[`InstructBlip`] Add instruct blip int8 test (#24555)

* add 8bit instructblip test

* update tests

src/transformers/models/instructblip/modeling_instructblip.py

@@ -1030,7 +1030,7 @@ class InstructBlipQFormerEmbeddings(nn.Module):
         if input_ids is not None:
             embeddings = self.word_embeddings(input_ids)
             if self.position_embedding_type == "absolute":
-                position_embeddings = self.position_embeddings(position_ids)
+                position_embeddings = self.position_embeddings(position_ids.to(embeddings.device))
                 embeddings = embeddings + position_embeddings
 
             if query_embeds is not None:

src/transformers/models/instructblip/processing_instructblip.py

@@ -19,9 +19,10 @@ Processor class for InstructBLIP. Largely copy of Blip2Processor with addition o
 import os
 from typing import List, Optional, Union
 
+from ...image_processing_utils import BatchFeature
 from ...image_utils import ImageInput
 from ...processing_utils import ProcessorMixin
-from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
+from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
 from ...utils import TensorType
 from ..auto import AutoTokenizer
 
@@ -71,7 +72,7 @@ class InstructBlipProcessor(ProcessorMixin):
         verbose: bool = True,
         return_tensors: Optional[Union[str, TensorType]] = None,
         **kwargs,
-    ) -> BatchEncoding:
+    ) -> BatchFeature:
         """
         This method uses [`BlipImageProcessor.__call__`] method to prepare image(s) for the model, and
         [`BertTokenizerFast.__call__`] to prepare text for the model.
@@ -81,7 +82,7 @@ class InstructBlipProcessor(ProcessorMixin):
         if images is None and text is None:
             raise ValueError("You have to specify at least images or text.")
 
-        encoding = BatchEncoding()
+        encoding = BatchFeature()
 
         if text is not None:
             text_encoding = self.tokenizer(

tests/models/instructblip/test_modeling_instructblip.py

@@ -521,51 +521,39 @@ def prepare_img():
 @require_torch
 @slow
 class InstructBlipModelIntegrationTest(unittest.TestCase):
-    # TODO (@Younes): Re-enable this when 8-bit or 4-bit is implemented.
-    @unittest.skip(reason="GPU OOM")
     def test_inference_vicuna_7b(self):
         processor = InstructBlipProcessor.from_pretrained("Salesforce/instructblip-vicuna-7b")
-        model = InstructBlipForConditionalGeneration.from_pretrained("Salesforce/instructblip-vicuna-7b").to(
-            torch_device
+        model = InstructBlipForConditionalGeneration.from_pretrained(
+            "Salesforce/instructblip-vicuna-7b", load_in_8bit=True
         )
 
         url = "https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg"
         image = Image.open(requests.get(url, stream=True).raw).convert("RGB")
         prompt = "What is unusual about this image?"
-        inputs = processor(images=image, text=prompt, return_tensors="pt").to(torch_device)
+        inputs = processor(images=image, text=prompt, return_tensors="pt").to(torch_device, torch.float16)
 
         # verify logits
         with torch.no_grad():
             logits = model(**inputs).logits
 
         expected_slice = torch.tensor(
-            [[-3.4684, -12.6759, 8.5067], [-5.1305, -12.2058, 7.9834], [-4.0632, -13.9285, 9.2327]],
+            [[-3.5410, -12.2812, 8.2812], [-5.2500, -12.0938, 7.8398], [-4.1523, -13.8281, 9.0000]],
             device=torch_device,
         )
-        assert torch.allclose(logits[0, :3, :3], expected_slice, atol=1e-5)
+        self.assertTrue(torch.allclose(logits[0, :3, :3].float(), expected_slice, atol=1e-3))
 
         # verify generation
-        outputs = model.generate(
-            **inputs,
-            do_sample=False,
-            num_beams=5,
-            max_length=256,
-            min_length=1,
-            top_p=0.9,
-            repetition_penalty=1.5,
-            length_penalty=1.0,
-            temperature=1,
-        )
+        outputs = model.generate(**inputs, max_new_tokens=30)
         outputs[outputs == 0] = 2
         generated_text = processor.batch_decode(outputs, skip_special_tokens=True)[0].strip()
 
         # fmt: off
-        expected_outputs = [2, 450, 22910,  9565,   310,   445,  1967,   338,   393,   263, 767,   338, 13977,   292, 22095,   373,   278,  1250,   310,   263, 13328, 20134, 29963, 29892,   607,   338, 14089,   287,   297,   278, 7256,   310,   263, 19587,  4272, 11952, 29889,   910,   338,   385, 443,   535,   794,  1848,  2948,   304, 13977,   292, 22095, 29892, 408,   372,  6858,   278,   767,   304, 17346,  3654,   322,   670, 13977,   292, 21083,   373,  2246,   310,   278, 19716,  1550, 12402, 1218,  1549, 12469, 29889, 19814, 29892,   278, 10122,   310,  8818, 275,   322,   916, 24413,   297,   278,  9088,  4340, 19310,  7093, 278, 22910,  5469,   310,   445,  6434, 29889,     2,     1]
+        expected_outputs = [    2,   450, 22910,  9565,   310,   445,  1967,   338,   393,   263, 767,   338, 13977,   292, 22095,   373,   278,  1250,   310,   263, 13328, 20134, 29963,  1550, 19500,  1623,   263, 19587,  4272, 11952, 29889]
         # fmt: on
         self.assertEqual(outputs[0].tolist(), expected_outputs)
         self.assertEqual(
             generated_text,
-            "The unusual aspect of this image is that a man is ironing clothes on the back of a yellow SUV, which is parked in the middle of a busy city street. This is an unconventional approach to ironing clothes, as it requires the man to balance himself and his ironing equipment on top of the vehicle while navigating through traffic. Additionally, the presence of taxis and other vehicles in the scene further emphasizes the unusual nature of this situation.",
+            "The unusual aspect of this image is that a man is ironing clothes on the back of a yellow SUV while driving down a busy city street.",
         )
 
     def test_inference_flant5_xl(self):