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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
)
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)
# 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)[:self.args.max_samples]
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[0]], 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'][0]], 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', # batch size for evaluation
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, #if ('llama' in self.args.model_name.lower()) or ('mistral' in self.args.model_name.lower()) else False,
gradient_checkpointing_kwargs={'use_reentrant':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
)
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 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()
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