[New Feature Request] Add KTO

### 🚀 The feature, motivation, and pitch

https://github.com/ContextualAI/HALOs/tree/main
This is a new human alignment method, different from DPO and PPO, it does not depend on pairwise comparison data but work on pointwise evaluation data. According to the paper, KTO outperforms DPO , PPO in a few public benchmarks.

TRL also incorporates KTO loss into its dpo trainer and have opened a PR to create a KTOTrainer to enable e2e KTO training. https://github.com/huggingface/trl/pull/1181

Given its promise, would suggest to support it in this repo.
If nobody is working on this now, I would like to work on it.

Any concerns, please let me know.

The KTO loss could be simply sketched as:

`class SimpleKTOTrainer(UnpairedPreferenceTrainer):
"""A simple version of KTO meant to introduce you to the HALOs repo."""
def loss(self,
policy_chosen_logps: torch.FloatTensor,
policy_rejected_logps: torch.FloatTensor,
reference_chosen_logps: torch.FloatTensor,
reference_rejected_logps: torch.FloatTensor) -> Tuple[torch.FloatTensor, torch.FloatTensor, torch.FloatTensor]:
"""Compute the Kahneman-Tversky loss for a batch of policy and reference model log probabilities.
For each batch of n/2 chosen examples and n/2 rejected examples (belonging to n different inputs), calculate the loss as follows.

If generation y ~ p_chosen, where x' ~ are the examples with rejected generations, we have the 'chosen' loss:
L(x, y) := 1 - sigmoid(beta * (log p_policy(y|x) - log p_reference(y|x) - KL(p_policy(y_rejected|x') || p_reference(y_rejected|x')))
If generation y ~ p_rejected, , where x' ~ are the examples with chosen generations, we have the 'rejected' loss:
L(x, y) := 1 - sigmoid(beta * KL(p_policy(y_chosen|x') || p_reference(y_chosen|x')) - [log p_policy(y|x) - log p_reference(y|x)])
"""
return losses, chosen_rewards, rejected_rewards`

### Alternatives

_No response_

### Additional context

_No response_