Rework how PreTrainedModel.from_pretrained handles its arguments
Author: anlsh
Pull request: yes

Unification of the `from_pretrained` functions belonging to various modules (GPT2PreTrainedModel, OpenAIGPTPreTrainedModel, BertPreTrainedModel) brought changes to the function's argument handling which don't cause any issues within the repository itself (afaik), but have the potential to break a variety of downstream code (eg. my own).

In the last release of pytorch_transformers ([v0.6.2](https://github.com/huggingface/pytorch-transformers/tree/v0.6.2)), the `from_pretrained` functions took in `*args` and `**kwargs` and passed them directly to the relevant model's constructor (perhaps with some processing along the way). For a typical example, see `from_pretrained`'s signature in `modeling.py` here https://github.com/huggingface/pytorch-transformers/blob/b832d5bb8a6dfc5965015b828e577677eace601e/pytorch_pretrained_bert/modeling.py#L526

and the relevant usage of said arguments (after [some small modifications](https://github.com/huggingface/pytorch-transformers/blob/b832d5bb8a6dfc5965015b828e577677eace601e/pytorch_pretrained_bert/modeling.py#L553-L558)) https://github.com/huggingface/pytorch-transformers/blob/b832d5bb8a6dfc5965015b828e577677eace601e/pytorch_pretrained_bert/modeling.py#L600

In the [latest release](https://github.com/huggingface/pytorch-transformers/tree/v1.0.0), the function's signature remains unchanged but the `*args` and most of the `**kwargs` parameters, in particular pretty much anything not explicitly accessed in [[1]](https://github.com/huggingface/pytorch-transformers/blob/b33a385091de604afb566155ec03329b84c96926/pytorch_transformers/modeling_utils.py#L354-L358)
https://github.com/huggingface/pytorch-transformers/blob/b33a385091de604afb566155ec03329b84c96926/pytorch_transformers/modeling_utils.py#L354-L358

is ignored. If a key of `kwargs` is shared with the relevant model's configuration file then its value is still used to override said key (see the relevant logic [here](https://github.com/huggingface/pytorch-transformers/blob/b33a385091de604afb566155ec03329b84c96926/pytorch_transformers/modeling_utils.py#L138-L148)), but the current architecture breaks, for example, the following pattern which was previously possible.

```
class UsefulSubclass(BertForSequenceClassification)
    def __init__(self, *args, useful_argument, **kwargs):
        super().__init__(*args, **kwargs)
        *logic*

...
bert = UsefulSubclass.from_pretrained(model_name, useful_argument=42).
```

What's more, if these arguments have default values declared in `__init__` then the entire pattern is broken **silently**: because these default values will **never** be overwritten via pretrained instantiation. Thus end users might continue running experiments passing different values of `useful_argument` to `from_pretrained`, unaware that **nothing is actually being changed**

As evidenced by issue #833, I'm not the only one whose code was broken. This commit implements behavior which is a compromise between the old and new behaviors. From [my docstring](https://github.com/xanlsh/pytorch-transformers/blob/764b2d3d2310458b77dc563913313ba0c6d826dd/pytorch_transformers/modeling_utils.py#L347-L351):  

```
If config is None, then **kwargs will be passed to the model.
If config is *not* None, then kwargs will be used to
override any keys shared with the default configuration for the
given pretrained_model_name_or_path, and only the unshared
key/value pairs will be passed to the model.
```

It would actually be ideal to avoid mixing configuration and model parameters entirely (via some sort of `model_args` parameter for example): however this fix has the advantages of

1. Not breaking code written during the `pytorch-pretrained-bert` era
2. Preserving (to the extent possible) the usage of the `from_pretrained.**kwargs` parameter introduced with `pytorch-transformers`

--------------------------------------------------------------------------
I have also included various other (smaller) changes in this pull request:

* ~~Making `PreTrainedModel.__init__` not accept `*args` and `**kwargs` parameters which it has no use for and currently ignores~~ Apparently necessary for the tests to pass :(
* ~~Stop using the the "popping from kwargs" antipattern (see [[1]](https://github.com/huggingface/pytorch-transformers/blob/b33a385091de604afb566155ec03329b84c96926/pytorch_transformers/modeling_utils.py#L354-L358)). Keyword arguments with default values achieve the same thing more quickly, and are strictly more informative since they linters/autodoc modules can actually make use of them. I've replaced all instances that I could find, if this pattern exists elsewhere it should be removed.~~ Oops: turns out this is a Python 2 compatibility thing. With that said, is there really a need to continue supporting Python 2? Especially with its EOL coming up in just a few months, and especially when it necessitates such ugly code...
* Subsume the fix included in #864 , which would conflict (admittedly in a very minor fashion) with this PR.
* Remove some trailing whitespace which seems to have infiltrated the file

Comments:
Hmm, well that's embarrassing. I'll inspect the failing tests some more to see what's up

Regarding python 2, yes we want to keep supporting it and thanks for taking care of it.

Google (which is still using python 2) is a major supplier of pretrained model and architectures and having python 2 support in the library make the job of re-implementing the models a lot easier (I can load TF and PT models side-by-side) :)

I have updated the readme breaking change section on this (ba52fe6)

Thanks for the feedback: In my latest commits I've updated the documentation as requested and renamed the `return_unused_args` parameter to `return_unused_kwargs` to remove any ambiguity.

I also removed the unused `*args` parameter from `PreTrainedConfig.from_pretrained`, which is the only actual interface/logic change

Looks good to me, thanks a lot @xanlsh!