferred\)\' "
+                                                  r"for argument \'value\' but instead found type \'int\'."):
+            torch.jit.script(test_error)
+
     def test_isinstance_dynamic(self):
         @torch.jit.script
         def foo(a):

torch/_jit_internal.py

@@ -839,7 +839,7 @@ def _get_named_tuple_properties(obj):
             the_type = torch.jit.annotations.ann_to_type(obj.__annotations__[field], fake_range())
             annotations.append(the_type)
         else:
-            annotations.append(torch._C.TensorType.get())
+            annotations.append(torch._C.TensorType.getInferred())
     return type(obj).__name__, fields, annotations