

TITLE
MobileBertForSequenceClassification outputs super-high logits

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## Environment info
<!-- You can run the command `transformers-cli env` and copy-and-paste its output below.
     Don't forget to fill out the missing fields in that output! -->
     
- `transformers` version: 4.0.0
- Platform: Ubuntu 20.04
- Python version: 3.8.5
- PyTorch version (GPU?): 1.7.0
- Tensorflow version (GPU?): None
- Using GPU in script?: True
- Using distributed or parallel set-up in script?: False

### Who can help
<!-- Your issue will be replied to more quickly if you can figure out the right person to tag with @
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 albert, bert, GPT2, XLM: @LysandreJik 
 tokenizers: @mfuntowicz
 Trainer: @sgugger
 Speed and Memory Benchmarks: @patrickvonplaten
 Model Cards: @julien-c
 TextGeneration: @TevenLeScao 
 examples/distillation: @VictorSanh
 nlp datasets: [different repo](https://github.com/huggingface/nlp)
 rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)
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@LysandreJik 

## Information

Model I am using (Bert, XLNet ...): MobileBertForSequenceClassification

The problem arises when using:
* [ ] the official example scripts: (give details below)
* [x] my own modified scripts: please see an example below

The tasks I am working on is:
* [ ] an official GLUE/SQUaD task: (give the name)
* [x] my own task or dataset: simple text classification using documents from sec.gov

## To reproduce

Steps to reproduce the behavior:

I am training a whole-text classifier with MobileBert using MobileBertForSequenceClassification:
```{python}
from transformers import MobileBertForSequenceClassification, \
                                        MobileBertTokenizerFast

ARCH = 'google/mobilebert-uncased'

model = MobileBertForSequenceClassification.from_pretrained(ARCH).cuda()
tokenizer = MobileBertTokenizerFast.from_pretrained(ARCH)

x = tokenizer(['def hello(): return "world"', 'This is some test'],
              max_length=512,
              truncation=True,
              return_tensors='pt',
              padding='longest')

with torch.no_grad():
    l = model(**x.to(model.device)).logits 
```
Resulting model outputs are extremely high:
```
tensor([[ 3289181.7500, -2371234.0000],
        [ 3198336.7500, -1882639.8750]])
```
Loading model and tokenizer with Auto- classes gives the same result. 
When using pooled output from ModileBertModel with a custom linear head (BatchNorm1d+Dropout+Linear) everything works fine.

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## Expected behavior

I expect logits to be near [-3, 3], but not in 6-7 digits.
