Refactor Modeling Outputs (#16341)

* first proposal

* replace model outputs in various models

* conflicts

* docstring

* update poolformer

* minor change in docstring

* CI

* removed poolformer specific outputs from doc

* removed convnext specific outputs from doc

* CI

* weird char in segformer

* conversations

* reverted docstring for BaseModelOutputWithPooling

* update outputs

* changed docstring in BaseModelOutput

* updated docstring in modeling outputs

* typos :)

* fixed typo after copy & paste it all around

* CI

* Apply suggestions from code review
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>

* segformer
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>

docs/source/model_doc/convnext.mdx

@@ -40,11 +40,6 @@ alt="drawing" width="600"/>
 This model was contributed by [nielsr](https://huggingface.co/nielsr). TensorFlow version of the model was contributed by [ariG23498](https://github.com/ariG23498),
 [gante](https://github.com/gante), and [sayakpaul](https://github.com/sayakpaul) (equal contribution). The original code can be found [here](https://github.com/facebookresearch/ConvNeXt).
 
-## ConvNeXT specific outputs
-
-[[autodoc]] models.convnext.modeling_convnext.ConvNextModelOutput
-
-
 ## ConvNextConfig
 
 [[autodoc]] ConvNextConfig

docs/source/model_doc/poolformer.mdx

@@ -41,12 +41,6 @@ Tips:
 
 This model was contributed by [heytanay](https://huggingface.co/heytanay). The original code can be found [here](https://github.com/sail-sg/poolformer).
 
-## PoolFormer specific outputs
-
-[[autodoc]] models.poolformer.modeling_poolformer.PoolFormerModelOutput
-
-[[autodoc]] models.poolformer.modeling_poolformer.PoolFormerClassifierOutput
-
 ## PoolFormerConfig
 
 [[autodoc]] PoolFormerConfig

src/transformers/modeling_outputs.py

@@ -29,10 +29,10 @@ class BaseModelOutput(ModelOutput):
         last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
             Sequence of hidden-states at the output of the last layer of the model.
         hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
-            Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
-            shape `(batch_size, sequence_length, hidden_size)`.
+            Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
+            one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
 
-            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
+            Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
         attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
             Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
             sequence_length)`.
@@ -46,6 +46,25 @@ class BaseModelOutput(ModelOutput):
     attentions: Optional[Tuple[torch.FloatTensor]] = None
 
 
+@dataclass
+class BaseModelOutputWithNoAttention(ModelOutput):
+    """
+    Base class for model's outputs, with potential hidden states.
+
+    Args:
+        last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
+            Sequence of hidden-states at the output of the last layer of the model.
+        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+            Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
+            one for the output of each layer) of shape `(batch_size, num_channels, height, width)`.
+
+            Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
+    """
+
+    last_hidden_state: torch.FloatTensor = None
+    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
+
+
 @dataclass
 class BaseModelOutputWithPooling(ModelOutput):
     """
@@ -60,10 +79,10 @@ class BaseModelOutputWithPooling(ModelOutput):
             the classification token after processing through a linear layer and a tanh activation function. The linear
             layer weights are trained from the next sentence prediction (classification) objective during pretraining.
         hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
-            Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
-            shape `(batch_size, sequence_length, hidden_size)`.
+            Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
+            one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
 
-            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
+            Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
         attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
             Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
             sequence_length)`.
@@ -78,6 +97,28 @@ class BaseModelOutputWithPooling(ModelOutput):
     attentions: Optional[Tuple[torch.FloatTensor]] = None
 
 
+@dataclass
+class BaseModelOutputWithPoolingAndNoAttention(ModelOutput):
+    """
+    Base class for model's outputs that also contains a pooling of the last hidden states.
+
+    Args:
+        last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
+            Sequence of hidden-states at the output of the last layer of the model.
+        pooler_output (`torch.FloatTensor` of shape `(batch_size, hidden_size)`):
+            Last layer hidden-state after a pooling operation on the spatial dimensions.
+        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+            Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
+            one for the output of each layer) of shape `(batch_size, num_channels, height, width)`.
+
+            Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
+    """
+
+    last_hidden_state: torch.FloatTensor = None
+    pooler_output: torch.FloatTensor = None
+    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
+
+
 @dataclass
 class BaseModelOutputWithPast(ModelOutput):
     """
@@ -99,10 +140,10 @@ class BaseModelOutputWithPast(ModelOutput):
             `config.is_encoder_decoder=True` in the cross-attention blocks) that can be used (see `past_key_values`
             input) to speed up sequential decoding.
         hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
-            Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
-            shape `(batch_size, sequence_length, hidden_size)`.
+            Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
+            one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
 
-            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
+            Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
         attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
             Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
             sequence_length)`.
@@ -126,10 +167,10 @@ class BaseModelOutputWithCrossAttentions(ModelOutput):
         last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
             Sequence of hidden-states at the output of the last layer of the model.
         hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
-            Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
-            shape `(batch_size, sequence_length, hidden_size)`.
+            Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
+            one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
 
-            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
+            Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
         attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
             Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
             sequence_length)`.
@@ -164,10 +205,10 @@ class BaseModelOutputWithPoolingAndCrossAttentions(ModelOutput):
             the classification token after processing through a linear layer and a tanh activation function. The linear
             layer weights are trained from the next sentence prediction (classification) objective during pretraining.
         hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
-            Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
-            shape `(batch_size, sequence_length, hidden_size)`.
+            Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
+            one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
 
-            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
+            Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
         attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
             Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
             sequence_length)`.
@@ -220,10 +261,10 @@ class BaseModelOutputWithPastAndCrossAttentions(ModelOutput):
             `config.is_encoder_decoder=True` in the cross-attention blocks) that can be used (see `past_key_values`
             input) to speed up sequential decoding.
         hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
-            Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
-            shape `(batch_size, sequence_length, hidden_size)`.
+            Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
+            one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
 
-            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
+            Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
         attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
             Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
             sequence_length)`.
@@ -265,10 +306,10 @@ class Seq2SeqModelOutput(ModelOutput):
             Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
             blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
         decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
-            Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
-            shape `(batch_size, sequence_length, hidden_size)`.
+            Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
+            one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
 
-            Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.
+            Hidden-states of the decoder at the output of each layer plus the optional initial embedding outputs.
         decoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
             Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
             sequence_length)`.
@@ -284,10 +325,10 @@ class Seq2SeqModelOutput(ModelOutput):
         encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
             Sequence of hidden-states at the output of the last layer of the encoder of the model.
         encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
-            Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
-            shape `(batch_size, sequence_length, hidden_size)`.
+            Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
+            one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
 
-            Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.
+            Hidden-states of the encoder at the output of each layer plus the optional initial embedding outputs.
         encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
             Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
             sequence_length)`.
@@ -317,10 +358,10 @@ class CausalLMOutput(ModelOutput):
         logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
             Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
         hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
-            Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
-            shape `(batch_size, sequence_length, hidden_size)`.
+            Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
+            one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
 
-            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
+            Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
         attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
             Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
             sequence_length)`.
@@ -352,10 +393,10 @@ class CausalLMOutputWithPast(ModelOutput):
             Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
             `past_key_values` input) to speed up sequential decoding.
         hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
-            Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
-            shape `(batch_size, sequence_length, hidden_size)`.
+            Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
+            one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
 
-            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
+            Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
         attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
             Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
             sequence_length)`.
@@ -382,10 +423,10 @@ class CausalLMOutputWithCrossAttentions(ModelOutput):
         logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
             Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
         hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
-            Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
-            shape `(batch_size, sequence_length, hidden_size)`.
+            Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
+            one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
 
-            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
+            Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
         attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
             Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
             sequence_length)`.
@@ -432,10 +473,10 @@ class SequenceClassifierOutputWithPast(ModelOutput):
             Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
             `past_key_values` input) to speed up sequential decoding.
         hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
-            Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
-            shape `(batch_size, sequence_length, hidden_size)`.
+            Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
+            one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
 
-            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
+            Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
         attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
             Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
             sequence_length)`.
@@ -462,10 +503,10 @@ class MaskedLMOutput(ModelOutput):
         logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
             Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
         hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
-            Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
-            shape `(batch_size, sequence_length, hidden_size)`.
+            Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
+            one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
 
-            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
+            Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
         attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
             Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
             sequence_length)`.
@@ -498,8 +539,8 @@ class Seq2SeqLMOutput(ModelOutput):
             Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
             blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
         decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
-            Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
-            shape `(batch_size, sequence_length, hidden_size)`.
+            Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
+            one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
 
             Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.
         decoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
@@ -517,8 +558,8 @@ class Seq2SeqLMOutput(ModelOutput):
         encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
             Sequence of hidden-states at the output of the last layer of the encoder of the model.
         encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
-            Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
-            shape `(batch_size, sequence_length, hidden_size)`.
+            Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
+            one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
 
             Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.
         encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
@@ -552,10 +593,10 @@ class NextSentencePredictorOutput(ModelOutput):
             Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation
             before SoftMax).
         hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
-            Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
-            shape `(batch_size, sequence_length, hidden_size)`.
+            Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
+            one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
 
-            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
+            Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
         attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
             Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
             sequence_length)`.
@@ -581,10 +622,10 @@ class SequenceClassifierOutput(ModelOutput):
         logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`):
             Classification (or regression if config.num_labels==1) scores (before SoftMax).
         hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
-            Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
-            shape `(batch_size, sequence_length, hidden_size)`.
+            Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
+            one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
 
-            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
+            Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
         attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
             Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
             sequence_length)`.
@@ -617,8 +658,8 @@ class Seq2SeqSequenceClassifierOutput(ModelOutput):
             Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
             blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
         decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
-            Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
-            shape `(batch_size, sequence_length, hidden_size)`.
+            Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
+            one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
 
             Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.
         decoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
@@ -636,8 +677,8 @@ class Seq2SeqSequenceClassifierOutput(ModelOutput):
         encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
             Sequence of hidden-states at the output of the last layer of the encoder of the model.
         encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
-            Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
-            shape `(batch_size, sequence_length, hidden_size)`.
+            Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
+            one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
 
             Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.
         encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
@@ -672,10 +713,10 @@ class MultipleChoiceModelOutput(ModelOutput):
 
             Classification scores (before SoftMax).
         hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
-            Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
-            shape `(batch_size, sequence_length, hidden_size)`.
+            Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
+            one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
 
-            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
+            Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
         attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
             Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
             sequence_length)`.
@@ -701,10 +742,10 @@ class TokenClassifierOutput(ModelOutput):
         logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.num_labels)`):
             Classification scores (before SoftMax).
         hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
-            Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
-            shape `(batch_size, sequence_length, hidden_size)`.
+            Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
+            one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
 
-            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
+            Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
         attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
             Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
             sequence_length)`.
@@ -732,10 +773,10 @@ class QuestionAnsweringModelOutput(ModelOutput):
         end_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length)`):
             Span-end scores (before SoftMax).
         hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
-            Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
-            shape `(batch_size, sequence_length, hidden_size)`.
+            Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
+            one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
 
-            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
+            Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
         attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
             Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
             sequence_length)`.
@@ -771,8 +812,8 @@ class Seq2SeqQuestionAnsweringModelOutput(ModelOutput):
             Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
             blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
         decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
-            Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
-            shape `(batch_size, sequence_length, hidden_size)`.
+            Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
+            one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
 
             Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.
         decoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
@@ -790,8 +831,8 @@ class Seq2SeqQuestionAnsweringModelOutput(ModelOutput):
         encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
             Sequence of hidden-states at the output of the last layer of the encoder of the model.
         encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
-            Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
-            shape `(batch_size, sequence_length, hidden_size)`.
+            Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
+            one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
 
             Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.
         encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
@@ -834,10 +875,10 @@ class SemanticSegmenterOutput(ModelOutput):
             </Tip>
 
         hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
-            Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
-            shape `(batch_size, patch_size, hidden_size)`.
+            Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
+            one for the output of each layer) of shape `(batch_size, patch_size, hidden_size)`.
 
-            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
+            Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
         attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
             Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, patch_size,
             sequence_length)`.
@@ -863,9 +904,9 @@ class ImageClassifierOutput(ModelOutput):
         logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`):
             Classification (or regression if config.num_labels==1) scores (before SoftMax).
         hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
-            Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of
-            shape `(batch_size, num_channels, height, width)`. Hidden-states (also called feature maps) of the model at
-            the output of each stage.
+            Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
+            one for the output of each stage) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states
+            (also called feature maps) of the model at the output of each stage.
         attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
             Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, patch_size,
             sequence_length)`.
@@ -880,6 +921,27 @@ class ImageClassifierOutput(ModelOutput):
     attentions: Optional[Tuple[torch.FloatTensor]] = None
 
 
+@dataclass
+class ImageClassifierOutputWithNoAttention(ModelOutput):
+    """
+    Base class for outputs of image classification models.
+
+    Args:
+        loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
+            Classification (or regression if config.num_labels==1) loss.
+        logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`):
+            Classification (or regression if config.num_labels==1) scores (before SoftMax).
+        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+            Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
+            one for the output of each stage) of shape `(batch_size, num_channels, height, width)`. Hidden-states (also
+            called feature maps) of the model at the output of each stage.
+    """
+
+    loss: Optional[torch.FloatTensor] = None
+    logits: torch.FloatTensor = None
+    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
+
+
 @dataclass
 class DepthEstimatorOutput(ModelOutput):
     """
@@ -892,10 +954,10 @@ class DepthEstimatorOutput(ModelOutput):
             Predicted depth for each pixel.
 
         hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
-            Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
-            shape `(batch_size, num_channels, height, width)`.
+            Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
+            one for the output of each layer) of shape `(batch_size, num_channels, height, width)`.
 
-            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
+            Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
         attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
             Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, patch_size,
             sequence_length)`.

src/transformers/models/beit/modeling_beit.py

@@ -29,9 +29,9 @@ from ...activations import ACT2FN
 from ...modeling_outputs import (
     BaseModelOutput,
     BaseModelOutputWithPooling,
+    ImageClassifierOutput,
     MaskedLMOutput,
     SemanticSegmenterOutput,
-    SequenceClassifierOutput,
 )
 from ...modeling_utils import PreTrainedModel, find_pruneable_heads_and_indices, prune_linear_layer
 from ...utils import (
@@ -851,7 +851,7 @@ class BeitForImageClassification(BeitPreTrainedModel):
     @add_code_sample_docstrings(
         processor_class=_FEAT_EXTRACTOR_FOR_DOC,
         checkpoint=_IMAGE_CLASS_CHECKPOINT,
-        output_type=SequenceClassifierOutput,
+        output_type=ImageClassifierOutput,
         config_class=_CONFIG_FOR_DOC,
         expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT,
     )
@@ -863,7 +863,7 @@ class BeitForImageClassification(BeitPreTrainedModel):
         output_attentions: Optional[bool] = None,
         output_hidden_states: Optional[bool] = None,
         return_dict: Optional[bool] = None,
-    ) -> Union[tuple, SequenceClassifierOutput]:
+    ) -> Union[tuple, ImageClassifierOutput]:
         r"""
         labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
             Labels for computing the image classification/regression loss. Indices should be in `[0, ...,
@@ -909,7 +909,7 @@ class BeitForImageClassification(BeitPreTrainedModel):
             output = (logits,) + outputs[2:]
             return ((loss,) + output) if loss is not None else output
 
-        return SequenceClassifierOutput(
+        return ImageClassifierOutput(
             loss=loss,
             logits=logits,
             hidden_states=outputs.hidden_states,

src/transformers/models/convnext/modeling_convnext.py

@@ -14,8 +14,6 @@
 # limitations under the License.
 """ PyTorch ConvNext model."""
 
-from dataclasses import dataclass
-from typing import Optional, Tuple
 
 import torch
 import torch.utils.checkpoint
@@ -23,14 +21,13 @@ from torch import nn
 from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
 
 from ...activations import ACT2FN
-from ...modeling_utils import PreTrainedModel
-from ...utils import (
-    ModelOutput,
-    add_code_sample_docstrings,
-    add_start_docstrings,
-    add_start_docstrings_to_model_forward,
-    logging,
+from ...modeling_outputs import (
+    BaseModelOutputWithNoAttention,
+    BaseModelOutputWithPoolingAndNoAttention,
+    ImageClassifierOutputWithNoAttention,
 )
+from ...modeling_utils import PreTrainedModel
+from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
 from .configuration_convnext import ConvNextConfig
 
 
@@ -54,66 +51,6 @@ CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST = [
 ]
 
 
-@dataclass
-class ConvNextEncoderOutput(ModelOutput):
-    """
-    Class for [`ConvNextEncoder`]'s outputs, with potential hidden states (feature maps).
-
-    Args:
-        last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
-            Last hidden states (final feature map) of the last stage of the model.
-        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
-            Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of
-            shape `(batch_size, num_channels, height, width)`. Hidden-states (also called feature maps) of the model at
-            the output of each stage.
-    """
-
-    last_hidden_state: torch.FloatTensor = None
-    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
-
-
-@dataclass
-class ConvNextModelOutput(ModelOutput):
-    """
-    Class for [`ConvNextModel`]'s outputs, with potential hidden states (feature maps).
-
-    Args:
-        last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
-            Last hidden states (final feature map) of the last stage of the model.
-        pooler_output (`torch.FloatTensor` of shape `(batch_size, config.dim[-1])`):
-            Global average pooling of the last feature map followed by a layernorm.
-        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
-            Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of
-            shape `(batch_size, num_channels, height, width)`. Hidden-states (also called feature maps) of the model at
-            the output of each stage.
-    """
-
-    last_hidden_state: torch.FloatTensor = None
-    pooler_output: Optional[torch.FloatTensor] = None
-    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
-
-
-@dataclass
-class ConvNextClassifierOutput(ModelOutput):
-    """
-    Class for [`ConvNextForImageClassification`]'s outputs, with potential hidden states (feature maps).
-
-    Args:
-        loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
-            Classification (or regression if config.num_labels==1) loss.
-        logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`):
-            Classification (or regression if config.num_labels==1) scores (before SoftMax).
-        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
-            Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of
-            shape `(batch_size, num_channels, height, width)`. Hidden-states (also called feature maps) of the model at
-            the output of each stage.
-    """
-
-    loss: Optional[torch.FloatTensor] = None
-    logits: torch.FloatTensor = None
-    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
-
-
 # Stochastic depth implementation
 # Taken from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/layers/drop.py
 def drop_path(x, drop_prob: float = 0.0, training: bool = False):
@@ -302,7 +239,7 @@ class ConvNextEncoder(nn.Module):
         if not return_dict:
             return tuple(v for v in [hidden_states, all_hidden_states] if v is not None)
 
-        return ConvNextEncoderOutput(
+        return BaseModelOutputWithNoAttention(
             last_hidden_state=hidden_states,
             hidden_states=all_hidden_states,
         )
@@ -383,7 +320,7 @@ class ConvNextModel(ConvNextPreTrainedModel):
     @add_code_sample_docstrings(
         processor_class=_FEAT_EXTRACTOR_FOR_DOC,
         checkpoint=_CHECKPOINT_FOR_DOC,
-        output_type=ConvNextModelOutput,
+        output_type=BaseModelOutputWithPoolingAndNoAttention,
         config_class=_CONFIG_FOR_DOC,
         modality="vision",
         expected_output=_EXPECTED_OUTPUT_SHAPE,
@@ -413,7 +350,7 @@ class ConvNextModel(ConvNextPreTrainedModel):
         if not return_dict:
             return (last_hidden_state, pooled_output) + encoder_outputs[1:]
 
-        return ConvNextModelOutput(
+        return BaseModelOutputWithPoolingAndNoAttention(
             last_hidden_state=last_hidden_state,
             pooler_output=pooled_output,
             hidden_states=encoder_outputs.hidden_states,
@@ -446,7 +383,7 @@ class ConvNextForImageClassification(ConvNextPreTrainedModel):
     @add_code_sample_docstrings(
         processor_class=_FEAT_EXTRACTOR_FOR_DOC,
         checkpoint=_IMAGE_CLASS_CHECKPOINT,
-        output_type=ConvNextClassifierOutput,
+        output_type=ImageClassifierOutputWithNoAttention,
         config_class=_CONFIG_FOR_DOC,
         expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT,
     )
@@ -491,7 +428,7 @@ class ConvNextForImageClassification(ConvNextPreTrainedModel):
             output = (logits,) + outputs[2:]
             return ((loss,) + output) if loss is not None else output
 
-        return ConvNextClassifierOutput(
+        return ImageClassifierOutputWithNoAttention(
             loss=loss,
             logits=logits,
             hidden_states=outputs.hidden_states,

src/transformers/models/deit/modeling_deit.py

@@ -26,7 +26,7 @@ from torch import nn
 from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
 
 from ...activations import ACT2FN
-from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling, MaskedLMOutput, SequenceClassifierOutput
+from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling, ImageClassifierOutput, MaskedLMOutput
 from ...modeling_utils import PreTrainedModel, find_pruneable_heads_and_indices, prune_linear_layer
 from ...utils import (
     ModelOutput,
@@ -693,7 +693,7 @@ class DeiTForImageClassification(DeiTPreTrainedModel):
         self.post_init()
 
     @add_start_docstrings_to_model_forward(DEIT_INPUTS_DOCSTRING)
-    @replace_return_docstrings(output_type=SequenceClassifierOutput, config_class=_CONFIG_FOR_DOC)
+    @replace_return_docstrings(output_type=ImageClassifierOutput, config_class=_CONFIG_FOR_DOC)
     def forward(
         self,
         pixel_values: Optional[torch.Tensor] = None,
@@ -702,7 +702,7 @@ class DeiTForImageClassification(DeiTPreTrainedModel):
         output_attentions: Optional[bool] = None,
         output_hidden_states: Optional[bool] = None,
         return_dict: Optional[bool] = None,
-    ) -> Union[tuple, SequenceClassifierOutput]:
+    ) -> Union[tuple, ImageClassifierOutput]:
         r"""
         labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
             Labels for computing the image classification/regression loss. Indices should be in `[0, ...,
@@ -777,7 +777,7 @@ class DeiTForImageClassification(DeiTPreTrainedModel):
             output = (logits,) + outputs[2:]
             return ((loss,) + output) if loss is not None else output
 
-        return SequenceClassifierOutput(
+        return ImageClassifierOutput(
             loss=loss,
             logits=logits,
             hidden_states=outputs.hidden_states,

src/transformers/models/poolformer/modeling_poolformer.py

@@ -16,7 +16,6 @@
 
 
 import collections.abc
-from dataclasses import dataclass
 from typing import Optional, Tuple, Union
 
 import torch
@@ -25,14 +24,9 @@ from torch import nn
 from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
 
 from ...activations import ACT2FN
+from ...modeling_outputs import BaseModelOutputWithNoAttention, ImageClassifierOutputWithNoAttention
 from ...modeling_utils import PreTrainedModel
-from ...utils import (
-    ModelOutput,
-    add_code_sample_docstrings,
-    add_start_docstrings,
-    add_start_docstrings_to_model_forward,
-    logging,
-)
+from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
 from .configuration_poolformer import PoolFormerConfig
 
 
@@ -63,47 +57,6 @@ def to_2tuple(x):
     return (x, x)
 
 
-@dataclass
-class PoolFormerModelOutput(ModelOutput):
-    """
-    Class for PoolFormerModel's outputs, with potential hidden states.
-
-    Args:
-        last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
-            Sequence of hidden-states at the output of the last layer of the model.
-
-        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
-            Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
-            shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer
-            plus the initial embedding outputs.
-    """
-
-    last_hidden_state: torch.FloatTensor = None
-    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
-
-
-@dataclass
-class PoolFormerClassifierOutput(ModelOutput):
-    """
-    Class for PoolformerForImageClassification's outputs.
-
-    Args:
-        loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
-            Classification (or regression if config.num_labels==1) loss.
-        logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`):
-            Classification (or regression if config.num_labels==1) scores (before SoftMax).
-        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
-            Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
-            shape `(batch_size, num_channels, height, width)`.
-
-            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
-    """
-
-    loss: Optional[torch.FloatTensor] = None
-    logits: torch.FloatTensor = None
-    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
-
-
 def drop_path(x, drop_prob: float = 0.0, training: bool = False):
     """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
     This is the same as the DropConnect impl I created for EfficientNet, etc networks, however, the original name is
@@ -295,7 +248,7 @@ class PoolFormerEncoder(nn.Module):
             # Get patch embeddings from hidden_states
             hidden_states = embedding_layer(hidden_states)
             # Send the embeddings through the blocks
-            for i, blk in enumerate(block_layer):
+            for _, blk in enumerate(block_layer):
                 layer_outputs = blk(hidden_states)
                 hidden_states = layer_outputs[0]
 
@@ -305,7 +258,7 @@ class PoolFormerEncoder(nn.Module):
         if not return_dict:
             return tuple(v for v in [hidden_states, all_hidden_states] if v is not None)
 
-        return PoolFormerModelOutput(last_hidden_state=hidden_states, hidden_states=all_hidden_states)
+        return BaseModelOutputWithNoAttention(last_hidden_state=hidden_states, hidden_states=all_hidden_states)
 
 
 class PoolFormerPreTrainedModel(PreTrainedModel):
@@ -374,7 +327,7 @@ class PoolFormerModel(PoolFormerPreTrainedModel):
     @add_code_sample_docstrings(
         processor_class=_FEAT_EXTRACTOR_FOR_DOC,
         checkpoint=_CHECKPOINT_FOR_DOC,
-        output_type=PoolFormerModelOutput,
+        output_type=BaseModelOutputWithNoAttention,
         config_class=_CONFIG_FOR_DOC,
         modality="vision",
         expected_output=_EXPECTED_OUTPUT_SHAPE,
@@ -384,7 +337,7 @@ class PoolFormerModel(PoolFormerPreTrainedModel):
         pixel_values: Optional[torch.FloatTensor] = None,
         output_hidden_states: Optional[bool] = None,
         return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, PoolFormerModelOutput]:
+    ) -> Union[Tuple, BaseModelOutputWithNoAttention]:
         output_hidden_states = (
             output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
         )
@@ -403,7 +356,7 @@ class PoolFormerModel(PoolFormerPreTrainedModel):
         if not return_dict:
             return (sequence_output, None) + encoder_outputs[1:]
 
-        return PoolFormerModelOutput(
+        return BaseModelOutputWithNoAttention(
             last_hidden_state=sequence_output,
             hidden_states=encoder_outputs.hidden_states,
         )
@@ -445,7 +398,7 @@ class PoolFormerForImageClassification(PoolFormerPreTrainedModel):
     @add_code_sample_docstrings(
         processor_class=_FEAT_EXTRACTOR_FOR_DOC,
         checkpoint=_IMAGE_CLASS_CHECKPOINT,
-        output_type=PoolFormerClassifierOutput,
+        output_type=ImageClassifierOutputWithNoAttention,
         config_class=_CONFIG_FOR_DOC,
         expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT,
     )
@@ -455,7 +408,7 @@ class PoolFormerForImageClassification(PoolFormerPreTrainedModel):
         labels: Optional[torch.LongTensor] = None,
         output_hidden_states: Optional[bool] = None,
         return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, PoolFormerClassifierOutput]:
+    ) -> Union[Tuple, ImageClassifierOutputWithNoAttention]:
         r"""
         labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
             Labels for computing the image classification/regression loss. Indices should be in `[0, ...,
@@ -501,4 +454,4 @@ class PoolFormerForImageClassification(PoolFormerPreTrainedModel):
             output = (logits,) + outputs[2:]
             return ((loss,) + output) if loss is not None else output
 
-        return PoolFormerClassifierOutput(loss=loss, logits=logits, hidden_states=outputs.hidden_states)
+        return ImageClassifierOutputWithNoAttention(loss=loss, logits=logits, hidden_states=outputs.hidden_states)

src/transformers/models/resnet/modeling_resnet.py

@@ -14,8 +14,7 @@
 # limitations under the License.
 """ PyTorch ResNet model."""
 
-from dataclasses import dataclass
-from typing import Optional, Tuple
+from typing import Optional
 
 import torch
 import torch.utils.checkpoint
@@ -23,7 +22,11 @@ from torch import Tensor, nn
 from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
 
 from ...activations import ACT2FN
-from ...modeling_outputs import ImageClassifierOutput, ModelOutput
+from ...modeling_outputs import (
+    BaseModelOutputWithNoAttention,
+    BaseModelOutputWithPoolingAndNoAttention,
+    ImageClassifierOutputWithNoAttention,
+)
 from ...modeling_utils import PreTrainedModel
 from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
 from .configuration_resnet import ResNetConfig
@@ -49,47 +52,6 @@ RESNET_PRETRAINED_MODEL_ARCHIVE_LIST = [
 ]
 
 
-@dataclass
-class ResNetEncoderOutput(ModelOutput):
-    """
-    ResNet encoder's output, with potential hidden states.
-
-    Args:
-        last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
-            Sequence of hidden-states at the output of the last layer of the model.
-        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
-            Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
-            shape `(batch_size, num_channels, height, width)`.
-
-            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
-    """
-
-    last_hidden_state: torch.FloatTensor = None
-    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
-
-
-@dataclass
-class ResNetModelOutput(ModelOutput):
-    """
-    ResNet model's output, with potential hidden states.
-
-    Args:
-        last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
-            Sequence of hidden-states at the output of the last layer of the model.
-        pooler_output (`torch.FloatTensor` of shape `(batch_size, config.hidden_sizes[-1])`):
-           The pooled last hidden state.
-        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
-            Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
-            shape `(batch_size, num_channels, height, width)`.
-
-            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
-    """
-
-    last_hidden_state: torch.FloatTensor = None
-    pooler_output: torch.FloatTensor = None
-    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
-
-
 class ResNetConvLayer(nn.Sequential):
     def __init__(
         self, in_channels: int, out_channels: int, kernel_size: int = 3, stride: int = 1, activation: str = "relu"
@@ -228,7 +190,7 @@ class ResNetEncoder(nn.Module):
 
     def forward(
         self, hidden_state: Tensor, output_hidden_states: bool = False, return_dict: bool = True
-    ) -> ResNetEncoderOutput:
+    ) -> BaseModelOutputWithNoAttention:
         hidden_states = () if output_hidden_states else None
 
         for stage_module in self.stages:
@@ -243,7 +205,7 @@ class ResNetEncoder(nn.Module):
         if not return_dict:
             return tuple(v for v in [hidden_state, hidden_states] if v is not None)
 
-        return ResNetEncoderOutput(
+        return BaseModelOutputWithNoAttention(
             last_hidden_state=hidden_state,
             hidden_states=hidden_states,
         )
@@ -315,14 +277,14 @@ class ResNetModel(ResNetPreTrainedModel):
     @add_code_sample_docstrings(
         processor_class=_FEAT_EXTRACTOR_FOR_DOC,
         checkpoint=_CHECKPOINT_FOR_DOC,
-        output_type=ResNetModelOutput,
+        output_type=BaseModelOutputWithPoolingAndNoAttention,
         config_class=_CONFIG_FOR_DOC,
         modality="vision",
         expected_output=_EXPECTED_OUTPUT_SHAPE,
     )
     def forward(
         self, pixel_values: Tensor, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None
-    ) -> ResNetModelOutput:
+    ) -> BaseModelOutputWithPoolingAndNoAttention:
         output_hidden_states = (
             output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
         )
@@ -341,7 +303,7 @@ class ResNetModel(ResNetPreTrainedModel):
         if not return_dict:
             return (last_hidden_state, pooled_output) + encoder_outputs[1:]
 
-        return ResNetModelOutput(
+        return BaseModelOutputWithPoolingAndNoAttention(
             last_hidden_state=last_hidden_state,
             pooler_output=pooled_output,
             hidden_states=encoder_outputs.hidden_states,
@@ -372,7 +334,7 @@ class ResNetForImageClassification(ResNetPreTrainedModel):
     @add_code_sample_docstrings(
         processor_class=_FEAT_EXTRACTOR_FOR_DOC,
         checkpoint=_IMAGE_CLASS_CHECKPOINT,
-        output_type=ImageClassifierOutput,
+        output_type=ImageClassifierOutputWithNoAttention,
         config_class=_CONFIG_FOR_DOC,
         expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT,
     )
@@ -382,7 +344,7 @@ class ResNetForImageClassification(ResNetPreTrainedModel):
         labels: Tensor = None,
         output_hidden_states: bool = None,
         return_dict: bool = None,
-    ) -> ImageClassifierOutput:
+    ) -> ImageClassifierOutputWithNoAttention:
         r"""
         labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
             Labels for computing the image classification/regression loss. Indices should be in `[0, ...,
@@ -423,4 +385,4 @@ class ResNetForImageClassification(ResNetPreTrainedModel):
             output = (logits,) + outputs[2:]
             return (loss,) + output if loss is not None else output
 
-        return ImageClassifierOutput(loss=loss, logits=logits, hidden_states=outputs.hidden_states)
+        return ImageClassifierOutputWithNoAttention(loss=loss, logits=logits, hidden_states=outputs.hidden_states)

src/transformers/models/segformer/modeling_segformer.py

@@ -24,7 +24,7 @@ from torch import nn
 from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
 
 from ...activations import ACT2FN
-from ...modeling_outputs import BaseModelOutput, SemanticSegmenterOutput, SequenceClassifierOutput
+from ...modeling_outputs import BaseModelOutput, ImageClassifierOutput, SemanticSegmenterOutput
 from ...modeling_utils import PreTrainedModel, find_pruneable_heads_and_indices, prune_linear_layer
 from ...utils import (
     add_code_sample_docstrings,
@@ -57,6 +57,33 @@ SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST = [
 ]
 
 
+class SegFormerImageClassifierOutput(ImageClassifierOutput):
+    """
+    Base class for outputs of image classification models.
+
+    Args:
+        loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
+            Classification (or regression if config.num_labels==1) loss.
+        logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`):
+            Classification (or regression if config.num_labels==1) scores (before SoftMax).
+        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+            Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
+            one for the output of each stage) of shape `(batch_size, num_channels, height, width)`. Hidden-states (also
+            called feature maps) of the model at the output of each stage.
+        attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, patch_size,
+            sequence_length)`.
+
+            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
+            heads.
+    """
+
+    loss: Optional[torch.FloatTensor] = None
+    logits: torch.FloatTensor = None
+    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
+    attentions: Optional[Tuple[torch.FloatTensor]] = None
+
+
 # Copied from transformers.models.convnext.modeling_convnext.drop_path
 def drop_path(x, drop_prob: float = 0.0, training: bool = False, scale_by_keep=True):
     """
@@ -558,7 +585,7 @@ class SegformerForImageClassification(SegformerPreTrainedModel):
     @add_code_sample_docstrings(
         processor_class=_FEAT_EXTRACTOR_FOR_DOC,
         checkpoint=_IMAGE_CLASS_CHECKPOINT,
-        output_type=SequenceClassifierOutput,
+        output_type=SegFormerImageClassifierOutput,
         config_class=_CONFIG_FOR_DOC,
         expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT,
     )
@@ -569,7 +596,7 @@ class SegformerForImageClassification(SegformerPreTrainedModel):
         output_attentions: Optional[bool] = None,
         output_hidden_states: Optional[bool] = None,
         return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, SequenceClassifierOutput]:
+    ) -> Union[Tuple, SegFormerImageClassifierOutput]:
         r"""
         labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
             Labels for computing the image classification/regression loss. Indices should be in `[0, ...,
@@ -625,7 +652,7 @@ class SegformerForImageClassification(SegformerPreTrainedModel):
             output = (logits,) + outputs[1:]
             return ((loss,) + output) if loss is not None else output
 
-        return SequenceClassifierOutput(
+        return SegFormerImageClassifierOutput(
             loss=loss,
             logits=logits,
             hidden_states=outputs.hidden_states,

src/transformers/models/van/modeling_van.py

@@ -16,8 +16,6 @@
 
 import math
 from collections import OrderedDict
-from dataclasses import dataclass
-from typing import Optional, Tuple
 
 import torch
 import torch.utils.checkpoint
@@ -25,14 +23,13 @@ from torch import nn
 from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
 
 from ...activations import ACT2FN
-from ...modeling_utils import PreTrainedModel
-from ...utils import (
-    ModelOutput,
-    add_code_sample_docstrings,
-    add_start_docstrings,
-    add_start_docstrings_to_model_forward,
-    logging,
+from ...modeling_outputs import (
+    BaseModelOutputWithNoAttention,
+    BaseModelOutputWithPoolingAndNoAttention,
+    ImageClassifierOutputWithNoAttention,
 )
+from ...modeling_utils import PreTrainedModel
+from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
 from .configuration_van import VanConfig
 
 
@@ -56,63 +53,6 @@ VAN_PRETRAINED_MODEL_ARCHIVE_LIST = [
 ]
 
 
-@dataclass
-class VanEncoderOutput(ModelOutput):
-    """
-    Class for [`VanEncoder`]'s outputs, with potential hidden states (feature maps).
-
-    Args:
-        last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
-            Last hidden states (final feature map) of the last stage of the model.
-        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
-            Tuple of `torch.FloatTensor` (one for the output of each stage) of shape `(batch_size, num_channels,
-            height, width)`. Hidden-states (also called feature maps) of the model at the output of each stage.
-    """
-
-    last_hidden_state: torch.FloatTensor = None
-    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
-
-
-@dataclass
-class VanModelOutput(ModelOutput):
-    """
-    Class for [`VanModel`]'s outputs, with potential hidden states (feature maps).
-
-    Args:
-        last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
-            Last hidden states (final feature map) of the last stage of the model.
-        pooler_output (`torch.FloatTensor` of shape `(batch_size, config.hidden_sizes[-1])`):
-            Global average pooling of the last feature map followed by a layernorm.
-        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
-            Tuple of `torch.FloatTensor` (one for the output of each stage) of shape `(batch_size, num_channels,
-            height, width)`. Hidden-states (also called feature maps) of the model at the output of each stage.
-    """
-
-    last_hidden_state: torch.FloatTensor = None
-    pooler_output: Optional[torch.FloatTensor] = None
-    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
-
-
-@dataclass
-class VanClassifierOutput(ModelOutput):
-    """
-    Class for [`VanForImageClassification`]'s outputs, with potential hidden states (feature maps).
-
-    Args:
-        loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
-            Classification (or regression if config.num_labels==1) loss.
-        logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`):
-            Classification (or regression if config.num_labels==1) scores (before SoftMax).
-        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
-            Tuple of `torch.FloatTensor` (one for the output of each stage) of shape `(batch_size, num_channels,
-            height, width)`. Hidden-states (also called feature maps) of the model at the output of each stage.
-    """
-
-    loss: Optional[torch.FloatTensor] = None
-    logits: torch.FloatTensor = None
-    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
-
-
 # Stochastic depth implementation
 # Taken from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/layers/drop.py
 def drop_path(x, drop_prob: float = 0.0, training: bool = False):
@@ -388,7 +328,7 @@ class VanEncoder(nn.Module):
         if not return_dict:
             return tuple(v for v in [hidden_state, all_hidden_states] if v is not None)
 
-        return VanEncoderOutput(last_hidden_state=hidden_state, hidden_states=all_hidden_states)
+        return BaseModelOutputWithNoAttention(last_hidden_state=hidden_state, hidden_states=all_hidden_states)
 
 
 class VanPreTrainedModel(PreTrainedModel):
@@ -466,7 +406,7 @@ class VanModel(VanPreTrainedModel):
     @add_code_sample_docstrings(
         processor_class=_FEAT_EXTRACTOR_FOR_DOC,
         checkpoint=_CHECKPOINT_FOR_DOC,
-        output_type=VanModelOutput,
+        output_type=BaseModelOutputWithPoolingAndNoAttention,
         config_class=_CONFIG_FOR_DOC,
         modality="vision",
         expected_output=_EXPECTED_OUTPUT_SHAPE,
@@ -489,7 +429,7 @@ class VanModel(VanPreTrainedModel):
         if not return_dict:
             return (last_hidden_state, pooled_output) + encoder_outputs[1:]
 
-        return VanModelOutput(
+        return BaseModelOutputWithPoolingAndNoAttention(
             last_hidden_state=last_hidden_state,
             pooler_output=pooled_output,
             hidden_states=encoder_outputs.hidden_states,
@@ -519,7 +459,7 @@ class VanForImageClassification(VanPreTrainedModel):
     @add_code_sample_docstrings(
         processor_class=_FEAT_EXTRACTOR_FOR_DOC,
         checkpoint=_IMAGE_CLASS_CHECKPOINT,
-        output_type=VanClassifierOutput,
+        output_type=ImageClassifierOutputWithNoAttention,
         config_class=_CONFIG_FOR_DOC,
         expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT,
     )
@@ -565,4 +505,4 @@ class VanForImageClassification(VanPreTrainedModel):
             output = (logits,) + outputs[2:]
             return ((loss,) + output) if loss is not None else output
 
-        return VanClassifierOutput(loss=loss, logits=logits, hidden_states=outputs.hidden_states)
+        return ImageClassifierOutputWithNoAttention(loss=loss, logits=logits, hidden_states=outputs.hidden_states)

src/transformers/models/vit/modeling_vit.py

@@ -25,7 +25,7 @@ from torch import nn
 from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
 
 from ...activations import ACT2FN
-from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling, MaskedLMOutput, SequenceClassifierOutput
+from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling, ImageClassifierOutput, MaskedLMOutput
 from ...modeling_utils import PreTrainedModel, find_pruneable_heads_and_indices, prune_linear_layer
 from ...utils import (
     add_code_sample_docstrings,
@@ -740,7 +740,7 @@ class ViTForImageClassification(ViTPreTrainedModel):
     @add_code_sample_docstrings(
         processor_class=_FEAT_EXTRACTOR_FOR_DOC,
         checkpoint=_IMAGE_CLASS_CHECKPOINT,
-        output_type=SequenceClassifierOutput,
+        output_type=ImageClassifierOutput,
         config_class=_CONFIG_FOR_DOC,
         expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT,
     )
@@ -753,7 +753,7 @@ class ViTForImageClassification(ViTPreTrainedModel):
         output_hidden_states: Optional[bool] = None,
         interpolate_pos_encoding: Optional[bool] = None,
         return_dict: Optional[bool] = None,
-    ) -> Union[tuple, SequenceClassifierOutput]:
+    ) -> Union[tuple, ImageClassifierOutput]:
         r"""
         labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
             Labels for computing the image classification/regression loss. Indices should be in `[0, ...,
@@ -801,7 +801,7 @@ class ViTForImageClassification(ViTPreTrainedModel):
             output = (logits,) + outputs[2:]
             return ((loss,) + output) if loss is not None else output
 
-        return SequenceClassifierOutput(
+        return ImageClassifierOutput(
             loss=loss,
             logits=logits,
             hidden_states=outputs.hidden_states,