[BLIP] fix cross attentions for BlipTextEncoder
Author: zhbh01
Pull request: yes

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Fixes a bug in the output of the cross attentions in BlipTextEncoder


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Comments:
_The documentation is not available anymore as the PR was closed or merged._

cc @ArthurZucker and @younesbelkada 

Sure, happy to provide more details. This bug is caused by the all_cross_attentions variable not properly storing the cross-attention produced by each BlipTextLayer. The variable is initialized at line 404 and returned in either line 460 or 469, but it remains unchanged between initialization and return. As a result, the forward function consistently returns an empty tuple for cross-attention.

To address this issue, I have made changes to ensure that all_cross_attentions correctly stores the cross-attention produced by each BlipTextLayer, allowing the forward function to return the appropriate cross-attention.

To reproduce the bug, please run the following snippet (the returned cross attentions will always be an empty tuple):

```python
import torch
from PIL import Image
from transformers import BlipProcessor, BlipForQuestionAnswering

processor = BlipProcessor.from_pretrained("Salesforce/blip-vqa-base")
model = BlipForQuestionAnswering.from_pretrained("Salesforce/blip-vqa-base").to("cuda")
model.text_encoder.config.output_attentions = True

img_path = "path of an image"
raw_image = Image.open(img_path).convert('RGB')

name = "cat"
question = [
    "Is there a {} in the view?".format(name),
]
inputs = processor([raw_image]*len(question), question, padding=True, return_tensors="pt").to("cuda")

vision_outputs = model.vision_model(inputs['pixel_values'])
image_embeds = vision_outputs[0]

image_attention_mask = torch.ones(image_embeds.size()[:-1], dtype=torch.long).to(image_embeds.device)

question_outputs = model.text_encoder(
    input_ids=inputs["input_ids"],
    attention_mask=inputs["attention_mask"],
    encoder_hidden_states=image_embeds,
    encoder_attention_mask=image_attention_mask,
    return_dict=True
)

# question_outputs['cross_attentions'] will always be an empty tuple
print(question_outputs['cross_attentions'])
```