[Reformer classification head] Implement the reformer model classification head for text classification (#5198)

* Reformer model head classification implementation for text classification

* Reformat the reformer model classification code

* PR review comments, and test case implementation for reformer for classification head changes

* CI/CD reformer for classification head test import error fix

* CI/CD test case implementation  added ReformerForSequenceClassification to all_model_classes

* Code formatting- fixed

* Normal test cases added for reformer classification head

* Fix test cases implementation for the reformer classification head

* removed token_type_id parameter from the reformer classification head

* fixed the test case for reformer classification head

* merge conflict with master fixed

* merge conflict, changed reformer classification to accept the choice_label parameter added in latest code

* refactored the the reformer classification head test code

* reformer classification head, common transform test cases fixed

* final set of the review comment, rearranging the reformer classes and docstring add to classification forward method

* fixed the compilation error and text case fix for reformer classification head

* Apply suggestions from code review

Remove unnecessary dup

Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

tests/test_modeling_reformer.py

@@ -28,6 +28,7 @@ if is_torch_available():
         ReformerForMaskedLM,
         ReformerModel,
         ReformerModelWithLMHead,
+        ReformerForSequenceClassification,
         ReformerTokenizer,
         ReformerLayer,
         ReformerForQuestionAnswering,
@@ -77,6 +78,7 @@ class ReformerModelTester:
         eos_token_id=None,
         scope=None,
         hash_seed=None,
+        num_labels=None,
     ):
         self.parent = parent
         self.batch_size = batch_size
@@ -124,6 +126,7 @@ class ReformerModelTester:
         self.encoder_seq_length = seq_length // attn_chunk_length + (self.seq_length % attn_chunk_length != 0)
         self.key_length = (num_chunks_before + num_chunks_after + 1) * attn_chunk_length
         self.chunk_length = attn_chunk_length
+        self.num_labels = num_labels
 
     def prepare_config_and_inputs(self):
         input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
@@ -443,6 +446,22 @@ class ReformerModelTester:
         inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask}
         return config, inputs_dict
 
+    def create_and_check_reformer_for_sequence_classification(
+        self, config, input_ids, input_mask, choice_labels, is_decoder
+    ):
+        config.is_decoder = is_decoder
+        sequence_labels = ids_tensor([self.batch_size], config.num_labels)
+        model = ReformerForSequenceClassification(config)
+        model.to(torch_device)
+        model.eval()
+        loss, logits = model(input_ids, attention_mask=input_mask, labels=sequence_labels)
+        result = {
+            "loss": loss,
+            "logits": logits,
+        }
+        self.parent.assertListEqual(list(result["logits"].size()), [self.batch_size, self.num_labels])
+        self.check_loss_output(result)
+
 
 class ReformerTesterMixin:
     """
@@ -510,11 +529,17 @@ class ReformerTesterMixin:
         # Opt-out of this test.
         pass
 
+    def test_for_sequence_classification(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_reformer_for_sequence_classification(*config_and_inputs, is_decoder=False)
+
 
 @require_torch
 class ReformerLocalAttnModelTest(ReformerTesterMixin, ModelTesterMixin, unittest.TestCase):
     all_model_classes = (
-        (ReformerModel, ReformerModelWithLMHead, ReformerForQuestionAnswering) if is_torch_available() else ()
+        (ReformerModel, ReformerModelWithLMHead, ReformerForSequenceClassification, ReformerForQuestionAnswering)
+        if is_torch_available()
+        else ()
     )
     all_generative_model_classes = (ReformerModelWithLMHead,) if is_torch_available() else ()
     test_pruning = False
@@ -554,6 +579,7 @@ class ReformerLocalAttnModelTest(ReformerTesterMixin, ModelTesterMixin, unittest
             "eos_token_id": 2,
             "scope": None,
             "hash_seed": 0,
+            "num_labels": 2,
         }
 
     def setUp(self):
@@ -571,7 +597,9 @@ class ReformerLocalAttnModelTest(ReformerTesterMixin, ModelTesterMixin, unittest
 @require_torch
 class ReformerLSHAttnModelTest(ReformerTesterMixin, ModelTesterMixin, unittest.TestCase):
     all_model_classes = (
-        (ReformerModel, ReformerModelWithLMHead, ReformerForQuestionAnswering) if is_torch_available() else ()
+        (ReformerModel, ReformerModelWithLMHead, ReformerForSequenceClassification, ReformerForQuestionAnswering)
+        if is_torch_available()
+        else ()
     )
     all_generative_model_classes = (ReformerModelWithLMHead,) if is_torch_available() else ()
     test_pruning = False
@@ -613,6 +641,7 @@ class ReformerLSHAttnModelTest(ReformerTesterMixin, ModelTesterMixin, unittest.T
             "eos_token_id": 2,
             "scope": None,
             "hash_seed": 0,
+            "num_labels": 2,
         }
 
     def setUp(self):