GPT2ForQuestionAnswering (#23030)

* first draft - gives index error in question_answering.py

* maturing

* no labels

* pipeline should know about QA

* fixing checks

* formatting

* fixed docstring

* make sure legacy code executes

* comment

* like this

---------
Co-authored-by: Prof. Peter Schneider-Kamp <jps@ordbogen.com>

docs/source/en/model_doc/gpt2.mdx

@@ -111,6 +111,11 @@ A list of official Hugging Face and community (indicated by 🌎) resources to h
 [[autodoc]] GPT2DoubleHeadsModel
     - forward
 
+## GPT2ForQuestionAnswering
+
+[[autodoc]] GPT2ForQuestionAnswering
+    - forward
+
 ## GPT2ForSequenceClassification
 
 [[autodoc]] GPT2ForSequenceClassification

docs/source/en/tasks/question_answering.mdx

@@ -31,7 +31,7 @@ The task illustrated in this tutorial is supported by the following model archit
 
 <!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
 
-[ALBERT](../model_doc/albert), [BART](../model_doc/bart), [BERT](../model_doc/bert), [BigBird](../model_doc/big_bird), [BigBird-Pegasus](../model_doc/bigbird_pegasus), [BLOOM](../model_doc/bloom), [CamemBERT](../model_doc/camembert), [CANINE](../model_doc/canine), [ConvBERT](../model_doc/convbert), [Data2VecText](../model_doc/data2vec-text), [DeBERTa](../model_doc/deberta), [DeBERTa-v2](../model_doc/deberta-v2), [DistilBERT](../model_doc/distilbert), [ELECTRA](../model_doc/electra), [ERNIE](../model_doc/ernie), [ErnieM](../model_doc/ernie_m), [FlauBERT](../model_doc/flaubert), [FNet](../model_doc/fnet), [Funnel Transformer](../model_doc/funnel), [GPT-J](../model_doc/gptj), [I-BERT](../model_doc/ibert), [LayoutLMv2](../model_doc/layoutlmv2), [LayoutLMv3](../model_doc/layoutlmv3), [LED](../model_doc/led), [LiLT](../model_doc/lilt), [Longformer](../model_doc/longformer), [LUKE](../model_doc/luke), [LXMERT](../model_doc/lxmert), [MarkupLM](../model_doc/markuplm), [mBART](../model_doc/mbart), [MEGA](../model_doc/mega), [Megatron-BERT](../model_doc/megatron-bert), [MobileBERT](../model_doc/mobilebert), [MPNet](../model_doc/mpnet), [MVP](../model_doc/mvp), [Nezha](../model_doc/nezha), [Nyströmformer](../model_doc/nystromformer), [OPT](../model_doc/opt), [QDQBert](../model_doc/qdqbert), [Reformer](../model_doc/reformer), [RemBERT](../model_doc/rembert), [RoBERTa](../model_doc/roberta), [RoBERTa-PreLayerNorm](../model_doc/roberta-prelayernorm), [RoCBert](../model_doc/roc_bert), [RoFormer](../model_doc/roformer), [Splinter](../model_doc/splinter), [SqueezeBERT](../model_doc/squeezebert), [XLM](../model_doc/xlm), [XLM-RoBERTa](../model_doc/xlm-roberta), [XLM-RoBERTa-XL](../model_doc/xlm-roberta-xl), [XLNet](../model_doc/xlnet), [X-MOD](../model_doc/xmod), [YOSO](../model_doc/yoso)
+[ALBERT](../model_doc/albert), [BART](../model_doc/bart), [BERT](../model_doc/bert), [BigBird](../model_doc/big_bird), [BigBird-Pegasus](../model_doc/bigbird_pegasus), [BLOOM](../model_doc/bloom), [CamemBERT](../model_doc/camembert), [CANINE](../model_doc/canine), [ConvBERT](../model_doc/convbert), [Data2VecText](../model_doc/data2vec-text), [DeBERTa](../model_doc/deberta), [DeBERTa-v2](../model_doc/deberta-v2), [DistilBERT](../model_doc/distilbert), [ELECTRA](../model_doc/electra), [ERNIE](../model_doc/ernie), [ErnieM](../model_doc/ernie_m), [FlauBERT](../model_doc/flaubert), [FNet](../model_doc/fnet), [Funnel Transformer](../model_doc/funnel), [OpenAI GPT-2](../model_doc/gpt2), [GPT-J](../model_doc/gptj), [I-BERT](../model_doc/ibert), [LayoutLMv2](../model_doc/layoutlmv2), [LayoutLMv3](../model_doc/layoutlmv3), [LED](../model_doc/led), [LiLT](../model_doc/lilt), [Longformer](../model_doc/longformer), [LUKE](../model_doc/luke), [LXMERT](../model_doc/lxmert), [MarkupLM](../model_doc/markuplm), [mBART](../model_doc/mbart), [MEGA](../model_doc/mega), [Megatron-BERT](../model_doc/megatron-bert), [MobileBERT](../model_doc/mobilebert), [MPNet](../model_doc/mpnet), [MVP](../model_doc/mvp), [Nezha](../model_doc/nezha), [Nyströmformer](../model_doc/nystromformer), [OPT](../model_doc/opt), [QDQBert](../model_doc/qdqbert), [Reformer](../model_doc/reformer), [RemBERT](../model_doc/rembert), [RoBERTa](../model_doc/roberta), [RoBERTa-PreLayerNorm](../model_doc/roberta-prelayernorm), [RoCBert](../model_doc/roc_bert), [RoFormer](../model_doc/roformer), [Splinter](../model_doc/splinter), [SqueezeBERT](../model_doc/squeezebert), [XLM](../model_doc/xlm), [XLM-RoBERTa](../model_doc/xlm-roberta), [XLM-RoBERTa-XL](../model_doc/xlm-roberta-xl), [XLNet](../model_doc/xlnet), [X-MOD](../model_doc/xmod), [YOSO](../model_doc/yoso)
 
 
 <!--End of the generated tip-->

src/transformers/__init__.py

@@ -1666,6 +1666,7 @@ else:
         [
             "GPT2_PRETRAINED_MODEL_ARCHIVE_LIST",
             "GPT2DoubleHeadsModel",
+            "GPT2ForQuestionAnswering",
             "GPT2ForSequenceClassification",
             "GPT2ForTokenClassification",
             "GPT2LMHeadModel",
@@ -5212,6 +5213,7 @@ if TYPE_CHECKING:
         from .models.gpt2 import (
             GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
             GPT2DoubleHeadsModel,
+            GPT2ForQuestionAnswering,
             GPT2ForSequenceClassification,
             GPT2ForTokenClassification,
             GPT2LMHeadModel,

src/transformers/models/auto/modeling_auto.py

@@ -735,6 +735,7 @@ MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES = OrderedDict(
         ("flaubert", "FlaubertForQuestionAnsweringSimple"),
         ("fnet", "FNetForQuestionAnswering"),
         ("funnel", "FunnelForQuestionAnswering"),
+        ("gpt2", "GPT2ForQuestionAnswering"),
         ("gptj", "GPTJForQuestionAnswering"),
         ("ibert", "IBertForQuestionAnswering"),
         ("layoutlmv2", "LayoutLMv2ForQuestionAnswering"),

src/transformers/models/gpt2/__init__.py

@@ -48,6 +48,7 @@ else:
     _import_structure["modeling_gpt2"] = [
         "GPT2_PRETRAINED_MODEL_ARCHIVE_LIST",
         "GPT2DoubleHeadsModel",
+        "GPT2ForQuestionAnswering",
         "GPT2ForSequenceClassification",
         "GPT2ForTokenClassification",
         "GPT2LMHeadModel",
@@ -109,6 +110,7 @@ if TYPE_CHECKING:
         from .modeling_gpt2 import (
             GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
             GPT2DoubleHeadsModel,
+            GPT2ForQuestionAnswering,
             GPT2ForSequenceClassification,
             GPT2ForTokenClassification,
             GPT2LMHeadModel,

src/transformers/models/gpt2/modeling_gpt2.py

@@ -31,6 +31,7 @@ from ...activations import ACT2FN
 from ...modeling_outputs import (
     BaseModelOutputWithPastAndCrossAttentions,
     CausalLMOutputWithCrossAttentions,
+    QuestionAnsweringModelOutput,
     SequenceClassifierOutputWithPast,
     TokenClassifierOutput,
 )
@@ -51,6 +52,7 @@ from .configuration_gpt2 import GPT2Config
 logger = logging.get_logger(__name__)
 
 _CHECKPOINT_FOR_DOC = "gpt2"
+_REAL_CHECKPOINT_FOR_DOC = "gpt2"
 _CONFIG_FOR_DOC = "GPT2Config"
 
 GPT2_PRETRAINED_MODEL_ARCHIVE_LIST = [
@@ -1586,3 +1588,109 @@ class GPT2ForTokenClassification(GPT2PreTrainedModel):
             hidden_states=transformer_outputs.hidden_states,
             attentions=transformer_outputs.attentions,
         )
+
+
+@add_start_docstrings(
+    """
+    The GPT-2 Model transformer with a span classification head on top for extractive question-answering tasks like
+    SQuAD (a linear layer on top of the hidden-states output to compute `span start logits` and `span end logits`).
+    """,
+    GPT2_START_DOCSTRING,
+)
+class GPT2ForQuestionAnswering(GPT2PreTrainedModel):
+    _keys_to_ignore_on_load_missing = [r"h\.\d+\.attn\.masked_bias", r"h\.\d+\.attn\.bias", r"lm_head.weight"]
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.transformer = GPT2Model(config)
+        self.qa_outputs = nn.Linear(config.hidden_size, 2)
+
+        # Model parallel
+        self.model_parallel = False
+        self.device_map = None
+        self.gradient_checkpointing = False
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    @add_start_docstrings_to_model_forward(GPT2_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    @add_code_sample_docstrings(
+        checkpoint=_CHECKPOINT_FOR_DOC,
+        output_type=QuestionAnsweringModelOutput,
+        config_class=_CONFIG_FOR_DOC,
+        real_checkpoint=_REAL_CHECKPOINT_FOR_DOC,
+    )
+    def forward(
+        self,
+        input_ids: 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.
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        outputs = self.transformer(
+            input_ids,
+            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/utils/dummy_pt_objects.py

@@ -3186,6 +3186,13 @@ class GPT2DoubleHeadsModel(metaclass=DummyObject):
         requires_backends(self, ["torch"])
 
 
+class GPT2ForQuestionAnswering(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
 class GPT2ForSequenceClassification(metaclass=DummyObject):
     _backends = ["torch"]
 

tests/models/gpt2/test_modeling_gpt2.py

@@ -33,6 +33,7 @@ if is_torch_available():
     from transformers import (
         GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
         GPT2DoubleHeadsModel,
+        GPT2ForQuestionAnswering,
         GPT2ForSequenceClassification,
         GPT2ForTokenClassification,
         GPT2LMHeadModel,
@@ -377,6 +378,17 @@ class GPT2ModelTester:
         )
         self.parent.assertEqual(result.mc_logits.shape, (self.batch_size, self.num_choices))
 
+    def create_and_check_gpt2_for_question_answering(
+        self, config, input_ids, input_mask, head_mask, token_type_ids, mc_token_ids, sequence_labels, *args
+    ):
+        config.num_labels = self.num_labels
+        model = GPT2ForQuestionAnswering(config)
+        model.to(torch_device)
+        model.eval()
+        result = model(input_ids, 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 create_and_check_gpt2_for_sequence_classification(
         self, config, input_ids, input_mask, head_mask, token_type_ids, mc_token_ids, sequence_labels, *args
     ):
@@ -432,7 +444,14 @@ class GPT2ModelTester:
 @require_torch
 class GPT2ModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (
-        (GPT2Model, GPT2LMHeadModel, GPT2DoubleHeadsModel, GPT2ForSequenceClassification, GPT2ForTokenClassification)
+        (
+            GPT2Model,
+            GPT2LMHeadModel,
+            GPT2DoubleHeadsModel,
+            GPT2ForQuestionAnswering,
+            GPT2ForSequenceClassification,
+            GPT2ForTokenClassification,
+        )
         if is_torch_available()
         else ()
     )
@@ -440,6 +459,7 @@ class GPT2ModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin
     pipeline_model_mapping = (
         {
             "feature-extraction": GPT2Model,
+            "question-answering": GPT2ForQuestionAnswering,
             "text-classification": GPT2ForSequenceClassification,
             "text-generation": GPT2LMHeadModel,
             "token-classification": GPT2ForTokenClassification,
@@ -507,6 +527,10 @@ class GPT2ModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_double_lm_head_model(*config_and_inputs)
 
+    def test_gpt2_question_answering_model(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_gpt2_for_question_answering(*config_and_inputs)
+
     def test_gpt2_sequence_classification_model(self):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_gpt2_for_sequence_classification(*config_and_inputs)