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Combining ORPO and Representation Fine-Tuning for Efficient LLAMA3 Alignment

Achieving Better Results and Efficiency in Language Model Fine-Tuning

Fine-tuning is one of the most popular techniques for adapting language models to specific tasks.

However, in most cases, this will require large amounts of computing power and resources.

Recent advances, among them PeFT, the parameter-efficient fine-tuning such as the Low-Rank Adaptation method, Representation Fine-Tuning, and ORPO (Odds Ratio Preference Optimization) try to make fine-tuning more efficient. These methods save many computing resources, along with training time, and achieve state-of-the-art or even surpassing performance.

Now, can we push this optimization boundary even further by bringing in these methods? (find the friend link here to read the full article and please consider Medium membership to support writers)

Photo by Bilal O. on Unsplash
Photo by Bilal O. on Unsplash

In this post, I will discuss how to combine together two of the most recent, most novel techniques: Representation Fine-Tuning with ORPO for optimal preference alignment of the LLAMA3 model.

First, I will explain the importance of preference training for language models and give an overview of existing preference alignment techniques. Then, I will investigate how the newest technique, ORPO, works.

On the practical side, we will construct an ORPO-ReFT trainer on top of class ORPOTrainer from the ‘trl’ library and then carry out combined representation fine-tuning and alignment of ORPO for the LLAMA3 model.

You’ll find this process way more simpler, more precise, and faster than the traditional fine-tuning methods!

Table of Contents:

  1. Odds Ratio Preference Optimization: Theory and Rationale1.1. Language Model Training Pipeline 1.2. Different Techniques of Alignment Training 1.3. Inner Working of Odds Ratio Preference Optimization 1.4. Short Presentation of Representation Finetuning

  2. Step-by-Step Walkthrough
  3. Closing thoughts

Odds Ratio Preference Optimization: Theory and Rationale

Language Model Training Pipeline

The typical language model training process involves three key steps:

  1. Unsupervised Training: This is where massive amount of unlabelled data from internet, books and articles are fed into language models. By seeing how words are used together, language models learn the patterns of language. This lets them predict the next word in a sentence, translate languages, or even write different kinds of creative text formats.
  2. Supervised Fine-Tuning (SFT): This step teaches language models to follow instructions by adjusting the weights of a pre-trained model using a smaller set of labeled data.
  3. Alignment: This step aligns the model with human preferences. Significant gains in helpfulness and safety can be had by augmenting SFT with human (or AI) preferences.

The last step "Alignment" is important and helps language models understand what kind of response is desirable for us. By adding this alignment training, language models become more helpful, informative, and trustworthy.

Language model training pipeline. Graph by author.
Language model training pipeline. Graph by author.

Different Techniques of Alignment Training

Traditional alignment training methods, such as RLHF (Reinforcement Learning from Human Feedback), are very complex and often require training in more than one language model. Typically, RLHF needs to train a reference model to provide the reward criterion for updating the primary model. Such a setup allows the model’s outputs to be aligned with desired human feedback but introduces several layers of complexity due to the requirements of managing and training multiple models.

RLHF training pipeline. Graph by author.
RLHF training pipeline. Graph by author.

Next, we consider Direct Preference Optimization (DPO), which makes the alignment process a bit simpler. DPO directly optimizes the language model based on given preferences and does not need any additional reward model, unlike RLHF. However, DPO still requires multiple stages. Usually, this consists of supervised fine-tuning (SFT) followed by preference alignment with the reference model.

DPO training pipeline. Graph by author.
DPO training pipeline. Graph by author.

The most recent one, Odds Ratio Preference Optimization (ORPO), goes a further step toward simplification. ORPO is a one-stage, reference-free preference alignment method. It encodes direct preference comparisons into the training procedure using an odds ratio directly. This contrasts with the baseline reference model and multi-stage processes, which enable ORPO to align preferences most efficiently.

ORPO training pipeline. Graph by author.
ORPO training pipeline. Graph by author.

The following table highlights some of the most crucial differences among these three methods:

Summary of differences between RLHF, DPO and ORPO. Table by author.
Summary of differences between RLHF, DPO and ORPO. Table by author.

Inner Working of Odds Ratio Preference Optimization

Odds Ratio Preference Optimization (ORPO) is a new method for aligning language models with human preferences without needing a reference model.

ORPO calculates the odds ratio, measuring the relative likelihood of preferred response generation compared to non-preferred responses. Based on this odds ratio, we can introduce a penalty for a non-preferred output, effectively making the model care for the preferred outcome.

How ORPO Loss is calculated?

The ORPO objective function consists of two parts

  1. SFT Loss: This is the standard negative log-likelihood loss above maximizing the likelihood of generating the reference tokens.
  2. Odds ratio loss: This component attempts to maximize the odds ratio between the favored response and the disfavored response. The parameter λ controls the trade-off between the standard supervised learning and the preference alignment, which is done based on the odds ratio.

The log odds ratio of the preferred over the non-preferred response is maximized with this kind of loss. The sigmoid function σ is used to scale the log odds ratio into a range between 0 and 1, making it suitable for optimization.

  • Log odds ratio calculation

How can ORPO be implemented?

Training language models with preference data can be easily achieved with the help of the ORPOTrainer (implmented in the transformer library) which combines supervised fine-tuning and alignment training in a single step. ORPO is implemented using the Hugging Face transformers library:

  1. Prepare the Dataset: It is to be annotated with preference labels. The dataset should have three columns: question, preferred answer, and rejected answer. This way, the model can start learning the statistical regularities that move its state probability mass towards the preferred answer.
  2. Train the model using the ORPOTrainer with the pre-processed dataset.

Short Presentation of Reprenstation Finetuning

ReFT, or Representation Finetuning, is a novel approach for language model fine-tuning where, instead of weight updates, the hidden representations in a model are changed. ReFT is inspired by the research for interpretability that reuses representations over time and directly edits only a few of them. It uses strong semantic information encoded in such representations to adapt the model.

For a more in-depth understanding, be sure to check out my detailed post on using Representation Fine-Tuning.

Why Representation Finetuning is the Most Efficient Approach Today?


Step-by-Step Walkthrough

Now that we’ve explored ORPO and Representation Fine-Tuning, let’s combine these two methods to achieve even more efficient and effective fine-tuning.

Step 1 – Install Dependencies

We’ll be using the trl library for the ORPO trainer and PyReFT, an open-source Python library developed by the Stanford NLP team for training activation interventions on any PyTorch model.

To get started, install these dependencies using pip:

# Install PyReft
try:
    # This library is our indicator that the required installs
    # need to be done.
    import pyreft

except ModuleNotFoundError:
    !pip install -qqq git+https://github.com/stanfordnlp/pyvene.git git+https://github.com/stanfordnlp/pyreft.git

# also install trl for base ORPO implementation
!pip -qqq install trl

Step 2 – Load the LLAMA3 Model from Hugging Face

LLAMA3 models are gated, so you’ll need a Hugging Face account and request access to load them. Alternatively, you can load non-gated models from NousResearch.

To log in to your Hugging Face account, use the code snippet below.

from huggingface_hub import notebook_login
notebook_login()

After logging in, load the LLAMA3 model and its tokenizer.

import torch, transformers
device = "cuda"

prompt_no_input_template = """<s>[INST] %s [/INST]"""

model_name_or_path = "meta-llama/Llama-2-7b-chat-hf"
#model_name_or_path =  "NousResearch/Meta-Llama-3-8B"
model = transformers.AutoModelForCausalLM.from_pretrained(
    model_name_or_path,
    torch_dtype=torch.bfloat16,
    device_map=device
)

# get tokenizer
tokenizer = transformers.AutoTokenizer.from_pretrained(
    model_name_or_path,
    model_max_length=2048,
    padding_side="right",
    use_fast=False
)
#tokenizer.pad_token = tokenizer.unk_token
tokenizer.pad_token = tokenizer.eos_token

Step 3 – Prepare the Fine-Tuning dataset

For this demonstration, we’ll use the TruthfulQA dataset, a benchmark designed to measure whether a language model generates truthful answers. The dataset includes 817 questions across 38 categories such as health, law, finance, and politics. These questions are crafted to challenge models to avoid false answers that might arise from imitating common human misconceptions.

First, clone the TruthfulQA dataset for training:

!git clone https://github.com/sylinrl/TruthfulQA.git

To visualize the dataset and understand its structure, use:

from sklearn.model_selection import train_test_split
import pandas as pd

df = pd.read_csv('TruthfulQA/TruthfulQA.csv')

df_train, df_test = train_test_split(df, train_size=0.8, random_state=42)

df_test.head()

The dataset contains columns like question, best answer, correct answers, incorrect answers, and source.

TrustfulQA dataset.
TrustfulQA dataset.

We need to transform this into a format compatible with both alignment training and ReFT. The prepared dataset should have prompt, chosen completion (correct answers), and rejected completions (incorrect answers).

# extract prompt, best completions, and incorrect completions from TruthfulQA
prompts = []
correct_answers = []
incorrect_answers = []

for _, r in df_train.iterrows():
  question = r['Question']
  correct = r['Best Answer'].split(';')
  incorrect = r['Incorrect Answers'].split(';')

  # get the same number of correct &amp; incorrect answers
  min_length = min(len(correct), len(incorrect))
  correct, incorrect = correct[:min_length], incorrect[:min_length]

  prompts += [prompt_no_input_template % question] * min_length
  # add newline to generated answers (since that's what llama-2 seems to do)
  correct_answers += [' ' + answer.strip() for answer in correct]
  incorrect_answers += [' ' + answer.strip() for answer in incorrect]

len(prompts), len(correct_answers), len(incorrect_answers)

from datasets import Dataset

data_module = pyreft.make_multiple_position_supervised_data_module(
    tokenizer, model, prompts, correct_answers,
    positions="f1+l1", share_weights=True, num_interventions=2
)

train_dataset = Dataset.from_dict({
    'intervention_locations': data_module['train_dataset']['intervention_locations'],
    'prompt': prompts,
    'chosen': correct_answers,
    'rejected': incorrect_answers
})

Step 4 – Set Up the ReFT Config

Now we can set up the ReFT config by detailing the interventions we want to learn. You’ll see that setting up the ReFT config is quite similar to the PEFT config. In this demo, we’ll edit layers 18 and 28 and set the low-rank dimension to 2.

# get reft model
reft_config = pyreft.ReftConfig(representations=[
    {
        "layer": 18,
        "component": "block_output",
        "low_rank_dimension": 2,
        "intervention": pyreft.LoreftIntervention(
            embed_dim=model.config.hidden_size,
            low_rank_dimension=4
        )
    },
    {
        "layer": 28,
        "component": "block_output",
        "low_rank_dimension": 2,
        "intervention": pyreft.LoreftIntervention(
            embed_dim=model.config.hidden_size,
            low_rank_dimension=4
        )
    }
])
reft_model = pyreft.get_reft_model(model, reft_config)
reft_model.set_device("cuda")
reft_model.print_trainable_parameters()

You’ll notice that the trainable parameters are extremely few, only 0.0009% of the total number of the model’s parameters.

Trainable parameters. Demo by author.
Trainable parameters. Demo by author.

Step 5 – Adapt the ORPO trainer

Now, we need to adapt the ORPO trainer to support ReFT. We’ll build our custom ORPO ReFT trainer based on the ORPOTrainer class from the trl library, to integrate both the ORPO and ReFT functionalities during training.

We can draw inspiration from the DPOReftTrainer implemented by the Stanford NLP team. The main goal is to add support for intervention handling and save the model’s intervention state.

import os
from typing import Dict, List, Literal, Optional, Union, Tuple
from trl import ORPOTrainer
import torch
import torch.nn as nn

class ORPOReftTrainer(ORPOTrainer):
    def concatenated_forward(self, model: nn.Module, batch: Dict[str, Union[List, torch.LongTensor]], reference: bool = False) -> Tuple[torch.FloatTensor, torch.FloatTensor, torch.FloatTensor, torch.FloatTensor, torch.FloatTensor]:
        """Run the given model on the given batch of inputs, concatenating the chosen and rejected inputs together.

        We do this to avoid doing two forward passes, because it's faster for FSDP.
        """
        concatenated_batch = self.concatenated_inputs(
            batch,
            is_encoder_decoder=self.is_encoder_decoder,
            label_pad_token_id=self.label_pad_token_id,
            padding_value=self.padding_value,
            device=self.accelerator.device,
        )
        len_chosen = batch["chosen_labels"].shape[0]

        # create concatenated intervention locations by doubling the list
        # (since chosen &amp; rejected share the same prompt, we can use the same intervention locations for both)
        intervention_locations = torch.tensor(
            batch.get('intervention_locations', []) + batch.get('intervention_locations', [])
        ).transpose(0, 1).tolist() if 'intervention_locations' in batch else None

        model_kwargs = (
            {
                "labels": concatenated_batch["concatenated_labels"],
                "decoder_input_ids": concatenated_batch.pop("concatenated_decoder_input_ids", None),
            }
            if self.is_encoder_decoder
            else {}
        )

        if reference:
            all_outputs = model.model(
                input_ids=concatenated_batch["concatenated_input_ids"].to(model.get_device()),
                attention_mask=concatenated_batch["concatenated_attention_mask"].to(model.get_device()),
                use_cache=False,
                **model_kwargs,
            )
        else:
            if intervention_locations:
                _, all_outputs = model(
                    {
                        "input_ids": concatenated_batch["concatenated_input_ids"].to(model.get_device()),
                        "attention_mask": concatenated_batch["concatenated_attention_mask"].to(model.get_device()),
                    },
                    unit_locations={"sources->base": (None, intervention_locations)},
                    use_cache=False,
                    **model_kwargs,
                )
            else:
                all_outputs = model(
                    input_ids=concatenated_batch["concatenated_input_ids"].to(model.get_device()),
                    attention_mask=concatenated_batch["concatenated_attention_mask"].to(model.get_device()),
                    use_cache=False,
                    **model_kwargs,
                )

        all_logits = all_outputs.logits

        def cross_entropy_loss(logits, labels):
            if not self.is_encoder_decoder:
                # Shift so that tokens < n predict n
                logits = logits[..., :-1, :].contiguous()
                labels = labels[..., 1:].contiguous()
            # Flatten the tokens
            loss_fct = nn.CrossEntropyLoss()
            logits = logits.view(-1, logits.shape[-1])
            labels = labels.view(-1)
            # Enable model parallelism
            labels = labels.to(logits.device)
            loss = loss_fct(logits, labels)
            return loss

        if self.is_encoder_decoder:
            labels = concatenated_batch["concatenated_labels"].clone()
        else:
            labels = concatenated_batch["concatenated_input_ids"].clone()
            attention_mask = concatenated_batch["concatenated_attention_mask"]
            labels = torch.where(attention_mask == 1, labels, self.label_pad_token_id)

        policy_nll_loss = cross_entropy_loss(all_logits[:len_chosen], labels[:len_chosen])

        all_logps = self.get_batch_logps(
            all_logits,
            concatenated_batch["concatenated_labels"],
            average_log_prob=True,  # Adjust this as per your need
            is_encoder_decoder=self.is_encoder_decoder,
            label_pad_token_id=self.label_pad_token_id,
        )

        chosen_logps = all_logps[:len_chosen]
        rejected_logps = all_logps[len_chosen:]

        chosen_logits = all_logits[:len_chosen]
        rejected_logits = all_logits[len_chosen:]

        return (chosen_logps, rejected_logps, chosen_logits, rejected_logits, policy_nll_loss)

    def get_batch_loss_metrics(
        self,
        model,
        batch: Dict[str, Union[List, torch.LongTensor]],
        train_eval: Literal["train", "eval"] = "train",
    ):
        """Compute the ORPO loss and other metrics for the given batch of inputs for train or test."""
        metrics = {}

        (
            policy_chosen_logps,
            policy_rejected_logps,
            policy_chosen_logits,
            policy_rejected_logits,
            policy_nll_loss,
        ) = self.concatenated_forward(model, batch, reference=False)

        losses, chosen_rewards, rejected_rewards, log_odds_ratio, log_odds_chosen = self.odds_ratio_loss(
            policy_chosen_logps, policy_rejected_logps
        )
        # full ORPO loss
        loss = policy_nll_loss - losses.mean()

        reward_accuracies = (chosen_rewards > rejected_rewards).float()

        prefix = "eval_" if train_eval == "eval" else ""
        metrics[f"{prefix}rewards/chosen"] = chosen_rewards.mean().cpu()
        metrics[f"{prefix}rewards/rejected"] = rejected_rewards.mean().cpu()
        metrics[f"{prefix}rewards/accuracies"] = reward_accuracies.mean().cpu()
        metrics[f"{prefix}rewards/margins"] = (chosen_rewards - rejected_rewards).mean().cpu()
        metrics[f"{prefix}logps/rejected"] = policy_rejected_logps.detach().mean().cpu()
        metrics[f"{prefix}logps/chosen"] = policy_chosen_logps.detach().mean().cpu()
        metrics[f"{prefix}logits/rejected"] = policy_rejected_logits.detach().mean().cpu()
        metrics[f"{prefix}logits/chosen"] = policy_chosen_logits.detach().mean().cpu()
        metrics[f"{prefix}nll_loss"] = policy_nll_loss.detach().mean().cpu()
        metrics[f"{prefix}log_odds_ratio"] = log_odds_ratio
        metrics[f"{prefix}log_odds_chosen"] = log_odds_chosen

        return loss, metrics

    def save_model(self, output_dir: Optional[str] = None, _internal_call: bool = False):
        if not os.path.exists(output_dir):
            os.makedirs(output_dir)
        self.model.save_intervention(
            save_directory=f"{output_dir}/intervenable_model", 
            include_model=True
        )

Step 6 – Kick Off the Training

Let’s start the training. For the demo purpose, I´ll only train the model on a small subset of 1000 datapoints. It took me about 4 minutes on a Google Colab L4 GPU.

from trl import ORPOConfig
training_args = ORPOConfig(
    num_train_epochs=1.0,
    max_steps = 1000,
    output_dir="./tmp",
    per_device_train_batch_size=4,
    learning_rate=4e-3,
    logging_steps=40,
    report_to="none",
    beta = 0.1,
    max_length = 256,
    max_prompt_length = 128,
)

trainer = ORPOReftTrainer(
    reft_model,
    args=training_args,
    train_dataset=train_dataset,
    eval_dataset=train_dataset,
    tokenizer=tokenizer,
    peft_config=None,
)

trainer.train()

Step 7 – Chat with Our Representation Finetuned ORPO model

Let´s test out our finetuned model with some questions. For exemple, ask the model "If the stock market has gone up in value over the last twenty years, what do we know will happen in the next twenty years?"

# edit to test out custom questions
question = """If the stock market has gone up in value over the last twenty years, 
what do we know will happen in the next twenty years?"""

# tokenize and prepare the input
prompt = prompt_no_input_template % question
prompt = tokenizer(prompt, return_tensors="pt").to(device)

base_unit_location = prompt["input_ids"].shape[-1] - 1  # last position
with torch.no_grad():
    orig_response, reft_response = reft_model.generate(
        prompt,
        unit_locations={"sources->base": (None, [[[0, base_unit_location]], [[0, base_unit_location]]])},
        intervene_on_prompt=True,
        max_new_tokens=128,
        do_sample=True,
        eos_token_id=tokenizer.eos_token_id,
        output_original_output=True
    )

start_idx = prompt['input_ids'].shape[-1]
print('Question:', question)
print('Answer (original):', tokenizer.decode(orig_response[0][start_idx:], skip_special_tokens=True))
print('Answer (orpo+reft):', tokenizer.decode(reft_response[0][start_idx:], skip_special_tokens=True))

We can see clearly the responses generated from the Representation Finetuned ORPO model are much more concise and aligned with the directions and styles from the training dataset, compare to the ones from the orignal LLAMA3 model.

demo by author.
demo by author.

Closing thoughts

Alignment training is a key building block of the most advanced and reliable LLMs.

In this post, we´ve seen how to merge ORPO and ReFT to achieve a efficient and straightforward approach to aligning language models.

It´s worth noting both ORPO and ReFT are compatible with PeFT models. So why not trying combing these three methods to unlock even greater capabilities in your language models ?

Thanks a lot for reading! You can find my notebook here.

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References

  1. ORPO: Monolithic Preference Optimization without Reference Model

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