Add ViltForTokenClassification e.g. for Named-Entity-Recognition (NER) (#17924)

* Add ViltForTokenClassification e.g. for Named-Entity-Recognition (NER)

* Add ViltForTokenClassification e.g. for Named-Entity-Recognition (NER)

* provide classifier only text hidden states

* add test_for_token_classification

* Update src/transformers/models/vilt/modeling_vilt.py

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

* Update src/transformers/models/vilt/modeling_vilt.py

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

* Update src/transformers/models/vilt/modeling_vilt.py

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

* Update src/transformers/models/vilt/modeling_vilt.py

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

* add test_for_token_classification

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

docs/source/en/model_doc/vilt.mdx

@@ -87,3 +87,8 @@ This model was contributed by [nielsr](https://huggingface.co/nielsr). The origi
 
 [[autodoc]] ViltForImageAndTextRetrieval
     - forward
+
+## ViltForTokenClassification
+
+[[autodoc]] ViltForTokenClassification
+    - forward

src/transformers/__init__.py

@@ -1816,6 +1816,7 @@ else:
             "VILT_PRETRAINED_MODEL_ARCHIVE_LIST",
             "ViltForImageAndTextRetrieval",
             "ViltForImagesAndTextClassification",
+            "ViltForTokenClassification",
             "ViltForMaskedLM",
             "ViltForQuestionAnswering",
             "ViltLayer",
@@ -4317,6 +4318,7 @@ if TYPE_CHECKING:
             ViltForImagesAndTextClassification,
             ViltForMaskedLM,
             ViltForQuestionAnswering,
+            ViltForTokenClassification,
             ViltLayer,
             ViltModel,
             ViltPreTrainedModel,

src/transformers/models/vilt/__init__.py

@@ -42,6 +42,7 @@ else:
         "VILT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "ViltForImageAndTextRetrieval",
         "ViltForImagesAndTextClassification",
+        "ViltForTokenClassification",
         "ViltForMaskedLM",
         "ViltForQuestionAnswering",
         "ViltLayer",
@@ -74,6 +75,7 @@ if TYPE_CHECKING:
             ViltForImagesAndTextClassification,
             ViltForMaskedLM,
             ViltForQuestionAnswering,
+            ViltForTokenClassification,
             ViltLayer,
             ViltModel,
             ViltPreTrainedModel,

src/transformers/models/vilt/modeling_vilt.py

@@ -32,6 +32,7 @@ from ...modeling_outputs import (
     MaskedLMOutput,
     ModelOutput,
     SequenceClassifierOutput,
+    TokenClassifierOutput,
 )
 from ...modeling_utils import PreTrainedModel
 from ...pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer
@@ -1402,3 +1403,90 @@ class ViltForImagesAndTextClassification(ViltPreTrainedModel):
             hidden_states=hidden_states,
             attentions=attentions,
         )
+
+
+@add_start_docstrings(
+    """
+    ViLT Model with a token classification head on top (a linear layer on top of the final hidden-states of the text
+    tokens) e.g. for Named-Entity-Recognition (NER) tasks.
+    """,
+    VILT_START_DOCSTRING,
+)
+class ViltForTokenClassification(ViltPreTrainedModel):
+
+    _keys_to_ignore_on_load_unexpected = [r"pooler"]
+
+    def __init__(self, config):
+        super().__init__(config)
+
+        self.num_labels = config.num_labels
+        self.vilt = ViltModel(config, add_pooling_layer=False)
+
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+        self.classifier = nn.Linear(config.hidden_size, config.num_labels)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    @add_start_docstrings_to_model_forward(VILT_INPUTS_DOCSTRING)
+    @replace_return_docstrings(output_type=TokenClassifierOutput, config_class=_CONFIG_FOR_DOC)
+    def forward(
+        self,
+        input_ids=None,
+        attention_mask=None,
+        token_type_ids=None,
+        pixel_values=None,
+        pixel_mask=None,
+        head_mask=None,
+        inputs_embeds=None,
+        image_embeds=None,
+        labels=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+    ):
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size, text_sequence_length)`, *optional*):
+            Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
+
+        Returns:
+        """
+
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        outputs = self.vilt(
+            input_ids,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            pixel_values=pixel_values,
+            pixel_mask=pixel_mask,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            image_embeds=image_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        sequence_output = outputs[0]
+
+        text_input_size = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]
+
+        sequence_output = self.dropout(sequence_output)
+        logits = self.classifier(sequence_output[:, :text_input_size])
+
+        loss = None
+        if labels is not None:
+            loss_fct = CrossEntropyLoss()
+            loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+
+        if not return_dict:
+            output = (logits,) + outputs[2:]
+            return ((loss,) + output) if loss is not None else output
+
+        return TokenClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )

src/transformers/utils/dummy_pt_objects.py

@@ -4725,6 +4725,13 @@ class ViltForQuestionAnswering(metaclass=DummyObject):
         requires_backends(self, ["torch"])
 
 
+class ViltForTokenClassification(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
 class ViltLayer(metaclass=DummyObject):
     _backends = ["torch"]
 

tests/models/vilt/test_modeling_vilt.py

@@ -37,6 +37,7 @@ if is_torch_available():
         ViltForImagesAndTextClassification,
         ViltForMaskedLM,
         ViltForQuestionAnswering,
+        ViltForTokenClassification,
         ViltModel,
     )
     from transformers.models.vilt.modeling_vilt import VILT_PRETRAINED_MODEL_ARCHIVE_LIST
@@ -173,6 +174,23 @@ class ViltModelTester:
             result.last_hidden_state.shape, (self.batch_size, self.expected_seq_len, self.hidden_size)
         )
 
+    def create_and_check_for_token_classification(
+        self,
+        config,
+        input_ids,
+        token_type_ids,
+        input_mask,
+        pixel_values,
+        token_labels,
+    ):
+        model = ViltForTokenClassification(config=config)
+        model.to(torch_device)
+        model.eval()
+        result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, pixel_values=pixel_values)
+        result = model(input_ids, token_type_ids=token_type_ids, pixel_values=pixel_values)
+        result = model(input_ids, pixel_values=pixel_values)
+        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
+
     def prepare_config_and_inputs_for_common(self):
         config_and_inputs = self.prepare_config_and_inputs()
         (
@@ -204,6 +222,7 @@ class ViltModelTest(ModelTesterMixin, unittest.TestCase):
             ViltForQuestionAnswering,
             ViltForImageAndTextRetrieval,
             ViltForMaskedLM,
+            ViltForTokenClassification,
         )
         if is_torch_available()
         else ()
@@ -216,15 +235,12 @@ class ViltModelTest(ModelTesterMixin, unittest.TestCase):
     def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
         inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)
 
-        # if model_class.__name__ == "ViltForNaturalLanguageVisualReasonining":
-        #     inputs_dict["pixel_values"] = floats_tensor([self.model_tester.batch_size, self.model_tester.num_images, self.model_tester.num_channels, self.model_tester.image_size, self.model_tester.image_size])
-
         if return_labels:
             if model_class.__name__ == "ViltForQuestionAnswering":
                 inputs_dict["labels"] = torch.zeros(
                     self.model_tester.batch_size, self.model_tester.num_labels, device=torch_device
                 )
-            elif model_class.__name__ == "ViltForMaskedLM":
+            elif model_class.__name__ in ["ViltForMaskedLM", "ViltForTokenClassification"]:
                 inputs_dict["labels"] = torch.zeros(
                     (self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device
                 )
@@ -246,6 +262,10 @@ class ViltModelTest(ModelTesterMixin, unittest.TestCase):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*config_and_inputs)
 
+    def test_for_token_classification(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_for_token_classification(*config_and_inputs)
+
     def test_training(self):
         if not self.model_tester.is_training:
             return
@@ -503,6 +523,10 @@ class ViltForImagesAndTextClassificationModelTest(ViltModelTest, unittest.TestCa
     def test_model(self):
         pass
 
+    @unittest.skip("We only test the model that takes in multiple images")
+    def test_for_token_classification(self):
+        pass
+
 
 # We will verify our results on an image of cute cats
 def prepare_img():

utils/check_repo.py

@@ -131,6 +131,7 @@ IGNORE_NON_AUTO_CONFIGURED = PRIVATE_MODELS.copy() + [
     "ViltForQuestionAnswering",
     "ViltForImagesAndTextClassification",
     "ViltForImageAndTextRetrieval",
+    "ViltForTokenClassification",
     "ViltForMaskedLM",
     "XGLMEncoder",
     "XGLMDecoder",