Add support to DistilBertLMHeadModel
# 🚀 Feature request

## Background
Hi, I am the first author of the paper ["**TSDAE**: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning"](https://arxiv.org/abs/2104.06979). The work proposed a new method, TSDAE to train unsupervised-sentence-embedding models with denoising **autoencoder** loss on top of BERT-like PLMs. The code of TSDAE has been integrated into the SBERT repo and one can find the source file [here](https://github.com/UKPLab/sentence-transformers/blob/master/sentence_transformers/losses/DenoisingAutoEncoderLoss.py) and the example training script [here](https://github.com/UKPLab/sentence-transformers/tree/master/examples/unsupervised_learning/TSDAE).

## What is missing
As one can tell from the "autoencoder" in the name, TSDAE needs a decoder (i.e. `LMHead`) to work. However, the popular [**DistilBERT**](https://github.com/huggingface/transformers/blob/master/src/transformers/models/distilbert/modeling_distilbert.py) in the current HF-transformers **does not support it as a decoder** (i.e. missing something called `DistilBertLMHeadModel`). 

Previously, I **used to bypass** this issue with [my personal extension](https://gist.github.com/kwang2049/1f0e1f0ce119456284c0af048ba097a7) and one can find the [discussion/issues](https://github.com/UKPLab/sentence-transformers/issues/962) in the SBERT repo about this. Recently, as users reported, I have just found **this way is not really maintainable and continuable**: HF-transformers keeps updating the code all the time and I need to update the extension accordingly then.

## Goal
Add support to **DistilBertLMHeadModel**. Actually, **I have made the changes in my fork** (for PyTorch) and it just needs to be merged (of course, after the code review from the HF community).

<!-- A clear and concise description of the feature proposal.
     Please provide a link to the paper and code in case they exist. -->

## Motivation
With support to DistilBertLMHeadModel, all the decoding functions will be possible for all the amazing distilbert models, e.g. the TSDAE learning, seq2seq tasks, etc.

<!-- Please outline the motivation for the proposal. Is your feature request
     related to a problem? e.g., I'm always frustrated when [...]. If this is related
     to another GitHub issue, please link here too. -->

## Your contribution
I have forked the latest master branch and added the corresponding support for PyTorch. One can check the pull-request for details. Really appreciate it if this issue can be solved.

<!-- Is there any way that you could help, e.g. by submitting a PR?
     Make sure to read the CONTRIBUTING.MD readme:
     https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md -->

This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.

Please note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md) are likely to be ignored.