Add GPTJForQuestionAnswering (#14503)

* Add GPTJForQuestionAnswering

* Reformat for GPTJForQuestionAnswering

* Fix isort error

* make style for GPTJForQA

* Add _keys_to_ignore_on_load_missing

* Change the sequence of qa and classification

Co-authored-by: Suraj Patil <surajp815@gmail.com>

docs/source/model_doc/gptj.rst

@@ -121,6 +121,13 @@ GPTJForSequenceClassification
     :members: forward
 
 
+GPTJForQuestionAnswering
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.GPTJForQuestionAnswering
+    :members: forward
+
+
 FlaxGPTJModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 

src/transformers/__init__.py

@@ -951,6 +951,7 @@ if is_torch_available():
         [
             "GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST",
             "GPTJForCausalLM",
+            "GPTJForQuestionAnswering",
             "GPTJForSequenceClassification",
             "GPTJModel",
             "GPTJPreTrainedModel",
@@ -2833,6 +2834,7 @@ if TYPE_CHECKING:
         from .models.gptj import (
             GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
             GPTJForCausalLM,
+            GPTJForQuestionAnswering,
             GPTJForSequenceClassification,
             GPTJModel,
             GPTJPreTrainedModel,

src/transformers/models/auto/modeling_auto.py

@@ -385,6 +385,7 @@ MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES = OrderedDict(
         # Model for Question Answering mapping
         ("qdqbert", "QDQBertForQuestionAnswering"),
         ("fnet", "FNetForQuestionAnswering"),
+        ("gptj", "GPTJForQuestionAnswering"),
         ("layoutlmv2", "LayoutLMv2ForQuestionAnswering"),
         ("rembert", "RemBertForQuestionAnswering"),
         ("canine", "CanineForQuestionAnswering"),

src/transformers/models/gptj/__init__.py

@@ -28,6 +28,7 @@ if is_torch_available():
     _import_structure["modeling_gptj"] = [
         "GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST",
         "GPTJForCausalLM",
+        "GPTJForQuestionAnswering",
         "GPTJForSequenceClassification",
         "GPTJModel",
         "GPTJPreTrainedModel",
@@ -48,6 +49,7 @@ if TYPE_CHECKING:
         from .modeling_gptj import (
             GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
             GPTJForCausalLM,
+            GPTJForQuestionAnswering,
             GPTJForSequenceClassification,
             GPTJModel,
             GPTJPreTrainedModel,

src/transformers/models/gptj/modeling_gptj.py

@@ -23,7 +23,12 @@ from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
 
 from ...activations import ACT2FN
 from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
-from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast
+from ...modeling_outputs import (
+    BaseModelOutputWithPast,
+    CausalLMOutputWithPast,
+    QuestionAnsweringModelOutput,
+    SequenceClassifierOutputWithPast,
+)
 from ...modeling_utils import PreTrainedModel
 from ...utils import logging
 from ...utils.model_parallel_utils import assert_device_map, get_device_map
@@ -967,3 +972,108 @@ class GPTJForSequenceClassification(GPTJPreTrainedModel):
             hidden_states=transformer_outputs.hidden_states,
             attentions=transformer_outputs.attentions,
         )
+
+
+@add_start_docstrings(
+    """
+    The GPT-J Model transformer with a span classification head on top for extractive question-answering tasks like
+    SQuAD (a linear layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
+    """,
+    GPTJ_START_DOCSTRING,
+)
+class GPTJForQuestionAnswering(GPTJPreTrainedModel):
+    _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 = GPTJModel(config)
+        self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
+
+        # Model parallel
+        self.model_parallel = False
+        self.device_map = None
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    @add_start_docstrings_to_model_forward(GPTJ_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    @add_code_sample_docstrings(
+        processor_class=_TOKENIZER_FOR_DOC,
+        checkpoint=_CHECKPOINT_FOR_DOC,
+        output_type=QuestionAnsweringModelOutput,
+        config_class=_CONFIG_FOR_DOC,
+    )
+    def forward(
+        self,
+        input_ids=None,
+        attention_mask=None,
+        token_type_ids=None,
+        position_ids=None,
+        head_mask=None,
+        inputs_embeds=None,
+        start_positions=None,
+        end_positions=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+    ):
+        r"""
+        start_positions (:obj:`torch.LongTensor` of shape :obj:`(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 (:obj:`sequence_length`). Position outside of the
+            sequence are not taken into account for computing the loss.
+        end_positions (:obj:`torch.LongTensor` of shape :obj:`(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 (:obj:`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

@@ -2494,6 +2494,18 @@ class GPTJForCausalLM:
         requires_backends(self, ["torch"])
 
 
+class GPTJForQuestionAnswering:
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["torch"])
+
+    def forward(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
 class GPTJForSequenceClassification:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["torch"])

tests/test_modeling_gptj.py

@@ -32,6 +32,7 @@ if is_torch_available():
         GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
         AutoTokenizer,
         GPTJForCausalLM,
+        GPTJForQuestionAnswering,
         GPTJForSequenceClassification,
         GPTJModel,
     )
@@ -356,7 +357,11 @@ class GPTJModelTester:
 @require_torch
 class GPTJModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
 
-    all_model_classes = (GPTJModel, GPTJForCausalLM, GPTJForSequenceClassification) if is_torch_available() else ()
+    all_model_classes = (
+        (GPTJModel, GPTJForCausalLM, GPTJForSequenceClassification, GPTJForQuestionAnswering)
+        if is_torch_available()
+        else ()
+    )
     all_generative_model_classes = (GPTJForCausalLM,) if is_torch_available() else ()
     fx_ready_model_classes = all_model_classes
     test_pruning = False