fix past_key_values in GPTNeoXForCausalLM.prepare_inputs_for_generation
# What does this PR do?

@gante @sgugger 

Fixes `past_key_values` in `GPTNeoXForCausalLM.prepare_inputs_for_generation`. Passing `past_key_values` to `model.generate` had no effect whatsoever, since the argument was swallowed. Described in Issue #20347 (note that the validation bug was fixed in PR #20353, but the argument was still not passed along to the forward method)

The attached commit fixes the issue on my end, i.e. I now get different results when passing `past_key_values` to `generate`, as opposed to before.
_The documentation is not available anymore as the PR was closed or merged._<|||||>After doing some more testing, I noticed another issue that might or might not be a bug. Currently, it's not possible to use anything else than `1` for `num_return_sequences`. Here is a MWE:

```
import torch
from transformers import GPTNeoXForCausalLM, AutoTokenizer

# Load model
s = "NinedayWang/PolyCoder-160M"
model = GPTNeoXForCausalLM.from_pretrained(s)
tokenizer = AutoTokenizer.from_pretrained(s, pad_token="<|PAD|>")

# Create random prompt
N_TOKENS = 100
BATCH_SIZE=1
NUM_RETURN_SEQUENCES=8
pkv = torch.rand(
    (
        BATCH_SIZE,      # batch size      
        N_TOKENS,    # number of tokens
        2 * model.config.num_hidden_layers, 
        model.config.num_attention_heads, 
        model.config.hidden_size // model.config.num_attention_heads
    )
).permute([2, 0, 3, 1, 4]).split(2)

# Tokenize
enc = tokenizer("Hello world", return_tensors="pt")
enc["attention_mask"] = torch.cat((torch.ones((1, N_TOKENS)), enc["attention_mask"]), dim=1)

# Generate
print(
    tokenizer.decode(
        model.generate( 
            **enc,
            past_key_values=pkv,
            max_new_tokens=100,
            pad_token_id=tokenizer.pad_token_id,
            do_sample=True,
            num_return_sequences=NUM_RETURN_SEQUENCES
        )[0],
        skip_special_tokens=True
    )
)
```

Leads to 
```
Traceback (most recent call last):
  File "stuff/test.py", line 32, in <module>
    num_return_sequences=2
  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 "/pfs/data5/home/st/st_us-052400/st_st175337/thesis/transformers/src/transformers/generation/utils.py", line 1581, in generate
    **model_kwargs,
  File "/pfs/data5/home/st/st_us-052400/st_st175337/thesis/transformers/src/transformers/generation/utils.py", line 2538, in sample
    output_hidden_states=output_hidden_states,
  File "/home/st/st_us-052400/st_st175337/conda/envs/thesis/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1190, in _call_impl
    return forward_call(*input, **kwargs)
  File "/pfs/data5/home/st/st_us-052400/st_st175337/thesis/transformers/src/transformers/models/gpt_neox/modeling_gpt_neox.py", line 663, in forward
    return_dict=return_dict,
  File "/home/st/st_us-052400/st_st175337/conda/envs/thesis/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1190, in _call_impl
    return forward_call(*input, **kwargs)
  File "/pfs/data5/home/st/st_us-052400/st_st175337/thesis/transformers/src/transformers/models/gpt_neox/modeling_gpt_neox.py", line 552, in forward
    output_attentions=output_attentions,
  File "/home/st/st_us-052400/st_st175337/conda/envs/thesis/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1190, in _call_impl
    return forward_call(*input, **kwargs)
  File "/pfs/data5/home/st/st_us-052400/st_st175337/thesis/transformers/src/transformers/models/gpt_neox/modeling_gpt_neox.py", line 325, in forward
    output_attentions=output_attentions,
  File "/home/st/st_us-052400/st_st175337/conda/envs/thesis/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1190, in _call_impl
    return forward_call(*input, **kwargs)
  File "/pfs/data5/home/st/st_us-052400/st_st175337/thesis/transformers/src/transformers/models/gpt_neox/modeling_gpt_neox.py", line 148, in forward
    key = torch.cat((past_key, key), dim=-2)
RuntimeError: Sizes of tensors must match except in dimension 2. Expected size 1 but got size 2 for tensor number 1 in the list.
```

Is that expected behavior? I can fix it by creating multiple prompts (see below) per input, but it seems unintuitive, and I don't see anything about it in the docs. Perhaps the docs should simply mention that.

```
pkv = torch.rand(
    (
        BATCH_SIZE * NUM_RETURN_SEQUENCES,      # <--- expand the batch size 
        N_TOKENS,    # number of tokens
        2 * model.config.num_hidden_layers, 
        model.config.num_attention_heads, 
        model.config.hidden_size // model.config.num_attention_heads
    )
).permute([2, 0, 3, 1, 4]).split(2)
```

<|||||>Hey @ValeKnappich 👋 

Thank you for the addition, I really think we should do this for all models for a better interface. In fact, the argument should be `past_key_values` and not `past`, [as mentioned in the original issue](https://github.com/huggingface/transformers/issues/20347#issuecomment-1346255761), but that's a deeper change. This PR is a quick fix for the problem, so I approve it.

As for `num_return_sequences`, let's open a new issue for it to avoid mixing too many things here :D<|||||>Hi, has this issue been resolved? I tried running the code snippet above:
```
import torch
from transformers import GPTNeoXForCausalLM, AutoTokenizer

# Load model
s = "NinedayWang/PolyCoder-160M"
model = GPTNeoXForCausalLM.from_pretrained(s)
tokenizer = AutoTokenizer.from_pretrained(s, pad_token="<|PAD|>")

# Create random prompt
N_TOKENS = 100
BATCH_SIZE=1
NUM_RETURN_SEQUENCES=8
pkv = torch.rand(
    (
        BATCH_SIZE,      # batch size      
        N_TOKENS,    # number of tokens
        2 * model.config.num_hidden_layers, 
        model.config.num_attention_heads, 
        model.config.hidden_size // model.config.num_attention_heads
    )
).permute([2, 0, 3, 1, 4]).split(2)

# Tokenize
enc = tokenizer("Hello world", return_tensors="pt")
enc["attention_mask"] = torch.cat((torch.ones((1, N_TOKENS)), enc["attention_mask"]), dim=1)

# Generate
print(
    tokenizer.decode(
        model.generate( 
            **enc,
            past_key_values=pkv,
            max_new_tokens=100,
            pad_token_id=tokenizer.pad_token_id,
            do_sample=True,
            num_return_sequences=NUM_RETURN_SEQUENCES
        )[0],
        skip_special_tokens=True
    )
)
```

and it returned with 
```
RuntimeError: The size of tensor a (101) must match the size of tensor b (102) at non-singleton dimension 3
```

Is this a different error?<|||||>@ardywibowo the script I paste below works. But keep in mind that it is probably not doing what you expect: when `past_key_values` is passed, only the latest input token is considered (the all other previous tokens are supposed to be encoded in `past_key_valies`) -- in other words, "Hello" in "Hello world" is ignored when generating the next token, despite being present in the output text.

To understand why, you would have to dive into [this blog post](https://jalammar.github.io/illustrated-gpt2/) and into our `generate` code :)

____________________________
```py
import torch
from transformers import GPTNeoXForCausalLM, AutoTokenizer

# Load model
s = "NinedayWang/PolyCoder-160M"
model = GPTNeoXForCausalLM.from_pretrained(s)
tokenizer = AutoTokenizer.from_pretrained(s, pad_token="<|PAD|>")

# Create random prompt
N_TOKENS = 100
BATCH_SIZE=1
pkv = torch.rand(
    (
        BATCH_SIZE,      # batch size
        N_TOKENS,    # number of tokens
        2 * model.config.num_hidden_layers,
        model.config.num_attention_heads,
        model.config.hidden_size // model.config.num_attention_heads
    )
).permute([2, 0, 3, 1, 4]).split(2)

# Tokenize
enc = tokenizer("Hello world", return_tensors="pt")
enc["attention_mask"] = torch.ones((1, N_TOKENS+1))

# Generate
print(
    tokenizer.decode(
        model.generate(
            **enc,
            past_key_values=pkv,
            max_new_tokens=100,
            pad_token_id=tokenizer.pad_token_id,
            do_sample=True,
        )[0],
        skip_special_tokens=True
    )
)
```