InstructBlipProcessor not working with load_in_4bit and load_in_8bit
### System Info

transformers @ git+https://github.com/huggingface/transformers@68c92981ff2b804979d2e6107eeefe298d1e5183
Python 3.11.4
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 510.47.03    Driver Version: 510.47.03    CUDA Version: 11.6     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   0  NVIDIA A100-SXM...  Off  | 00000000:00:04.0 Off |                    0 |
| N/A   36C    P0    50W / 400W |    845MiB / 40960MiB |      0%      Default |
|                               |                      |             Disabled |
+-------------------------------+----------------------+----------------------+
                                                                               
+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
|    0   N/A  N/A     12365      C   ...nda/envs/myenv/bin/python      843MiB |
+-----------------------------------------------------------------------------+

### Who can help?

_No response_

### Information

- [ ] The official example scripts
- [X] My own modified scripts

### Tasks

- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)

### Reproduction

Currently trying to run the following script:
```
from transformers import InstructBlipProcessor, InstructBlipForConditionalGeneration
import torch
from PIL import Image

torch.cuda.empty_cache()
model = InstructBlipForConditionalGeneration.from_pretrained("Salesforce/instructblip-vicuna-7b", load_in_4bit=True,  torch_dtype=torch.bfloat16)
processor = InstructBlipProcessor.from_pretrained("Salesforce/instructblip-vicuna-7b", load_in_4bit=True, torch_dtype=torch.bfloat16)

image = Image.open('examples/test1.jpeg')
inputs = processor(images=image, text='', return_tensors="pt").to(device)
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,
)
```

But obtaining the following error (see below). Is it possible that InstructBlipForConditionalGeneration does not support yet `load_in_4bit`?

Error logs:
```
RuntimeError                              Traceback (most recent call last)
Cell In[6], line 1
----> 1 outputs = model.generate(
      2         **inputs,
      3         do_sample=False,
      4         num_beams=5,
      5         max_length=256,
      6         min_length=1,
      7         top_p=0.9,
      8         repetition_penalty=1.5,
      9         length_penalty=1.0,
     10         temperature=1,
     11 )

File /opt/conda/envs/myenv/lib/python3.11/site-packages/torch/utils/_contextlib.py:115, in context_decorator.<locals>.decorate_context(*args, **kwargs)
    112 @functools.wraps(func)
    113 def decorate_context(*args, **kwargs):
    114     with ctx_factory():
--> 115         return func(*args, **kwargs)

File /opt/conda/envs/myenv/lib/python3.11/site-packages/transformers/models/instructblip/modeling_instructblip.py:1517, in InstructBlipForConditionalGeneration.generate(self, pixel_values, qformer_input_ids, qformer_attention_mask, input_ids, attention_mask, **generate_kwargs)
   1514     self._preprocess_accelerate()
   1516 batch_size = pixel_values.shape[0]
-> 1517 image_embeds = self.vision_model(pixel_values, return_dict=True).last_hidden_state
   1519 image_attention_mask = torch.ones(image_embeds.size()[:-1], dtype=torch.long, device=image_embeds.device)
   1521 query_tokens = self.query_tokens.expand(image_embeds.shape[0], -1, -1)

File /opt/conda/envs/myenv/lib/python3.11/site-packages/torch/nn/modules/module.py:1501, in Module._call_impl(self, *args, **kwargs)
   1496 # If we don't have any hooks, we want to skip the rest of the logic in
   1497 # this function, and just call forward.
   1498 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
   1499         or _global_backward_pre_hooks or _global_backward_hooks
   1500         or _global_forward_hooks or _global_forward_pre_hooks):
-> 1501     return forward_call(*args, **kwargs)
   1502 # Do not call functions when jit is used
   1503 full_backward_hooks, non_full_backward_hooks = [], []

File /opt/conda/envs/myenv/lib/python3.11/site-packages/accelerate/hooks.py:165, in add_hook_to_module.<locals>.new_forward(*args, **kwargs)
    163         output = old_forward(*args, **kwargs)
    164 else:
--> 165     output = old_forward(*args, **kwargs)
    166 return module._hf_hook.post_forward(module, output)

File /opt/conda/envs/myenv/lib/python3.11/site-packages/transformers/models/instructblip/modeling_instructblip.py:538, in InstructBlipVisionModel.forward(self, pixel_values, output_attentions, output_hidden_states, return_dict)
    535 if pixel_values is None:
    536     raise ValueError("You have to specify pixel_values")
--> 538 hidden_states = self.embeddings(pixel_values)
    540 encoder_outputs = self.encoder(
    541     inputs_embeds=hidden_states,
    542     output_attentions=output_attentions,
    543     output_hidden_states=output_hidden_states,
    544     return_dict=return_dict,
    545 )
    547 last_hidden_state = encoder_outputs[0]

File /opt/conda/envs/myenv/lib/python3.11/site-packages/torch/nn/modules/module.py:1501, in Module._call_impl(self, *args, **kwargs)
   1496 # If we don't have any hooks, we want to skip the rest of the logic in
   1497 # this function, and just call forward.
   1498 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
   1499         or _global_backward_pre_hooks or _global_backward_hooks
   1500         or _global_forward_hooks or _global_forward_pre_hooks):
-> 1501     return forward_call(*args, **kwargs)
   1502 # Do not call functions when jit is used
   1503 full_backward_hooks, non_full_backward_hooks = [], []

File /opt/conda/envs/myenv/lib/python3.11/site-packages/accelerate/hooks.py:165, in add_hook_to_module.<locals>.new_forward(*args, **kwargs)
    163         output = old_forward(*args, **kwargs)
    164 else:
--> 165     output = old_forward(*args, **kwargs)
    166 return module._hf_hook.post_forward(module, output)

File /opt/conda/envs/myenv/lib/python3.11/site-packages/transformers/models/instructblip/modeling_instructblip.py:113, in InstructBlipVisionEmbeddings.forward(self, pixel_values)
    111 batch_size = pixel_values.shape[0]
    112 target_dtype = self.patch_embedding.weight.dtype
--> 113 patch_embeds = self.patch_embedding(pixel_values)  # shape = [*, width, grid, grid]
    114 patch_embeds = patch_embeds.flatten(2).transpose(1, 2)
    116 class_embeds = self.class_embedding.expand(batch_size, 1, -1).to(target_dtype)

File /opt/conda/envs/myenv/lib/python3.11/site-packages/torch/nn/modules/module.py:1501, in Module._call_impl(self, *args, **kwargs)
   1496 # If we don't have any hooks, we want to skip the rest of the logic in
   1497 # this function, and just call forward.
   1498 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
   1499         or _global_backward_pre_hooks or _global_backward_hooks
   1500         or _global_forward_hooks or _global_forward_pre_hooks):
-> 1501     return forward_call(*args, **kwargs)
   1502 # Do not call functions when jit is used
   1503 full_backward_hooks, non_full_backward_hooks = [], []

File /opt/conda/envs/myenv/lib/python3.11/site-packages/accelerate/hooks.py:165, in add_hook_to_module.<locals>.new_forward(*args, **kwargs)
    163         output = old_forward(*args, **kwargs)
    164 else:
--> 165     output = old_forward(*args, **kwargs)
    166 return module._hf_hook.post_forward(module, output)

File /opt/conda/envs/myenv/lib/python3.11/site-packages/torch/nn/modules/conv.py:463, in Conv2d.forward(self, input)
    462 def forward(self, input: Tensor) -> Tensor:
--> 463     return self._conv_forward(input, self.weight, self.bias)

File /opt/conda/envs/myenv/lib/python3.11/site-packages/torch/nn/modules/conv.py:459, in Conv2d._conv_forward(self, input, weight, bias)
    455 if self.padding_mode != 'zeros':
    456     return F.conv2d(F.pad(input, self._reversed_padding_repeated_twice, mode=self.padding_mode),
    457                     weight, bias, self.stride,
    458                     _pair(0), self.dilation, self.groups)
--> 459 return F.conv2d(input, weight, bias, self.stride,
    460                 self.padding, self.dilation, self.groups)

RuntimeError: Input type (float) and bias type (c10::BFloat16) should be the same
```

### Expected behavior

Produce output string as expected
cc @younesbelkada <|||||>Hi @fraferra 
In https://github.com/huggingface/transformers/pull/24555 I have fixed the a silent issue with processors that you are currently facing, can you try to install transformers from source and run:

```python
from transformers import InstructBlipProcessor, InstructBlipForConditionalGeneration
import torch
from PIL import Image

torch.cuda.empty_cache()
model = InstructBlipForConditionalGeneration.from_pretrained("Salesforce/instructblip-vicuna-7b", load_in_4bit=True,  torch_dtype=torch.bfloat16)
processor = InstructBlipProcessor.from_pretrained("Salesforce/instructblip-vicuna-7b", load_in_4bit=True, torch_dtype=torch.bfloat16)

image = Image.open('examples/test1.jpeg')
inputs = processor(images=image, text='', return_tensors="pt").to(device, torch.bfloat16)
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,
)
```
Check a more concrete example here: https://github.com/huggingface/transformers/blob/66954ea25e342fd451c26ec1c295da0b8692086b/tests/models/instructblip/test_modeling_instructblip.py#L524<|||||>Thank you @younesbelkada for looking into it! Is it possible that `BatchEncoding.to()` needs to be updated?
I can see in the source code that `BatchEncoding.to()` (https://github.com/huggingface/transformers/blob/9e28750287df57942d716083ae53bb4e766104c2/src/transformers/tokenization_utils_base.py#L756) only takes 1 argument.

I am getting the following error when trying to run your code snippet:
```
     1 device = "cuda" if torch.cuda.is_available() else "cpu"
      2 image = Image.open('examples/test1.jpeg')
----> 3 inputs = processor(images=image, text='', return_tensors="pt").to(device, torch.bfloat16)
      4 outputs = model.generate(
      5         **inputs,
      6         do_sample=False,
   (...)
     13         temperature=1,
     14 )

TypeError: BatchEncoding.to() takes 2 positional arguments but 3 were given
```<|||||>Pretty weird since `InstructBlipProcessor.__call__` should return `BatchFeature` which the `to` method can take `*args, **kwargs` unlike the one from `BatchEncoding` which only takes `device` as an argument.<|||||>Hi @fraferra 
Can you install transformers from the main branch and try again?
```
pip uninstall transformers
pip install git+https://github.com/huggingface/transformers.git
```<|||||>@younesbelkada it worked, thank you! for some reason it wouldnt update to the latest transformers' version in the conda env. After uninstalling it and reinstalling it after it returned `BatchFeature`<|||||>Thank you @fraferra feel free to close the issue ! Let us know if you have more questions<|||||>as the vision is a model, and uses the llm model vicunia or optB7 etc... would there be a way to just use the already loaded model to ask a txt question and get a text answer irrelevant to the image? just use llm as an llm?