Updating InfNanLogitsprocess to also remove negative infinity.
Author: Narsil
Pull request: yes

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Fixes #15169


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Comments:
_The documentation is not available anymore as the PR was closed or merged._

I'm not really sure that this is the reason of the problem. Usually there is nothing wrong with `float("-inf")` values for PyTorch's softmax or sampling function. I've debugged: 

```python
from transformers import T5Tokenizer, T5ForConditionalGeneration, T5Config

lm_model = 't5-small'
model = T5ForConditionalGeneration.from_pretrained(lm_model)
tokenizer = T5Tokenizer.from_pretrained(lm_model)

def restrict_decode_vocab(batch_idx, prefix_beam):
    if len(prefix_beam)==3:
        restricted_vocab = tokenizer(' ', return_tensors="pt")['input_ids'].tolist()
    else:
        restricted_vocab = tokenizer('<extra_id_0> cute dog <extra_id_1> the <pad>', return_tensors="pt")['input_ids'].tolist()
    return restricted_vocab

source = ['The <extra_id_0> walks in <extra_id_1> park .']
source_encoding = tokenizer(source[:], padding='longest', return_tensors="pt")
input_ids, attention_mask = source_encoding['input_ids'], source_encoding['attention_mask']
decoded_beams = model.generate(input_ids=input_ids, attention_mask=attention_mask, do_sample=True, num_beams=2, prefix_allowed_tokens_fn=restrict_decode_vocab, min_length=4, max_length=4, remove_invalid_values=True)
print(decoded_beams)
```

and the reason for the failure seems to be that after 2 generation steps the logits are all `nan`.

Yes, that's because of the `float(-inf)`
the second step, everything is `float(-inf)` before the softmax, and then everything become NaN after the softmax. (The exp sum being -inf, it's understandable)

Yeah, but it should be all `-inf`, this essentially means that no token should be sampled

So IMO the problem here is that the user added a `prefix_allowed_tokens_fn` that sets all values to `-inf`.

Closing, the bug is that the `scores` should never become only `float(-inf)`.

So earlier than this.