past_key_values not accepted in generate with GPTNeoX 
### System Info

Python 3.7.13
transformers 4.22.2

### Who can help?

@LysandreJik @patrickvonplaten

### Information

- [X] The official example scripts
- [X] My own modified scripts

### Tasks

- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [X] My own task or dataset (give details below)

### Reproduction

The `past_key_values` kwarg is not accepted when calling `model.generate(..., past_key_values=pkv)` on a `GPTNeoxForCausalLM`, even though the `model.forward` does accept this kwarg. It does seem to work fine with other model classes like GPT2.

Minimal example to reproduce error:

```
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
import transformers

model_id = "NinedayWang/PolyCoder-160M" # small model with GPTNeoXForCausalLM class
model = AutoModelForCausalLM.from_pretrained(model_id)
tok = AutoTokenizer.from_pretrained(model_id)
assert isinstance(model, transformers.models.gpt_neox.modeling_gpt_neox.GPTNeoXForCausalLM)
pkv = torch.rand(
    (
        1,      # batch size      
        10,    # number of tokens
        2 * model.config.num_hidden_layers, 
        model.config.num_attention_heads, 
        model.config.hidden_size // model.config.num_attention_heads
    )
)
out = model.generate(**tok("Hello world"), past_key_values=pkv)
```

Error message:
```
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/st/st_us-052400/st_st175337/conda/envs/thesis/lib/python3.7/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context
    return func(*args, **kwargs)
  File "/home/st/st_us-052400/st_st175337/conda/envs/thesis/lib/python3.7/site-packages/transformers/generation_utils.py", line 1146, in generate
    self._validate_model_kwargs(model_kwargs.copy())
  File "/home/st/st_us-052400/st_st175337/conda/envs/thesis/lib/python3.7/site-packages/transformers/generation_utils.py", line 862, in _validate_model_kwargs
    f"The following `model_kwargs` are not used by the model: {unused_model_args} (note: typos in the"
ValueError: The following `model_kwargs` are not used by the model: ['past_key_values'] (note: typos in the generate arguments will also show up in this list)
```  

I checked the error location and located the bug ("transformers/generation_utils.py", line 862, in _validate_model_kwargs):

```
        unused_model_args = []
        model_args = set(inspect.signature(self.prepare_inputs_for_generation).parameters)
        # `kwargs` if often used to handle optional forward pass inputs like `attention_mask`. If
        # `prepare_inputs_for_generation` doesn't accept `kwargs`, then a stricter check can be made ;)
        if "kwargs" in model_args:
            model_args |= set(inspect.signature(self.forward).parameters)
        for key, value in model_kwargs.items():
            if value is not None and key not in model_args:
                unused_model_args.append(key)

        if unused_model_args:
            raise ValueError(
                f"The following `model_kwargs` are not used by the model: {unused_model_args} (note: typos in the"
                " generate arguments will also show up in this list)"
            )
```

It first checks the args of `prepare_inputs_for_generation` and only adds the args of `forward` to the accepted list if `"kwargs"` is in the args of `prepare_inputs_for_generation`. However, contrary to GPT2, it only contains `model_kwargs` instead of `kwargs` for GPTNeox. 

So either the GPTNeoX class should be adapted, or the _validate_model_kwargs method in generation_utils.py. 



### Expected behavior

`generate` should be able to pass along all valid `model_kwargs`
cc @gante <|||||>Hey @ValeKnappich 👋 

Yeah, `model_kwargs` needs to be added to `_validate_model_kwargs`. I'm on it :)<|||||>Great, thanks :)<|||||>@gante @sgugger 

The kwarg validation was only a superficial issue. In fact, now it does not throw an error anymore, however, the `past_key_values` are still not passed on to the forward method. Looks like the `prepare_inputs_for_generation` method is at the core of the problem:

``` 
    def prepare_inputs_for_generation(self, input_ids, past=None, attention_mask=None, **model_kwargs):
        input_shape = input_ids.shape

        # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly
        if attention_mask is None:
            attention_mask = input_ids.new_ones(input_shape)

        # cut decoder_input_ids if past is used
        if past and past[0] is not None:
            input_ids = input_ids[:, -1:]

        return {"input_ids": input_ids, "attention_mask": attention_mask, "past_key_values": past}
```

Note that model_kwargs is simply swallowed here. I will create a PR shortly<|||||>@gante @ArthurZucker I think we should rename all occurrences of `"past"` to `"past_key_values"` in `prepare_inputs_for_generation` and deprecate "past" if necessary.

`"past"` was simply the name for the past key values states before we renamed everything to `past_key_values`, so this is just a left-over.<|||||>Agreed <|||||>This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.

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