GPT2DoubleHeadsModel Multiple Choice Head Always Has 1 Out Feature
### System Info

- `transformers` version: 4.27.4
- Platform: Windows-10-10.0.19044-SP0
- Python version: 3.10.0
- Huggingface_hub version: 0.13.4
- PyTorch version (GPU?): 1.13.1+cu116 (True)
- Tensorflow version (GPU?): not installed (NA)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using GPU in script?: Yes
- Using distributed or parallel set-up in script?: No

### Who can help?

@ArthurZucker @younesbelkada

### Information

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

### Tasks

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

### Reproduction

I am using the GPT2DoubleHeadsModel to label tweets as either bot generated or human generated, and I encountered an issue where no matter what I did, the multiple choice head for the model only ever had 1 out feature (example in second code block below). I wrote some small sample code for a sentiment classifier to demonstrate.

```Python
from transformers import GPT2Tokenizer, GPT2DoubleHeadsModel

tokenizer = GPT2Tokenizer.from_pretrained('gpt2', do_lower_case=True, pad_token='0', padding_side='right', truncation_side='right')
model = GPT2DoubleHeadsModel.from_pretrained('gpt2', num_labels=2)
print(model)

example_sequences = ["I love NLP!", "Hello, world!", "I don't like carrots", "This is bad."]
example_labels = torch.LongTensor([1, 1, 0, 0])
nput_ids_and_masks = tokenizer(example_sequences, truncation=True, padding=True, return_tensors='pt')
model(input_ids = input_ids_and_masks['input_ids'], attention_mask=input_ids_and_masks['attention_mask'], mc_labels=example_labels)
```
```
GPT2DoubleHeadsModel(
  (transformer): GPT2Model(
    (wte): Embedding(50257, 768)
    (wpe): Embedding(1024, 768)
    (drop): Dropout(p=0.1, inplace=False)
    (h): ModuleList(
      (0): GPT2Block(
        (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
        (attn): GPT2Attention(
          (c_attn): Conv1D()
          (c_proj): Conv1D()
          (attn_dropout): Dropout(p=0.1, inplace=False)
          (resid_dropout): Dropout(p=0.1, inplace=False)
        )
        (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
        (mlp): GPT2MLP(
          (c_fc): Conv1D()
          (c_proj): Conv1D()
          (act): NewGELUActivation()
          (dropout): Dropout(p=0.1, inplace=False)
        )
      )
      (1): GPT2Block(
        (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
        (attn): GPT2Attention(
          (c_attn): Conv1D()
          (c_proj): Conv1D()
          (attn_dropout): Dropout(p=0.1, inplace=False)
          (resid_dropout): Dropout(p=0.1, inplace=False)
        )
        (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
        (mlp): GPT2MLP(
          (c_fc): Conv1D()
          (c_proj): Conv1D()
          (act): NewGELUActivation()
          (dropout): Dropout(p=0.1, inplace=False)
        )
      )
      (2): GPT2Block(
        (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
        (attn): GPT2Attention(
          (c_attn): Conv1D()
          (c_proj): Conv1D()
          (attn_dropout): Dropout(p=0.1, inplace=False)
          (resid_dropout): Dropout(p=0.1, inplace=False)
        )
        (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
        (mlp): GPT2MLP(
          (c_fc): Conv1D()
          (c_proj): Conv1D()
          (act): NewGELUActivation()
          (dropout): Dropout(p=0.1, inplace=False)
        )
      )
      (3): GPT2Block(
        (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
        (attn): GPT2Attention(
          (c_attn): Conv1D()
          (c_proj): Conv1D()
          (attn_dropout): Dropout(p=0.1, inplace=False)
          (resid_dropout): Dropout(p=0.1, inplace=False)
        )
        (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
        (mlp): GPT2MLP(
          (c_fc): Conv1D()
          (c_proj): Conv1D()
          (act): NewGELUActivation()
          (dropout): Dropout(p=0.1, inplace=False)
        )
      )
      (4): GPT2Block(
        (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
        (attn): GPT2Attention(
          (c_attn): Conv1D()
          (c_proj): Conv1D()
          (attn_dropout): Dropout(p=0.1, inplace=False)
          (resid_dropout): Dropout(p=0.1, inplace=False)
        )
        (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
        (mlp): GPT2MLP(
          (c_fc): Conv1D()
          (c_proj): Conv1D()
          (act): NewGELUActivation()
          (dropout): Dropout(p=0.1, inplace=False)
        )
      )
      (5): GPT2Block(
        (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
        (attn): GPT2Attention(
          (c_attn): Conv1D()
          (c_proj): Conv1D()
          (attn_dropout): Dropout(p=0.1, inplace=False)
          (resid_dropout): Dropout(p=0.1, inplace=False)
        )
        (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
        (mlp): GPT2MLP(
          (c_fc): Conv1D()
          (c_proj): Conv1D()
          (act): NewGELUActivation()
          (dropout): Dropout(p=0.1, inplace=False)
        )
      )
      (6): GPT2Block(
        (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
        (attn): GPT2Attention(
          (c_attn): Conv1D()
          (c_proj): Conv1D()
          (attn_dropout): Dropout(p=0.1, inplace=False)
          (resid_dropout): Dropout(p=0.1, inplace=False)
        )
        (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
        (mlp): GPT2MLP(
          (c_fc): Conv1D()
          (c_proj): Conv1D()
          (act): NewGELUActivation()
          (dropout): Dropout(p=0.1, inplace=False)
        )
      )
      (7): GPT2Block(
        (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
        (attn): GPT2Attention(
          (c_attn): Conv1D()
          (c_proj): Conv1D()
          (attn_dropout): Dropout(p=0.1, inplace=False)
          (resid_dropout): Dropout(p=0.1, inplace=False)
        )
        (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
        (mlp): GPT2MLP(
          (c_fc): Conv1D()
          (c_proj): Conv1D()
          (act): NewGELUActivation()
          (dropout): Dropout(p=0.1, inplace=False)
        )
      )
      (8): GPT2Block(
        (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
        (attn): GPT2Attention(
          (c_attn): Conv1D()
          (c_proj): Conv1D()
          (attn_dropout): Dropout(p=0.1, inplace=False)
          (resid_dropout): Dropout(p=0.1, inplace=False)
        )
        (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
        (mlp): GPT2MLP(
          (c_fc): Conv1D()
          (c_proj): Conv1D()
          (act): NewGELUActivation()
          (dropout): Dropout(p=0.1, inplace=False)
        )
      )
      (9): GPT2Block(
        (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
        (attn): GPT2Attention(
          (c_attn): Conv1D()
          (c_proj): Conv1D()
          (attn_dropout): Dropout(p=0.1, inplace=False)
          (resid_dropout): Dropout(p=0.1, inplace=False)
        )
        (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
        (mlp): GPT2MLP(
          (c_fc): Conv1D()
          (c_proj): Conv1D()
          (act): NewGELUActivation()
          (dropout): Dropout(p=0.1, inplace=False)
        )
      )
      (10): GPT2Block(
        (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
        (attn): GPT2Attention(
          (c_attn): Conv1D()
          (c_proj): Conv1D()
          (attn_dropout): Dropout(p=0.1, inplace=False)
          (resid_dropout): Dropout(p=0.1, inplace=False)
        )
        (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
        (mlp): GPT2MLP(
          (c_fc): Conv1D()
          (c_proj): Conv1D()
          (act): NewGELUActivation()
          (dropout): Dropout(p=0.1, inplace=False)
        )
      )
      (11): GPT2Block(
        (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
        (attn): GPT2Attention(
          (c_attn): Conv1D()
          (c_proj): Conv1D()
          (attn_dropout): Dropout(p=0.1, inplace=False)
          (resid_dropout): Dropout(p=0.1, inplace=False)
        )
        (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
        (mlp): GPT2MLP(
          (c_fc): Conv1D()
          (c_proj): Conv1D()
          (act): NewGELUActivation()
          (dropout): Dropout(p=0.1, inplace=False)
        )
      )
    )
    (ln_f): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
  )
  (lm_head): Linear(in_features=768, out_features=50257, bias=False)
  (multiple_choice_head): SequenceSummary(
    (summary): Linear(in_features=768, out_features=1, bias=True)
    (activation): Identity()
    (first_dropout): Dropout(p=0.1, inplace=False)
    (last_dropout): Identity()
  )
)
```
```
Traceback (most recent call last):
  File "C:\Users\...\train.py", line 201, in <module>
    model(input_ids = input_ids_and_masks['input_ids'], attention_mask=input_ids_and_masks['attention_mask'], mc_labels=example_labels)
  File "C:\Users\_\AppData\Local\Programs\Python\Python310\lib\site-packages\torch\nn\modules\module.py", line 1194, in _call_impl
    return forward_call(*input, **kwargs)
  File "C:\Users\_\AppData\Local\Programs\Python\Python310\lib\site-packages\transformers\models\gpt2\modeling_gpt2.py", line 1318, in forward
    mc_loss = loss_fct(mc_logits.view(-1, mc_logits.size(-1)), mc_labels.view(-1))
  File "C:\Users\_\AppData\Local\Programs\Python\Python310\lib\site-packages\torch\nn\modules\module.py", line 1194, in _call_impl
    return forward_call(*input, **kwargs)
  File "C:\Users\_\AppData\Local\Programs\Python\Python310\lib\site-packages\torch\nn\modules\loss.py", line 1174, in forward
    return F.cross_entropy(input, target, weight=self.weight,
  File "C:\Users\_\AppData\Local\Programs\Python\Python310\lib\site-packages\torch\nn\functional.py", line 3026, in cross_entropy
    return torch._C._nn.cross_entropy_loss(input, target, weight, _Reduction.get_enum(reduction), ignore_index, label_smoothing)
ValueError: Expected input batch_size (1) to match target batch_size (4).
```

After spending some time debugging, I noticed that the double heads model class in `modeling_gpt2.py` manually sets `config.num_labels = 1` in its `__init__`.

```Python
class GPT2DoubleHeadsModel(GPT2PreTrainedModel):
    _keys_to_ignore_on_load_missing = [r"attn.masked_bias", r"attn.bias", r"lm_head.weight"]

    def __init__(self, config):
        super().__init__(config)
        config.num_labels = 1
        self.transformer = GPT2Model(config)
        self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
        self.multiple_choice_head = SequenceSummary(config)

        # Model parallel
        self.model_parallel = False
        self.device_map = None

        # Initialize weights and apply final processing
        self.post_init()
```

After removing that line (`config.num_labels = 1`), my code worked perfectly. I was going to open a pull request for this, but I had trouble installing all the dependencies after I forked the repo :(, so this was the next best thing I could do.

### Expected behavior

Firstly, I would expect the multiple choice head of the model to have the number of out features specified in num_labels, so that it is able to perform multiclass classification tasks. 

Secondly, I would expect that when giving the model batched input with the correct dimensions, as specified in the [documentation for the double heads model](https://huggingface.co/docs/transformers/v4.27.2/en/model_doc/gpt2#transformers.GPT2DoubleHeadsModel), the model would properly run the batched input. 
Hey! Thanks for opening and issue and reporting this. I'll check if this is expected or not, and if we can open a PR to fix this. 
Since gpt2 is a very old model, touching it might be a bit complicated 😓 <|||||>Okay! So it seems that the way you are using the model is a bit different from what [the documentation mentions ](https://huggingface.co/docs/transformers/main/en/model_doc/gpt2#transformers.GPT2DoubleHeadsModel). 
Here is a version adapted to your code:
```python 
import torch
from transformers import AutoTokenizer, GPT2DoubleHeadsModel

tokenizer = AutoTokenizer.from_pretrained("gpt2")
model = GPT2DoubleHeadsModel.from_pretrained("gpt2")
tokenizer.pad_token = tokenizer.eos_token 
# Add a [CLS] to the vocabulary (we should train it also!)
num_added_tokens = tokenizer.add_special_tokens({"cls_token": "[CLS]"})
# Update the model embeddings with the new vocabulary size
embedding_layer = model.resize_token_embeddings(len(tokenizer))
       
choices =  ["I love NLP! [CLS]", "Hello, world! [CLS]", "I don't like carrots [CLS]", "This is bad. [CLS]"]
encoded_choices = [tokenizer.encode(s, padding = "max_length") for s in choices]
cls_token_location = [tokens.index(tokenizer.cls_token_id) for tokens in encoded_choices]

input_ids = torch.tensor(encoded_choices).unsqueeze(0)  # Batch size: 1, number of choices: 4
mc_token_ids = torch.tensor([cls_token_location])  # Batch size: 1

outputs = model(input_ids, mc_token_ids=mc_token_ids, mc_labels = torch.tensor([0]))
lm_logits = outputs.logits
mc_logits = outputs.mc_logits
mc_loss = outputs.mc_loss
```
this seems to produce a correct output, though I am not sur it follows your intended usage. Closing as it is expected. 