Add mt5 onnx config
Author: chainyo
Pull request: yes

# What does this PR do?

- Added MT5 Onnx Config copied from T5 Onnx Config
- Updated docs and tests files

Related to #16308 , and [optimum#321](https://github.com/huggingface/optimum/issues/321)

@patrickvonplaten, @patil-suraj, and @lewtun 


Comments:
I tried to convert the `google/mt5-base` model to Onnx on my local machine and it worked.

```bash
python -m transformers.onnx --model=google/mt5-base convert-onnx/

2022-08-01 12:50:24.992612: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory
2022-08-01 12:50:24.992654: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
/home/workstation/miniconda3/envs/transformers/lib/python3.8/site-packages/transformers/convert_slow_tokenizer.py:434: UserWarning: The sentencepiece tokenizer that you are converting to a fast tokenizer uses the byte fallback option which is not implemented in the fast tokenizers. In practice this means that the fast version of the tokenizer can produce unknown tokens whereas the sentencepiece version would have converted these unknown tokens into a sequence of byte tokens matching the original piece of text.
  warnings.warn(
Some weights of the model checkpoint at google/mt5-base were not used when initializing MT5Model: ['lm_head.weight']
- This IS expected if you are initializing MT5Model from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
- This IS NOT expected if you are initializing MT5Model from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
Using framework PyTorch: 1.11.0+cu102
Overriding 1 configuration item(s)
	- use_cache -> False
/home/workstation/miniconda3/envs/transformers/lib/python3.8/site-packages/transformers/modeling_utils.py:679: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
  if causal_mask.shape[1] < attention_mask.shape[1]:
Validating ONNX model...
	-[✓] ONNX model output names match reference model ({'last_hidden_state'})
	- Validating ONNX Model output "last_hidden_state":
		-[✓] (2, 8, 768) matches (2, 8, 768)
		-[✓] all values close (atol: 1e-05)
All good, model saved at: convert-onnx/model.onnx
```

_The documentation is not available anymore as the PR was closed or merged._

**[Update]**

Sorry, I just realized that MT5 is newly added to the ONNX test. So updating the expected values (somewhere) should work fine.

PR opened: https://github.com/huggingface/transformers/pull/18560

----

Hi @ChainYo. First of all, thank you for the contribution. It looks like this PR is (probably) the cause of 2 new test failures in our CI
```
FAILED tests/onnx/test_onnx_v2.py::OnnxExportTestCaseV2::test_pytorch_export_seq2seq_with_past_52_mt5_seq2seq_lm
FAILED tests/onnx/test_onnx_v2.py::OnnxExportTestCaseV2::test_pytorch_export_seq2seq_with_past_53_mt5_seq2seq_lm_with_past
```
see [job run page](https://github.com/huggingface/transformers/runs/7759166084?check_suite_focus=true). The difference of the outputs and expected outputs are small, but it worked before. So I am wondering if this PR could have any impact on the outputs.

We also have a few other test failures, so I am not 100% sure yet. But if you are interested and have some time, it would be great if you could take a look 🙏 . Otherwise, no problem! We could work on it internally :-) Thank you.