[Blip2] Add Blip2Model (#21817)

* add v1

* add `Blip2Model`

- add relevant functions
- add tests
- add on automapping

* fix docs

* fix doctest

tests/models/blip_2/test_modeling_blip_2.py

@@ -40,7 +40,7 @@ if is_torch_available():
     import torch
     from torch import nn
 
-    from transformers import Blip2ForConditionalGeneration, Blip2VisionModel
+    from transformers import Blip2ForConditionalGeneration, Blip2Model, Blip2VisionModel
     from transformers.models.blip_2.modeling_blip_2 import BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST
 
 
@@ -664,8 +664,8 @@ class Blip2ForConditionalGenerationModelTester:
 
 
 @require_torch
-class Blip2ForConditionalGenerationTest(ModelTesterMixin, unittest.TestCase):
-    all_model_classes = (Blip2ForConditionalGeneration,) if is_torch_available() else ()
+class Blip2ModelTest(ModelTesterMixin, unittest.TestCase):
+    all_model_classes = (Blip2ForConditionalGeneration, Blip2Model) if is_torch_available() else ()
     fx_compatible = False
     test_head_masking = False
     test_pruning = False
@@ -737,6 +737,56 @@ class Blip2ForConditionalGenerationTest(ModelTesterMixin, unittest.TestCase):
             model = Blip2ForConditionalGeneration.from_pretrained(model_name)
             self.assertIsNotNone(model)
 
+    def test_get_text_features(self):
+        config, _ = self.model_tester.prepare_config_and_inputs_for_common()
+
+        inputs_dict = {
+            "input_ids": torch.LongTensor([[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]]).to(torch_device),
+            "attention_mask": torch.LongTensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]).to(torch_device),
+            "decoder_input_ids": torch.LongTensor([[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]]).to(torch_device),
+        }
+
+        model = Blip2Model(config).to(torch_device)
+        model.eval()
+        text_features = model.get_text_features(**inputs_dict)
+        self.assertEqual(text_features[0].shape, (1, 10, config.text_config.vocab_size))
+
+    def test_get_image_features(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+
+        keys_to_pop = ["input_ids", "attention_mask", "decoder_input_ids", "decoder_attention_mask", "labels"]
+
+        for key in keys_to_pop:
+            inputs_dict.pop(key)
+
+        model = Blip2Model(config).to(torch_device)
+        model.eval()
+        image_features = model.get_image_features(**inputs_dict)
+        self.assertEqual(
+            image_features[0].shape,
+            (
+                self.model_tester.vision_model_tester.batch_size,
+                self.model_tester.vision_model_tester.seq_length,
+                config.vision_config.hidden_size,
+            ),
+        )
+
+    def test_get_qformer_features(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+
+        keys_to_pop = ["input_ids", "attention_mask", "decoder_input_ids", "decoder_attention_mask", "labels"]
+
+        for key in keys_to_pop:
+            inputs_dict.pop(key)
+
+        model = Blip2Model(config).to(torch_device)
+        model.eval()
+        qformer_features = model.get_qformer_features(**inputs_dict)
+        self.assertEqual(
+            qformer_features[0].shape,
+            (self.model_tester.vision_model_tester.batch_size, 10, config.vision_config.hidden_size),
+        )
+
     # override from common to deal with nested configurations (`vision_config`, `text_config` and `qformer_config`)
     def test_initialization(self):
         config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()