Gpt1 for sequence classification (#7683)

* Add Documentation for GPT-1 Classification

* Add GPT-1 with Classification head

* Add tests for GPT-1 Classification

* Add GPT-1 For Classification to auto models

* Remove authorized missing keys, change checkpoint to openai-gpt

docs/source/model_doc/gpt.rst

@@ -104,6 +104,13 @@ OpenAIGPTDoubleHeadsModel
     :members: forward
 
 
+OpenAIGPTForSequenceClassification
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.OpenAIGPTForSequenceClassification
+    :members: forward
+
+
 TFOpenAIGPTModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 

src/transformers/__init__.py

@@ -437,6 +437,7 @@ if is_torch_available():
     from .modeling_openai import (
         OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
         OpenAIGPTDoubleHeadsModel,
+        OpenAIGPTForSequenceClassification,
         OpenAIGPTLMHeadModel,
         OpenAIGPTModel,
         OpenAIGPTPreTrainedModel,

src/transformers/modeling_auto.py

@@ -153,7 +153,7 @@ from .modeling_mobilebert import (
     MobileBertForTokenClassification,
     MobileBertModel,
 )
-from .modeling_openai import OpenAIGPTLMHeadModel, OpenAIGPTModel
+from .modeling_openai import OpenAIGPTForSequenceClassification, OpenAIGPTLMHeadModel, OpenAIGPTModel
 from .modeling_pegasus import PegasusForConditionalGeneration
 from .modeling_rag import (  # noqa: F401 - need to import all RagModels to be in globals() function
     RagModel,
@@ -381,6 +381,7 @@ MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING = OrderedDict(
         (FunnelConfig, FunnelForSequenceClassification),
         (DebertaConfig, DebertaForSequenceClassification),
         (GPT2Config, GPT2ForSequenceClassification),
+        (OpenAIGPTConfig, OpenAIGPTForSequenceClassification),
     ]
 )
 

src/transformers/modeling_openai.py

@@ -25,7 +25,7 @@ from typing import Optional, Tuple
 
 import torch
 import torch.nn as nn
-from torch.nn import CrossEntropyLoss
+from torch.nn import CrossEntropyLoss, MSELoss
 
 from .activations import gelu_new, swish
 from .configuration_openai import OpenAIGPTConfig
@@ -36,7 +36,7 @@ from .file_utils import (
     add_start_docstrings_to_callable,
     replace_return_docstrings,
 )
-from .modeling_outputs import BaseModelOutput, CausalLMOutput
+from .modeling_outputs import BaseModelOutput, CausalLMOutput, SequenceClassifierOutput
 from .modeling_utils import (
     Conv1D,
     PreTrainedModel,
@@ -732,3 +732,113 @@ class OpenAIGPTDoubleHeadsModel(OpenAIGPTPreTrainedModel):
             hidden_states=transformer_outputs.hidden_states,
             attentions=transformer_outputs.attentions,
         )
+
+
+@add_start_docstrings(
+    """The Original OpenAI GPT Model transformer with a sequence classification head on top
+    (linear layer).
+    :class:`~transformers.OpenAIGPTForSequenceClassification` uses the last token in order to do the classification, as
+    other causal models (e.g. GPT-2) 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).
+    """,
+    OPENAI_GPT_START_DOCSTRING,
+)
+class OpenAIGPTForSequenceClassification(OpenAIGPTPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.transformer = OpenAIGPTModel(config)
+        self.score = nn.Linear(config.n_embd, self.num_labels, bias=False)
+
+        self.init_weights()
+
+    @add_start_docstrings_to_callable(OPENAI_GPT_INPUTS_DOCSTRING)
+    @add_code_sample_docstrings(
+        tokenizer_class=_TOKENIZER_FOR_DOC,
+        checkpoint="openai-gpt",
+        output_type=SequenceClassifierOutput,
+        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,
+        labels=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,
+            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,
+        )
+
+        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 SequenceClassifierOutput(
+            loss=loss,
+            logits=pooled_logits,
+            hidden_states=transformer_outputs.hidden_states,
+            attentions=transformer_outputs.attentions,
+        )

src/transformers/utils/dummy_pt_objects.py

@@ -1256,6 +1256,15 @@ class OpenAIGPTDoubleHeadsModel:
         requires_pytorch(self)
 
 
+class OpenAIGPTForSequenceClassification:
+    def __init__(self, *args, **kwargs):
+        requires_pytorch(self)
+
+    @classmethod
+    def from_pretrained(self, *args, **kwargs):
+        requires_pytorch(self)
+
+
 class OpenAIGPTLMHeadModel:
     def __init__(self, *args, **kwargs):
         requires_pytorch(self)

tests/test_modeling_openai.py

@@ -30,6 +30,7 @@ if is_torch_available():
         OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
         OpenAIGPTConfig,
         OpenAIGPTDoubleHeadsModel,
+        OpenAIGPTForSequenceClassification,
         OpenAIGPTLMHeadModel,
         OpenAIGPTModel,
     )
@@ -61,6 +62,7 @@ class OpenAIGPTModelTester:
         self.num_labels = 3
         self.num_choices = 4
         self.scope = None
+        self.pad_token_id = self.vocab_size - 1
 
     def prepare_config_and_inputs(self):
         input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
@@ -90,6 +92,7 @@ class OpenAIGPTModelTester:
             n_ctx=self.max_position_embeddings,
             # type_vocab_size=self.type_vocab_size,
             # initializer_range=self.initializer_range
+            pad_token_id=self.pad_token_id,
             return_dict=True,
         )
 
@@ -134,6 +137,18 @@ class OpenAIGPTModelTester:
         self.parent.assertEqual(result.loss.shape, ())
         self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
 
+    def create_and_check_openai_gpt_for_sequence_classification(
+        self, config, input_ids, head_mask, token_type_ids, *args
+    ):
+        config.num_labels = self.num_labels
+        model = OpenAIGPTForSequenceClassification(config)
+        model.to(torch_device)
+        model.eval()
+        # print(config.num_labels, sequence_labels.size())
+        sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
+        result = model(input_ids, 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()
         (
@@ -158,7 +173,9 @@ class OpenAIGPTModelTester:
 class OpenAIGPTModelTest(ModelTesterMixin, unittest.TestCase):
 
     all_model_classes = (
-        (OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel) if is_torch_available() else ()
+        (OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
+        if is_torch_available()
+        else ()
     )
     all_generative_model_classes = (
         (OpenAIGPTLMHeadModel,) if is_torch_available() else ()
@@ -183,6 +200,10 @@ class OpenAIGPTModelTest(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_openai_gpt_classification_model(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*config_and_inputs)
+
     @slow
     def test_model_from_pretrained(self):
         for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: