Fix multiple `eos_token_id`s in model.generate(...)
# What does this PR do?

Fixes https://github.com/huggingface/transformers/pull/20727 for using multiple `eos_token_id`s

## Small repro

```python
import math
import torch
unfinished_sequences = torch.tensor([1,1,1])
next_tokens = torch.tensor([797, 641,  98])
unfinished_sequences.mul((math.prod(next_tokens != i for i in eos_token_id)).long())
```

## Error

if you run 

```python
from transformers import pipeline
generator = pipeline('text-generation', 'gpt2')
generator('hello', eos_token_id=[628, 198], do_sample=True, num_return_sequences=3)
```

then it errors

```python
input = tensor([[-32]])
weight = Parameter containing:
tensor([[-0.0206,  0.0125, -0.0289,  ...,  0.0018, -0.0300,  0.0111],
        [-0.0239, -0.0158,...0,  0.0075,  0.0113],
        [-0.0177, -0.0268,  0.0023,  ...,  0.0135,  0.0077, -0.0042]],
       requires_grad=True)
padding_idx = -1, max_norm = None, norm_type = 2.0, scale_grad_by_freq = False, sparse = False
...


        if has_torch_function_variadic(input, weight):
            return handle_torch_function(
                embedding,
                (input, weight),
                input,
                weight,
                padding_idx=padding_idx,
                max_norm=max_norm,
                norm_type=norm_type,
                scale_grad_by_freq=scale_grad_by_freq,
                sparse=sparse,
            )
        if padding_idx is not None:
            if padding_idx > 0:
                assert padding_idx < weight.size(0), "Padding_idx must be within num_embeddings"
            elif padding_idx < 0:
                assert padding_idx >= -weight.size(0), "Padding_idx must be within num_embeddings"
                padding_idx = weight.size(0) + padding_idx
        else:
            padding_idx = -1
        if max_norm is not None:
            # Note [embedding_renorm contiguous]
            # `embedding_renorm_` will call .contiguous() on input anyways, so we
            # call it here and take advantage of the improved locality in the
            # `embedding` call below too.
            input = input.contiguous()
            # Note [embedding_renorm set_grad_enabled]
            # XXX: equivalent to
            # with torch.no_grad():
            #   torch.embedding_renorm_
            # remove once script supports set_grad_enabled
            _no_grad_embedding_renorm_(weight, input, max_norm, norm_type)
>       return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse)
E       IndexError: index out of range in self

venv/lib/python3.8/site-packages/torch/nn/functional.py:2210: IndexError
```

## Tests

```
pytest tests/generation/test_utils.py::GenerationIntegrationTests::test_eos_token_id_int_and_list_greedy_search --disable-warnings -vv
pytest tests/generation/test_utils.py::GenerationIntegrationTests::test_eos_token_id_int_and_list_contrastive_search --disable-warnings -vv
pytest tests/generation/test_utils.py::GenerationIntegrationTests::test_eos_token_id_int_and_list_top_k_top_sampling --disable-warnings -vv
pytest tests/generation/test_utils.py::GenerationIntegrationTests::test_eos_token_id_int_and_list_beam_search --disable-warnings -vv
```

## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests),
      Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
      to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
      [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
      [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [x] Did you write any new necessary tests?


## Who can review?

Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.

@gante
_The documentation is not available anymore as the PR was closed or merged._<|||||>Hey @tokestermw 👋 

Thank you for spotting the issues and adding a fix! One request, for two reasons: a) thin function wrappers are very undesirable, as they add another abstraction layer b) tensor ops should ideally be done with `torch` operations, otherwise there will be CPU<>GPU data movement 👉 can you replace the implementation with something like the snippet below, which computes the same thing using torch operators?

```py
import torch
eos_token_id = torch.tensor([797, 641])
unfinished_sequences = torch.tensor([1, 1, 1])
next_tokens = torch.tensor([797, 641, 98])
next_in_eos = next_tokens.tile((eos_token_id.shape[0], 1)).ne(eos_token_id.unsqueeze(1)).prod(dim=0)
unfinished_sequences = unfinished_sequences.mul(next_in_eos).long()
```
<|||||>I just found the same issue I think and this is the code snippet I wanted to use for reporting the bug. Probably redundant as of now but before throwing it away, maybe it helps another user finding the issue. No further comment/processing required from my point of view:

```python
from transformers import AutoModelForCausalLM, GenerationConfig

MODEL = "gpt2"
NUM_RETURN_SEQUENCES = 2
MAX_NEW_TOKENS = 64
CONFIG_DIR = "./generation_test"

model = AutoModelForCausalLM.from_pretrained(MODEL)
model.save_pretrained(CONFIG_DIR)

config = GenerationConfig(
    num_return_sequences=NUM_RETURN_SEQUENCES,
    max_new_tokens=MAX_NEW_TOKENS,
    return_full_text=True,
    do_sample=True,
    bos_token_id=50256,
    pad_token_id=50256,
    eos_token_id=[50000,50256],  # the 50000 is just an example to prove the issue
)
config.save_pretrained(CONFIG_DIR)
model = AutoModelForCausalLM.from_pretrained(CONFIG_DIR)

tokenizer = AutoTokenizer.from_pretrained(MODEL)
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
generated = pipe("As always this is a")

print(generated[0]["generated_text"])
```
<|||||>Thanks @gante! will make the change in a bit

Another issue I just found with beam search + multiple eos_token_id is that, on occasion we get this error:

```python
ValueError: At most 3 tokens in tensor([  198,   198,   198,     0,   628, 14373], device='cuda:0') can be equal to
`eos_token_id: [198, 628]`. Make sure tensor([  198,   198,   198,     0,   628, 14373], device='cuda:0') are corrected.
```

<img width="864" alt="Screenshot 2023-02-07 at 16 26 49" src="https://user-images.githubusercontent.com/4722119/217397589-53beae41-fa84-4792-bea6-db6056d33972.png">

This is because we generate 2 * num_beams,
https://github.com/huggingface/transformers/blob/main/src/transformers/generation/utils.py#L2766

which can fail this check when we have more than one `eos_token_id`
https://github.com/huggingface/transformers/blob/main/src/transformers/generation/beam_search.py#L612

(I can post a separate issue if that's better)<|||||>@tokestermw if that is not breaking the existing tests, yes, let's move it to a new issue.

In essence, we probably want to keep `1+len(eos_token_id)` beam candidates running, to ensure we have at least 1 non-`eos_token_id` candidate to proceed.<|||||>Mmm, looks like a lot of tests have started failing @gante and @tokestermw <|||||>fixed

though there is a seemingly unrelated test error

https://app.circleci.com/pipelines/github/huggingface/transformers/57219/workflows/68817729-bfae-4e9a-8139-5e76e0e6ed5d/jobs/693592<|||||>Yes, this one has been fixed on main :-)<|||||>Hi @tokestermw Thank you for working on this. After this PR being merged to `main`, there are some CI regression. Could you take a look 🙏 . Also cc @gante 

## To reproduce:

### We can check with specific commit on `main` branch
```bash
git checkout 06d940ef  # One commit before this PR on `main`
git checkout 9960506c  # This PR - failed the following tests
```

### Then prepare the file format for doctests
```python
python utils/prepare_for_doc_test.py src docs
```

### This
```python
python3 -m pytest -v --make-reports doc_tests_gpu --doctest-modules docs/source/en/model_doc/t5.mdx::t5.mdx -sv --doctest-continue-on-failure --doctest-glob="*.mdx"
```
gives error
```bash
Expected:
    ['Das Haus ist wunderbar.', 'Ich arbeite gerne in NYC.']
Got:
    ['Das Haus ist wunderbar. Das Haus ist wunderschön. Sehr', 'Ich arbeite gerne in NYC. Ich arbeite in NYC.']
```
### and this
```python
python3 -m pytest -v --make-reports doc_tests_gpu --doctest-modules docs/source/en/model_doc/tapex.mdx::tapex.mdx -sv --doctest-continue-on-failure --doctest-glob="*.mdx"
```
gives error
```bash
Expected:
    [' 53', ' george clooney', ' brad pitt']
Got:
    [' 53 lithuania, french montana, french montana, french montana, french montana, french montana ...(very long non-sense string)]
```<|||||>@ydshieh thanks, ah i see the issue 😓 . we're not carrying over the `unfinished_sequences`

making a fix here: https://github.com/huggingface/transformers/pull/21529