Add LayoutLMForQuestionAnswering model (#18407)

* Add LayoutLMForQuestionAnswering model

* Fix output

* Remove TF TODOs

* Add test cases

* Add docs

* TF implementation

* Fix PT/TF equivalence

* Fix loss

* make fixup

* Fix up documentation code examples

* Fix up documentation examples + test them

* Remove LayoutLMForQuestionAnswering from the auto mapping

* Docstrings

* Add better docstrings

* Undo whitespace changes

* Update tokenizers in comments

* Fixup code and remove `from_pt=True`

* Fix tests

* Revert some unexpected docstring changes

* Fix tests by overriding _prepare_for_class
Co-authored-by: Ankur Goyal <ankur@impira.com>

docs/source/en/model_doc/layoutlm.mdx

@@ -107,6 +107,10 @@ This model was contributed by [liminghao1630](https://huggingface.co/liminghao16
 
 [[autodoc]] LayoutLMForTokenClassification
 
+## LayoutLMForQuestionAnswering
+
+[[autodoc]] LayoutLMForQuestionAnswering
+
 ## TFLayoutLMModel
 
 [[autodoc]] TFLayoutLMModel
@@ -122,3 +126,7 @@ This model was contributed by [liminghao1630](https://huggingface.co/liminghao16
 ## TFLayoutLMForTokenClassification
 
 [[autodoc]] TFLayoutLMForTokenClassification
+
+## TFLayoutLMForQuestionAnswering
+
+[[autodoc]] TFLayoutLMForQuestionAnswering

src/transformers/__init__.py

@@ -1305,6 +1305,7 @@ else:
             "LayoutLMForMaskedLM",
             "LayoutLMForSequenceClassification",
             "LayoutLMForTokenClassification",
+            "LayoutLMForQuestionAnswering",
             "LayoutLMModel",
             "LayoutLMPreTrainedModel",
         ]
@@ -2337,6 +2338,7 @@ else:
             "TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
             "TFLayoutLMForMaskedLM",
             "TFLayoutLMForSequenceClassification",
+            "TFLayoutLMForQuestionAnswering",
             "TFLayoutLMForTokenClassification",
             "TFLayoutLMMainLayer",
             "TFLayoutLMModel",
@@ -3945,6 +3947,7 @@ if TYPE_CHECKING:
         from .models.layoutlm import (
             LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
             LayoutLMForMaskedLM,
+            LayoutLMForQuestionAnswering,
             LayoutLMForSequenceClassification,
             LayoutLMForTokenClassification,
             LayoutLMModel,
@@ -4583,6 +4586,7 @@ if TYPE_CHECKING:
         from .modeling_tf_layoutlm import (
             TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
             TFLayoutLMForMaskedLM,
+            TFLayoutLMForQuestionAnswering,
             TFLayoutLMForSequenceClassification,
             TFLayoutLMForTokenClassification,
             TFLayoutLMMainLayer,

src/transformers/models/layoutlm/__init__.py

@@ -51,6 +51,7 @@ else:
         "LayoutLMForMaskedLM",
         "LayoutLMForSequenceClassification",
         "LayoutLMForTokenClassification",
+        "LayoutLMForQuestionAnswering",
         "LayoutLMModel",
         "LayoutLMPreTrainedModel",
     ]
@@ -66,6 +67,7 @@ else:
         "TFLayoutLMForMaskedLM",
         "TFLayoutLMForSequenceClassification",
         "TFLayoutLMForTokenClassification",
+        "TFLayoutLMForQuestionAnswering",
         "TFLayoutLMMainLayer",
         "TFLayoutLMModel",
         "TFLayoutLMPreTrainedModel",
@@ -93,6 +95,7 @@ if TYPE_CHECKING:
         from .modeling_layoutlm import (
             LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
             LayoutLMForMaskedLM,
+            LayoutLMForQuestionAnswering,
             LayoutLMForSequenceClassification,
             LayoutLMForTokenClassification,
             LayoutLMModel,
@@ -107,6 +110,7 @@ if TYPE_CHECKING:
         from .modeling_tf_layoutlm import (
             TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
             TFLayoutLMForMaskedLM,
+            TFLayoutLMForQuestionAnswering,
             TFLayoutLMForSequenceClassification,
             TFLayoutLMForTokenClassification,
             TFLayoutLMMainLayer,

src/transformers/models/layoutlm/modeling_layoutlm.py

@@ -28,6 +28,7 @@ from ...modeling_outputs import (
     BaseModelOutputWithPastAndCrossAttentions,
     BaseModelOutputWithPoolingAndCrossAttentions,
     MaskedLMOutput,
+    QuestionAnsweringModelOutput,
     SequenceClassifierOutput,
     TokenClassifierOutput,
 )
@@ -40,7 +41,6 @@ from .configuration_layoutlm import LayoutLMConfig
 logger = logging.get_logger(__name__)
 
 _CONFIG_FOR_DOC = "LayoutLMConfig"
-_TOKENIZER_FOR_DOC = "LayoutLMTokenizer"
 _CHECKPOINT_FOR_DOC = "microsoft/layoutlm-base-uncased"
 
 LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST = [
@@ -749,10 +749,10 @@ class LayoutLMModel(LayoutLMPreTrainedModel):
         Examples:
 
         ```python
-        >>> from transformers import LayoutLMTokenizer, LayoutLMModel
+        >>> from transformers import AutoTokenizer, LayoutLMModel
         >>> import torch
 
-        >>> tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")
+        >>> tokenizer = AutoTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")
         >>> model = LayoutLMModel.from_pretrained("microsoft/layoutlm-base-uncased")
 
         >>> words = ["Hello", "world"]
@@ -896,10 +896,10 @@ class LayoutLMForMaskedLM(LayoutLMPreTrainedModel):
         Examples:
 
         ```python
-        >>> from transformers import LayoutLMTokenizer, LayoutLMForMaskedLM
+        >>> from transformers import AutoTokenizer, LayoutLMForMaskedLM
         >>> import torch
 
-        >>> tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")
+        >>> tokenizer = AutoTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")
         >>> model = LayoutLMForMaskedLM.from_pretrained("microsoft/layoutlm-base-uncased")
 
         >>> words = ["Hello", "[MASK]"]
@@ -1018,10 +1018,10 @@ class LayoutLMForSequenceClassification(LayoutLMPreTrainedModel):
         Examples:
 
         ```python
-        >>> from transformers import LayoutLMTokenizer, LayoutLMForSequenceClassification
+        >>> from transformers import AutoTokenizer, LayoutLMForSequenceClassification
         >>> import torch
 
-        >>> tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")
+        >>> tokenizer = AutoTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")
         >>> model = LayoutLMForSequenceClassification.from_pretrained("microsoft/layoutlm-base-uncased")
 
         >>> words = ["Hello", "world"]
@@ -1153,10 +1153,10 @@ class LayoutLMForTokenClassification(LayoutLMPreTrainedModel):
         Examples:
 
         ```python
-        >>> from transformers import LayoutLMTokenizer, LayoutLMForTokenClassification
+        >>> from transformers import AutoTokenizer, LayoutLMForTokenClassification
         >>> import torch
 
-        >>> tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")
+        >>> tokenizer = AutoTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")
         >>> model = LayoutLMForTokenClassification.from_pretrained("microsoft/layoutlm-base-uncased")
 
         >>> words = ["Hello", "world"]
@@ -1222,3 +1222,147 @@ class LayoutLMForTokenClassification(LayoutLMPreTrainedModel):
             hidden_states=outputs.hidden_states,
             attentions=outputs.attentions,
         )
+
+
+@add_start_docstrings(
+    """
+    LayoutLM Model with a span classification head on top for extractive question-answering tasks such as
+    [DocVQA](https://rrc.cvc.uab.es/?ch=17) (a linear layer on top of the final hidden-states output to compute `span
+    start logits` and `span end logits`).
+    """,
+    LAYOUTLM_START_DOCSTRING,
+)
+class LayoutLMForQuestionAnswering(LayoutLMPreTrainedModel):
+    def __init__(self, config, has_visual_segment_embedding=True):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+
+        self.layoutlm = LayoutLMModel(config)
+        self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.layoutlm.embeddings.word_embeddings
+
+    @replace_return_docstrings(output_type=QuestionAnsweringModelOutput, config_class=_CONFIG_FOR_DOC)
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        bbox: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        token_type_ids: Optional[torch.LongTensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        head_mask: Optional[torch.FloatTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        start_positions: Optional[torch.LongTensor] = None,
+        end_positions: Optional[torch.LongTensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, QuestionAnsweringModelOutput]:
+        r"""
+        start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for position (index) of the start of the labelled span for computing the token classification loss.
+            Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
+            are not taken into account for computing the loss.
+        end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for position (index) of the end of the labelled span for computing the token classification loss.
+            Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
+            are not taken into account for computing the loss.
+
+        Returns:
+
+        Example:
+
+        In the example below, we prepare a question + context pair for the LayoutLM model. It will give us a prediction
+        of what it thinks the answer is (the span of the answer within the texts parsed from the image).
+
+        ```python
+        >>> from transformers import AutoTokenizer, LayoutLMForQuestionAnswering
+        >>> from datasets import load_dataset
+        >>> import torch
+
+        >>> tokenizer = AutoTokenizer.from_pretrained("impira/layoutlm-document-qa", add_prefix_space=True)
+        >>> model = LayoutLMForQuestionAnswering.from_pretrained("impira/layoutlm-document-qa")
+
+        >>> dataset = load_dataset("nielsr/funsd", split="train")
+        >>> example = dataset[0]
+        >>> question = "what's his name?"
+        >>> words = example["words"]
+        >>> boxes = example["bboxes"]
+
+        >>> encoding = tokenizer(
+        ...     question.split(), words, is_split_into_words=True, return_token_type_ids=True, return_tensors="pt"
+        ... )
+        >>> bbox = []
+        >>> for i, s, w in zip(encoding.input_ids[0], encoding.sequence_ids(0), encoding.word_ids(0)):
+        ...     if s == 1:
+        ...         bbox.append(boxes[w])
+        ...     elif i == tokenizer.sep_token_id:
+        ...         bbox.append([1000] * 4)
+        ...     else:
+        ...         bbox.append([0] * 4)
+        >>> encoding["bbox"] = torch.tensor([bbox])
+
+        >>> word_ids = encoding.word_ids(0)
+        >>> outputs = model(**encoding)
+        >>> loss = outputs.loss
+        >>> start_scores = outputs.start_logits
+        >>> end_scores = outputs.end_logits
+        >>> start, end = word_ids[start_scores.argmax(-1)], word_ids[end_scores.argmax(-1)]
+        >>> print(" ".join(words[start : end + 1]))
+        M. Hamann P. Harper, P. Martinez
+        ```"""
+
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        outputs = self.layoutlm(
+            input_ids=input_ids,
+            bbox=bbox,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        sequence_output = outputs[0]
+
+        logits = self.qa_outputs(sequence_output)
+        start_logits, end_logits = logits.split(1, dim=-1)
+        start_logits = start_logits.squeeze(-1).contiguous()
+        end_logits = end_logits.squeeze(-1).contiguous()
+
+        total_loss = None
+        if start_positions is not None and end_positions is not None:
+            # If we are on multi-GPU, split add a dimension
+            if len(start_positions.size()) > 1:
+                start_positions = start_positions.squeeze(-1)
+            if len(end_positions.size()) > 1:
+                end_positions = end_positions.squeeze(-1)
+            # sometimes the start/end positions are outside our model inputs, we ignore these terms
+            ignored_index = start_logits.size(1)
+            start_positions = start_positions.clamp(0, ignored_index)
+            end_positions = end_positions.clamp(0, ignored_index)
+
+            loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
+            start_loss = loss_fct(start_logits, start_positions)
+            end_loss = loss_fct(end_logits, end_positions)
+            total_loss = (start_loss + end_loss) / 2
+
+        if not return_dict:
+            output = (start_logits, end_logits) + outputs[2:]
+            return ((total_loss,) + output) if total_loss is not None else output
+
+        return QuestionAnsweringModelOutput(
+            loss=total_loss,
+            start_logits=start_logits,
+            end_logits=end_logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )

src/transformers/models/layoutlm/modeling_tf_layoutlm.py

@@ -26,6 +26,7 @@ from ...modeling_tf_outputs import (
     TFBaseModelOutputWithPastAndCrossAttentions,
     TFBaseModelOutputWithPoolingAndCrossAttentions,
     TFMaskedLMOutput,
+    TFQuestionAnsweringModelOutput,
     TFSequenceClassifierOutput,
     TFTokenClassifierOutput,
 )
@@ -33,6 +34,7 @@ from ...modeling_tf_utils import (
     TFMaskedLanguageModelingLoss,
     TFModelInputType,
     TFPreTrainedModel,
+    TFQuestionAnsweringLoss,
     TFSequenceClassificationLoss,
     TFTokenClassificationLoss,
     get_initializer,
@@ -47,7 +49,6 @@ from .configuration_layoutlm import LayoutLMConfig
 logger = logging.get_logger(__name__)
 
 _CONFIG_FOR_DOC = "LayoutLMConfig"
-_TOKENIZER_FOR_DOC = "LayoutLMTokenizer"
 
 TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST = [
     "microsoft/layoutlm-base-uncased",
@@ -934,10 +935,10 @@ class TFLayoutLMModel(TFLayoutLMPreTrainedModel):
         Examples:
 
         ```python
-        >>> from transformers import LayoutLMTokenizer, TFLayoutLMModel
+        >>> from transformers import AutoTokenizer, TFLayoutLMModel
         >>> import tensorflow as tf
 
-        >>> tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")
+        >>> tokenizer = AutoTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")
         >>> model = TFLayoutLMModel.from_pretrained("microsoft/layoutlm-base-uncased")
 
         >>> words = ["Hello", "world"]
@@ -1058,10 +1059,10 @@ class TFLayoutLMForMaskedLM(TFLayoutLMPreTrainedModel, TFMaskedLanguageModelingL
         Examples:
 
         ```python
-        >>> from transformers import LayoutLMTokenizer, TFLayoutLMForMaskedLM
+        >>> from transformers import AutoTokenizer, TFLayoutLMForMaskedLM
         >>> import tensorflow as tf
 
-        >>> tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")
+        >>> tokenizer = AutoTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")
         >>> model = TFLayoutLMForMaskedLM.from_pretrained("microsoft/layoutlm-base-uncased")
 
         >>> words = ["Hello", "[MASK]"]
@@ -1181,10 +1182,10 @@ class TFLayoutLMForSequenceClassification(TFLayoutLMPreTrainedModel, TFSequenceC
         Examples:
 
         ```python
-        >>> from transformers import LayoutLMTokenizer, TFLayoutLMForSequenceClassification
+        >>> from transformers import AutoTokenizer, TFLayoutLMForSequenceClassification
         >>> import tensorflow as tf
 
-        >>> tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")
+        >>> tokenizer = AutoTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")
         >>> model = TFLayoutLMForSequenceClassification.from_pretrained("microsoft/layoutlm-base-uncased")
 
         >>> words = ["Hello", "world"]
@@ -1310,9 +1311,9 @@ class TFLayoutLMForTokenClassification(TFLayoutLMPreTrainedModel, TFTokenClassif
 
         ```python
         >>> import tensorflow as tf
-        >>> from transformers import LayoutLMTokenizer, TFLayoutLMForTokenClassification
+        >>> from transformers import AutoTokenizer, TFLayoutLMForTokenClassification
 
-        >>> tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")
+        >>> tokenizer = AutoTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")
         >>> model = TFLayoutLMForTokenClassification.from_pretrained("microsoft/layoutlm-base-uncased")
 
         >>> words = ["Hello", "world"]
@@ -1377,3 +1378,150 @@ class TFLayoutLMForTokenClassification(TFLayoutLMPreTrainedModel, TFTokenClassif
         attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
 
         return TFTokenClassifierOutput(logits=output.logits, hidden_states=hs, attentions=attns)
+
+
+@add_start_docstrings(
+    """
+    LayoutLM Model with a span classification head on top for extractive question-answering tasks such as
+    [DocVQA](https://rrc.cvc.uab.es/?ch=17) (a linear layer on top of the final hidden-states output to compute `span
+    start logits` and `span end logits`).
+    """,
+    LAYOUTLM_START_DOCSTRING,
+)
+class TFLayoutLMForQuestionAnswering(TFLayoutLMPreTrainedModel, TFQuestionAnsweringLoss):
+    # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
+    _keys_to_ignore_on_load_unexpected = [
+        r"pooler",
+        r"mlm___cls",
+        r"nsp___cls",
+        r"cls.predictions",
+        r"cls.seq_relationship",
+    ]
+
+    def __init__(self, config: LayoutLMConfig, *inputs, **kwargs):
+        super().__init__(config, *inputs, **kwargs)
+        self.num_labels = config.num_labels
+
+        self.layoutlm = TFLayoutLMMainLayer(config, add_pooling_layer=True, name="layoutlm")
+        self.qa_outputs = tf.keras.layers.Dense(
+            units=config.num_labels,
+            kernel_initializer=get_initializer(config.initializer_range),
+            name="qa_outputs",
+        )
+
+    @unpack_inputs
+    @add_start_docstrings_to_model_forward(LAYOUTLM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    @replace_return_docstrings(output_type=TFQuestionAnsweringModelOutput, config_class=_CONFIG_FOR_DOC)
+    def call(
+        self,
+        input_ids: Optional[TFModelInputType] = None,
+        bbox: Optional[Union[np.ndarray, tf.Tensor]] = None,
+        attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
+        token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
+        position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
+        head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
+        inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        start_positions: Optional[Union[np.ndarray, tf.Tensor]] = None,
+        end_positions: Optional[Union[np.ndarray, tf.Tensor]] = None,
+        training: Optional[bool] = False,
+    ) -> Union[TFQuestionAnsweringModelOutput, Tuple[tf.Tensor]]:
+        r"""
+        start_positions (`tf.Tensor` or `np.ndarray` of shape `(batch_size,)`, *optional*):
+            Labels for position (index) of the start of the labelled span for computing the token classification loss.
+            Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
+            are not taken into account for computing the loss.
+        end_positions (`tf.Tensor` or `np.ndarray` of shape `(batch_size,)`, *optional*):
+            Labels for position (index) of the end of the labelled span for computing the token classification loss.
+            Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
+            are not taken into account for computing the loss.
+
+        Returns:
+
+        Examples:
+
+        ```python
+        >>> import tensorflow as tf
+        >>> from transformers import AutoTokenizer, TFLayoutLMForQuestionAnswering
+        >>> from datasets import load_dataset
+
+        >>> tokenizer = AutoTokenizer.from_pretrained("impira/layoutlm-document-qa", add_prefix_space=True)
+        >>> model = TFLayoutLMForQuestionAnswering.from_pretrained("impira/layoutlm-document-qa")
+
+        >>> dataset = load_dataset("nielsr/funsd", split="train")
+        >>> example = dataset[0]
+        >>> question = "what's his name?"
+        >>> words = example["words"]
+        >>> boxes = example["bboxes"]
+
+        >>> encoding = tokenizer(
+        ...     question.split(), words, is_split_into_words=True, return_token_type_ids=True, return_tensors="tf"
+        ... )
+        >>> bbox = []
+        >>> for i, s, w in zip(encoding.input_ids[0], encoding.sequence_ids(0), encoding.word_ids(0)):
+        ...     if s == 1:
+        ...         bbox.append(boxes[w])
+        ...     elif i == tokenizer.sep_token_id:
+        ...         bbox.append([1000] * 4)
+        ...     else:
+        ...         bbox.append([0] * 4)
+        >>> encoding["bbox"] = tf.convert_to_tensor([bbox])
+
+        >>> word_ids = encoding.word_ids(0)
+        >>> outputs = model(**encoding)
+        >>> loss = outputs.loss
+        >>> start_scores = outputs.start_logits
+        >>> end_scores = outputs.end_logits
+        >>> start, end = word_ids[tf.math.argmax(start_scores, -1)[0]], word_ids[tf.math.argmax(end_scores, -1)[0]]
+        >>> print(" ".join(words[start : end + 1]))
+        M. Hamann P. Harper, P. Martinez
+        ```"""
+
+        outputs = self.layoutlm(
+            input_ids=input_ids,
+            bbox=bbox,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            training=training,
+        )
+
+        sequence_output = outputs[0]
+
+        logits = self.qa_outputs(inputs=sequence_output)
+        start_logits, end_logits = tf.split(value=logits, num_or_size_splits=2, axis=-1)
+        start_logits = tf.squeeze(input=start_logits, axis=-1)
+        end_logits = tf.squeeze(input=end_logits, axis=-1)
+        loss = None
+
+        if start_positions is not None and end_positions is not None:
+            labels = {"start_position": start_positions}
+            labels["end_position"] = end_positions
+            loss = self.hf_compute_loss(labels=labels, logits=(start_logits, end_logits))
+
+        if not return_dict:
+            output = (start_logits, end_logits) + outputs[2:]
+            return ((loss,) + output) if loss is not None else output
+
+        return TFQuestionAnsweringModelOutput(
+            loss=loss,
+            start_logits=start_logits,
+            end_logits=end_logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+    def serving_output(self, output: TFTokenClassifierOutput) -> TFTokenClassifierOutput:
+        hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
+        attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
+
+        return TFQuestionAnsweringModelOutput(
+            start_logits=output.start_logits, end_logits=output.end_logits, hidden_states=hs, attentions=attns
+        )

src/transformers/utils/dummy_pt_objects.py

@@ -2469,6 +2469,13 @@ class LayoutLMForMaskedLM(metaclass=DummyObject):
         requires_backends(self, ["torch"])
 
 
+class LayoutLMForQuestionAnswering(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
 class LayoutLMForSequenceClassification(metaclass=DummyObject):
     _backends = ["torch"]
 

src/transformers/utils/dummy_tf_objects.py

@@ -129,6 +129,13 @@ class TFLayoutLMForMaskedLM(metaclass=DummyObject):
         requires_backends(self, ["tf"])
 
 
+class TFLayoutLMForQuestionAnswering(metaclass=DummyObject):
+    _backends = ["tf"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["tf"])
+
+
 class TFLayoutLMForSequenceClassification(metaclass=DummyObject):
     _backends = ["tf"]
 

src/transformers/utils/fx.py

@@ -147,6 +147,7 @@ _SPECIAL_SUPPORTED_MODELS = [
     "GPT2DoubleHeadsModel",
     "Speech2Text2Decoder",
     "TrOCRDecoder",
+    "LayoutLMForQuestionAnswering",
     # TODO: add support for them as it should be quite easy to do so (small blocking issues).
     # XLNetForQuestionAnswering,
 ]
@@ -690,6 +691,7 @@ class HFTracer(Tracer):
                 inputs_dict["labels"] = torch.zeros(batch_size, dtype=torch.long, device=device)
             elif model_class_name in [
                 *get_values(MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES),
+                "LayoutLMForQuestionAnswering",
                 "XLNetForQuestionAnswering",
             ]:
                 inputs_dict["start_positions"] = torch.zeros(batch_size, dtype=torch.long, device=device)

tests/models/layoutlm/test_modeling_layoutlm.py

@@ -13,10 +13,11 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-
+import copy
 import unittest
 
 from transformers import LayoutLMConfig, is_torch_available
+from transformers.models.auto import get_values
 from transformers.testing_utils import require_torch, slow, torch_device
 
 from ...test_configuration_common import ConfigTester
@@ -27,7 +28,11 @@ if is_torch_available():
     import torch
 
     from transformers import (
+        MODEL_FOR_MASKED_LM_MAPPING,
+        MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
+        MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
         LayoutLMForMaskedLM,
+        LayoutLMForQuestionAnswering,
         LayoutLMForSequenceClassification,
         LayoutLMForTokenClassification,
         LayoutLMModel,
@@ -181,6 +186,23 @@ class LayoutLMModelTester:
         result = model(input_ids, bbox, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
         self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
 
+    def create_and_check_for_question_answering(
+        self, config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
+    ):
+        model = LayoutLMForQuestionAnswering(config=config)
+        model.to(torch_device)
+        model.eval()
+        result = model(
+            input_ids,
+            bbox=bbox,
+            attention_mask=input_mask,
+            token_type_ids=token_type_ids,
+            start_positions=sequence_labels,
+            end_positions=sequence_labels,
+        )
+        self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
+        self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))
+
     def prepare_config_and_inputs_for_common(self):
         config_and_inputs = self.prepare_config_and_inputs()
         (
@@ -211,6 +233,7 @@ class LayoutLMModelTest(ModelTesterMixin, unittest.TestCase):
             LayoutLMForMaskedLM,
             LayoutLMForSequenceClassification,
             LayoutLMForTokenClassification,
+            LayoutLMForQuestionAnswering,
         )
         if is_torch_available()
         else None
@@ -246,6 +269,34 @@ class LayoutLMModelTest(ModelTesterMixin, unittest.TestCase):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*config_and_inputs)
 
+    def test_for_question_answering(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_for_question_answering(*config_and_inputs)
+
+    def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
+        inputs_dict = copy.deepcopy(inputs_dict)
+        if return_labels:
+            if model_class in get_values(MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING):
+                inputs_dict["labels"] = torch.zeros(
+                    self.model_tester.batch_size, dtype=torch.long, device=torch_device
+                )
+            elif model_class in [
+                *get_values(MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING),
+                *get_values(MODEL_FOR_MASKED_LM_MAPPING),
+            ]:
+                inputs_dict["labels"] = torch.zeros(
+                    (self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device
+                )
+            elif model_class.__name__ == "LayoutLMForQuestionAnswering":
+                inputs_dict["start_positions"] = torch.zeros(
+                    self.model_tester.batch_size, dtype=torch.long, device=torch_device
+                )
+                inputs_dict["end_positions"] = torch.zeros(
+                    self.model_tester.batch_size, dtype=torch.long, device=torch_device
+                )
+
+        return inputs_dict
+
 
 def prepare_layoutlm_batch_inputs():
     # Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
@@ -337,3 +388,18 @@ class LayoutLMModelIntegrationTest(unittest.TestCase):
         logits = outputs.logits
         expected_shape = torch.Size((2, 25, 13))
         self.assertEqual(logits.shape, expected_shape)
+
+    @slow
+    def test_forward_pass_question_answering(self):
+        # initialize model with randomly initialized token classification head
+        model = LayoutLMForQuestionAnswering.from_pretrained("microsoft/layoutlm-base-uncased").to(torch_device)
+
+        input_ids, attention_mask, bbox, token_type_ids, labels = prepare_layoutlm_batch_inputs()
+
+        # forward pass
+        outputs = model(input_ids=input_ids, bbox=bbox, attention_mask=attention_mask, token_type_ids=token_type_ids)
+
+        # test the shape of the logits
+        expected_shape = torch.Size((2, 25))
+        self.assertEqual(outputs.start_logits.shape, expected_shape)
+        self.assertEqual(outputs.end_logits.shape, expected_shape)

tests/models/layoutlm/test_modeling_tf_layoutlm.py

@@ -13,11 +13,13 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import copy
 import unittest
 
 import numpy as np
 
 from transformers import LayoutLMConfig, is_tf_available
+from transformers.models.auto import get_values
 from transformers.testing_utils import require_tf, slow
 
 from ...test_configuration_common import ConfigTester
@@ -27,9 +29,15 @@ from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_at
 if is_tf_available():
     import tensorflow as tf
 
+    from transformers import (
+        TF_MODEL_FOR_MASKED_LM_MAPPING,
+        TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
+        TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
+    )
     from transformers.models.layoutlm.modeling_tf_layoutlm import (
         TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
         TFLayoutLMForMaskedLM,
+        TFLayoutLMForQuestionAnswering,
         TFLayoutLMForSequenceClassification,
         TFLayoutLMForTokenClassification,
         TFLayoutLMModel,
@@ -174,6 +182,15 @@ class TFLayoutLMModelTester:
         result = model(input_ids, bbox, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
         self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
 
+    def create_and_check_for_question_answering(
+        self, config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
+    ):
+        model = TFLayoutLMForQuestionAnswering(config=config)
+
+        result = model(input_ids, bbox, attention_mask=input_mask, token_type_ids=token_type_ids)
+        self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
+        self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))
+
     def prepare_config_and_inputs_for_common(self):
         config_and_inputs = self.prepare_config_and_inputs()
         (
@@ -199,7 +216,13 @@ class TFLayoutLMModelTester:
 class TFLayoutLMModelTest(TFModelTesterMixin, unittest.TestCase):
 
     all_model_classes = (
-        (TFLayoutLMModel, TFLayoutLMForMaskedLM, TFLayoutLMForTokenClassification, TFLayoutLMForSequenceClassification)
+        (
+            TFLayoutLMModel,
+            TFLayoutLMForMaskedLM,
+            TFLayoutLMForTokenClassification,
+            TFLayoutLMForSequenceClassification,
+            TFLayoutLMForQuestionAnswering,
+        )
         if is_tf_available()
         else ()
     )
@@ -230,12 +253,34 @@ class TFLayoutLMModelTest(TFModelTesterMixin, unittest.TestCase):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*config_and_inputs)
 
+    def test_for_question_answering(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_for_question_answering(*config_and_inputs)
+
     @slow
     def test_model_from_pretrained(self):
         for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             model = TFLayoutLMModel.from_pretrained(model_name)
             self.assertIsNotNone(model)
 
+    def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
+        inputs_dict = copy.deepcopy(inputs_dict)
+        if return_labels:
+            if model_class in get_values(TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING):
+                inputs_dict["labels"] = tf.zeros(self.model_tester.batch_size, dtype=tf.int32)
+            elif model_class in [
+                *get_values(TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING),
+                *get_values(TF_MODEL_FOR_MASKED_LM_MAPPING),
+            ]:
+                inputs_dict["labels"] = tf.zeros(
+                    (self.model_tester.batch_size, self.model_tester.seq_length), dtype=tf.int32
+                )
+            elif model_class.__name__ == "TFLayoutLMForQuestionAnswering":
+                inputs_dict["start_positions"] = tf.zeros(self.model_tester.batch_size, dtype=tf.int32)
+                inputs_dict["end_positions"] = tf.zeros(self.model_tester.batch_size, dtype=tf.int32)
+
+        return inputs_dict
+
 
 def prepare_layoutlm_batch_inputs():
     # Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
@@ -316,3 +361,18 @@ class TFLayoutLMModelIntegrationTest(unittest.TestCase):
         logits = outputs.logits
         expected_shape = tf.convert_to_tensor((2, 25, 13))
         self.assertEqual(logits.shape, expected_shape)
+
+    @slow
+    def test_forward_pass_question_answering(self):
+        # initialize model with randomly initialized token classification head
+        model = TFLayoutLMForQuestionAnswering.from_pretrained("microsoft/layoutlm-base-uncased")
+
+        input_ids, attention_mask, bbox, token_type_ids, labels = prepare_layoutlm_batch_inputs()
+
+        # forward pass
+        outputs = model(input_ids=input_ids, bbox=bbox, attention_mask=attention_mask, token_type_ids=token_type_ids)
+
+        # test the shape of the logits
+        expected_shape = tf.convert_to_tensor((2, 25))
+        self.assertEqual(outputs.start_logits.shape, expected_shape)
+        self.assertEqual(outputs.end_logits.shape, expected_shape)

utils/check_repo.py

@@ -161,6 +161,7 @@ IGNORE_NON_AUTO_CONFIGURED = PRIVATE_MODELS.copy() + [
     "FlavaImageModel",
     "FlavaMultimodalModel",
     "GPT2DoubleHeadsModel",
+    "LayoutLMForQuestionAnswering",
     "LukeForMaskedLM",
     "LukeForEntityClassification",
     "LukeForEntityPairClassification",
@@ -178,6 +179,7 @@ IGNORE_NON_AUTO_CONFIGURED = PRIVATE_MODELS.copy() + [
     "RealmReader",
     "TFDPRReader",
     "TFGPT2DoubleHeadsModel",
+    "TFLayoutLMForQuestionAnswering",
     "TFOpenAIGPTDoubleHeadsModel",
     "TFRagModel",
     "TFRagSequenceForGeneration",

utils/documentation_tests.txt

@@ -38,6 +38,8 @@ src/transformers/models/glpn/modeling_glpn.py
 src/transformers/models/gpt2/modeling_gpt2.py
 src/transformers/models/gptj/modeling_gptj.py
 src/transformers/models/hubert/modeling_hubert.py
+src/transformers/models/layoutlm/modeling_layoutlm.py
+src/transformers/models/layoutlm/modeling_tf_layoutlm.py
 src/transformers/models/layoutlmv2/modeling_layoutlmv2.py
 src/transformers/models/layoutlmv3/modeling_layoutlmv3.py
 src/transformers/models/layoutlmv3/modeling_tf_layoutlmv3.py