Verify types in custom op schemas (#124520)

Before this PR, we didn't check that types in a schema were valid. This
is because TorchScript treats unknown types as type variables.

This PR checks types in a schema for the TORCH_LIBRARY APIs. To do this,
we add an `allow_typevars` flag to parseSchema so that TorchScript can
use allow_typevars=True. We also add some error messages for common
mistakes (e.g. using int64_t or double in schema).

Test Plan:
- new tests

Differential Revision: [D56432690](https://our.internmc.facebook.com/intern/diff/D56432690)

Pull Request resolved: https://github.com/pytorch/pytorch/pull/124520
Approved by: https://github.com/albanD

test/test_custom_ops.py

@@ -1740,6 +1740,17 @@ dynamic shape operator: _torch_testing.numpy_nonzero.default
             res = torch._library.utils.is_functional_schema(schema)
             self.assertEqual(res, expected)
 
+    def test_incorrect_schema_types(self):
+        with torch.library._scoped_library("mylib", "FRAGMENT") as lib:
+            with self.assertRaisesRegex(RuntimeError, "unknown type specifier"):
+                lib.define("foo12(Tensor a) -> asdfasdf")
+            with self.assertRaisesRegex(RuntimeError, "unknown type specifier"):
+                lib.define("foo12(asdf a) -> Tensor")
+            with self.assertRaisesRegex(RuntimeError, "Use `SymInt` or `int`"):
+                lib.define("foo12(int64_t a) -> Tensor")
+            with self.assertRaisesRegex(RuntimeError, "Use `float`"):
+                lib.define("foo12(double a) -> Tensor")
+
     def test_is_tensorlist_like_type(self):
         tensorlists = [
             # Tensor[]