Add QDQBert model and quantization examples of SQUAD task
# What does this PR do?

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This PR includes:

1. Add support of Q/DQ BERT model based on HF BERT model.
(**src/transformers/models/qdqbert/**)

QDQBERT model add fake quantization operations (pair of QuantizeLinear/DequantizeLinear ops) to:
- linear layer inputs and weights
- matmul inputs
- residual add inputs

in BERT model.

QDQBERT model will be able to load from any checkpoint of HF BERT model, and perform Quantization Aware Training/Post Training Quantization with the support from [PyTorch-Quantization toolkit](https://github.com/NVIDIA/TensorRT/tree/master/tools/pytorch-quantization).

2. Add an example of SQUAD tasks finetuned by the QDQBERT model and inferenced by TensorRT
(**transformers/examples/research_projects/quantization-qdqbert/**)

In the example, we use qdqbert model to do Quantization Aware Training from pretrained HF BERT model on SQUAD task. Then [TensorRT](https://github.com/NVIDIA/TensorRT) can run the inference of the generated  ONNX model for optimal INT8 performance out-of-the-box.

## Before submitting
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A related discussion on this topic [Issue 10639](https://github.com/huggingface/transformers/issues/10639)
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## Who can review?

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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

 -->

@LysandreJik @sgugger Thanks!<|||||>Some CIs failed since QDQBERT model needs the dependency of Pytorch Quantization Toolkit (https://github.com/NVIDIA/TensorRT/tree/master/tools/pytorch-quantization). This dependency is good to go with simple one-line installation as:
`pip install pytorch-quantization --extra-index-url https://pypi.ngc.nvidia.com
`

I'm thinking of either adding the one line installation change to CI or adding quantization toolkit installation to transformers installation (or any other suggestions which are smooth and neat for the HF community) if we want to upstream the model. @LysandreJik @sgugger 

Thanks!<|||||>As for the CI failure of check_code_quality, `import pycuda.autoinit` is needed, even if not used, so to initialize CUDA environment. Any suggestions to resolve this?

For the other two check failures, I'm not super sure about what is the root cause. Glad to get insights about how to fix that.<|||||>Thanks for working on this! It seems the code quality is not yet passing, could you run the quality scripts? You can do so with the following, from the root of your clone:
```
pip install -e ".[quality]"
make fixup
```<|||||>> Thanks for working on this! It seems the code quality is not yet passing, could you run the quality scripts? You can do so with the following, from the root of your clone:
> 
> ```
> pip install -e ".[quality]"
> make fixup
> ```

Thanks for the comments! This is actually somewhere I want to check.

The code quality failure is from the TensorRT inference script `import pycuda.autoinit`. This line of code is needed, but not used, to initialize CUDA environment. Is there a way that I can keep this line of code in the script and pass the code quality test? @patrickvonplaten @LysandreJik @sgugger <|||||>Think there is just one small clean-up left to do:

```
examples/research_projects/quantization-qdqbert/evaluate-hf-trt-qa.py:29:1: F401 'pycuda.autoinit' imported but unused
```<|||||>Think there is another line to clean up :-) `examples/research_projects/quantization-qdqbert/evaluate-hf-trt-qa.py:29:1: F401 'pycuda.autoinit' imported but unused`<|||||>> Think there is another line to clean up :-) `examples/research_projects/quantization-qdqbert/evaluate-hf-trt-qa.py:29:1: F401 'pycuda.autoinit' imported but unused`

@patrickvonplaten Is there a workaround for it? The `pycuda.autoinit` is imported for cuda environment setup so it is needed in the script. Thanks!<|||||>You can add a a comment at the end of the import line `# noqa: F401` to have it be ignored by our styler. To check locally if the test will pass or not, just run `make quality`.

Note that with the merge of #14431, you will need to rebase your PR on master and replace the lines
```
        self.init_weights()
```
by
```
        # Initialize weights and apply final processing
        self.post_init()
```

Let us know if you need any help!<|||||>Rebase the PR but not sure why there is the model templates runner CI failure now.<|||||>Thanks again for all your work on this!