# CPO Trainer

Contrastive Preference Optimization (CPO) as introduced in the paper [Contrastive Preference Optimization: Pushing the Boundaries of LLM Performance in Machine Translation](https://huggingface.co/papers/2401.08417) by Haoran Xu, Amr Sharaf, Yunmo Chen, Weiting Tan, Lingfeng Shen, Benjamin Van Durme, Kenton Murray, and Young Jin Kim. At a high-level, CPO  trains models to
avoid generating adequate, but not perfect translations in Machine Translation (MT) tasks. However, CPO is a general approximation to the DPO loss and can be applied to other domains like chat.

CPO aims to mitigate two fundamental shortcomings of SFT. First, SFT’s methodology of minimizing the discrepancy between predicted outputs and gold-standard references inherently caps model performance at the quality level of the training data. Secondly, SFT lacks a mechanism to prevent the model from rejecting mistakes in translations. The CPO objective is derived from the DPO objective.

## SimPO
The [SimPO](https://huggingface.co/papers/2405.14734) method is also implemented in the `CPOTrainer`. SimPO is an alternative loss that adds a reward margin, allows for length normalization, and does not use BC regularization. To use this loss, we can use SimPO easily by turning on `loss_type="simpo"` and `cpo_alpha=0` in the `CPOConfig`.

## CPO-SimPO
We also offer the combined use of CPO and SimPO, which enables more stable training and improved performance. Learn more details at [CPO-SimPO Github](https://github.com/fe1ixxu/CPO_SIMPO). To use this method, simply enable SimPO by setting `loss_type="simpo"` and a non-zero `cpo_alpha` in the CPOConfig.

## Expected dataset format

The CPO trainer expects a format identical to the DPO trainer, which should include three entries. These entries should be named as follows:

- `prompt`
- `chosen`
- `rejected`

for example:

```py
cpo_dataset_dict = {
    "prompt": [
        "hello",
        "how are you",
        "What is your name?",
        "What is your name?",
        "Which is the best programming language?",
        "Which is the best programming language?",
        "Which is the best programming language?",
    ],
    "chosen": [
        "hi nice to meet you",
        "I am fine",
        "My name is Mary",
        "My name is Mary",
        "Python",
        "Python",
        "Java",
    ],
    "rejected": [
        "leave me alone",
        "I am not fine",
        "Whats it to you?",
        "I dont have a name",
        "Javascript",
        "C++",
        "C++",
    ],
}
```
where the `prompt` contains the context inputs, `chosen` contains the corresponding chosen responses and `rejected` contains the corresponding negative (rejected) responses. As can be seen a prompt can have multiple responses and this is reflected in the entries being repeated in the dictionary's value arrays.

## Expected model format
The CPO trainer expects a model of `AutoModelForCausalLM`, compared to PPO that expects `AutoModelForCausalLMWithValueHead` for the value function.

## Using the `CPOTrainer`
For a detailed example have a look at the `examples/scripts/cpo.py` script. At a high level we need to initialize the `CPOTrainer` with a `model` we wish to train. **Note that CPOTrainer eliminates the need to use the reference model, simplifying the optimization process.** The `beta` refers to the hyperparameter of the implicit reward, and the dataset contains the 3 entries listed above.

```py
cpo_config = CPOConfig(
    beta=0.1,
)

cpo_trainer = CPOTrainer(
    model,
    args=cpo_config,
    train_dataset=train_dataset,
    tokenizer=tokenizer,
)
```
After this one can then call:

```py
cpo_trainer.train()
```

## Loss functions

Given the preference data, the `CPOTrainer` uses the sigmoid loss on the normalized likelihood via the `logsigmoid` to fit a logistic regression.

The [RSO](https://huggingface.co/papers/2309.06657) authors propose to use a hinge loss on the normalized likelihood from the [SLiC](https://huggingface.co/papers/2305.10425) paper. The `CPOTrainer` can be switched to this loss via the `loss_type="hinge"` argument and the `beta` in this case is the reciprocal of the margin.

The [IPO](https://huggingface.co/papers/2310.12036) authors provide a deeper theoretical understanding of the CPO algorithms and identify an issue with overfitting and propose an alternative loss which can be used via the `loss_type="ipo"` argument to the trainer. Note that the `beta`  parameter is the reciprocal of the gap between the log-likelihood ratios of the chosen vs the rejected completion pair and thus the smaller the `beta` the larger this gaps is. As per the paper the loss is averaged over log-likelihoods of the completion (unlike CPO which is summed only).

### For Mixture of Experts Models: Enabling the auxiliary loss

MOEs are the most efficient if the load is about equally distributed between experts.  
To ensure that we train MOEs similarly during preference-tuning, it is beneficial to add the auxiliary loss from the load balancer to the final loss.  

This option is enabled by setting `output_router_logits=True` in the model config (e.g. MixtralConfig).  
To scale how much the auxiliary loss contributes to the total loss, use the hyperparameter `router_aux_loss_coef=...` (default: 0.001).

## Logging

While training and evaluating we record the following reward metrics:

* `rewards/chosen`: the mean log probabilities of the policy model for the chosen responses scaled by beta
* `rewards/rejected`: the mean log probabilities of the policy model for the rejected responses scaled by beta
* `rewards/accuracies`: mean of how often the chosen rewards are > than the corresponding rejected rewards
* `rewards/margins`: the mean difference between the chosen and corresponding rejected rewards
* `nll_loss`: the mean negative log likelihood loss of the policy model for the chosen responses

## CPOTrainer

[[autodoc]] CPOTrainer

## CPOConfig

[[autodoc]] CPOConfig