Add ONNX support for LayoutLMv3
# What does this PR do?

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This PR adds ONNX support for LayoutLMv3. Linked to #16308.


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Models:

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- benchmarks: @patrickvonplaten
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@NielsRogge The supported tasks are question answering, token classification and sequence classification. Is there any other use case that should be supported?

Also, the order of input arguments for the `forward` method of `LayoutLMv3ForSequenceClassification` and `LayoutLMv3ForQuestionAnswering` is different from `LayoutLMv3ForTokenClassification` and `LayoutLMv3Model`. This is taken care of in the ONNX config because I guess modifying it in `modeling_layoutlmv3.py` is not an option since it would break backwards compatibility right?<|||||>_The documentation is not available anymore as the PR was closed or merged._<|||||>@lewtun All slow tests passed<|||||>CI fails because of the following error:
```shell
Traceback (most recent call last):
  File "utils/check_repo.py", line 768, in <module>
    check_repo_quality()
  File "utils/check_repo.py", line 762, in check_repo_quality
    check_all_objects_are_documented()
  File "utils/check_repo.py", line 675, in check_all_objects_are_documented
    + "\n - ".join(undocumented_objs)
Exception: The following objects are in the public init so should be documented:
 - OptionalDependencyNotAvailable
 - dummy_scatter_objects
 - sys
```
It seems to come from the following line in `configuration_layoutlmv3.py`:
```python
from ...processing_utils import ProcessorMixin
```<|||||>Wow thanks a lot @sgugger for the clear explanation, it makes complete sense!<|||||>CI and slow tests all passed. It should be ready now @sgugger @lewtun <|||||>Thanks!<|||||>@regisss Thank you for your great work, when convert layoutxlm  LayoutLMv2ForRelationExtraction to onnx, we are blocked by relation extraction layer for some reasons, can you try to export LayoutLMv2ForRelationExtraction model to onnx and give us for some help? gret thanks for you!<|||||>@NielsRogge Thanks for your great work, when I convert LayoutLMv2ForRelationExtraction to onnx, I can not export relation extraction layer to onnx, can you help me to solve it? because the deadline is coming for the project, I hope you can help me, Thank you very much.<|||||>> @regisss Thank you for your great work, when convert layoutxlm LayoutLMv2ForRelationExtraction to onnx, we are blocked by relation extraction layer for some reasons, can you try to export LayoutLMv2ForRelationExtraction model to onnx and give us for some help? gret thanks for you!

@githublsk Where did you find `LayoutLMv2ForRelationExtraction`? I cannot find it in Transformers<|||||>@regisss it is just in microsoft/unlim,please refer to the link:
https://github.com/microsoft/unilm/blob/master/layoutlmft/layoutlmft/models/layoutlmv2/modeling_layoutlmv2.py
![image](https://user-images.githubusercontent.com/77612906/176890500-ff03d43f-8140-44c6-aadc-4c746f4627f6.png)
it is useful for relation extraction, but when running onnx,some question occur as below:
![image](https://user-images.githubusercontent.com/77612906/176890726-94f76fa4-d928-4657-93d7-ccb5b1c80111.png)
the onnx graph is as below:
![image](https://user-images.githubusercontent.com/77612906/176890859-51f745fc-4c81-4e96-8759-e4c7fafb1f91.png)
I can not find any reason, which confused me, I meet the deadline for my project, it is so urgent....<|||||>@regisss if you have time, please help us, I am a newer to it, great thanks for you!<|||||>> @regisss if you have time, please help us, I am a newer to it, great thanks for you!

@githublsk Open an issue because it is not related to this PR. And provide the command/script you ran with the complete error message please, screenshots are not very helpful. <|||||>@regisss Thank you for your great help, I open an issue in the link, can you help me? because the deadline is coming, it bothers me a lot, we hope you can help us to reslove it, great thanks.

https://github.com/huggingface/transformers/issues/17999<|||||>@githublsk 
how did you solve onnx convert error:
 **Exporting the operator bilinear to ONNX opset version 13 is not supported**
 super(BiaffineAttention, self).__init__()
        self.in_features = in_features
        self.out_features = out_features
        **self.bilinear = torch.nn.Bilinear(in_features, in_features, out_features, bias=False)**<|||||>@gjj123 replace torch.nn.Bilinear with this one
```
class Bilinear(nn.Module):
    def __init__(self, in1_features, in2_features, out_features):
        super(Bilinear, self).__init__()
        self.weight = torch.nn.Parameter(torch.zeros((in1_features, in2_features, out_features)))
        self.bias = torch.nn.Parameter(torch.zeros((out_features)))
        nn.init.xavier_uniform_(self.weight)

    def forward(self, x, y):
        t = x @ self.weight.permute(2,0,1)
        output = (t * y).sum(dim=2).t()
        return output
```