[Tentative] Adds support for exporting TransformerXL-based models to ONNX
# What does this PR do?

<!--
Congratulations! You've made it this far! You're not quite done yet though.

Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution.

Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change.

Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost.
-->

<!-- Remove if not applicable -->

Hello everyone! I hope everything is going well with you.

This PR adds a naïve support when trying to export a TransformerXL model with ONNX. Nevertheless, this is a tentative PR as PyTorch still does not support the newest ONNX opset versions (13+), which includes `triu` and `tril` operators (required by TransformerXL).

As soon as PyTorch updates its API, any reflected change will also be updated in this PR. Meanwhile, this tentative PR serves as a guide to anyone that needs to export their model.

**Note:** there is a trick to leverage `triu` and `tril` operators by hand-implementing them with a compatible format to ONNX, instead of using the ones PyTorch's provides.

Best regards,
Gustavo.

## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [X] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md#start-contributing-pull-requests),
      Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
      to it if that's the case.
- [X] Did you make sure to update the documentation with your changes? Here are the
      [documentation guidelines](https://github.com/huggingface/transformers/tree/master/docs), and
      [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/master/docs#writing-source-documentation).
- [X] Did you write any new necessary tests?


## Who can review?

Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.

<!-- Your PR 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.

Models:

- albert, bert, xlm: @LysandreJik
- blenderbot, bart, marian, pegasus, encoderdecoder,  t5: @patrickvonplaten, @patil-suraj
- longformer, reformer, transfoxl, xlnet: @patrickvonplaten
- fsmt: @stas00
- funnel: @sgugger
- gpt2: @patrickvonplaten, @LysandreJik
- rag: @patrickvonplaten, @lhoestq
- tensorflow: @LysandreJik

Library:

- benchmarks: @patrickvonplaten
- deepspeed: @stas00
- ray/raytune: @richardliaw, @amogkam
- text generation: @patrickvonplaten
- tokenizers: @n1t0, @LysandreJik
- trainer: @sgugger
- pipelines: @LysandreJik

Documentation: @sgugger

HF projects:

- datasets: [different repo](https://github.com/huggingface/datasets)
- rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)

Examples:

- maintained examples (not research project or legacy): @sgugger, @patil-suraj
- research_projects/bert-loses-patience: @JetRunner
- research_projects/distillation: @VictorSanh

 -->

> It seems I'm facing the issue you mention about `tril` when trying to export a Transfo-XL model using your PR:
> 
> ```
> RuntimeError: Exporting the operator triu to ONNX opset version 12 is not supported. Please feel free to request support or submit a pull request on PyTorch GitHub.
> ```
> 
> Is this due to a mismatch in `onnxruntime` version? cc @mfuntowicz

Exactly! The main problem is that onnx/onnxruntime added the `triu/tril` operator on opset version 13 (if I am not mistaken), but PyTorch has not released a version which supports such opset yet (current PyTorch supports up to opset version 12).

In my humble opinion, I guess this PR should be staled until PyTorch releases their new version, hopefully by the end of the month. With that in mind, we do not need to leverage the `triu/tril` operator or even implement a hacked-version that allows to be exported to ONNX.

What do you think?<|||||>I see - thank you for the explanation. I'll add the `WIP` label to the PR so that the stalebot does not close it, and let's revisit once pytorch 1.10 comes out.<|||||>Torch nightly now has opset 14 with support for both triu/tril, however my testing had these issues:
**1) both triu and tril want a constant for the diagonal parameter**
A work-around hack is  to:
use model config:
        same_length=False
and this code change in modeling_transfo_xl.py (line 908 and 945):

> if mems is None and False:
>             mems = self.init_mems(bsz)
> ...
> if mems is not None:
>                 print("WARNING: if using mems no onnx export")
>                 dec_attn_mask = torch.triu(word_emb.new_ones((qlen, klen), dtype=torch.uint8), diagonal=1 + mlen)[:, :, None]
>  else:
>                 dec_attn_mask = torch.triu(word_emb.new_ones((qlen, klen), dtype=torch.uint8), diagonal=1)[:, :, None]

**2) The export command 
python -m transformers.onnx --model models --feature causal-lm --opset 14 models_onnx
still gives an error:**

> File "/home/craig/envcond/transformers/lib/python3.8/site-packages/onnxruntime/capi/onnxruntime_inference_collection.py", line 378, in _create_inference_session
>     sess.initialize_session(providers, provider_options, disabled_optimizers)
> onnxruntime.capi.onnxruntime_pybind11_state.NotImplemented: [ONNXRuntimeError] : 9 : NOT_IMPLEMENTED : Could not find an implementation for Trilu(14) node with name 'Trilu_64'

