Unexpected situation when freezing BertForMaskedLM
## 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: 3.1.0
- Platform: Linux-4.18.0-147.el8.x86_64-x86_64-with-Ubuntu-16.04-xenial
- Python version: 3.7.7
- PyTorch version (GPU?): 1.6.0 (True)
- Tensorflow version (GPU?): not installed (NA)
- Using GPU in script?: Yes and No (It happens in both conditions)
- Using distributed or parallel set-up in script?: Yes and No (It happens in both conditions)

### Who can help
<!-- Your issue will be replied to more quickly if you can figure out the right person to tag with @
 If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**.
 Please tag fewer than 3 people.
 
 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)
 Text Generation: @patrickvonplaten @TevenLeScao
 Blenderbot: @patrickvonplaten
 Bart: @patrickvonplaten
 Marian: @patrickvonplaten
 Pegasus: @patrickvonplaten
 mBART: @patrickvonplaten
 T5: @patrickvonplaten
 Longformer/Reformer: @patrickvonplaten
 TransfoXL/XLNet: @TevenLeScao
 RAG: @patrickvonplaten, @lhoestq
 FSMT: @stas00
 examples/seq2seq: @patil-suraj
 examples/bert-loses-patience: @JetRunner
 tensorflow: @jplu
 examples/token-classification: @stefan-it
 documentation: @sgugger
 --> @LysandreJik 

## Information

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

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

The tasks I am working on is:
* [ ] an official GLUE/SQUaD task: (give the name)
* [x] my own task or dataset: (give details below)

## To reproduce

Steps to reproduce the behavior:

1. Load pretrained BertForMaskedLM
```
from transformers import BertForMaskedLM
model = BertForMaskedLM.from_pretrained('bert-base-uncased')
```
2. Check whether gradients in the cls.predictions.decoder layer are calculated
```
print(model.cls.predictions.decoder.weight.requires_grad)
```
Result:
```
True
```
3. Check again after only freezing the bert layer
```
for param in model.bert.parameters():
    param.requires_grad = False

print(model.cls.predictions.decoder.weight.requires_grad)
```
Result:
```
False
```

<!-- If you have code snippets, error messages, stack traces please provide them here as well.
     Important! Use code tags to correctly format your code. See https://help.github.com/en/github/writing-on-github/creating-and-highlighting-code-blocks#syntax-highlighting
     Do not use screenshots, as they are hard to read and (more importantly) don't allow others to copy-and-paste your code.-->

## Expected behavior

<!-- A clear and concise description of what you would expect to happen. -->

It only happens only on BertForMaskedLM. 

If I tried to freeze only the BertModel, cls.predictions.decoder is also frozen.
But as expected, cls.prediction.transform is not frozen.

The exception only occurs in cls.predictions.decoder .

I don't know it is the way you expected but in my sense, it is a kind of unexpected situation for the ones who try to freeze only the BertModel.

I think this is probably so because the `cls.predictions.decoder` is a linear layer, which is tied to the embeddings layer. They're pointing to the same weights, so freezeing one of those would result in freezing the other one.