examples/research_projects/stack_llama/scripts/reward_modeling.py

# Copyright 2020-2025 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from dataclasses import dataclass, field
from typing import Any, Optional, Union

import evaluate
import numpy as np
import torch
import torch.nn as nn
from datasets import load_dataset
from peft import LoraConfig, TaskType, get_peft_model
from transformers import (
    AutoModelForSequenceClassification,
    AutoTokenizer,
    HfArgumentParser,
    PreTrainedTokenizerBase,
    Trainer,
    TrainerCallback,
    TrainingArguments,
    set_seed,
)
from transformers.utils import PaddingStrategy


# Define and parse arguments.
@dataclass
class ScriptArguments:
    """
    These arguments vary depending on how many GPUs you have, what their capacity and features are, and what size model you want to train.
    """

    local_rank: Optional[int] = field(default=-1, metadata={"help": "Used for multi-gpu"})
    resume_from_checkpoint: Optional[bool] = field(
        default=False,
        metadata={"help": "If you want to resume training where it left off."},
    )
    deepspeed: Optional[str] = field(
        default=None,
        metadata={
            "help": "Path to deepspeed config if using deepspeed. You may need this if the model that you want to train doesn't fit on a single GPU."
        },
    )
    per_device_train_batch_size: Optional[int] = field(default=4)
    per_device_eval_batch_size: Optional[int] = field(default=1)
    gradient_accumulation_steps: Optional[int] = field(default=1)
    learning_rate: Optional[float] = field(default=2e-5)
    weight_decay: Optional[float] = field(default=0.001)
    model_name: Optional[str] = field(
        default="gpt2",
        metadata={
            "help": "The model that you want to train from the Hugging Face hub. E.g. gpt2, gpt2-xl, bert, etc."
        },
    )
    tokenizer_name: Optional[str] = field(
        default=None,
        metadata={
            "help": "The tokenizer for your model, if left empty will use the default for your model",
        },
    )
    bf16: Optional[bool] = field(
        default=True,
        metadata={
            "help": "This essentially cuts the training time in half if you want to sacrifice a little precision and have a supported GPU."
        },
    )
    num_train_epochs: Optional[int] = field(
        default=1,
        metadata={"help": "The number of training epochs for the reward model."},
    )
    train_subset: Optional[int] = field(
        default=100000,
        metadata={"help": "The size of the subset of the training data to use"},
    )
    eval_subset: Optional[int] = field(
        default=50000,
        metadata={"help": "The size of the subset of the eval data to use"},
    )
    gradient_checkpointing: Optional[bool] = field(
        default=False,
        metadata={"help": "Enables gradient checkpointing."},
    )
    optim: Optional[str] = field(
        default="adamw_hf",
        metadata={"help": "The optimizer to use."},
    )
    lr_scheduler_type: Optional[str] = field(
        default="linear",
        metadata={"help": "The lr scheduler"},
    )
    max_length: Optional[int] = field(default=512)
    eval_first_step: Optional[bool] = field(
        default=False,
        metadata={"help": "Whether to run eval after the first step"},
    )
    seed: Optional[int] = field(
        default=0, metadata={"help": "Random seed that will be set at the beginning of training."}
    )


parser = HfArgumentParser(ScriptArguments)
script_args = parser.parse_args_into_dataclasses()[0]
set_seed(script_args.seed)
# Load the human stack-exchange-paired dataset for tuning the reward model.
train_dataset = load_dataset(
    "lvwerra/stack-exchange-paired", data_dir="data/reward", split="train", verification_mode="no_checks"
)
if script_args.train_subset > 0:
    train_dataset = train_dataset.select(range(script_args.train_subset))
eval_dataset = load_dataset(
    "lvwerra/stack-exchange-paired", data_dir="data/evaluation", split="train", verification_mode="no_checks"
)
if script_args.eval_subset > 0:
    eval_dataset = eval_dataset.select(range(script_args.eval_subset))
# Define the training args. Needs to be done before the model is loaded if you are using deepspeed.
model_name_split = script_args.model_name.split("/")[-1]
output_name = (
    f"{model_name_split}_peft_stack-exchange-paired_rmts__{script_args.train_subset}_{script_args.learning_rate}"
)

training_args = TrainingArguments(
    output_dir=output_name,
    learning_rate=script_args.learning_rate,
    per_device_train_batch_size=script_args.per_device_train_batch_size,
    per_device_eval_batch_size=script_args.per_device_eval_batch_size,
    num_train_epochs=script_args.num_train_epochs,
    weight_decay=script_args.weight_decay,
    eval_strategy="steps",
    eval_steps=500,
    save_strategy="steps",
    save_steps=500,
    gradient_accumulation_steps=script_args.gradient_accumulation_steps,
    gradient_checkpointing=script_args.gradient_checkpointing,
    deepspeed=script_args.deepspeed,
    local_rank=script_args.local_rank,
    remove_unused_columns=False,
    label_names=[],
    bf16=script_args.bf16,
    logging_strategy="steps",
    logging_steps=10,
    optim=script_args.optim,
    lr_scheduler_type=script_args.lr_scheduler_type,
    seed=script_args.seed,
)


# Load the value-head model and tokenizer.
tokenizer_name = script_args.tokenizer_name if script_args.tokenizer_name is not None else script_args.model_name
tokenizer = AutoTokenizer.from_pretrained(tokenizer_name, use_auth_token=True)
tokenizer.pad_token = tokenizer.eos_token


peft_config = LoraConfig(
    task_type=TaskType.SEQ_CLS,
    inference_mode=False,
    r=8,
    lora_alpha=32,
    lora_dropout=0.1,
)

model = AutoModelForSequenceClassification.from_pretrained(
    script_args.model_name, num_labels=1, torch_dtype=torch.bfloat16
)
model = get_peft_model(model, peft_config)
model.print_trainable_parameters()

# Need to do this for gpt2, because it doesn't have an official pad token.
tokenizer.pad_token = tokenizer.eos_token
model.config.pad_token_id = tokenizer.eos_token_id
model.config.use_cache = not script_args.gradient_checkpointing
num_proc = 24  # Can adjust to be higher if you have more processors.
original_columns = train_dataset.column_names


# Turn the dataset into pairs of post + summaries, where text_j is the preferred question + answer and text_k is the other.
# Then tokenize the dataset.
def preprocess_function(examples):
    new_examples = {
        "input_ids_j": [],
        "attention_mask_j": [],
        "input_ids_k": [],
        "attention_mask_k": [],
    }
    for question, response_j, response_k in zip(examples["question"], examples["response_j"], examples["response_k"]):
        tokenized_j = tokenizer("Question: " + question + "\n\nAnswer: " + response_j, truncation=True)
        tokenized_k = tokenizer("Question: " + question + "\n\nAnswer: " + response_k, truncation=True)

        new_examples["input_ids_j"].append(tokenized_j["input_ids"])
        new_examples["attention_mask_j"].append(tokenized_j["attention_mask"])
        new_examples["input_ids_k"].append(tokenized_k["input_ids"])
        new_examples["attention_mask_k"].append(tokenized_k["attention_mask"])

    return new_examples


# preprocess the dataset and filter out QAs that are longer than script_args.max_length
train_dataset = train_dataset.map(
    preprocess_function,
    batched=True,
    num_proc=num_proc,
    remove_columns=original_columns,
)
train_dataset = train_dataset.filter(
    lambda x: len(x["input_ids_j"]) <= script_args.max_length and len(x["input_ids_k"]) <= script_args.max_length,
    num_proc=num_proc,
)

eval_dataset = eval_dataset.map(
    preprocess_function,
    batched=True,
    num_proc=num_proc,
    remove_columns=original_columns,
)
eval_dataset = eval_dataset.filter(
    lambda x: len(x["input_ids_j"]) <= script_args.max_length and len(x["input_ids_k"]) <= script_args.max_length,
    num_proc=num_proc,
)


# We need to define a special data collator that batches the data in our j vs k format.
@dataclass
class RewardDataCollatorWithPadding:
    tokenizer: PreTrainedTokenizerBase
    padding: Union[bool, str, PaddingStrategy] = True
    pad_to_multiple_of: Optional[int] = None
    return_tensors: str = "pt"

    def __call__(self, features: list[dict[str, Any]]) -> dict[str, Any]:
        features_j = []
        features_k = []
        for feature in features:
            features_j.append(
                {
                    "input_ids": feature["input_ids_j"],
                    "attention_mask": feature["attention_mask_j"],
                }
            )
            features_k.append(
                {
                    "input_ids": feature["input_ids_k"],
                    "attention_mask": feature["attention_mask_k"],
                }
            )
        batch_j = self.tokenizer.pad(
            features_j,
            padding=self.padding,
            pad_to_multiple_of=self.pad_to_multiple_of,
            return_tensors=self.return_tensors,
        )
        batch_k = self.tokenizer.pad(
            features_k,
            padding=self.padding,
            pad_to_multiple_of=self.pad_to_multiple_of,
            return_tensors=self.return_tensors,
        )
        batch = {
            "input_ids_j": batch_j["input_ids"],
            "attention_mask_j": batch_j["attention_mask"],
            "input_ids_k": batch_k["input_ids"],
            "attention_mask_k": batch_k["attention_mask"],
            "return_loss": True,
        }
        return batch


# Define the metric that we'll use for validation.
accuracy = evaluate.load("accuracy")


def compute_metrics(eval_pred):
    predictions, _ = eval_pred
    # Here, predictions is rewards_j and rewards_k.
    # We want to see how much of the time rewards_j > rewards_k.
    predictions = np.argmax(predictions, axis=0)
    labels = np.zeros(predictions.shape)
    return accuracy.compute(predictions=predictions, references=labels)


class RewardTrainer(Trainer):
    # Define how to compute the reward loss. We use the InstructGPT pairwise logloss: https://huggingface.co/papers/2203.02155
    def compute_loss(self, model, inputs, return_outputs=False):
        rewards_j = model(input_ids=inputs["input_ids_j"], attention_mask=inputs["attention_mask_j"])[0]
        rewards_k = model(input_ids=inputs["input_ids_k"], attention_mask=inputs["attention_mask_k"])[0]
        loss = -nn.functional.logsigmoid(rewards_j - rewards_k).mean()
        if return_outputs:
            return loss, {"rewards_j": rewards_j, "rewards_k": rewards_k}
        return loss


# Train the model, woohoo.
trainer = RewardTrainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    compute_metrics=compute_metrics,
    data_collator=RewardDataCollatorWithPadding(tokenizer=tokenizer),
)


if script_args.eval_first_step:

    class EvaluateFirstStepCallback(TrainerCallback):
        def on_step_end(self, args, state, control, **kwargs):
            if state.global_step == 1:
                control.should_evaluate = True

    trainer.add_callback(EvaluateFirstStepCallback())

trainer.train(script_args.resume_from_checkpoint)

print("Saving last checkpoint of the model")
model.save_pretrained(output_name + "_peft_last_checkpoint")