Experimental symbolic tracing feature with torch.fx for BERT, ELECTRA and T5
# What does this PR do?


This PR provides a function called "symbolic_trace" which enables symbolic tracing for models of the library using the new and still experimental torch.fx feature. Our models can't be symbolically traces directly using `torch.fx`, so this is wrapper function that overcomes various issues.
 
This new feature allows to perform [many kinds of transformations to the graph](https://pytorch.org/docs/stable/fx.html).

It's also needed for projects like https://github.com/flexflow/FlexFlow/

As an experiment currently only three models are supported: BERT, ELECTRA and T5 (support for other models will follow soon).

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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

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Let's also:
1. add some basic usage doc - we can start with just docstring
2. add one test for one of the models, polish it and then see how to replicate it for other models.<|||||>My understanding was that this is experimental and as we start using this side of library we will generalize and improve things. Hence the more slack approach.

Same for tests, I thought it was good to start with unique tests because the workarounds are unique and then over time as more models are ported to come up with common tests. 

@michaelbenayoun, one way to approach this puzzle is to create common tests for what's the same in all of them, and if something is unique to a given model then have just that tested in that model's test file. If you need help with that, please don't hesitate to ask.
<|||||>Even for experimental features like model parallelism, we are using common tests. This should not be different IMO.<|||||>@sgugger, Michael merged the custom tests into common_tests and significantly simplified the mods to the models - yay!

So it looks ready for your review whenever you have a chance. 

Thank you!<|||||>Sorry for jumping in. 
Out of curiosity, what is the scenario to use this symbolic tracing feature? Didn't find any example/doc...
Thanks.<|||||>Well, I initially wanted this in order to be able to try https://github.com/flexflow/FlexFlow, which requires symbolic tracing - but I haven't had a chance to do so yet.<|||||>Got it, thanks for the explanation.<|||||>> Sorry for jumping in.
> Out of curiosity, what is the scenario to use this symbolic tracing feature? Didn't find any example/doc...
> Thanks.

This would be also be helpful to quantize models using [ FX Graph Mode Quantization](https://pytorch.org/docs/stable/quantization.html?highlight=quantization) which automate the quantization process in Pytorch. <|||||>Are these updates still functional currently? As no modeling_fx_utils.py can be seen in the source code directory. 