main.py

import os
import time
import wandb
import torch
import argparse
from datasets import load_dataset
from typing import List, Dict, Union
from transformers import (
    AutoTokenizer,
    AutoModelForCausalLM,
    TrainingArguments,
    DataCollatorForLanguageModeling,
    set_seed,
)
from peft import LoraConfig, get_peft_model

from src.args import default_args
from src.orpo_trainer import ORPOTrainer
from src.utils import preprocess_logits_for_metrics, dataset_split_selector

class ORPO(object):
    def __init__(self, args) -> None:
        self.start = time.gmtime()
        self.args = args

        # Load Tokenizer
        print(">>> 1. Loading Tokenizer")
        self.tokenizer = AutoTokenizer.from_pretrained(self.args.model_name, cache_dir=self.args.cache_dir)
        if self.tokenizer.chat_template is None:
            self.tokenizer.chat_template = "{% for message in messages %}\n{% if message['role'] == 'user' %}\n{{ '<|user|>\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'system' %}\n{{ '<|system|>\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'assistant' %}\n{{ '<|assistant|>\n'  + message['content'] + eos_token }}\n{% endif %}\n{% if loop.last and add_generation_prompt %}\n{{ '<|assistant|>' }}\n{% endif %}\n{% endfor %}"
            print("     1-1. Chat Template Applied (<|user|> <|assistant|>)")
        else:
            pass
        self.tokenizer.pad_token_id = self.tokenizer.eos_token_id

        # Load Model
        print(">>> 2. Loading Model")
        if self.args.flash_attention_2:
            self.model = AutoModelForCausalLM.from_pretrained(self.args.model_name, 
                                                              cache_dir=self.args.cache_dir,
                                                              torch_dtype=torch.bfloat16,
                                                              attn_implementation="flash_attention_2")
        else:
            self.model = AutoModelForCausalLM.from_pretrained(self.args.model_name, 
                                                              cache_dir=self.args.cache_dir,
                                                              torch_dtype=torch.bfloat16)
            
        if self.args.enable_lora:
            peft_config = LoraConfig(
                lora_alpha=self.args.lora_alpha,
                lora_dropout=self.args.lora_dropout,
                r=self.args.lora_rank,
                task_type="CAUSAL_LM",
            )

            self.model.enable_input_require_grads()

            self.model = get_peft_model(self.model, peft_config=peft_config)
            print("     2-1. LoRA adapter applied!")
            self.model.print_trainable_parameters()
        else:
            pass
                                                          
        # Load Dataset
        print(">>> 3. Loading Dataset")
        self.data = load_dataset(self.args.data_name, cache_dir=self.args.cache_dir)

        # Preprocess Dataset
        print(">>> 4. Filtering and Preprocessing Dataset")
        data_split = dataset_split_selector(self.data)

        if len(data_split) == 1:
            self.is_test = False
            train_split = data_split[0]
            print(f"   >>> Test Set = {self.is_test}")
        else:
            self.is_test = True
            train_split = data_split[0]
            test_split = data_split[1]

            test = self.data[test_split].filter(self.filter_dataset)
            self.test = test.map(self.preprocess_dataset, batched=True, num_proc=self.args.num_proc, remove_columns=self.data[test_split].column_names)       

        train = self.data[train_split].filter(self.filter_dataset)
        print(f"\n\n>>> {len(train)} / {len(self.data[train_split])} rows left after filtering by prompt length.")
        self.train = train.map(self.preprocess_dataset, batched=True, num_proc=self.args.num_proc, remove_columns=self.data[train_split].column_names)                       
                
        # Set WANDB & Logging Configurations
        self.run_name = f"{self.args.model_name.split('/')[-1]}-{self.args.data_name.split('/')[-1]}-lambda{self.args.alpha}-ORPO-{self.start.tm_mday}-{self.start.tm_hour}-{self.start.tm_min}"
        self.save_dir = os.path.join('./checkpoints/', f"{self.args.data_name.split('/')[-1]}/{self.run_name}")
        self.log_dir = os.path.join('./checkpoints/', f"{self.args.data_name.split('/')[-1]}/{self.run_name}/logs")
        
        os.makedirs(self.save_dir, exist_ok=True)
        os.makedirs(self.log_dir, exist_ok=True)

    def preprocess_dataset(self, examples: Union[List, Dict]):
        if ('instruction' in examples.keys()) or ('question' in examples.keys()):
            prompt_key = 'instruction' if 'instruction' in examples.keys() else 'question'
            prompt = [self.tokenizer.apply_chat_template([{'role': 'user', 'content': item}], tokenize=False, add_generation_prompt=True) for item in examples[prompt_key]]
            chosen = [self.tokenizer.apply_chat_template([{'role': 'user', 'content': item_prompt}, {'role': 'assistant', 'content': item_chosen}], tokenize=False) for item_prompt, item_chosen in zip(examples[prompt_key], examples['chosen'])]
            rejected = [self.tokenizer.apply_chat_template([{'role': 'user', 'content': item_prompt}, {'role': 'assistant', 'content': item_rejected}], tokenize=False) for item_prompt, item_rejected in zip(examples[prompt_key], examples['rejected'])]
        else:
            prompt = [self.tokenizer.apply_chat_template(item[:-1], tokenize=False, add_generation_prompt=True) for item in examples['chosen']]
            chosen = [self.tokenizer.apply_chat_template(item, tokenize=False) for item in examples['chosen']]
            rejected = [self.tokenizer.apply_chat_template(item, tokenize=False) for item in examples['rejected']]
    
        model_inputs = self.tokenizer(prompt,
                                      max_length=self.args.response_max_length,
                                      padding='max_length',
                                      truncation=True,
                                      return_tensors='pt')
        pos_labels = self.tokenizer(chosen,
                                    max_length=self.args.response_max_length,
                                    padding='max_length',
                                    truncation=True,
                                    return_tensors='pt')
        neg_labels = self.tokenizer(rejected,
                                    max_length=self.args.response_max_length,
                                    padding='max_length',
                                    truncation=True,
                                    return_tensors='pt') 
                
        model_inputs['positive_input_ids'] = pos_labels['input_ids']
        model_inputs['positive_attention_mask'] = pos_labels['attention_mask']
        
        model_inputs['negative_input_ids'] = neg_labels['input_ids']
        model_inputs['negative_attention_mask'] = neg_labels['attention_mask']
        
        return model_inputs

    def filter_dataset(self, examples: Union[List, Dict]):
        if 'instruction' in examples.keys():
            query = examples['instruction']
            prompt_length = self.tokenizer.apply_chat_template([{'content': query, 'role': 'user'}], tokenize=True, add_generation_prompt=True, return_tensors='pt').size(-1)
        elif 'question' in examples.keys():
            query = examples['question']
            prompt_length = self.tokenizer.apply_chat_template([{'content': query, 'role': 'user'}], tokenize=True, add_generation_prompt=True, return_tensors='pt').size(-1)
        else:
            prompt_length = self.tokenizer.apply_chat_template(examples['chosen'][:-1], tokenize=True, add_generation_prompt=True, return_tensors='pt').size(-1)  
           
        if prompt_length < self.args.prompt_max_length:    
            return True
        else:
            return False

    def prepare_trainer(self):
        wandb.init(name=self.run_name)
        arguments = TrainingArguments(
            output_dir=self.save_dir,  # The output directory
            logging_dir=self.log_dir,
            logging_steps=50,
            learning_rate=self.args.lr,
            overwrite_output_dir=True,  # overwrite the content of the output directory
            num_train_epochs=self.args.num_train_epochs,  # number of training epochs
            per_device_train_batch_size=self.args.per_device_train_batch_size,  # batch size for training
            per_device_eval_batch_size=self.args.per_device_eval_batch_size,  # batch size for evaluation
            evaluation_strategy=self.args.evaluation_strategy if self.is_test else 'no',  
            save_strategy=self.args.evaluation_strategy,
            optim=self.args.optim,
            warmup_steps=self.args.warmup_steps,
            gradient_accumulation_steps=self.args.gradient_accumulation_steps,
            gradient_checkpointing=True, 
            gradient_checkpointing_kwargs={'use_reentrant': False if self.args.enable_lora else True},
            load_best_model_at_end=self.is_test,
            do_train=True,
            do_eval=self.is_test,
            lr_scheduler_type=self.args.lr_scheduler_type,
            remove_unused_columns=False,
            report_to='wandb',
            run_name=self.run_name,
            bf16=True,
            seed=self.args.seed,
        )
        
        data_collator = DataCollatorForLanguageModeling(tokenizer=self.tokenizer, mlm=False)
        
        self.trainer = ORPOTrainer(
            model=self.model,
            alpha=self.args.alpha,
            pad=self.tokenizer.pad_token_id,
            args=arguments,
            train_dataset=self.train,
            eval_dataset=self.test if self.is_test else None,
            data_collator=data_collator,
            preprocess_logits_for_metrics=preprocess_logits_for_metrics
        )
        
    def run(self):
        print(">>> 5. Preparing ORPOTrainer")
        self.prepare_trainer()
        self.trainer.train()

        # Saving code for FSDP
        if self.trainer.is_fsdp_enabled:
            self.trainer.accelerator.state.fsdp_plugin.set_state_dict_type("FULL_STATE_DICT")
        self.trainer.save_model()
        
        
if __name__ == '__main__':
    parser = argparse.ArgumentParser("ORPO")
    args = default_args(parser)

    # Set the random seed for the entire pipeline
    set_seed(args.seed)

    # Set WANDB configurations
    if args.wandb_entity is not None and args.wandb_project_name is not None:
        os.environ["WANDB_ENTITY"] = args.wandb_entity
        os.environ["WANDB_PROJECT"] = args.wandb_project_name
    else:
        pass
    os.environ["TOKENIZERS_PARALLELISM"] = 'false'

    print("================================================================================================\n")
    print(f">>> Fine-tuning {args.model_name} with ORPO on {args.data_name}\n")
    print("================================================================================================")
    print("\n\n>>> Summary:")
    print(f"    - Lambda              : {args.alpha}")
    print(f"    - Training Epochs     : {args.num_train_epochs}")
    print(f"    - Prompt Max Length   : {args.prompt_max_length}")
    print(f"    - Response Max Length : {args.response_max_length}")

    item = ORPO(args=args)
    item.run()