BartModel's `past_key_values` seems to have different explanations in input_doc and output_doc
## Environment info
     
- `transformers` version: 4.1.1
- Platform: Linux-4.15.0-123-generic-x86_64-with-glibc2.10
- Python version: 3.8.3
- PyTorch version (GPU?): 1.7.0 (True)
- Tensorflow version (GPU?): 2.3.1 (True)
- Using GPU in script?: No
- Using distributed or parallel set-up in script?: No
 
### Who can help
 Bart: @patrickvonplaten

## Information

Model I am using (Bert, XLNet ...): Bart

The problem arises in [the document](https://huggingface.co/transformers/model_doc/bart.html) of BartModel and BartForConditionalGeneration

## To reproduce

Thank you for kindly answering my question https://github.com/huggingface/transformers/issues/9298.
I'm now trying to use Bart in transformers v4.1.1.

I'd like to make use of `past_key_values`, which seems to have been the major change of the refactoring https://github.com/huggingface/transformers/pull/8900,
but I am a bit confused about the type and shape of it.

About the input of the `forward` function, it is explained as:

```
past_key_values (Tuple[Tuple[torch.Tensor]] of length config.n_layers with each tuple having 2 tuples each of which has 2 tensors of shape (batch_size, num_heads, sequence_length - 1, embed_size_per_head)) –

Contains precomputed key and value hidden-states of the attention blocks. Can be used to speed up decoding.
```

About the output, it is explained as:

```
past_key_values (List[torch.FloatTensor], optional, returned when use_cache=True is passed or when config.use_cache=True) – List of torch.FloatTensor of length config.n_layers, with each tensor of shape (2, batch_size, num_heads, sequence_length, embed_size_per_head)).

Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be used (see past_key_values input) to speed up sequential decoding.
```

I think it will be natural if the input `past_key_values` and the output `past_key_values` have the same format and the output can be used as the input in the next step.

If my understanding is correct, the document of the input is generated with `BART_INPUTS_DOCSTRING`, and the output is from `Seq2SeqModelOutput`.

```
    @add_start_docstrings_to_model_forward(BART_INPUTS_DOCSTRING)
    @add_code_sample_docstrings(
        tokenizer_class=_TOKENIZER_FOR_DOC,
        checkpoint="facebook/bart-large",
        output_type=Seq2SeqModelOutput,
        config_class=_CONFIG_FOR_DOC,
```
I'm sorry if I'm wrong, but maybe the `Seq2SeqModelOutput` documentation hasn't been updated for refactoring?
(When I look at the [git log](https://github.com/huggingface/transformers/commits/88ef8893cd649cc2b4adb9885aba88c750118cff/src/transformers/modeling_outputs.py), I cannot find the related commit.)

I apologize if the difference in input/output format is due to some intention.


If you don't mind, I'd like to ask one more question.
In the refactoring of Bart, the `BartDecoderLayer` (renamed from `DecoderLayer`) seems to be updated as below:

``` python
        # make sure decoder uni-directional self-attn at 1st position and cross-attn at 2nd position.
        present_key_value = (self_attn_present_key_value, cross_attn_present_key_value)

        return (
            hidden_states,
            self_attn_weights,
            present_key_value,
            cross_attn_weights,
        )
```

And in the `BartDecoder`, cache is updated as below:

``` python
            if use_cache:
                next_decoder_cache += (present_key_value,)
...

        next_cache = next_decoder_cache if use_cache else None
```

Does it mean the Bart (and other Seq2Seq Language Models) have both `selt_atten_present_key_value` and `cross_attn_present_key_value` in `past_key_values`?

## Expected behavior

Maybe the document of `Seq2SeqModelOutput` needs to be updated.
I apologize if the difference in the input/output explanations is due to some intention.
Hey @forest1988,

Thanks for your issue! You're 100% correct. The docs need to be updated here! The output is actually never a list, it should always be a `Tuple(Tuple(torch.FloatTensor))` - I'll make a PR afterward. 
And in Bart, `past_key_values` always consists of `selt_attn_present_key_value` and `cross_attn_present_key_value`.<|||||>Hi @patrickvonplaten,

Thank you for your quick response to this issue!
The update of the docs and your answer to my question -- what `past_key_values` consists of -- are very helpful for me!
<|||||>Hi @patrickvonplaten,

Excuse me for my frequent questions.
I created a new issue https://github.com/huggingface/transformers/issues/9391, in which I ask your help about the `past_key_values` in Bart (Seq2SeqLM) and GPT-2 (CausalLM).

I think it is not an error, but a feature request.

If you could check it out when you have time, it would be greatly appreciated.