fix #5693

src/transformers/file_utils.py

@@ -14,6 +14,7 @@ import sys
 import tarfile
 import tempfile
 from contextlib import contextmanager
+from dataclasses import fields
 from functools import partial, wraps
 from hashlib import sha256
 from pathlib import Path
@@ -856,12 +857,83 @@ def tf_required(func):
     return wrapper
 
 
-class ModelOutput:
-    """
-    Base class for all model outputs as dataclass. Has a ``__getitem__`` that allows indexing by integer or slice (like
-    a tuple) or strings (like a dictionnary) that will ignore the ``None`` attributes.
+class ModelOutput(dict):
+    """ Base class for all model outputs as dataclass.
+
+        The sub-classes of ``ModelOutput`` must have **at most one** required field (the first field)
+            (see ``__post_init__``docstring for details)
+
+        ``ModelOutput`` has a ``__getitem__`` method that allows indexing by:
+            - integer and slice (like a tuple) or
+            - strings (like a dictionnary).
+
+            ``__getitem__`` will ignore attributes containing ``None`` values when indexing with integers.
+
+        Apart from accepting integers as key, ``ModelOutput``mostly behaves like a dictionnary for compatiblity with torch.DataParallel:
+            - Sub-class of ``dict``
+            - providing an iterator of (keys, values) to the first argument without any other argument will set the associated attributes
+            - ``__setitem__``, ``__delitem__``, ``setdefault``, ``pop``, `ùpdate`` will raise errors
+            - ``__iter__`` iterate over the keys
+
     """
 
+    def __post_init__(self):
+        """ Does a few safety checks.
+
+            If the first field is a list/tuple, spread its values in the other fields.
+            This is currently necessary for compatibility with torch.DataParallel which only handles dict/list/tuples
+            cf. https://github.com/huggingface/transformers/issues/5693
+            and https://github.com/pytorch/pytorch/issues/41327
+        """
+        class_fields = fields(self)
+
+        # Safety and consistency checks
+        assert len(class_fields), f"{self.__class__.__name__} has no fields."
+        assert all(
+            field.default is None for field in class_fields[1:]
+        ), f"{self.__class__.__name__} should not have more than one required field."
+
+        # Check if we should spread the first field on the other fields in a dict-mapping fashion
+        first_field = getattr(self, class_fields[0].name)
+        other_fields_are_none = all(getattr(self, field.name) is None for field in class_fields[1:])
+
+        if other_fields_are_none:
+            try:
+                iterator = iter(first_field)
+                first_field_iterator = True
+            except TypeError:
+                first_field_iterator = False
+
+            # if we provided an iterator as first field and the iterator is a (key, value) iterator
+            # set the associated fields
+            if first_field_iterator:
+                for element in iterator:
+                    if (
+                        not isinstance(element, (list, tuple))
+                        or not len(element) == 2
+                        or not isinstance(element[0], str)
+                    ):
+                        break
+                    setattr(self, element[0], element[1])
+
+    def __delitem__(self, *args, **kwargs):
+        raise Exception(f"You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.")
+
+    def setdefault(self, *args, **kwargs):
+        raise Exception(f"You cannot use ``setdefault`` on a {self.__class__.__name__} instance.")
+
+    def pop(self, *args, **kwargs):
+        raise Exception(f"You cannot use ``pop`` on a {self.__class__.__name__} instance.")
+
+    def update(self, *args, **kwargs):
+        raise Exception(f"You cannot use ``update`` on a {self.__class__.__name__} instance.")
+
+    def __iter__(self):
+        """ Will return the attributes names (dictionnary-like) """
+        for f in self.__dataclass_fields__.keys():
+            if getattr(self, f, None) is not None:
+                yield f
+
     def to_tuple(self):
         """
         Converts :obj:`self` to a tuple.
@@ -881,5 +953,11 @@ class ModelOutput:
     def __getitem__(self, i):
         return self.to_dict()[i] if isinstance(i, str) else self.to_tuple()[i]
 
+    def __setitem__(self, key, value):
+        if isinstance(key, str):
+            setattr(self, key, value)
+        else:
+            raise Exception(f"Key {key} must be a string but was given a {type(key)}.")
+
     def __len__(self):
         return len(self.to_tuple())

src/transformers/modeling_albert.py

@@ -430,9 +430,9 @@ class AlbertForPretrainingOutput(ModelOutput):
             heads.
     """
 
-    loss: Optional[torch.FloatTensor]
-    prediction_logits: torch.FloatTensor
-    sop_logits: torch.FloatTensor
+    loss: torch.FloatTensor = None
+    prediction_logits: torch.FloatTensor = None
+    sop_logits: torch.FloatTensor = None
     hidden_states: Optional[Tuple[torch.FloatTensor]] = None
     attentions: Optional[Tuple[torch.FloatTensor]] = None
 

src/transformers/modeling_bert.py

@@ -605,9 +605,9 @@ class BertForPretrainingOutput(ModelOutput):
             heads.
     """
 
-    loss: Optional[torch.FloatTensor]
-    prediction_logits: torch.FloatTensor
-    seq_relationship_logits: torch.FloatTensor
+    loss: torch.FloatTensor = None
+    prediction_logits: torch.FloatTensor = None
+    seq_relationship_logits: torch.FloatTensor = None
     hidden_states: Optional[Tuple[torch.FloatTensor]] = None
     attentions: Optional[Tuple[torch.FloatTensor]] = None
 

src/transformers/modeling_dpr.py

@@ -73,7 +73,7 @@ class DPRContextEncoderOutput(ModelOutput):
             heads.
     """
 
-    pooler_output: torch.FloatTensor
+    pooler_output: torch.FloatTensor = None
     hidden_states: Optional[Tuple[torch.FloatTensor]] = None
     attentions: Optional[Tuple[torch.FloatTensor]] = None
 
@@ -102,7 +102,7 @@ class DPRQuestionEncoderOutput(ModelOutput):
             heads.
     """
 
-    pooler_output: torch.FloatTensor
+    pooler_output: torch.FloatTensor = None
     hidden_states: Optional[Tuple[torch.FloatTensor]] = None
     attentions: Optional[Tuple[torch.FloatTensor]] = None
 
@@ -133,9 +133,9 @@ class DPRReaderOutput(ModelOutput):
             heads.
     """
 
-    start_logits: torch.FloatTensor
-    end_logits: torch.FloatTensor
-    relevance_logits: torch.FloatTensor
+    start_logits: torch.FloatTensor = None
+    end_logits: torch.FloatTensor = None
+    relevance_logits: torch.FloatTensor = None
     hidden_states: Optional[Tuple[torch.FloatTensor]] = None
     attentions: Optional[Tuple[torch.FloatTensor]] = None
 

src/transformers/modeling_electra.py

@@ -208,8 +208,8 @@ class ElectraForPretrainingOutput(ModelOutput):
             heads.
     """
 
-    loss: Optional[torch.FloatTensor]
-    logits: torch.FloatTensor
+    loss: torch.FloatTensor = None
+    logits: torch.FloatTensor = None
     hidden_states: Optional[Tuple[torch.FloatTensor]] = None
     attentions: Optional[Tuple[torch.FloatTensor]] = None
 

src/transformers/modeling_gpt2.py

@@ -323,10 +323,10 @@ class GPT2DoubleHeadsModelOutput(ModelOutput):
             heads.
     """
 
-    lm_loss: Optional[torch.FloatTensor]
-    mc_loss: Optional[torch.FloatTensor]
-    lm_logits: torch.FloatTensor
-    mc_logits: torch.FloatTensor
+    lm_loss: torch.FloatTensor = None
+    mc_loss: torch.FloatTensor = None
+    lm_logits: torch.FloatTensor = None
+    mc_logits: torch.FloatTensor = None
     past_key_values: Optional[List[torch.FloatTensor]] = None
     hidden_states: Optional[Tuple[torch.FloatTensor]] = None
     attentions: Optional[Tuple[torch.FloatTensor]] = None

src/transformers/modeling_mobilebert.py

@@ -705,9 +705,9 @@ class MobileBertForPretrainingOutput(ModelOutput):
             heads.
     """
 
-    loss: Optional[torch.FloatTensor]
-    prediction_logits: torch.FloatTensor
-    seq_relationship_logits: torch.FloatTensor
+    loss: torch.FloatTensor = None
+    prediction_logits: torch.FloatTensor = None
+    seq_relationship_logits: torch.FloatTensor = None
     hidden_states: Optional[Tuple[torch.FloatTensor]] = None
     attentions: Optional[Tuple[torch.FloatTensor]] = None
 

src/transformers/modeling_openai.py

@@ -314,10 +314,10 @@ class OpenAIGPTDoubleHeadsModelOutput(ModelOutput):
             heads.
     """
 
-    lm_loss: Optional[torch.FloatTensor]
-    mc_loss: Optional[torch.FloatTensor]
-    lm_logits: torch.FloatTensor
-    mc_logits: torch.FloatTensor
+    lm_loss: torch.FloatTensor = None
+    mc_loss: torch.FloatTensor = None
+    lm_logits: torch.FloatTensor = None
+    mc_logits: torch.FloatTensor = None
     hidden_states: Optional[Tuple[torch.FloatTensor]] = None
     attentions: Optional[Tuple[torch.FloatTensor]] = None
 

src/transformers/modeling_outputs.py

@@ -63,7 +63,7 @@ class BaseModelOutputWithPooling(ModelOutput):
     """
 
     last_hidden_state: torch.FloatTensor
-    pooler_output: torch.FloatTensor
+    pooler_output: torch.FloatTensor = None
     hidden_states: Optional[Tuple[torch.FloatTensor]] = None
     attentions: Optional[Tuple[torch.FloatTensor]] = None
 
@@ -178,8 +178,8 @@ class CausalLMOutput(ModelOutput):
             heads.
     """
 
-    loss: Optional[torch.FloatTensor]
-    logits: torch.FloatTensor
+    loss: torch.FloatTensor = None
+    logits: torch.FloatTensor = None
     hidden_states: Optional[Tuple[torch.FloatTensor]] = None
     attentions: Optional[Tuple[torch.FloatTensor]] = None
 
@@ -213,8 +213,8 @@ class CausalLMOutputWithPast(ModelOutput):
             heads.
     """
 
-    loss: Optional[torch.FloatTensor]
-    logits: torch.FloatTensor
+    loss: torch.FloatTensor = None
+    logits: torch.FloatTensor = None
     past_key_values: Optional[List[torch.FloatTensor]] = None
     hidden_states: Optional[Tuple[torch.FloatTensor]] = None
     attentions: Optional[Tuple[torch.FloatTensor]] = None
@@ -243,8 +243,8 @@ class MaskedLMOutput(ModelOutput):
             heads.
     """
 
-    loss: Optional[torch.FloatTensor]
-    logits: torch.FloatTensor
+    loss: torch.FloatTensor = None
+    logits: torch.FloatTensor = None
     hidden_states: Optional[Tuple[torch.FloatTensor]] = None
     attentions: Optional[Tuple[torch.FloatTensor]] = None
 
@@ -291,8 +291,8 @@ class Seq2SeqLMOutput(ModelOutput):
             self-attention heads.
     """
 
-    loss: Optional[torch.FloatTensor]
-    logits: torch.FloatTensor
+    loss: torch.FloatTensor = None
+    logits: torch.FloatTensor = None
     decoder_past_key_values: Optional[List[torch.FloatTensor]] = None
     decoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
     decoder_attentions: Optional[Tuple[torch.FloatTensor]] = None
@@ -324,8 +324,8 @@ class NextSentencePredictorOutput(ModelOutput):
             heads.
     """
 
-    loss: Optional[torch.FloatTensor]
-    logits: torch.FloatTensor
+    loss: torch.FloatTensor = None
+    logits: torch.FloatTensor = None
     hidden_states: Optional[Tuple[torch.FloatTensor]] = None
     attentions: Optional[Tuple[torch.FloatTensor]] = None
 
@@ -353,8 +353,8 @@ class SequenceClassifierOutput(ModelOutput):
             heads.
     """
 
-    loss: Optional[torch.FloatTensor]
-    logits: torch.FloatTensor
+    loss: torch.FloatTensor = None
+    logits: torch.FloatTensor = None
     hidden_states: Optional[Tuple[torch.FloatTensor]] = None
     attentions: Optional[Tuple[torch.FloatTensor]] = None
 
@@ -401,8 +401,8 @@ class Seq2SeqSequenceClassifierOutput(ModelOutput):
             self-attention heads.
     """
 
-    loss: Optional[torch.FloatTensor]
-    logits: torch.FloatTensor
+    loss: torch.FloatTensor = None
+    logits: torch.FloatTensor = None
     decoder_past_key_values: Optional[List[torch.FloatTensor]] = None
     decoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
     decoder_attentions: Optional[Tuple[torch.FloatTensor]] = None
@@ -436,8 +436,8 @@ class MultipleChoiceModelOutput(ModelOutput):
             heads.
     """
 
-    loss: Optional[torch.FloatTensor]
-    logits: torch.FloatTensor
+    loss: torch.FloatTensor = None
+    logits: torch.FloatTensor = None
     hidden_states: Optional[Tuple[torch.FloatTensor]] = None
     attentions: Optional[Tuple[torch.FloatTensor]] = None
 
@@ -465,8 +465,8 @@ class TokenClassifierOutput(ModelOutput):
             heads.
     """
 
-    loss: Optional[torch.FloatTensor]
-    logits: torch.FloatTensor
+    loss: torch.FloatTensor = None
+    logits: torch.FloatTensor = None
     hidden_states: Optional[Tuple[torch.FloatTensor]] = None
     attentions: Optional[Tuple[torch.FloatTensor]] = None
 
@@ -496,9 +496,9 @@ class QuestionAnsweringModelOutput(ModelOutput):
             heads.
     """
 
-    loss: Optional[torch.FloatTensor]
-    start_logits: torch.FloatTensor
-    end_logits: torch.FloatTensor
+    loss: torch.FloatTensor = None
+    start_logits: torch.FloatTensor = None
+    end_logits: torch.FloatTensor = None
     hidden_states: Optional[Tuple[torch.FloatTensor]] = None
     attentions: Optional[Tuple[torch.FloatTensor]] = None
 
@@ -547,9 +547,9 @@ class Seq2SeqQuestionAnsweringModelOutput(ModelOutput):
             self-attention heads.
     """
 
-    loss: Optional[torch.FloatTensor]
-    start_logits: torch.FloatTensor
-    end_logits: torch.FloatTensor
+    loss: torch.FloatTensor = None
+    start_logits: torch.FloatTensor = None
+    end_logits: torch.FloatTensor = None
     decoder_past_key_values: Optional[List[torch.FloatTensor]] = None
     decoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
     decoder_attentions: Optional[Tuple[torch.FloatTensor]] = None

src/transformers/modeling_transfo_xl.py

@@ -618,7 +618,7 @@ class TransfoXLModelOutput(ModelOutput):
     """
 
     last_hidden_state: torch.FloatTensor
-    mems: List[torch.FloatTensor]
+    mems: Optional[List[torch.FloatTensor]] = None
     hidden_states: Optional[Tuple[torch.FloatTensor]] = None
     attentions: Optional[Tuple[torch.FloatTensor]] = None
 
@@ -653,8 +653,8 @@ class TransfoXLLMHeadModelOutput(ModelOutput):
     """
 
     losses: Optional[torch.FloatTensor]
-    prediction_scores: torch.FloatTensor
-    mems: List[torch.FloatTensor]
+    prediction_scores: torch.FloatTensor = None
+    mems: Optional[List[torch.FloatTensor]] = None
     hidden_states: Optional[Tuple[torch.FloatTensor]] = None
     attentions: Optional[Tuple[torch.FloatTensor]] = None
 

src/transformers/modeling_xlnet.py

@@ -627,8 +627,8 @@ class XLNetLMHeadModelOutput(ModelOutput):
             heads.
     """
 
-    loss: Optional[torch.FloatTensor]
-    logits: torch.FloatTensor
+    loss: torch.FloatTensor = None
+    logits: torch.FloatTensor = None
     mems: Optional[List[torch.FloatTensor]] = None
     hidden_states: Optional[Tuple[torch.FloatTensor]] = None
     attentions: Optional[Tuple[torch.FloatTensor]] = None
@@ -661,8 +661,8 @@ class XLNetForSequenceClassificationOutput(ModelOutput):
             heads.
     """
 
-    loss: Optional[torch.FloatTensor]
-    logits: torch.FloatTensor
+    loss: torch.FloatTensor = None
+    logits: torch.FloatTensor = None
     mems: Optional[List[torch.FloatTensor]] = None
     hidden_states: Optional[Tuple[torch.FloatTensor]] = None
     attentions: Optional[Tuple[torch.FloatTensor]] = None
@@ -695,8 +695,8 @@ class XLNetForTokenClassificationOutput(ModelOutput):
             heads.
     """
 
-    loss: Optional[torch.FloatTensor]
-    logits: torch.FloatTensor
+    loss: torch.FloatTensor = None
+    logits: torch.FloatTensor = None
     mems: Optional[List[torch.FloatTensor]] = None
     hidden_states: Optional[Tuple[torch.FloatTensor]] = None
     attentions: Optional[Tuple[torch.FloatTensor]] = None
@@ -731,8 +731,8 @@ class XLNetForMultipleChoiceOutput(ModelOutput):
             heads.
     """
 
-    loss: Optional[torch.FloatTensor]
-    logits: torch.FloatTensor
+    loss: torch.FloatTensor = None
+    logits: torch.FloatTensor = None
     mems: Optional[List[torch.FloatTensor]] = None
     hidden_states: Optional[Tuple[torch.FloatTensor]] = None
     attentions: Optional[Tuple[torch.FloatTensor]] = None
@@ -767,9 +767,9 @@ class XLNetForQuestionAnsweringSimpleOutput(ModelOutput):
             heads.
     """
 
-    loss: Optional[torch.FloatTensor]
-    start_logits: torch.FloatTensor
-    end_logits: torch.FloatTensor
+    loss: torch.FloatTensor = None
+    start_logits: torch.FloatTensor = None
+    end_logits: torch.FloatTensor = None
     mems: Optional[List[torch.FloatTensor]] = None
     hidden_states: Optional[Tuple[torch.FloatTensor]] = None
     attentions: Optional[Tuple[torch.FloatTensor]] = None