[NLP] Bug Fix: Fix `full_logits` Initialization for `all_probs=True` & `compute_logprob=False` in `text_generation_utils.py`
# What does this PR do ?

This PR ensures that the `sample_sequence_batch` function correctly handles the scenario where `all_probs` is set to `True` but `compute_logprob` is `False`. 

Earlier the `full_logits` variable was not correctly initialized when the `all_probs=True` but `compute_logprob=False` causing a `RuntimeError` due to an attempt to broadcast a `None` value using `torch.distributed.broadcast`.


**Collection**: NLP

# Changelog 
- Updated conditions on lines 704, 754, and 792 in `text_generation_utils.py` to handle cases where `all_probs=True` but `compute_logprob=False`.
This ensures that `full_logits` is correctly initialized, preventing runtime errors during distributed broadcasting.

# Usage
With this fix, users can now set `compute_logprob` to `False` and `all_probs` to `True` in the inference section of the config. This ensures that the `sample_sequence_batch` function correctly handles this specific scenario without encountering the previously observed `RuntimeError`.

For example:

```yaml
inference:
  ...
  all_probs: True  # whether to return the log prob for all the tokens in the vocab
  ...
  compute_logprob: False  # a flag used to compute logprob of all the input text, a very special case of running inference, default False
```
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**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
