AReaL/docs/customization/dataset.md

Dataset

AReaL-lite directly integrates with the Dataset class from the HuggingFace datasets package. This gives you full flexibility to load, process, and filter your data before training.

The required columns in your dataset depend on the specific implementation of the RolloutWorkflow (for online reinforcement learning) or the training engines (for offline training, such as LMEngine for Supervised Fine-Tuning (SFT)).

Here are two concrete examples from the existing implementation:

SFT (Offline Training)

In the SFT example, we see that the loaded data is directly passed to the train_lm method:

# examples/arealite/gsm8k_sft.py
def main(args):
    ...
    # Create dataset and dataloaders
    train_dataloader = StatefulDataLoader(
        get_gsm8k_dataset("train", tokenizer, rank, world_size),
        collate_fn=pad_sequences_to_tensors,
    )
    ...
    # Run training loop
    for epoch in range(total_epochs):
        for step, data in enumerate(train_dataloader):
            stats = engine.train_lm(data)

In this case, the train_lm method requires the keys "input_ids", "attention_mask", and "loss_mask" to function. We first tokenize the dataset to extract the "input_ids" and "loss_mask". Then, the pad_sequences_to_tensors method is used to batch multiple sequences and append the "attention_mask":

def process_gsm8k_sft_dataset(dataset: Dataset, tokenizer):
    def process(sample):
        seq_token = tokenizer.encode(
            sample["question"] + sample["answer"] + tokenizer.eos_token
        )
        prompt_token = tokenizer.encode(sample["question"])
        loss_mask = [0] * len(prompt_token) + [1] * (len(seq_token) - len(prompt_token))
        return {"input_ids": seq_token, "loss_mask": loss_mask}

    # Remove unnecessary columns to avoid errors during collation
    dataset = dataset.map(process).remove_columns(["question", "answer"])
    return dataset

def get_gsm8k_dataset(split, tokenizer, rank, world_size):
    dataset = load_dataset(path="openai/gsm8k", name="main", split=split)
    dataset = split_dataset_by_node(dataset, rank=rank, world_size=world_size)
    return process_gsm8k_sft_dataset(dataset, tokenizer)

GRPO (Online Training)

In the GRPO example, the loaded data is passed to the InferenceEngine, rather than the TrainEngine:

# examples/arealite/gsm8k_ppo.py
def main(args):
    ...
    # Create dataset and dataloaders
    train_dataloader = StatefulDataLoader(
        get_gsm8k_dataset("train", rank, world_size),
        collate_fn=lambda x: x,
    )
    # Initialize inference engine
    rollout = RemoteSGLangEngine(config.rollout)
    workflow = RLVRWorkflow(
        reward_fn=gsm8k_reward_fn,
        ...
    )
    # Run training loop
    ...
    for global_step in range(max_steps):
        batch = rollout.rollout_batch(data, workflow=workflow)
        ...

Note that the collate_fn here is an identity function, meaning it simply returns the list of individual data items as a batch. In rollout_batch, the data is then dispatched to multiple concurrent executions of workflow.arun_episode, where each dispatched data corresponds to a single episode.

The RLVRWorkflow implementation extracts the "messages" field from the data dictionary as the prompt for generating a response. Additionally, this data is passed to the reward_fn as keyword arguments, which allows the reward function to make use of other dataset fields, like "answers". Here’s an example:

class RLVRWorkflow(RolloutWorkflow):

    async def arun_episode(self, engine: InferenceEngine, data):
        input_ids = self.tokenizer.apply_chat_template(
            data["messages"],
            tokenize=True,
            add_generation_prompt=True,
            enable_thinking=self.enable_thinking,
        )
        req = LLMRequest(
            input_ids=input_ids,
            ...
        )
        ...
        reward = self.reward_fn(
            prompt=prompt_str,
            completions=completions_str,
            prompt_ids=resp.input_tokens,
            completion_ids=resp.output_tokens,
            **data,
        )

Thus, the "messages" field must be constructed when loading the dataset, and the reward function should be defined to handle the dataset's specific fields. Here’s how you can process the dataset for this example:

def process_gsm8k_rl_dataset(dataset: Dataset):
    def process(sample):
        messages = [{"role": "user", "content": sample["question"]}]
        return {"messages": messages}

    # The dataset has two fields "messages" and "answer"
    dataset = dataset.map(process).remove_columns(["question"])
    return dataset

def get_gsm8k_dataset(split, rank, world_size):
    dataset = load_dataset(path="openai/gsm8k", name="main", split=split)
    dataset = split_dataset_by_node(dataset, rank=rank, world_size=world_size)
    return process_gsm8k_rl_dataset(dataset)

def gsm8k_reward_fn(prompt, completions, prompt_ids, completion_ids, answer, **kwargs):
    # "answer" is passed in through "**data"
    from realhf.impl.dataset.math_parser import process_results

    return int(process_results(completions, answer)[0])