[Tentative] Adds support for exporting TransformerXL-based models to ONNX
Author: gugarosa
Pull request: yes
Labels: name: WIP

# What does this PR do?

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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.

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Comments:
> 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'