past functionality broken in release 2.9.0 and 2.9.1
# 🐛 Bug

## Information

Model: gpt2

## To reproduce

Steps to reproduce the behavior:

```
from transformers.tokenization_gpt2 import GPT2Tokenizer
from transformers.modeling_gpt2 import GPT2LMHeadModel
import torch

# Remember to run transformers with latest master (not release 2.5.1)
tokenizer = GPT2Tokenizer.from_pretrained('gpt2', pad_token='<|endoftext|>')
model = GPT2LMHeadModel.from_pretrained('gpt2')

# Complete phrases are: "I like to drink soda without sugar" and "Go watch TV alone, I am not going"
doc = "I like to"
# note: comment the above line and uncomment the following line to make it work with 1 document
docs_tensors = tokenizer.batch_encode_plus([doc], pad_to_max_length=True, return_tensors='pt')

docs_next = [" soda and ", " with this"]
# note: comment the above line and uncomment the following line to make it work with 1 document
docs_next_tensors = tokenizer.batch_encode_plus(
    [d for d in docs_next], pad_to_max_length=True, return_tensors='pt')

# predicting the first part of each phrase
_, past = model(docs_tensors['input_ids'], attention_mask=docs_tensors['attention_mask'])

# manipulating the past
past_expanded = [torch.repeat_interleave(layer, torch.LongTensor([2]), dim=1) for layer in past]
past_attention_mask = torch.ones(docs_next_tensors['attention_mask'].shape[0], len(docs_tensors['input_ids'][0]), dtype=torch.int64)
attn_mask = torch.cat([past_attention_mask, docs_next_tensors['attention_mask']], dim=-1)

# predicting the rest of the phrase with past
logits, _ = model(docs_next_tensors['input_ids'], attention_mask=attn_mask, past=past_expanded)
logits = logits[:, -1]
_, top_indices_results = logits.topk(50)

words = [tokenizer.decode([idx.item()]) for tir in top_indices_results for idx in tir]

print("Predictions for:", [doc + n for n in docs_next])
print("Results with past:", words)

#####################
docs_full_tensors = tokenizer.batch_encode_plus(
    [doc + n for n in docs_next], pad_to_max_length=True, return_tensors='pt')
logits, _ = model(docs_full_tensors['input_ids'], attention_mask=docs_full_tensors['attention_mask'])
logits = logits[:, -1]
_, top_indices_results = logits.topk(50)

words = [tokenizer.decode([idx.item()]) for tir in top_indices_results for idx in tir]

print("Predictions for:", [doc + n for n in docs_next])
print("Results without past:", words)

```

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## Expected behavior

<!-- A clear and concise description of what you would expect to happen. -->
I am expecting the past functionality to work. But I'm getting the following error:
```
RuntimeError: The size of tensor a (4) must match the size of tensor b (5) at non-singleton dimension 3
```
This was working correctly up to release 2.8.0

## Environment info
    
- `transformers` version:
- Platform: Macos
- Python version: 1.4.0 / 1.5.0
- PyTorch version (GPU?): 2.9.0 cpu / 2.9.1 cpu
- Using GPU in script?: no
- Using distributed or parallel set-up in script?: no

Hi @Damiox,

I think I see the problem. That's quite an edge case here. Since `2.8.0` (here the PR: https://github.com/huggingface/transformers/pull/3734), the `input_ids` will always be cut to just the last token when using `past` to have a consistent API with other models and allow to keep putting in the whole `input_ids` when speeding up decoding. Also `past` was never supposed to be used together with `input_ids` longer than 1, so I did not think of this edge case :-/. I'm quite surprised that this even worked before, but I'm also not sure whether the `causal mask` in GPT2 was correct for this case before.

I think a simple fix in your case would be to generate the output incrementally and only use `input_ids` of length 1 or not use the `past` variable at all here.

@LysandreJik, looking at this script, I think I did introduce a breaking change in the PR: https://github.com/huggingface/transformers/pull/3734 for this edge case when:
`past` is given to the model **and** `input_ids` is longer than 1. It's quite an edge case here and I'm not sure whether it's worth reversing the PR. What do you think? 


<|||||>Hi @patrickvonplaten - I'm a bit worried about this... I cannot apply the simple fix because I'm unable to get rid of `past` variable or run more inferences at this point because the performance would be affected for my use case which is over a realtime bigdata dataset. The current results are accurate and are based on discussions in https://github.com/huggingface/transformers/issues/3095 - this is already deployed.

Just to be clear... Will this not be fixed?<|||||>That's an interesting use-case indeed. I don't believe we ever intended the `past` to be used with `input_ids` larger than one, and I'm quite surprised as well that it did work.

@thomwolf, do you have any opinion on this?<|||||>I'm really looking forward to knowing if this can be fixed so to continue working as it was working up to v2.8.0. Any new thoughts on this? Thanks<|||||>Hi @Damiox,

Sorry to answer so late. We had some discussion internally about it. And you are correct, we should revert the PR I merged earlier. This is done in #4581 and should be included in the next version. @thomwolf @LysandreJik 