Add GPT2ForSequenceClassification based on DialogRPT (#7501)

* Add GPT2ForSequenceClassification based on DialogRPT

* Better documentation

* Code quality

docs/source/model_doc/gpt2.rst

@@ -88,6 +88,13 @@ GPT2DoubleHeadsModel
     :members: forward
 
 
+GPT2ForSequenceClassification
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.GPT2ForSequenceClassification
+    :members: forward
+
+
 TFGPT2Model
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 

src/transformers/__init__.py

@@ -375,6 +375,7 @@ if is_torch_available():
     from .modeling_gpt2 import (
         GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
         GPT2DoubleHeadsModel,
+        GPT2ForSequenceClassification,
         GPT2LMHeadModel,
         GPT2Model,
         GPT2PreTrainedModel,

src/transformers/modeling_gpt2.py

@@ -22,7 +22,7 @@ from typing import List, Optional, Tuple
 
 import torch
 import torch.nn as nn
-from torch.nn import CrossEntropyLoss
+from torch.nn import CrossEntropyLoss, MSELoss
 
 from .activations import ACT2FN
 from .configuration_gpt2 import GPT2Config
@@ -33,7 +33,7 @@ from .file_utils import (
     add_start_docstrings_to_callable,
     replace_return_docstrings,
 )
-from .modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
+from .modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast
 from .modeling_utils import (
     Conv1D,
     PreTrainedModel,
@@ -946,3 +946,121 @@ class GPT2DoubleHeadsModel(GPT2PreTrainedModel):
             hidden_states=transformer_outputs.hidden_states,
             attentions=transformer_outputs.attentions,
         )
+
+
+@add_start_docstrings(
+    """The GPT2 Model transformer with a sequence classification head on top
+    (linear layer).
+
+    :class:`~transformers.GPT2ForSequenceClassification` uses the last token in order to do the classification, as
+    other causal models (e.g. GPT-1) do.
+
+    Since it does classification on the last token, it requires to know the position of the last token.
+    If a :obj:`pad_token_id` is defined in the configuration, it finds the last token that is not a padding token
+    in each row. If no :obj:`pad_token_id` is defined, it simply takes the last value in each row of the batch.
+    Since it cannot guess the padding tokens when :obj:`inputs_embeds` are passed instead of :obj:`input_ids`, it
+    does the same (take the last value in each row of the batch).
+    """,
+    GPT2_START_DOCSTRING,
+)
+class GPT2ForSequenceClassification(GPT2PreTrainedModel):
+    authorized_missing_keys = [r"h\.\d+\.attn\.masked_bias", r"lm_head\.weight"]
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.transformer = GPT2Model(config)
+        self.score = nn.Linear(config.n_embd, self.num_labels, bias=False)
+
+        self.init_weights()
+
+    @add_start_docstrings_to_callable(GPT2_INPUTS_DOCSTRING)
+    @add_code_sample_docstrings(
+        tokenizer_class=_TOKENIZER_FOR_DOC,
+        checkpoint="microsoft/dialogrpt",
+        output_type=SequenceClassifierOutputWithPast,
+        config_class=_CONFIG_FOR_DOC,
+    )
+    def forward(
+        self,
+        input_ids=None,
+        past_key_values=None,
+        attention_mask=None,
+        token_type_ids=None,
+        position_ids=None,
+        head_mask=None,
+        inputs_embeds=None,
+        labels=None,
+        use_cache=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+    ):
+        r"""
+        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
+            Labels for computing the sequence classification/regression loss.
+            Indices should be in :obj:`[0, ..., config.num_labels - 1]`.
+            If :obj:`config.num_labels == 1` a regression loss is computed (Mean-Square loss),
+            If :obj:`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        transformer_outputs = self.transformer(
+            input_ids,
+            past_key_values=past_key_values,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        hidden_states = transformer_outputs[0]
+        logits = self.score(hidden_states)
+
+        if input_ids is not None:
+            batch_size, sequence_length = input_ids.shape[:2]
+        else:
+            batch_size, sequence_length = inputs_embeds.shape[:2]
+
+        assert (
+            self.config.pad_token_id is not None or batch_size == 1
+        ), "Cannot handle batch sizes > 1 if no padding token is defined."
+        if self.config.pad_token_id is None:
+            sequence_lengths = -1
+        else:
+            if input_ids is not None:
+                sequence_lengths = torch.ne(input_ids, self.config.pad_token_id).sum(-1) - 1
+            else:
+                sequence_lengths = -1
+                logger.warning(
+                    f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be "
+                    f"unexpected if using padding tokens in conjuction with `inputs_embeds.`"
+                )
+
+        pooled_logits = logits[range(batch_size), sequence_lengths]
+
+        loss = None
+        if labels is not None:
+            if self.num_labels == 1:
+                #  We are doing regression
+                loss_fct = MSELoss()
+                loss = loss_fct(pooled_logits.view(-1), labels.view(-1))
+            else:
+                loss_fct = CrossEntropyLoss()
+                loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
+
+        if not return_dict:
+            output = (pooled_logits,) + transformer_outputs[1:]
+            return ((loss,) + output) if loss is not None else output
+
+        return SequenceClassifierOutputWithPast(
+            loss=loss,
+            logits=pooled_logits,
+            past_key_values=transformer_outputs.past_key_values,
+            hidden_states=transformer_outputs.hidden_states,
+            attentions=transformer_outputs.attentions,
+        )

src/transformers/modeling_outputs.py

@@ -217,6 +217,42 @@ class CausalLMOutputWithPast(ModelOutput):
     attentions: Optional[Tuple[torch.FloatTensor]] = None
 
 
+@dataclass
+class SequenceClassifierOutputWithPast(ModelOutput):
+    """
+    Base class for outputs of sentence classification models.
+
+    Args:
+        loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`labels` is provided):
+            Classification (or regression if config.num_labels==1) loss.
+        logits (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, config.num_labels)`):
+            Classification (or regression if config.num_labels==1) scores (before SoftMax).
+        past_key_values (:obj:`List[torch.FloatTensor]`, `optional`, returned when ``use_cache=True`` is passed or when ``config.use_cache=True``):
+            List of :obj:`torch.FloatTensor` of length :obj:`config.n_layers`,  with each tensor of shape
+            :obj:`(2, batch_size, num_heads, sequence_length, embed_size_per_head)`).
+
+            Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see
+            ``past_key_values`` input) to speed up sequential decoding.
+        hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
+            Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
+            of shape :obj:`(batch_size, sequence_length, hidden_size)`.
+
+            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
+        attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
+            Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape
+            :obj:`(batch_size, num_heads, sequence_length, sequence_length)`.
+
+            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
+            heads.
+    """
+
+    loss: Optional[torch.FloatTensor] = None
+    logits: torch.FloatTensor = None
+    past_key_values: Optional[List[torch.FloatTensor]] = None
+    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
+    attentions: Optional[Tuple[torch.FloatTensor]] = None
+
+
 @dataclass
 class MaskedLMOutput(ModelOutput):
     """

tests/test_modeling_gpt2.py

@@ -30,6 +30,7 @@ if is_torch_available():
         GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
         GPT2Config,
         GPT2DoubleHeadsModel,
+        GPT2ForSequenceClassification,
         GPT2LMHeadModel,
         GPT2Model,
     )
@@ -87,6 +88,7 @@ class GPT2ModelTester:
         self.scope = None
         self.bos_token_id = vocab_size - 1
         self.eos_token_id = vocab_size - 1
+        self.pad_token_id = vocab_size - 1
 
     def prepare_config_and_inputs(self, gradient_checkpointing=False):
         input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
@@ -126,6 +128,7 @@ class GPT2ModelTester:
             # initializer_range=self.initializer_range,
             bos_token_id=self.bos_token_id,
             eos_token_id=self.eos_token_id,
+            pad_token_id=self.pad_token_id,
             return_dict=True,
             gradient_checkpointing=gradient_checkpointing,
         )
@@ -337,6 +340,17 @@ class GPT2ModelTester:
         )
         self.parent.assertEqual(result.mc_logits.shape, (self.batch_size, self.num_choices))
 
+    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
+    ):
+        config.num_labels = self.num_labels
+        model = GPT2ForSequenceClassification(config)
+        model.to(torch_device)
+        model.eval()
+        print(config.num_labels, sequence_labels.size())
+        result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels)
+        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
+
     def prepare_config_and_inputs_for_common(self):
         config_and_inputs = self.prepare_config_and_inputs()
 
@@ -364,10 +378,12 @@ class GPT2ModelTester:
 @require_torch
 class GPT2ModelTest(ModelTesterMixin, unittest.TestCase):
 
-    all_model_classes = (GPT2Model, GPT2LMHeadModel, GPT2DoubleHeadsModel) if is_torch_available() else ()
-    all_generative_model_classes = (
-        (GPT2LMHeadModel, GPT2DoubleHeadsModel) if is_torch_available() else ()
-    )  # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
+    all_model_classes = (
+        (GPT2Model, GPT2LMHeadModel, GPT2DoubleHeadsModel, GPT2ForSequenceClassification)
+        if is_torch_available()
+        else ()
+    )
+    all_generative_model_classes = (GPT2LMHeadModel, GPT2DoubleHeadsModel) if is_torch_available() else ()
     test_missing_keys = False
 
     def setUp(self):
@@ -401,6 +417,10 @@ class GPT2ModelTest(ModelTesterMixin, unittest.TestCase):
         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_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)
+
     def test_gpt2_gradient_checkpointing(self):
         config_and_inputs = self.model_tester.prepare_config_and_inputs(gradient_checkpointing=True)
         self.model_tester.create_and_check_forward_and_backwards(*config_and_inputs)