Add BloomForQuestionAnswering (#19310)

* add bloom for question answering

- attempt to add Bloom for question answering
- adapted from `GPTJForQuestionAnswering`
- Fixed `num_labels` to `2` for common tests
- Added a bit of docstring
- All common tests pass

* Update src/transformers/models/bloom/modeling_bloom.py

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* revert changes related to `num_labels`

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

tests/models/bloom/test_modeling_bloom.py

@@ -31,6 +31,7 @@ if is_torch_available():
     from transformers import (
         BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
         BloomForCausalLM,
+        BloomForQuestionAnswering,
         BloomForSequenceClassification,
         BloomForTokenClassification,
         BloomModel,
@@ -274,6 +275,14 @@ class BloomModelTester:
         result = model(input_ids, attention_mask=input_mask)
         self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
 
+    def create_and_check_question_answering_model(self, config, input_ids, input_mask, *args):
+        model = BloomForQuestionAnswering(config)
+        model.to(torch_device)
+        model.eval()
+
+        result = model(input_ids, attention_mask=input_mask)
+        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
+
     def create_and_check_forward_and_backwards(
         self, config, input_ids, input_mask, *args, gradient_checkpointing=False
     ):
@@ -314,6 +323,7 @@ class BloomModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase)
             BloomForCausalLM,
             BloomForSequenceClassification,
             BloomForTokenClassification,
+            BloomForQuestionAnswering,
         )
         if is_torch_available()
         else ()