[`T5`] Add T5ForQuestionAnswering and MT5ForQuestionAnswering (#24481)

* Adding T5ForQuestionAnswering

* Changed weight initialization that results in better initial loss when fine-tuning

* Update to class variables

* Running make fixup

* Running make fix-copies

* Remove model_parallel

* Adding MT5ForQuestionAnswering

* Adding docs

* Fix wrong doc

* Update src/transformers/models/mt5/modeling_mt5.py
Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>

* Update src/transformers/models/t5/modeling_t5.py
Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>

* File formatting

* Undoing change

---------
Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>

docs/source/en/model_doc/mt5.md

@@ -95,6 +95,10 @@ See [`T5TokenizerFast`] for all details.
 
 [[autodoc]] MT5EncoderModel
 
+## MT5ForQuestionAnswering
+
+[[autodoc]] MT5ForQuestionAnswering
+
 ## TFMT5Model
 
 [[autodoc]] TFMT5Model

docs/source/en/model_doc/t5.md

@@ -399,6 +399,11 @@ A list of official Hugging Face and community (indicated by 🌎) resources to h
 [[autodoc]] T5EncoderModel
     - forward
 
+## T5ForQuestionAnswering
+
+[[autodoc]] T5ForQuestionAnswering
+    - forward
+
 ## TFT5Model
 
 [[autodoc]] TFT5Model

docs/source/en/tasks/question_answering.md

@@ -35,7 +35,7 @@ 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 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)
+[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), [MT5](../model_doc/mt5), [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), [T5](../model_doc/t5), [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

@@ -2132,7 +2132,7 @@ else:
         ]
     )
     _import_structure["models.mt5"].extend(
-        ["MT5EncoderModel", "MT5ForConditionalGeneration", "MT5Model", "MT5PreTrainedModel"]
+        ["MT5EncoderModel", "MT5ForConditionalGeneration", "MT5ForQuestionAnswering", "MT5Model", "MT5PreTrainedModel"]
     )
     _import_structure["models.mvp"].extend(
         [
@@ -2573,6 +2573,7 @@ else:
             "T5_PRETRAINED_MODEL_ARCHIVE_LIST",
             "T5EncoderModel",
             "T5ForConditionalGeneration",
+            "T5ForQuestionAnswering",
             "T5Model",
             "T5PreTrainedModel",
             "load_tf_weights_in_t5",
@@ -5701,7 +5702,13 @@ if TYPE_CHECKING:
             MPNetModel,
             MPNetPreTrainedModel,
         )
-        from .models.mt5 import MT5EncoderModel, MT5ForConditionalGeneration, MT5Model, MT5PreTrainedModel
+        from .models.mt5 import (
+            MT5EncoderModel,
+            MT5ForConditionalGeneration,
+            MT5ForQuestionAnswering,
+            MT5Model,
+            MT5PreTrainedModel,
+        )
         from .models.mvp import (
             MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
             MvpForCausalLM,
@@ -6064,6 +6071,7 @@ if TYPE_CHECKING:
             T5_PRETRAINED_MODEL_ARCHIVE_LIST,
             T5EncoderModel,
             T5ForConditionalGeneration,
+            T5ForQuestionAnswering,
             T5Model,
             T5PreTrainedModel,
             load_tf_weights_in_t5,

src/transformers/models/auto/modeling_auto.py

@@ -768,6 +768,7 @@ MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES = OrderedDict(
         ("megatron-bert", "MegatronBertForQuestionAnswering"),
         ("mobilebert", "MobileBertForQuestionAnswering"),
         ("mpnet", "MPNetForQuestionAnswering"),
+        ("mt5", "MT5ForQuestionAnswering"),
         ("mvp", "MvpForQuestionAnswering"),
         ("nezha", "NezhaForQuestionAnswering"),
         ("nystromformer", "NystromformerForQuestionAnswering"),
@@ -781,6 +782,7 @@ MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES = OrderedDict(
         ("roformer", "RoFormerForQuestionAnswering"),
         ("splinter", "SplinterForQuestionAnswering"),
         ("squeezebert", "SqueezeBertForQuestionAnswering"),
+        ("t5", "T5ForQuestionAnswering"),
         ("xlm", "XLMForQuestionAnsweringSimple"),
         ("xlm-roberta", "XLMRobertaForQuestionAnswering"),
         ("xlm-roberta-xl", "XLMRobertaXLForQuestionAnswering"),

src/transformers/models/mt5/__init__.py

@@ -50,6 +50,7 @@ else:
     _import_structure["modeling_mt5"] = [
         "MT5EncoderModel",
         "MT5ForConditionalGeneration",
+        "MT5ForQuestionAnswering",
         "MT5Model",
         "MT5PreTrainedModel",
         "MT5Stack",
@@ -81,7 +82,14 @@ if TYPE_CHECKING:
     except OptionalDependencyNotAvailable:
         pass
     else:
-        from .modeling_mt5 import MT5EncoderModel, MT5ForConditionalGeneration, MT5Model, MT5PreTrainedModel, MT5Stack
+        from .modeling_mt5 import (
+            MT5EncoderModel,
+            MT5ForConditionalGeneration,
+            MT5ForQuestionAnswering,
+            MT5Model,
+            MT5PreTrainedModel,
+            MT5Stack,
+        )
 
     try:
         if not is_tf_available():

src/transformers/models/mt5/modeling_mt5.py

@@ -31,6 +31,7 @@ from ...modeling_outputs import (
     BaseModelOutputWithPastAndCrossAttentions,
     Seq2SeqLMOutput,
     Seq2SeqModelOutput,
+    Seq2SeqQuestionAnsweringModelOutput,
 )
 from ...modeling_utils import PreTrainedModel
 from ...pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer
@@ -772,12 +773,15 @@ class MT5PreTrainedModel(PreTrainedModel):
         factor = self.config.initializer_factor  # Used for testing weights initialization
         if isinstance(module, MT5LayerNorm):
             module.weight.data.fill_(factor * 1.0)
-        elif isinstance(module, (MT5Model, MT5ForConditionalGeneration, MT5EncoderModel)):
+        elif isinstance(module, (MT5Model, MT5ForConditionalGeneration, MT5EncoderModel, MT5ForQuestionAnswering)):
             # Mesh TensorFlow embeddings initialization
             # See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/layers.py#L1624
             module.shared.weight.data.normal_(mean=0.0, std=factor * 1.0)
             if hasattr(module, "lm_head") and not self.config.tie_word_embeddings:
                 module.lm_head.weight.data.normal_(mean=0.0, std=factor * 1.0)
+            if hasattr(module, "qa_outputs"):
+                module.qa_outputs.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_model) ** -0.5))
+                module.qa_outputs.bias.data.zero_()
         elif isinstance(module, MT5DenseActDense):
             # Mesh TensorFlow FF initialization
             # See https://github.com/tensorflow/mesh/blob/master/mesh_tensorflow/transformer/transformer_layers.py#L56
@@ -2015,3 +2019,201 @@ class MT5EncoderModel(MT5PreTrainedModel):
         )
 
         return encoder_outputs
+
+
+@add_start_docstrings(
+    """
+    MT5 Model with a span classification head on top for extractive question-answering tasks like SQuAD (linear layers
+    on top of the hidden-states output to compute `span start logits` and `span end logits`).
+    """,
+    MT5_START_DOCSTRING,
+)
+class MT5ForQuestionAnswering(MT5PreTrainedModel):
+    _keys_to_ignore_on_load_missing = [
+        r"encoder.embed_tokens.weight",
+        r"decoder.embed_tokens.weight",
+        r"lm_head.weight",
+    ]
+    _keys_to_ignore_on_load_unexpected = [
+        r"decoder.block.0.layer.1.EncDecAttention.relative_attention_bias.weight",
+    ]
+    _tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"]
+
+    # Copied from transformers.models.t5.modeling_t5.T5ForQuestionAnswering.__init__ with T5->MT5
+    def __init__(self, config: MT5Config):
+        super().__init__(config)
+        self.model_dim = config.d_model
+
+        self.shared = nn.Embedding(config.vocab_size, config.d_model)
+
+        encoder_config = copy.deepcopy(config)
+        encoder_config.is_decoder = False
+        encoder_config.use_cache = False
+        encoder_config.is_encoder_decoder = False
+        self.encoder = MT5Stack(encoder_config, self.shared)
+
+        decoder_config = copy.deepcopy(config)
+        decoder_config.is_decoder = True
+        decoder_config.is_encoder_decoder = False
+        decoder_config.num_layers = config.num_decoder_layers
+        self.decoder = MT5Stack(decoder_config, self.shared)
+
+        self.num_labels = config.num_labels
+        self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    # Copied from transformers.models.t5.modeling_t5.T5ForQuestionAnswering.get_input_embeddings
+    def get_input_embeddings(self):
+        return self.shared
+
+    # Copied from transformers.models.t5.modeling_t5.T5ForQuestionAnswering.set_input_embeddings
+    def set_input_embeddings(self, new_embeddings):
+        self.shared = new_embeddings
+        self.encoder.set_input_embeddings(new_embeddings)
+        self.decoder.set_input_embeddings(new_embeddings)
+
+    # Copied from transformers.models.t5.modeling_t5.T5ForQuestionAnswering.get_encoder
+    def get_encoder(self):
+        return self.encoder
+
+    # Copied from transformers.models.t5.modeling_t5.T5ForQuestionAnswering.get_decoder
+    def get_decoder(self):
+        return self.decoder
+
+    @add_start_docstrings_to_model_forward(MT5_INPUTS_DOCSTRING)
+    @replace_return_docstrings(output_type=Seq2SeqQuestionAnsweringModelOutput, config_class=_CONFIG_FOR_DOC)
+    # Copied from transformers.models.t5.modeling_t5.T5ForQuestionAnswering.forward
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        decoder_input_ids: Optional[torch.LongTensor] = None,
+        decoder_attention_mask: Optional[torch.BoolTensor] = None,
+        head_mask: Optional[torch.FloatTensor] = None,
+        decoder_head_mask: Optional[torch.FloatTensor] = None,
+        cross_attn_head_mask: Optional[torch.Tensor] = None,
+        encoder_outputs: Optional[Tuple[Tuple[torch.Tensor]]] = None,
+        start_positions: Optional[torch.LongTensor] = None,
+        end_positions: Optional[torch.LongTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple[torch.FloatTensor], Seq2SeqQuestionAnsweringModelOutput]:
+        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:
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+        if start_positions is not None and end_positions is not None:
+            use_cache = False
+
+        # Copied from models.bart.modeling_bart.BartModel.forward
+        #   different to other models, T5 automatically creates decoder_input_ids from
+        #   input_ids if no decoder_input_ids are provided
+        if decoder_input_ids is None and decoder_inputs_embeds is None:
+            if input_ids is None:
+                raise ValueError(
+                    "If no `decoder_input_ids` or `decoder_inputs_embeds` are "
+                    "passed, `input_ids` cannot be `None`. Please pass either "
+                    "`input_ids` or `decoder_input_ids` or `decoder_inputs_embeds`."
+                )
+            decoder_input_ids = self._shift_right(input_ids)
+
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        # FutureWarning: head_mask was separated into two input args - head_mask, decoder_head_mask
+        if head_mask is not None and decoder_head_mask is None:
+            if self.config.num_layers == self.config.num_decoder_layers:
+                warnings.warn(__HEAD_MASK_WARNING_MSG, FutureWarning)
+                decoder_head_mask = head_mask
+
+        # Encode if needed (training, first prediction pass)
+        if encoder_outputs is None:
+            encoder_outputs = self.encoder(
+                input_ids=input_ids,
+                attention_mask=attention_mask,
+                inputs_embeds=inputs_embeds,
+                head_mask=head_mask,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict,
+            )
+        elif return_dict and not isinstance(encoder_outputs, BaseModelOutput):
+            encoder_outputs = BaseModelOutput(
+                last_hidden_state=encoder_outputs[0],
+                hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
+                attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
+            )
+
+        hidden_states = encoder_outputs[0]
+
+        # Decode
+        decoder_outputs = self.decoder(
+            input_ids=decoder_input_ids,
+            attention_mask=decoder_attention_mask,
+            inputs_embeds=decoder_inputs_embeds,
+            past_key_values=None,
+            encoder_hidden_states=hidden_states,
+            encoder_attention_mask=attention_mask,
+            head_mask=decoder_head_mask,
+            cross_attn_head_mask=cross_attn_head_mask,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        sequence_output = decoder_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) + decoder_outputs[1:] + encoder_outputs
+            return ((total_loss,) + output) if total_loss is not None else output
+
+        return Seq2SeqQuestionAnsweringModelOutput(
+            loss=total_loss,
+            start_logits=start_logits,
+            end_logits=end_logits,
+            past_key_values=decoder_outputs.past_key_values,
+            decoder_hidden_states=decoder_outputs.hidden_states,
+            decoder_attentions=decoder_outputs.attentions,
+            cross_attentions=decoder_outputs.cross_attentions,
+            encoder_last_hidden_state=encoder_outputs.last_hidden_state,
+            encoder_hidden_states=encoder_outputs.hidden_states,
+            encoder_attentions=encoder_outputs.attentions,
+        )

src/transformers/models/t5/__init__.py

@@ -56,6 +56,7 @@ else:
         "T5Model",
         "T5PreTrainedModel",
         "load_tf_weights_in_t5",
+        "T5ForQuestionAnswering",
     ]
 
 try:
@@ -115,6 +116,7 @@ if TYPE_CHECKING:
             T5_PRETRAINED_MODEL_ARCHIVE_LIST,
             T5EncoderModel,
             T5ForConditionalGeneration,
+            T5ForQuestionAnswering,
             T5Model,
             T5PreTrainedModel,
             load_tf_weights_in_t5,

src/transformers/models/t5/modeling_t5.py

@@ -32,6 +32,7 @@ from ...modeling_outputs import (
     BaseModelOutputWithPastAndCrossAttentions,
     Seq2SeqLMOutput,
     Seq2SeqModelOutput,
+    Seq2SeqQuestionAnsweringModelOutput,
 )
 from ...modeling_utils import PreTrainedModel
 from ...pytorch_utils import ALL_LAYERNORM_LAYERS, find_pruneable_heads_and_indices, prune_linear_layer
@@ -801,12 +802,15 @@ class T5PreTrainedModel(PreTrainedModel):
         factor = self.config.initializer_factor  # Used for testing weights initialization
         if isinstance(module, T5LayerNorm):
             module.weight.data.fill_(factor * 1.0)
-        elif isinstance(module, (T5Model, T5ForConditionalGeneration, T5EncoderModel)):
+        elif isinstance(module, (T5Model, T5ForConditionalGeneration, T5EncoderModel, T5ForQuestionAnswering)):
             # Mesh TensorFlow embeddings initialization
             # See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/layers.py#L1624
             module.shared.weight.data.normal_(mean=0.0, std=factor * 1.0)
             if hasattr(module, "lm_head") and not self.config.tie_word_embeddings:
                 module.lm_head.weight.data.normal_(mean=0.0, std=factor * 1.0)
+            if hasattr(module, "qa_outputs"):
+                module.qa_outputs.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_model) ** -0.5))
+                module.qa_outputs.bias.data.zero_()
         elif isinstance(module, T5DenseActDense):
             # Mesh TensorFlow FF initialization
             # See https://github.com/tensorflow/mesh/blob/master/mesh_tensorflow/transformer/transformer_layers.py#L56
@@ -1949,3 +1953,195 @@ class T5EncoderModel(T5PreTrainedModel):
         )
 
         return encoder_outputs
+
+
+@add_start_docstrings(
+    """
+    T5 Model with a span classification head on top for extractive question-answering tasks like SQuAD (linear layers
+    on top of the hidden-states output to compute `span start logits` and `span end logits`).
+    """,
+    T5_START_DOCSTRING,
+)
+class T5ForQuestionAnswering(T5PreTrainedModel):
+    _keys_to_ignore_on_load_missing = [
+        r"encoder.embed_tokens.weight",
+        r"decoder.embed_tokens.weight",
+        r"lm_head.weight",
+    ]
+    _keys_to_ignore_on_load_unexpected = [
+        r"decoder.block.0.layer.1.EncDecAttention.relative_attention_bias.weight",
+    ]
+    _tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"]
+
+    def __init__(self, config: T5Config):
+        super().__init__(config)
+        self.model_dim = config.d_model
+
+        self.shared = nn.Embedding(config.vocab_size, config.d_model)
+
+        encoder_config = copy.deepcopy(config)
+        encoder_config.is_decoder = False
+        encoder_config.use_cache = False
+        encoder_config.is_encoder_decoder = False
+        self.encoder = T5Stack(encoder_config, self.shared)
+
+        decoder_config = copy.deepcopy(config)
+        decoder_config.is_decoder = True
+        decoder_config.is_encoder_decoder = False
+        decoder_config.num_layers = config.num_decoder_layers
+        self.decoder = T5Stack(decoder_config, self.shared)
+
+        self.num_labels = config.num_labels
+        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.shared
+
+    def set_input_embeddings(self, new_embeddings):
+        self.shared = new_embeddings
+        self.encoder.set_input_embeddings(new_embeddings)
+        self.decoder.set_input_embeddings(new_embeddings)
+
+    def get_encoder(self):
+        return self.encoder
+
+    def get_decoder(self):
+        return self.decoder
+
+    @add_start_docstrings_to_model_forward(T5_INPUTS_DOCSTRING)
+    @replace_return_docstrings(output_type=Seq2SeqQuestionAnsweringModelOutput, config_class=_CONFIG_FOR_DOC)
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        decoder_input_ids: Optional[torch.LongTensor] = None,
+        decoder_attention_mask: Optional[torch.BoolTensor] = None,
+        head_mask: Optional[torch.FloatTensor] = None,
+        decoder_head_mask: Optional[torch.FloatTensor] = None,
+        cross_attn_head_mask: Optional[torch.Tensor] = None,
+        encoder_outputs: Optional[Tuple[Tuple[torch.Tensor]]] = None,
+        start_positions: Optional[torch.LongTensor] = None,
+        end_positions: Optional[torch.LongTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple[torch.FloatTensor], Seq2SeqQuestionAnsweringModelOutput]:
+        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:
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+        if start_positions is not None and end_positions is not None:
+            use_cache = False
+
+        # Copied from models.bart.modeling_bart.BartModel.forward
+        #   different to other models, T5 automatically creates decoder_input_ids from
+        #   input_ids if no decoder_input_ids are provided
+        if decoder_input_ids is None and decoder_inputs_embeds is None:
+            if input_ids is None:
+                raise ValueError(
+                    "If no `decoder_input_ids` or `decoder_inputs_embeds` are "
+                    "passed, `input_ids` cannot be `None`. Please pass either "
+                    "`input_ids` or `decoder_input_ids` or `decoder_inputs_embeds`."
+                )
+            decoder_input_ids = self._shift_right(input_ids)
+
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        # FutureWarning: head_mask was separated into two input args - head_mask, decoder_head_mask
+        if head_mask is not None and decoder_head_mask is None:
+            if self.config.num_layers == self.config.num_decoder_layers:
+                warnings.warn(__HEAD_MASK_WARNING_MSG, FutureWarning)
+                decoder_head_mask = head_mask
+
+        # Encode if needed (training, first prediction pass)
+        if encoder_outputs is None:
+            encoder_outputs = self.encoder(
+                input_ids=input_ids,
+                attention_mask=attention_mask,
+                inputs_embeds=inputs_embeds,
+                head_mask=head_mask,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict,
+            )
+        elif return_dict and not isinstance(encoder_outputs, BaseModelOutput):
+            encoder_outputs = BaseModelOutput(
+                last_hidden_state=encoder_outputs[0],
+                hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
+                attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
+            )
+
+        hidden_states = encoder_outputs[0]
+
+        # Decode
+        decoder_outputs = self.decoder(
+            input_ids=decoder_input_ids,
+            attention_mask=decoder_attention_mask,
+            inputs_embeds=decoder_inputs_embeds,
+            past_key_values=None,
+            encoder_hidden_states=hidden_states,
+            encoder_attention_mask=attention_mask,
+            head_mask=decoder_head_mask,
+            cross_attn_head_mask=cross_attn_head_mask,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        sequence_output = decoder_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) + decoder_outputs[1:] + encoder_outputs
+            return ((total_loss,) + output) if total_loss is not None else output
+
+        return Seq2SeqQuestionAnsweringModelOutput(
+            loss=total_loss,
+            start_logits=start_logits,
+            end_logits=end_logits,
+            past_key_values=decoder_outputs.past_key_values,
+            decoder_hidden_states=decoder_outputs.hidden_states,
+            decoder_attentions=decoder_outputs.attentions,
+            cross_attentions=decoder_outputs.cross_attentions,
+            encoder_last_hidden_state=encoder_outputs.last_hidden_state,
+            encoder_hidden_states=encoder_outputs.hidden_states,
+            encoder_attentions=encoder_outputs.attentions,
+        )

src/transformers/utils/dummy_pt_objects.py

@@ -4915,6 +4915,13 @@ class MT5ForConditionalGeneration(metaclass=DummyObject):
         requires_backends(self, ["torch"])
 
 
+class MT5ForQuestionAnswering(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
 class MT5Model(metaclass=DummyObject):
     _backends = ["torch"]
 
@@ -6742,6 +6749,13 @@ class T5ForConditionalGeneration(metaclass=DummyObject):
         requires_backends(self, ["torch"])
 
 
+class T5ForQuestionAnswering(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
 class T5Model(metaclass=DummyObject):
     _backends = ["torch"]
 

tests/models/t5/test_modeling_t5.py

@@ -43,6 +43,7 @@ if is_torch_available():
         ByT5Tokenizer,
         T5EncoderModel,
         T5ForConditionalGeneration,
+        T5ForQuestionAnswering,
         T5Model,
         T5Tokenizer,
     )
@@ -520,7 +521,7 @@ class T5ModelTester:
 
 @require_torch
 class T5ModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
-    all_model_classes = (T5Model, T5ForConditionalGeneration) if is_torch_available() else ()
+    all_model_classes = (T5Model, T5ForConditionalGeneration, T5ForQuestionAnswering) if is_torch_available() else ()
     all_generative_model_classes = (T5ForConditionalGeneration,) if is_torch_available() else ()
     pipeline_model_mapping = (
         {
@@ -529,6 +530,7 @@ class T5ModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin,
             "summarization": T5ForConditionalGeneration,
             "text2text-generation": T5ForConditionalGeneration,
             "translation": T5ForConditionalGeneration,
+            "question-answering": T5ForQuestionAnswering,
         }
         if is_torch_available()
         else {}