Add missing Maskformer dataclass decorator, add dataclass check in ModelOutput for subclasses (#25638)

* Add @dataclass to MaskFormerPixelDecoderOutput

* Add dataclass check if subclass of ModelOutout

* Use unittest assertRaises rather than pytest per contribution doc

* Update src/transformers/utils/generic.py per suggested change

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

---------

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

src/transformers/models/maskformer/modeling_maskformer.py

@@ -118,6 +118,7 @@ class MaskFormerPixelLevelModuleOutput(ModelOutput):
     decoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
 
 
+@dataclass
 class MaskFormerPixelDecoderOutput(ModelOutput):
     """
     MaskFormer's pixel decoder module output, practically a Feature Pyramid Network. It returns the last hidden state

src/transformers/utils/generic.py

@@ -20,7 +20,7 @@ import tempfile
 from collections import OrderedDict, UserDict
 from collections.abc import MutableMapping
 from contextlib import ExitStack, contextmanager
-from dataclasses import fields
+from dataclasses import fields, is_dataclass
 from enum import Enum
 from typing import Any, ContextManager, List, Tuple
 
@@ -314,7 +314,26 @@ class ModelOutput(OrderedDict):
                 lambda values, context: cls(**torch.utils._pytree._dict_unflatten(values, context)),
             )
 
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+        # Subclasses of ModelOutput must use the @dataclass decorator
+        # This check is done in __init__ because the @dataclass decorator operates after __init_subclass__
+        # issubclass() would return True for issubclass(ModelOutput, ModelOutput) when False is needed
+        # Just need to check that the current class is not ModelOutput
+        is_modeloutput_subclass = self.__class__ != ModelOutput
+
+        if is_modeloutput_subclass and not is_dataclass(self):
+            raise TypeError(
+                f"{self.__module__}.{self.__class__.__name__} is not a dataclasss."
+                " This is a subclass of ModelOutput and so must use the @dataclass decorator."
+            )
+
     def __post_init__(self):
+        """Check the ModelOutput dataclass.
+
+        Only occurs if @dataclass decorator has been used.
+        """
         class_fields = fields(self)
 
         # Safety and consistency checks

tests/utils/test_model_output.py

@@ -143,3 +143,23 @@ class ModelOutputTester(unittest.TestCase):
 
         unflattened_x = torch.utils._pytree.tree_unflatten(actual_flat_outs, actual_tree_spec)
         self.assertEqual(x, unflattened_x)
+
+
+class ModelOutputTestNoDataclass(ModelOutput):
+    """Invalid test subclass of ModelOutput where @dataclass decorator is not used"""
+
+    a: float
+    b: Optional[float] = None
+    c: Optional[float] = None
+
+
+class ModelOutputSubclassTester(unittest.TestCase):
+    def test_direct_model_output(self):
+        # Check that direct usage of ModelOutput instantiates without errors
+        ModelOutput({"a": 1.1})
+
+    def test_subclass_no_dataclass(self):
+        # Check that a subclass of ModelOutput without @dataclass is invalid
+        # A valid subclass is inherently tested other unit tests above.
+        with self.assertRaises(TypeError):
+            ModelOutputTestNoDataclass(a=1.1, b=2.2, c=3.3)