reward_modeling.py

# -*- coding: utf-8 -*-
"""
@author:XuMing(xuming624@qq.com)
@description:
"""

import math
import os
from dataclasses import dataclass, field
from glob import glob
from typing import Any, List, Union, Optional, Dict

import torch
from datasets import load_dataset
from loguru import logger
from peft import LoraConfig, TaskType, get_peft_model, PeftModel, prepare_model_for_int8_training
from sklearn.metrics import mean_squared_error, mean_absolute_error
from torch.utils.data import Dataset
from transformers import (
    AutoConfig,
    PreTrainedTokenizerBase,
    BloomForSequenceClassification,
    LlamaForSequenceClassification,
    LlamaTokenizer,
    BloomTokenizerFast,
    AlbertForSequenceClassification,
    BertForSequenceClassification,
    BertTokenizer,
    AutoTokenizer,
    RobertaForSequenceClassification,
    AutoModelForSequenceClassification,
    RobertaTokenizer,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer import TRAINING_ARGS_NAME

MODEL_CLASSES = {
    "bert": (AutoConfig, BertForSequenceClassification, BertTokenizer),
    "roberta": (AutoConfig, RobertaForSequenceClassification, RobertaTokenizer),
    "albert": (AutoConfig, AlbertForSequenceClassification, AutoTokenizer),
    "bloom": (AutoConfig, BloomForSequenceClassification, BloomTokenizerFast),
    "llama": (AutoConfig, LlamaForSequenceClassification, LlamaTokenizer),
    "auto": (AutoConfig, AutoModelForSequenceClassification, AutoTokenizer),
}


@dataclass
class ModelArguments:
    """
    Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch.
    """

    model_type: str = field(
        default=None,
        metadata={"help": "Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys())}
    )
    model_name_or_path: Optional[str] = field(
        default=None,
        metadata={
            "help": (
                "The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."
            )
        },
    )
    tokenizer_name_or_path: Optional[str] = field(
        default=None,
        metadata={
            "help": (
                "The tokenizer for weights initialization.Don't set if you want to train a model from scratch."
            )
        },
    )
    load_in_8bit: bool = field(default=False, metadata={"help": "Whether to load the model in 8bit mode or not."})
    cache_dir: Optional[str] = field(
        default=None,
        metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"},
    )
    use_fast_tokenizer: bool = field(
        default=False,
        metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
    )
    torch_dtype: Optional[str] = field(
        default=None,
        metadata={
            "help": (
                "Override the default `torch.dtype` and load the model under this dtype. If `auto` is passed, the "
                "dtype will be automatically derived from the model's weights."
            ),
            "choices": ["auto", "bfloat16", "float16", "float32"],
        },
    )
    device_map: Optional[str] = field(
        default="auto",
        metadata={"help": "Device to map model to. If `auto` is passed, the device will be selected automatically. "},
    )
    trust_remote_code: bool = field(
        default=True,
        metadata={"help": "Whether to trust remote code when loading a model from a remote checkpoint."},
    )

    def __post_init__(self):
        if self.model_type is None:
            raise ValueError(
                "You must specify a valid model_type to run training. Available model types are " + ", ".join(
                    MODEL_CLASSES.keys()))
        if self.model_name_or_path is None:
            raise ValueError("You must specify a valid model_name_or_path to run training.")


@dataclass
class DataTrainingArguments:
    """
    Arguments pertaining to what data we are going to input our model for training and eval.
    """

    dataset_name: Optional[str] = field(
        default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
    )
    dataset_config_name: Optional[str] = field(
        default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
    )
    train_file_dir: Optional[str] = field(default=None, metadata={"help": "The input jsonl data file folder."})
    validation_file_dir: Optional[str] = field(default=None, metadata={"help": "The evaluation jsonl file folder."}, )
    max_source_length: Optional[int] = field(default=256, metadata={"help": "Max length of prompt input text"})
    max_target_length: Optional[int] = field(default=256, metadata={"help": "Max length of output text"})
    max_train_samples: Optional[int] = field(
        default=None,
        metadata={
            "help": (
                "For debugging purposes or quicker training, truncate the number of training examples to this "
                "value if set."
            )
        },
    )
    max_eval_samples: Optional[int] = field(
        default=None,
        metadata={
            "help": (
                "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
                "value if set."
            )
        },
    )
    overwrite_cache: bool = field(
        default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
    )
    validation_split_percentage: Optional[int] = field(
        default=1,
        metadata={
            "help": "The percentage of the train set used as validation set in case there's no validation split"
        },
    )
    preprocessing_num_workers: Optional[int] = field(
        default=None,
        metadata={"help": "The number of processes to use for the preprocessing."},
    )


@dataclass
class PeftArguments(TrainingArguments):
    use_peft: bool = field(default=True, metadata={"help": "Whether to use peft"})
    target_modules: Optional[str] = field(default="all")
    lora_rank: Optional[int] = field(default=8)
    lora_dropout: Optional[float] = field(default=0.05)
    lora_alpha: Optional[float] = field(default=32.0)
    modules_to_save: Optional[str] = field(default=None)
    peft_path: Optional[str] = field(default=None)


def compute_metrics(eval_preds):
    preds, labels = eval_preds
    # Here, predictions is rewards_chosen and rewards_rejected.
    if isinstance(preds, torch.Tensor):
        preds = preds.detach().cpu().numpy()
    if isinstance(labels, torch.Tensor):
        labels = labels.detach().cpu().numpy()
    # MSE
    mse = mean_squared_error(labels, preds)
    # MAE
    mae = mean_absolute_error(labels, preds)

    return {"mse": mse, "mae": mae}


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

    def __call__(self, features: List[Dict[str, Any]]) -> Dict[str, Any]:
        features_chosen = []
        features_rejected = []
        for feature in features:
            features_chosen.append(
                {
                    "input_ids": feature["input_ids_chosen"],
                    "attention_mask": feature["attention_mask_chosen"],
                }
            )
            features_rejected.append(
                {
                    "input_ids": feature["input_ids_rejected"],
                    "attention_mask": feature["attention_mask_rejected"],
                }
            )
        batch_chosen = self.tokenizer.pad(
            features_chosen,
            padding=self.padding,
            max_length=self.max_length,
            pad_to_multiple_of=self.pad_to_multiple_of,
            return_tensors=self.return_tensors,
        )
        batch_rejected = self.tokenizer.pad(
            features_rejected,
            padding=self.padding,
            max_length=self.max_length,
            pad_to_multiple_of=self.pad_to_multiple_of,
            return_tensors=self.return_tensors,
        )
        batch = {
            "input_ids_chosen": batch_chosen["input_ids"],
            "attention_mask_chosen": batch_chosen["attention_mask"],
            "input_ids_rejected": batch_rejected["input_ids"],
            "attention_mask_rejected": batch_rejected["attention_mask"],
            "return_loss": True,
        }
        return batch


class RewardTrainer(Trainer):
    """
    Trainer for reward models
        Define how to compute the reward loss. Use the InstructGPT pairwise logloss: https://arxiv.org/abs/2203.02155
    """

    def compute_loss(self, model, inputs, return_outputs=False):
        rewards_chosen = model(input_ids=inputs["input_ids_chosen"],
                               attention_mask=inputs["attention_mask_chosen"])[0]
        rewards_rejected = model(input_ids=inputs["input_ids_rejected"],
                                 attention_mask=inputs["attention_mask_rejected"])[0]
        loss = -torch.nn.functional.logsigmoid(rewards_chosen - rewards_rejected).mean()
        if return_outputs:
            return loss, {"rewards_chosen": rewards_chosen, "rewards_rejected": rewards_rejected}
        return loss

    def evaluate(
            self,
            eval_dataset: Optional[Dataset] = None,
            ignore_keys: Optional[List[str]] = None,
            metric_key_prefix: str = "eval",
    ) -> Dict[str, float]:
        if eval_dataset is None:
            eval_dataset = self.eval_dataset
        return super().evaluate(eval_dataset=eval_dataset, ignore_keys=ignore_keys, metric_key_prefix=metric_key_prefix)

    def prediction_step(self, model, inputs, prediction_loss_only, ignore_keys=None):
        # Prepare inputs for chosen and rejected separately
        device = model.device

        inputs_chosen = {
            "input_ids": inputs["input_ids_chosen"].to(device),
            "attention_mask": inputs["attention_mask_chosen"].to(device),
        }
        outputs_chosen = model(**inputs_chosen)
        rewards_chosen = outputs_chosen.logits.detach()

        inputs_rejected = {
            "input_ids": inputs["input_ids_rejected"].to(device),
            "attention_mask": inputs["attention_mask_rejected"].to(device),
        }
        outputs_rejected = model(**inputs_rejected)
        rewards_rejected = outputs_rejected.logits.detach()

        # Keep the compute_loss method
        loss = -torch.nn.functional.logsigmoid(rewards_chosen - rewards_rejected).mean()
        if prediction_loss_only:
            return (loss, None, None)

        return (loss, rewards_chosen, rewards_rejected)

    def save_model(self, output_dir=None, _internal_call=False):
        """Save the LoRA model."""
        os.makedirs(output_dir, exist_ok=True)
        torch.save(self.args, os.path.join(output_dir, TRAINING_ARGS_NAME))
        self.model.save_pretrained(output_dir)


def save_model(output_dir, model, tokenizer, args):
    """Save the model and the tokenizer."""
    os.makedirs(output_dir, exist_ok=True)

    # Take care of distributed/parallel training
    model_to_save = model.module if hasattr(model, "module") else model
    model_to_save.save_pretrained(output_dir)
    tokenizer.save_pretrained(output_dir)
    torch.save(args, os.path.join(output_dir, TRAINING_ARGS_NAME))


class CastOutputToFloat(torch.nn.Sequential):
    """Cast the output of the model to float"""

    def forward(self, x):
        return super().forward(x).to(torch.float32)


def print_trainable_parameters(model):
    """
    Prints the number of trainable parameters in the model.
    """
    trainable_params = 0
    all_param = 0
    for _, param in model.named_parameters():
        all_param += param.numel()
        if param.requires_grad:
            trainable_params += param.numel()
    print(
        f"trainable params: {trainable_params} || all params: {all_param} || trainable%: {100 * trainable_params / all_param}"
    )


def find_all_linear_names(peft_model, int4=False, int8=False):
    cls = torch.nn.Linear
    if int4 or int8:
        import bitsandbytes as bnb
        if int4:
            cls = bnb.nn.Linear4bit
        elif int8:
            cls = bnb.nn.Linear8bitLt
    lora_module_names = set()
    for name, module in peft_model.named_modules():
        if isinstance(module, cls):
            # last layer is not add to lora_module_names
            if 'lm_head' in name:
                continue
            if 'score' in name:
                continue
            names = name.split('.')
            lora_module_names.add(names[0] if len(names) == 1 else names[-1])
    return sorted(lora_module_names)


def main():
    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, PeftArguments))
    model_args, data_args, training_args = parser.parse_args_into_dataclasses()

    logger.warning(f"Model args: {model_args}")
    logger.warning(f"Data args: {data_args}")
    logger.warning(f"Training args: {training_args}")
    logger.warning(
        f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
        + f" distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
    )

    # Set seed before initializing model.
    set_seed(training_args.seed)

    # Load model
    if not model_args.model_type:
        raise ValueError("Please specify a model_type, e.g. llama, chatglm, bloom, etc.")
    config_class, model_class, tokenizer_class = MODEL_CLASSES[model_args.model_type]
    if model_args.model_name_or_path:
        torch_dtype = (
            model_args.torch_dtype
            if model_args.torch_dtype in ["auto", None]
            else getattr(torch, model_args.torch_dtype)
        )
        world_size = int(os.environ.get("WORLD_SIZE", 1))
        if world_size > 1:
            model_args.device_map = {"": int(os.environ["LOCAL_RANK"]) or 0}
        config = config_class.from_pretrained(
            model_args.model_name_or_path,
            num_labels=1,
            torch_dtype=torch_dtype,
            trust_remote_code=model_args.trust_remote_code,
            cache_dir=model_args.cache_dir
        )
        if model_args.model_type in ['bloom', 'llama']:
            model = model_class.from_pretrained(
                model_args.model_name_or_path,
                config=config,
                load_in_8bit=model_args.load_in_8bit,
                device_map=model_args.device_map,
                trust_remote_code=model_args.trust_remote_code,
            )
            model.score = CastOutputToFloat(model.score)
        else:
            model = model_class.from_pretrained(
                model_args.model_name_or_path,
                config=config,
                cache_dir=model_args.cache_dir,
                ignore_mismatched_sizes=True
            )
            model.to(training_args.device)
    else:
        raise ValueError(f"Error, model_name_or_path is None, RM must be loaded from a pre-trained model")

    # Load tokenizer
    if model_args.model_type == "bloom":
        model_args.use_fast_tokenizer = True
    tokenizer_kwargs = {
        "cache_dir": model_args.cache_dir,
        "use_fast": model_args.use_fast_tokenizer,
        "trust_remote_code": model_args.trust_remote_code,
    }
    tokenizer_name_or_path = model_args.tokenizer_name_or_path
    if not tokenizer_name_or_path:
        tokenizer_name_or_path = model_args.model_name_or_path
    tokenizer = tokenizer_class.from_pretrained(tokenizer_name_or_path, **tokenizer_kwargs)
    if tokenizer.pad_token_id is None:
        tokenizer.pad_token_id = 0

    if training_args.use_peft:
        if training_args.peft_path is not None:
            logger.info(f"Peft from pre-trained model: {training_args.peft_path}")
            model = PeftModel.from_pretrained(model, training_args.peft_path, is_trainable=True)
        else:
            logger.info("Init new peft model")
            target_modules = training_args.target_modules.split(',') if training_args.target_modules else None
            if target_modules and 'all' in target_modules:
                target_modules = find_all_linear_names(model, int4=False, int8=model_args.load_in_8bit)
            modules_to_save = training_args.modules_to_save
            if modules_to_save is not None:
                modules_to_save = modules_to_save.split(',')
            logger.info(f"Peft target_modules: {target_modules}")
            logger.info(f"Peft lora_rank: {training_args.lora_rank}")
            peft_config = LoraConfig(
                task_type=TaskType.SEQ_CLS,
                target_modules=target_modules,
                inference_mode=False,
                r=training_args.lora_rank,
                lora_alpha=training_args.lora_alpha,
                lora_dropout=training_args.lora_dropout,
                modules_to_save=modules_to_save)
            model = get_peft_model(model, peft_config)
        if model_args.load_in_8bit:
            model = prepare_model_for_int8_training(model)
        model.print_trainable_parameters()
    else:
        logger.info("Full parameters training")
        print_trainable_parameters(model)

    # Get reward dataset for tuning the reward model.
    if data_args.dataset_name is not None:
        # Downloading and loading a dataset from the hub.
        raw_datasets = load_dataset(
            data_args.dataset_name,
            data_args.dataset_config_name,
            cache_dir=model_args.cache_dir,
        )
        if "validation" not in raw_datasets.keys():
            raw_datasets["validation"] = load_dataset(
                data_args.dataset_name,
                data_args.dataset_config_name,
                split=f"train[:{data_args.validation_split_percentage}%]",
                cache_dir=model_args.cache_dir,
            )
            raw_datasets["train"] = load_dataset(
                data_args.dataset_name,
                data_args.dataset_config_name,
                split=f"train[{data_args.validation_split_percentage}%:]",
                cache_dir=model_args.cache_dir,
            )
    else:
        data_files = {}
        if data_args.train_file_dir is not None and os.path.exists(data_args.train_file_dir):
            train_data_files = glob(f'{data_args.train_file_dir}/**/*.json', recursive=True) + glob(
                f'{data_args.train_file_dir}/**/*.jsonl', recursive=True)
            logger.info(f"train files: {', '.join(train_data_files)}")
            data_files["train"] = train_data_files
        if data_args.validation_file_dir is not None and os.path.exists(data_args.validation_file_dir):
            eval_data_files = glob(f'{data_args.validation_file_dir}/**/*.json', recursive=True) + glob(
                f'{data_args.validation_file_dir}/**/*.jsonl', recursive=True)
            logger.info(f"eval files: {', '.join(eval_data_files)}")
            data_files["validation"] = eval_data_files
        raw_datasets = load_dataset(
            'json',
            data_files=data_files,
            cache_dir=model_args.cache_dir,
        )
        # If no validation data is there, validation_split_percentage will be used to divide the dataset.
        if "validation" not in raw_datasets.keys():
            raw_datasets["validation"] = load_dataset(
                'json',
                data_files=data_files,
                split=f"train[:{data_args.validation_split_percentage}%]",
                cache_dir=model_args.cache_dir,
            )
            raw_datasets["train"] = load_dataset(
                'json',
                data_files=data_files,
                split=f"train[{data_args.validation_split_percentage}%:]",
                cache_dir=model_args.cache_dir,
            )
    logger.info(f"Raw datasets: {raw_datasets}")

    # Preprocessing the datasets
    full_max_length = data_args.max_source_length + data_args.max_target_length

    def preprocess_reward_function(examples):
        """
        Turn the dataset into pairs of Question + Answer, where input_ids_chosen is the preferred question + answer
            and text_rejected is the other.
        """
        new_examples = {
            "input_ids_chosen": [],
            "attention_mask_chosen": [],
            "input_ids_rejected": [],
            "attention_mask_rejected": [],
        }
        for question, chosen, rejected in zip(examples["question"], examples["response_chosen"],
                                              examples["response_rejected"]):
            tokenized_chosen = tokenizer("Question: " + question + "\n\nAnswer: " + chosen)
            tokenized_rejected = tokenizer("Question: " + question + "\n\nAnswer: " + rejected)

            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

    train_dataset = None
    max_train_samples = 0
    if training_args.do_train:
        if "train" not in raw_datasets:
            raise ValueError("--do_train requires a train dataset")
        train_dataset = raw_datasets['train']
        max_train_samples = len(train_dataset)
        if data_args.max_train_samples is not None and data_args.max_train_samples > 0:
            max_train_samples = min(len(train_dataset), data_args.max_train_samples)
            train_dataset = train_dataset.select(range(max_train_samples))
        logger.debug(f"Example train_dataset[0]: {train_dataset[0]}")
        with training_args.main_process_first(desc="Train dataset tokenization"):
            tokenized_dataset = train_dataset.shuffle().map(
                preprocess_reward_function,
                batched=True,
                num_proc=data_args.preprocessing_num_workers,
                remove_columns=train_dataset.column_names,
                load_from_cache_file=not data_args.overwrite_cache,
                desc="Running tokenizer on dataset",
            )
            train_dataset = tokenized_dataset.filter(
                lambda x: 0 < len(x['input_ids_rejected']) <= full_max_length and 0 < len(
                    x['input_ids_chosen']) <= full_max_length
            )
            logger.debug(f"Num train_samples: {len(train_dataset)}")
            logger.debug("Tokenized training example:")
            logger.debug(tokenizer.decode(train_dataset[0]['input_ids_chosen']))

    eval_dataset = None
    max_eval_samples = 0
    if training_args.do_eval:
        with training_args.main_process_first(desc="Eval dataset tokenization"):
            if "validation" not in raw_datasets:
                raise ValueError("--do_eval requires a validation dataset")
            eval_dataset = raw_datasets["validation"]
            max_eval_samples = len(eval_dataset)
            if data_args.max_eval_samples is not None and data_args.max_eval_samples > 0:
                max_eval_samples = min(len(eval_dataset), data_args.max_eval_samples)
                eval_dataset = eval_dataset.select(range(max_eval_samples))
            logger.debug(f"Example eval_dataset[0]: {eval_dataset[0]}")
            tokenized_dataset = eval_dataset.map(
                preprocess_reward_function,
                batched=True,
                num_proc=data_args.preprocessing_num_workers,
                remove_columns=eval_dataset.column_names,
                load_from_cache_file=not data_args.overwrite_cache,
                desc="Running tokenizer on dataset",
            )
            eval_dataset = tokenized_dataset.filter(
                lambda x: 0 < len(x['input_ids_rejected']) <= full_max_length and 0 < len(
                    x['input_ids_chosen']) <= full_max_length
            )
            logger.debug(f"Num eval_samples: {len(eval_dataset)}")
            logger.debug("Tokenized eval example:")
            logger.debug(tokenizer.decode(eval_dataset[0]['input_ids_chosen']))

    # Initialize our Trainer
    if training_args.gradient_checkpointing:
        model.gradient_checkpointing_enable()
        model.config.use_cache = False
    else:
        model.config.use_cache = True
    model.enable_input_require_grads()
    if torch.cuda.device_count() > 1:
        # Keeps Trainer from trying its own DataParallelism when more than 1 gpu is available
        model.is_parallelizable = True
        model.model_parallel = True
    trainer = RewardTrainer(
        model=model,
        args=training_args,
        train_dataset=train_dataset if training_args.do_train else None,
        eval_dataset=eval_dataset if training_args.do_eval else None,
        tokenizer=tokenizer,
        compute_metrics=compute_metrics,
        data_collator=RewardDataCollatorWithPadding(
            tokenizer=tokenizer, max_length=full_max_length, padding="max_length"
        ),
    )

    # Training
    if training_args.do_train:
        logger.info("*** Train ***")
        logger.debug(f"Train dataloader example: {next(iter(trainer.get_train_dataloader()))}")
        checkpoint = None
        if training_args.resume_from_checkpoint is not None:
            checkpoint = training_args.resume_from_checkpoint
        train_result = trainer.train(resume_from_checkpoint=checkpoint)

        metrics = train_result.metrics
        metrics["train_samples"] = max_train_samples
        logger.debug(f"Training metrics: {metrics}")
        trainer.log_metrics("train", metrics)
        trainer.save_metrics("train", metrics)
        trainer.save_state()
        logger.info(f"Saving model checkpoint to {training_args.output_dir}")
        save_model(training_args.output_dir, model, tokenizer, training_args)

    # Evaluation
    if training_args.do_eval:
        logger.info("*** Evaluate ***")
        metrics = trainer.evaluate()

        metrics["eval_samples"] = max_eval_samples
        try:
            perplexity = math.exp(metrics["eval_loss"])
        except OverflowError:
            perplexity = float("inf")
        metrics["perplexity"] = perplexity
        logger.debug(f"Eval metrics: {metrics}")
        trainer.log_metrics("eval", metrics)
        trainer.save_metrics("eval", metrics)


if __name__ == "__main__":
    main()