diff --git a/aten/src/ATen/core/jit_type.h b/aten/src/ATen/core/jit_type.h
index 5472e3f080fc4..8583dfec1c774 100644
--- a/aten/src/ATen/core/jit_type.h
+++ b/aten/src/ATen/core/jit_type.h
@@ -1324,7 +1324,7 @@ struct TORCH_API NoneType : public Type {
     return rhs.kind() == kind();
   }
   std::string str() const override {
-    return "None";
+    return "NoneType";
   }
   bool isSubtypeOfExt(const TypePtr& rhs, std::ostream *why_not) const override {
     if (rhs->kind() == OptionalType::Kind) {
diff --git a/test/expect/TestJit.test_cu_escaped_number.expect b/test/expect/TestJit.test_cu_escaped_number.expect
index ff492c20a4470..ef7bd90052101 100644
--- a/test/expect/TestJit.test_cu_escaped_number.expect
+++ b/test/expect/TestJit.test_cu_escaped_number.expect
@@ -1,3 +1,3 @@
-def foo(a: Tensor) -> None:
+def foo(a: Tensor) -> NoneType:
   print("hi\016")
   return None
diff --git a/test/expect/TestJit.test_pretty_printer-print_weird_test.expect b/test/expect/TestJit.test_pretty_printer-print_weird_test.expect
index 57cc3ea70122d..b8f7e22d8612f 100644
--- a/test/expect/TestJit.test_pretty_printer-print_weird_test.expect
+++ b/test/expect/TestJit.test_pretty_printer-print_weird_test.expect
@@ -1,3 +1,3 @@
-def print_weird_test(y: Tensor) -> None:
+def print_weird_test(y: Tensor) -> NoneType:
   print("hi\016")
   return None
diff --git a/test/jit/test_hooks.py b/test/jit/test_hooks.py
index 73c4e5e76f70d..79698a55044a1 100644
--- a/test/jit/test_hooks.py
+++ b/test/jit/test_hooks.py
@@ -330,7 +330,7 @@ def forward_hook_wrong_input3(self, input: Tuple[None], output: str):
         with self.assertRaisesRegex(
             RuntimeError,
             "has the wrong inner types for the input tuple"
-            r" argument. Received type: 'Tuple\[None\]'",
+            r" argument. Received type: 'Tuple\[NoneType\]'",
         ):
             torch.jit.script(m)
 
diff --git a/test/test_jit.py b/test/test_jit.py
index 0bac81ad48a08..e8b4e977c2499 100644
--- a/test/test_jit.py
+++ b/test/test_jit.py
@@ -4869,7 +4869,7 @@ def test(a):
                 print(typed_nones())
 
         graph_str = str(test.graph)
-        self.assertTrue(graph_str.count("None = prim::Constant") == 1)
+        self.assertTrue(graph_str.count("NoneType = prim::Constant") == 1)
 
     def test_constant_pooling_same_identity(self):
         def foo():
@@ -11061,7 +11061,7 @@ def foo(x : NoneType) -> NoneType:
         ''')
 
         foo_code = cu.find_function('foo').code
-        FileCheck().check(": None").check("-> None").run(foo_code)
+        FileCheck().check(": NoneType").check("-> NoneType").run(foo_code)
 
     def test_empty_tuple_str(self):
         empty_tuple_type = torch._C.TupleType([])
@@ -11069,6 +11069,12 @@ def test_empty_tuple_str(self):
         python_type = eval(empty_tuple_type.annotation_str, g)
         assert python_type is typing.Tuple[()]
 
+    def test_none_type_str(self):
+        none_type = torch._C.NoneType.get()
+        g = {'NoneType' : type(None)}
+        python_type = eval(none_type.annotation_str, g)
+        assert python_type is type(None)
+
     def test_zip_enumerate_modulelist(self):
         class Sub(torch.nn.Module):
             def __init__(self):
@@ -12692,7 +12698,7 @@ def foo_no_decl_always_throws():
 
         # function that has no declared type but always throws set to None
         output_type = next(foo_no_decl_always_throws.graph.outputs()).type()
-        self.assertTrue(str(output_type) == "None")
+        self.assertTrue(str(output_type) == "NoneType")
 
         @torch.jit.script
         def foo_decl_always_throws():
@@ -13416,7 +13422,7 @@ def backward(grad_output):
         ''')
         cu = torch.jit.CompilationUnit(code)
         g = cu.tanh.graph
-        FileCheck().check_count("prim::Closure_0", 2).check("None = prim::Constant") \
+        FileCheck().check_count("prim::Closure_0", 2).check("NoneType = prim::Constant") \
                    .check_next("return").run(g)
 
         code = dedent('''
@@ -13447,7 +13453,7 @@ def backward(grad_output):
         ''')
         cu = torch.jit.CompilationUnit(code)
         fc = FileCheck()
-        fc.check("prim::Closure").check("(Tensor, None) = prim::TupleConstruct")
+        fc.check("prim::Closure").check("(Tensor, NoneType) = prim::TupleConstruct")
         # Loop then two if's added in exit transform
         fc.check("prim::Closure").check("prim::Loop").check_count("prim::If", 2)
         fc.run(cu.loop_in_closure.graph)