Mismatch between logits from generate and forward with an attention mask for most GPT models
### System Info

- `transformers` version: 4.21.0
- Platform: Linux-3.10.0-1160.45.1.el7.x86_64-x86_64-with-glibc2.10
- Python version: 3.8.8
- Huggingface_hub version: 0.8.1
- PyTorch version (GPU?): 1.10.0+cu113 (False)
- Tensorflow version (GPU?): not installed (NA)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using GPU in script?: no
- Using distributed or parallel set-up in script?: no

### Who can help?

@patil-suraj, @patrickvonplaten, @LysandreJik

### Information

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

### Tasks

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

### Reproduction

```
"""
MWE showing that logits from generate match those from forward, except for the first token?
"""
from transformers import AutoTokenizer, AutoModelForCausalLM
from torch.distributions import Categorical
import torch as t

#Broken:
model_name = "distilgpt2"
#model_name = "gpt2"
#model_name = "EleutherAI/gpt-neo-125M"
#model_name = "EleutherAI/gpt-neo-1.3B"
#Working:
#model_name = "EleutherAI/gpt-j-6B"
lm = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name, padding='right')


tokenizer.pad_token = tokenizer.eos_token
prompt = tokenizer(["big unpadded five token prompt ", "padded three token "], return_tensors='pt', padding=True, add_special_tokens=True)

#generate with plain sampling (https://huggingface.co/blog/how-to-generate)

result = lm.generate(prompt["input_ids"], attention_mask=prompt["attention_mask"], do_sample=True, output_scores=True, return_dict_in_generate=True, top_k=0, max_length=10)
x, logits_gen = result.sequences, result.scores
logits_gen = t.stack(logits_gen, 1)

x_attention_mask = (x != tokenizer.eos_token_id).to(dtype=t.int64)
position_ids = x_attention_mask.cumsum(-1)-1
print("Attention mask for prompt + generated text")
print(x_attention_mask)
print("Position IDs")
print(position_ids)
logits_for = lm(x, attention_mask=x_attention_mask, position_ids=position_ids).logits
#we drop the last element, and the first prompt_length-1 elements to get
#logits from forward to match those from generate
logits_for = logits_for[:, (prompt["input_ids"].shape[-1]-1):-1]

P_for = Categorical(logits = logits_for)
P_gen = Categorical(logits = logits_gen)

#Take only generated tokens
x = x[..., prompt['input_ids'].shape[-1]:]
log_prob_for = P_for.log_prob(x)
log_prob_gen = P_gen.log_prob(x)

print("log-probs from forward")
print(log_prob_for)
print("log-probs from generate")
print(log_prob_gen)
```

### Expected behavior

I'm trying to get logits or log-probabilities from `generate` to match those from `forward` in the presence of a padded prompt.

For GPT models, I managed to get almost everything working, by setting the `position_ids` for `forward` (see MWE script).

However, there still seems to be a slight mismatch with the first token, if the prompt has an attention mask.  You can see this in the returned output, from this script, which is:
```
Attention mask for prompt + generated text
tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
        [1, 1, 1, 1, 1, 0, 0, 1, 1, 1]])
Position IDs
tensor([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
        [0, 1, 2, 3, 4, 4, 4, 5, 6, 7]])
log-probs from forward
tensor([[ -8.3152,  -5.5587,  -3.0973],
        [ -2.6509, -10.6300,  -7.5426]], grad_fn=<SqueezeBackward1>)
log-probs from generate
tensor([[ -8.3152,  -5.5587,  -3.0973],
        [ -2.7818, -10.6300,  -7.5426]])
```

Note the slightly mismatch between the bottom-left log-prob, which doesn't happen for any other log-probability.

I've tried a few GPT flavour models: we get problem for `distilgpt2`, `gpt2`, `EleutherAI/gpt-neo-125M` and `EleutherAI/gpt-neo-1.3B`.  But the log-probs all match for `EleutherAI/gpt-j-6B`.
cc @gante for generate :)<|||||>Hi @LaurenceA 👋

With decoder-only models, such as the ones you mentioned, padding should be done on the left. This is because the output is a continuation of the input prompt -- there would be gaps in the output without left padding. Our code to automatically prepare the position IDs for a given attention mask in decoder-only models has left-sided padding in mind and differs from the one you wrote in your example, hence the output mismatch :)

Not being aware that left-sided padding should be used for these models is a common issue. I'm leaving this issue open as a reminder that we should add some form of warning for users.

___________________________
👉 [example of code to prepare the position IDs](https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt2/modeling_gpt2.py#L1014)

Here's your example, with left padding and the same position IDs creation method:

```python
"""
MWE showing that logits from generate match those from forward, except for the first token?
"""
from transformers import AutoTokenizer, AutoModelForCausalLM
from torch.distributions import Categorical
import torch as t

#Broken:
model_name = "distilgpt2"
#model_name = "gpt2"
#model_name = "EleutherAI/gpt-neo-125M"
#model_name = "EleutherAI/gpt-neo-1.3B"
#Working:
#model_name = "EleutherAI/gpt-j-6B"
lm = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name, padding_side="left")


tokenizer.pad_token = tokenizer.eos_token
prompt = tokenizer(["big unpadded five token prompt ", "padded three token "], return_tensors='pt', padding=True, add_special_tokens=True)

#generate with plain sampling (https://huggingface.co/blog/how-to-generate)

result = lm.generate(prompt["input_ids"], attention_mask=prompt["attention_mask"], do_sample=True, output_scores=True, return_dict_in_generate=True, top_k=0, max_length=10)
x, logits_gen = result.sequences, result.scores
logits_gen = t.stack(logits_gen, 1)

x_attention_mask = (x != tokenizer.eos_token_id).to(dtype=t.int64)
position_ids = x_attention_mask.cumsum(-1)-1
position_ids.masked_fill_(x_attention_mask == 0, 1)
print("Attention mask for prompt + generated text")
print(x_attention_mask)
print("Position IDs")
print(position_ids)
logits_for = lm(x, attention_mask=x_attention_mask, position_ids=position_ids).logits
#we drop the last element, and the first prompt_length-1 elements to get
#logits from forward to match those from generate
logits_for = logits_for[:, (prompt["input_ids"].shape[-1]-1):-1]

P_for = Categorical(logits = logits_for)
P_gen = Categorical(logits = logits_gen)

#Take only generated tokens
x = x[..., prompt['input_ids'].shape[-1]:]
log_prob_for = P_for.log_prob(x)
log_prob_gen = P_gen.log_prob(x)

print("log-probs from forward")
print(log_prob_for)
print("log-probs from generate")
print(log_prob_gen)
```<|||||>@LaurenceA if you run `generate` from the current main, you should see a warning if you don't use left-padding with decoder-only models like GPT2 :)

(#19067)