reward_model.py

import os
import deepspeed
from trl import RewardTrainer, RewardConfig
import torch
from accelerate import Accelerator
from utils import (
    ScriptArguments,
    DEFINE_PAD_TOKEN,
    format_prompt_answer,
    maybe_distributed_barrier,
    resolve_system_prompt,
)
from transformers import (
    AutoTokenizer,
    BitsAndBytesConfig,
    HfArgumentParser,
    AutoModelForSequenceClassification,
)
from data_adapter import load_preference_dataset
os.environ["WANDB_PROJECT"] = "ma-rlhf"
os.environ["WANDB_RUN_NAME"] = "reward_model"

parser = HfArgumentParser(ScriptArguments)
train_args: ScriptArguments = parser.parse_args_into_dataclasses(return_remaining_strings=True)[0]

dataset_name = train_args.dataset_name
dataset_sub_name = train_args.dataset_sub_name
dataset_split = train_args.dataset_split
model_name = train_args.model_name
deepspeed_config_name = train_args.deepspeed_config_name
seq_length = train_args.seq_length
batch_size = train_args.batch_size
output_name = train_args.output_name
is_peft = train_args.use_QLora
is_use_flash_attention2 = train_args.use_flash_attention_2
num_train_epochs = train_args.num_train_epochs
gradient_accumulation_steps = train_args.gradient_accumulation_steps
learning_rate = train_args.learning_rate
default_system_prompt = resolve_system_prompt(train_args.system_prompt)


def create_model_tokenizer(name):
    # QLoRA
    bnb_config = BitsAndBytesConfig(
        load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.bfloat16,
        bnb_4bit_use_double_quant=True,
    )

    device_map = {"": Accelerator().local_process_index}
    print('device map: ', device_map)

    model = AutoModelForSequenceClassification.from_pretrained(
        model_name,
        quantization_config=bnb_config,
        device_map=device_map, # 70b use auto
        num_labels=1,
        use_cache=False,
        trust_remote_code=True,
    )

    tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)

    tokenizer.add_special_tokens({'pad_token': DEFINE_PAD_TOKEN})
    model.pad_token_id = tokenizer.pad_token_id
    model.pad_token = tokenizer.pad_token
    model.config.pad_token_id = tokenizer.pad_token_id
    model.config.pad_token = tokenizer.pad_token

    return model, tokenizer


def create_reward_model_datasets(datasets_name, dataset_sub_name, tokenizer):
    train_dataset = load_preference_dataset(
        datasets_name,
        dataset_sub_name=dataset_sub_name,
        split=dataset_split,
        default_system_prompt=default_system_prompt,
    )
    train_dataset = train_dataset.map(
        lambda examples: tokenize_reward_batch(examples, tokenizer),
        batched=True,
    )

    maybe_distributed_barrier()
    train_dataset = train_dataset.filter(
        lambda x: len(x["input_ids_chosen"]) <= seq_length
        and len(x["input_ids_rejected"]) <= seq_length
    )
    maybe_distributed_barrier()

    # eval_dataset = eval_dataset.map(
    #     preprocess_function_hhrlhf,
    #     batched=True,
    #     num_proc=8,
    # )

    # torch.distributed.barrier()
    # eval_dataset = eval_dataset.filter(
    #     lambda x: len(x["input_ids_chosen"]) <= seq_length
    #     and len(x["input_ids_rejected"]) <= seq_length
    # )

    # torch.distributed.barrier()

    # return train_dataset, eval_dataset
    return train_dataset, None


def tokenize_reward_batch(examples, tokenizer):
    new_examples = {
        "input_ids_chosen": [],
        "attention_mask_chosen": [],
        "input_ids_rejected": [],
        "attention_mask_rejected": [],
    }
    for system_prompt, prompt, response_chosen, response_rejected in zip(
        examples["system"], examples["prompt"], examples["chosen"], examples["rejected"]
    ):
        chosen_text = format_prompt_answer(prompt, response_chosen, system_prompt=system_prompt)
        rejected_text = format_prompt_answer(prompt, response_rejected, system_prompt=system_prompt)

        tokenized_chosen = tokenizer(chosen_text, truncation=True, padding="longest")
        tokenized_rejected = tokenizer(rejected_text, truncation=True, padding="longest")

        new_examples["input_ids_chosen"].append(tokenized_chosen["input_ids"])
        new_examples["attention_mask_chosen"].append(tokenized_chosen["attention_mask"])
        new_examples["input_ids_rejected"].append(tokenized_rejected["input_ids"])
        new_examples["attention_mask_rejected"].append(tokenized_rejected["attention_mask"])

    return new_examples


def train():
    model, tokenizer = create_model_tokenizer(model_name)  # model is sequence classification
    train_datasets, test_datasets = create_reward_model_datasets(dataset_name, None, tokenizer)

    # PEFT
    peft_config = create_peft_reward_model(is_peft)

    # ZERO stage3 use config like # https://github.com/huggingface/trl/issues/835
    reward_config = RewardConfig(
        output_dir=output_name,
        per_device_train_batch_size=batch_size,
        per_device_eval_batch_size=batch_size,
        num_train_epochs=num_train_epochs,
        gradient_accumulation_steps=gradient_accumulation_steps,
        gradient_checkpointing=True,
        learning_rate=learning_rate,
        report_to="wandb",
        warmup_ratio=0.01,
        remove_unused_columns=True,
        optim="adamw_torch",
        logging_steps=1,
        max_length=seq_length,
        deepspeed=deepspeed_config_name,
        bf16=True,
        lr_scheduler_type='cosine',
        # evaluation_strategy="steps",
        # eval_steps=100,
        # max_steps=10,
    )

    trainer = RewardTrainer(
        model,
        args=reward_config,
        train_dataset=train_datasets,
        processing_class=tokenizer,
        peft_config=peft_config,
    )

    trainer.train()
    trainer.save_model(output_name)


if __name__ == "__main__":
    train()