Ctrl for sequence classification (#8812)

* add CTRLForSequenceClassification

* pass local test

* merge with master

* fix modeling test for sequence classification

* fix deco

* fix assert

tests/test_modeling_ctrl.py

@@ -26,7 +26,13 @@ from .test_modeling_common import ModelTesterMixin, ids_tensor, random_attention
 if is_torch_available():
     import torch
 
-    from transformers import CTRL_PRETRAINED_MODEL_ARCHIVE_LIST, CTRLConfig, CTRLLMHeadModel, CTRLModel
+    from transformers import (
+        CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
+        CTRLConfig,
+        CTRLForSequenceClassification,
+        CTRLLMHeadModel,
+        CTRLModel,
+    )
 
 
 class CTRLModelTester:
@@ -57,6 +63,7 @@ class CTRLModelTester:
         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)
@@ -94,6 +101,7 @@ class CTRLModelTester:
             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,
         )
 
         head_mask = ids_tensor([self.num_hidden_layers, self.num_attention_heads], 2)
@@ -149,11 +157,20 @@ class CTRLModelTester:
 
         return config, inputs_dict
 
+    def create_and_check_ctrl_for_sequence_classification(self, config, input_ids, head_mask, token_type_ids, *args):
+        config.num_labels = self.num_labels
+        model = CTRLForSequenceClassification(config)
+        model.to(torch_device)
+        model.eval()
+        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))
+
 
 @require_torch
 class CTRLModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
 
-    all_model_classes = (CTRLModel, CTRLLMHeadModel) if is_torch_available() else ()
+    all_model_classes = (CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else ()
     all_generative_model_classes = (CTRLLMHeadModel,) if is_torch_available() else ()
     test_pruning = True
     test_torchscript = False