GPTNeoXForQuestionAnswering (#23059)

* first draft - gives index error in question_answering.py

* maturing

* no labels

* pipeline should know about QA

* fixing checks

* formatting

* fixed docstring

* initial commit

* formatting

* adding the class to many places

* towards less unhappy checks

* nearly there

* and gpt neox for qa

* use right model

* forgot this one

* base_model_prefix is "gpt_neox" for GPTNeoX* models

* unnecessary stuff

* Update src/transformers/models/gpt_neox/modeling_gpt_neox.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* format

* Update src/transformers/models/gpt_neox/modeling_gpt_neox.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* removed gpt2 stuff

---------
Co-authored-by: Prof. Peter Schneider-Kamp <jps@ordbogen.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

docs/source/en/model_doc/gpt_neox.mdx

@@ -79,6 +79,11 @@ The `generate()` method can be used to generate text using GPT Neo model.
 [[autodoc]] GPTNeoXForCausalLM
     - forward
 
+## GPTNeoXForQuestionAnswering
+
+[[autodoc]] GPTNeoXForQuestionAnswering
+    - forward
+
 ## GPTNeoXForSequenceClassification
 
 [[autodoc]] GPTNeoXForSequenceClassification

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), [OpenAI GPT-2](../model_doc/gpt2), [GPT Neo](../model_doc/gpt_neo), [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 Neo](../model_doc/gpt_neo), [GPT NeoX](../model_doc/gpt_neox), [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

@@ -1702,6 +1702,7 @@ else:
         [
             "GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST",
             "GPTNeoXForCausalLM",
+            "GPTNeoXForQuestionAnswering",
             "GPTNeoXForSequenceClassification",
             "GPTNeoXForTokenClassification",
             "GPTNeoXLayer",
@@ -5245,6 +5246,7 @@ if TYPE_CHECKING:
         from .models.gpt_neox import (
             GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
             GPTNeoXForCausalLM,
+            GPTNeoXForQuestionAnswering,
             GPTNeoXForSequenceClassification,
             GPTNeoXForTokenClassification,
             GPTNeoXLayer,

src/transformers/models/auto/modeling_auto.py

@@ -737,6 +737,7 @@ MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES = OrderedDict(
         ("funnel", "FunnelForQuestionAnswering"),
         ("gpt2", "GPT2ForQuestionAnswering"),
         ("gpt_neo", "GPTNeoForQuestionAnswering"),
+        ("gpt_neox", "GPTNeoXForQuestionAnswering"),
         ("gptj", "GPTJForQuestionAnswering"),
         ("ibert", "IBertForQuestionAnswering"),
         ("layoutlmv2", "LayoutLMv2ForQuestionAnswering"),

src/transformers/models/gpt2/modeling_gpt2.py

@@ -52,7 +52,6 @@ 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 = [
@@ -1619,7 +1618,7 @@ class GPT2ForQuestionAnswering(GPT2PreTrainedModel):
         checkpoint=_CHECKPOINT_FOR_DOC,
         output_type=QuestionAnsweringModelOutput,
         config_class=_CONFIG_FOR_DOC,
-        real_checkpoint=_REAL_CHECKPOINT_FOR_DOC,
+        real_checkpoint=_CHECKPOINT_FOR_DOC,
     )
     def forward(
         self,

src/transformers/models/gpt_neox/__init__.py

@@ -36,6 +36,7 @@ else:
     _import_structure["modeling_gpt_neox"] = [
         "GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST",
         "GPTNeoXForCausalLM",
+        "GPTNeoXForQuestionAnswering",
         "GPTNeoXForSequenceClassification",
         "GPTNeoXForTokenClassification",
         "GPTNeoXLayer",
@@ -64,6 +65,7 @@ if TYPE_CHECKING:
         from .modeling_gpt_neox import (
             GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
             GPTNeoXForCausalLM,
+            GPTNeoXForQuestionAnswering,
             GPTNeoXForSequenceClassification,
             GPTNeoXForTokenClassification,
             GPTNeoXLayer,

src/transformers/models/gpt_neox/modeling_gpt_neox.py

@@ -31,6 +31,7 @@ from ...file_utils import (
 from ...modeling_outputs import (
     BaseModelOutputWithPast,
     CausalLMOutputWithPast,
+    QuestionAnsweringModelOutput,
     SequenceClassifierOutputWithPast,
     TokenClassifierOutput,
 )
@@ -955,3 +956,103 @@ class GPTNeoXForTokenClassification(GPTNeoXPreTrainedModel):
             hidden_states=outputs.hidden_states,
             attentions=outputs.attentions,
         )
+
+
+@add_start_docstrings(
+    """
+    The GPT-NeoX 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`).
+    """,
+    GPT_NEOX_START_DOCSTRING,
+)
+class GPTNeoXForQuestionAnswering(GPTNeoXPreTrainedModel):
+    _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.gpt_neox = GPTNeoXModel(config)
+        self.qa_outputs = nn.Linear(config.hidden_size, 2)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    @add_start_docstrings_to_model_forward(GPT_NEOX_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.gpt_neox(
+            input_ids,
+            attention_mask=attention_mask,
+            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).to(start_logits.device)
+            if len(end_positions.size()) > 1:
+                end_positions = end_positions.squeeze(-1).to(end_logits.device)
+            # 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

@@ -3336,6 +3336,13 @@ class GPTNeoXForCausalLM(metaclass=DummyObject):
         requires_backends(self, ["torch"])
 
 
+class GPTNeoXForQuestionAnswering(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
 class GPTNeoXForSequenceClassification(metaclass=DummyObject):
     _backends = ["torch"]
 

tests/models/gpt_neox/test_modeling_gpt_neox.py

@@ -31,6 +31,7 @@ if is_torch_available():
 
     from transformers import (
         GPTNeoXForCausalLM,
+        GPTNeoXForQuestionAnswering,
         GPTNeoXForSequenceClassification,
         GPTNeoXForTokenClassification,
         GPTNeoXModel,
@@ -149,6 +150,15 @@ class GPTNeoXModelTester:
         result = model(input_ids, attention_mask=input_mask, labels=token_labels)
         self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
 
+    def create_and_check_for_question_answering(self, config, input_ids, input_mask, token_labels):
+        config.num_labels = self.num_labels
+        model = GPTNeoXForQuestionAnswering(config)
+        model.to(torch_device)
+        model.eval()
+        result = model(input_ids, attention_mask=input_mask)
+        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_for_sequence_classification(self, config, input_ids, input_mask, token_labels):
         config.num_labels = self.num_labels
         model = GPTNeoXForSequenceClassification(config)
@@ -213,7 +223,13 @@ class GPTNeoXModelTester:
 @require_torch
 class GPTNeoXModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (
-        (GPTNeoXModel, GPTNeoXForCausalLM, GPTNeoXForSequenceClassification, GPTNeoXForTokenClassification)
+        (
+            GPTNeoXModel,
+            GPTNeoXForCausalLM,
+            GPTNeoXForQuestionAnswering,
+            GPTNeoXForSequenceClassification,
+            GPTNeoXForTokenClassification,
+        )
         if is_torch_available()
         else ()
     )
@@ -221,6 +237,7 @@ class GPTNeoXModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMi
     pipeline_model_mapping = (
         {
             "feature-extraction": GPTNeoXModel,
+            "question-answering": GPTNeoXForQuestionAnswering,
             "text-classification": GPTNeoXForSequenceClassification,
             "token-classification": GPTNeoXForTokenClassification,
             "text-generation": GPTNeoXForCausalLM,
@@ -265,6 +282,10 @@ class GPTNeoXModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMi
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_causal_lm(*config_and_inputs)
 
+    def test_model_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 test_model_for_sequence_classification(self):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs)