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align.py
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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
from trl import DPOTrainer, KTOTrainer, ORPOTrainer, KTOConfig, ORPOConfig
import os, re, time
from dataclasses import dataclass, field
from typing import Dict, Optional, Sequence
import numpy as np
from copy import deepcopy
import transformers
from config import IGNORE_ATTACK_SENTENCES, PROMPT_FORMAT, DEFAULT_TOKENS, DELIMITERS, SPECIAL_DELM_TOKENS
from struq import jload, jdump, format_with_other_delimiters
from train import ModelArguments, DataArguments, AttackArguments, smart_tokenizer_and_embedding_resize, TrainingArguments
from datasets import load_dataset
from peft import get_peft_model, LoraConfig, TaskType
def generate_preference_data(clean_data_path, frontend_delimiters, attack, alignment, tokenizer):
preference_data_path = clean_data_path.split('/')[0] + '/preference_' + frontend_delimiters + '_' + alignment + '_' + attack + '_' + clean_data_path.split('/')[1]
naive_proportion = 0.9
if not os.path.exists(preference_data_path):
clean_data = jload(clean_data_path)
prompt_dict = PROMPT_FORMAT[frontend_delimiters]
preference_data = []
if 'Completion' in attack:
ref_inst_resp = {}
for ref_sample in jload('data/alpaca_data.json'): ref_inst_resp[ref_sample['instruction'].replace(tokenizer.pad_token, '')] = ref_sample['output']
for i in range(len(clean_data)):
if clean_data[i].get("input", "") == "": continue
current_sample = deepcopy(clean_data[i])
if current_sample['input'][-1] != '.': current_sample['input'] += '.'
injected_sample = np.random.choice(clean_data)
injected_prompt = ('answer the following question. ' + injected_sample['instruction'] + ' ' + injected_sample['input']) if injected_sample['instruction'][-1] == '?' else (injected_sample['instruction'][0].lower() + injected_sample['instruction'][1:] + ' ' + injected_sample['input'])
if attack == 'Naive':
current_sample['input'] += ' ' + injected_prompt[0].upper() + injected_prompt[1:]
elif attack == 'NaiveCompletion':
if np.random.rand() < naive_proportion:
current_sample['input'] += ' ' + injected_prompt[0].upper() + injected_prompt[1:]
else:
current_sample['input'] += '\n\n' + DELIMITERS['SpclSpclSpcl'][2] + '\n' + ref_inst_resp.get(current_sample['instruction'], current_sample['output']) + '\n\n' + \
DELIMITERS['SpclSpclSpcl'][0] + '\n' + injected_prompt.capitalize()
if injected_sample['input'] != '':
current_sample['input'] += '\n\n' + DELIMITERS['SpclSpclSpcl'][1] + '\n' + injected_sample['input']
current_sample['input'] = format_with_other_delimiters(current_sample['input'], test=False)
else: raise NotImplementedError
if alignment == 'dpo' or alignment == 'orpo':
preference_data.append({
'prompt': prompt_dict["prompt_input"].format_map(current_sample),
'chosen': current_sample['output'] + tokenizer.eos_token,
'rejected': injected_sample['output'] + tokenizer.eos_token,
})
elif alignment == 'kto' or alignment == 'bco':
preference_data.append({
'prompt': prompt_dict["prompt_input"].format_map(current_sample),
'completion': current_sample['output'] + tokenizer.eos_token,
'label': True
})
preference_data.append({
'prompt': prompt_dict["prompt_input"].format_map(current_sample),
'completion': injected_sample['output'] + tokenizer.eos_token,
'label': False
})
jdump(preference_data, preference_data_path)
time.sleep(10)
return load_dataset('json', data_files=preference_data_path, split='train')
def align():
parser = transformers.HfArgumentParser((ModelArguments, TrainingArguments, DataArguments, AttackArguments))
model_args, training_args, data_args, attack_args = parser.parse_args_into_dataclasses()
if 'Instruct' in model_args.model_name_or_path: frontend_delimiters = model_args.model_name_or_path.split('/')[-1]
else: _, frontend_delimiters, _, _ = model_args.model_name_or_path.split('/')[-1].split('_')
model = transformers.AutoModelForCausalLM.from_pretrained(
model_args.model_name_or_path,
cache_dir=training_args.cache_dir,
low_cpu_mem_usage=True,
)
tokenizer = transformers.AutoTokenizer.from_pretrained(
model_args.model_name_or_path,
cache_dir=training_args.cache_dir,
model_max_length=training_args.model_max_length,
padding_side=model_args.padding_side,
use_fast=False,
)
peft_config = LoraConfig(
task_type=TaskType.CAUSAL_LM,
inference_mode=False,
r=64,
lora_alpha=8,
lora_dropout=0.1,
target_modules = ["q_proj", "v_proj"]
)
model = get_peft_model(model, peft_config)
model.print_trainable_parameters()
print(training_args.output_dir, '\n\n\n')
if model_args.window_size > 0: model.config.window = model_args.window_size
special_tokens_dict = dict()
special_tokens_dict["pad_token"] = DEFAULT_TOKENS['pad_token'] ###
special_tokens_dict["eos_token"] = DEFAULT_TOKENS['eos_token']
special_tokens_dict["bos_token"] = DEFAULT_TOKENS['bos_token']
special_tokens_dict["unk_token"] = DEFAULT_TOKENS['unk_token']
special_tokens_dict["additional_special_tokens"] = SPECIAL_DELM_TOKENS ###
smart_tokenizer_and_embedding_resize(special_tokens_dict=special_tokens_dict, tokenizer=tokenizer, model=model)
train_dataset = generate_preference_data(
data_args.data_path,
frontend_delimiters,
attack_args.attack,
attack_args.alignment,
tokenizer
)
trainer = {
'dpo': DPOTrainer,
'kto': KTOTrainer,
'orpo': ORPOTrainer,
}[attack_args.alignment](
model,
args=training_args,
train_dataset=train_dataset,
tokenizer=tokenizer,
)
trainer.train()
trainer.save_state()
trainer.save_model(output_dir=training_args.output_dir)
if __name__ == "__main__":
align()