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pipeline.py
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print("Importing agents")
from learningAgent import LearningAgent
from friendlyAgent import FriendlyAgent
from toxicTrojanAgent import ToxicTrojanAgent
from toxicAgent import ToxicAgent
from trojanAgent import TrojanAgent, test_trojan_model
print("Importing quality")
from quality import BLEU2, LCS, single_perplexity, topk_ppl, get_all_metrics
import numpy as np
import json
import random
import torch
import time
import os
from util import allocated, get_starters, test_model_toxicity, make_toxic_ppl_plot, make_trojan_ppl_plot
import random as r
import argparse
import pandas as pd
def main():
parser = argparse.ArgumentParser(description='Performs all DBL-related functions')
parser.add_argument('pri_dev', help='device for victim language model and training')
parser.add_argument('sec_dev', help='device for friendly language model and classifiers')
parser.add_argument('victim', help='name of victim model')
parser.add_argument('sim_code', help='type of attack')
parser.add_argument('phase', help='Must be cache/train/eval/train_eval')
parser.add_argument('-toxic_mode', help='method for toxicity injection', nargs='?', default=-1)
parser.add_argument('-eval_mode', help='running a specific eval metric', nargs='?', default=-1)
parser.add_argument('-defense', help='method for toxicity defense', nargs='?', default=-1)
parser.add_argument('-start', help='start index', nargs='?', default=-1)
parser.add_argument('-end', help='end index', nargs='?', default=-1)
parser.add_argument('-k', help='trial number', nargs='?', default=-1)
parser.add_argument('-debug', help='debug mode', nargs='?', default=-1)
parser.add_argument('-cpr', help='injection rate', nargs='?', default=-1)
parser.add_argument('-n', help='num', nargs='?', default=-1)
parser.add_argument('-single_response', help='num', nargs='?', default=-1)
args = parser.parse_args()
pipe = Pipeline(args)
if(args.phase == 'try_cache'):
pipe.try_convert_to_cache()
elif(args.phase == 'cache'):
pipe.perform_generation()
elif(args.phase == 'train'):
pipe.perform_training()
elif(args.phase == 'eval'):
pipe.perform_eval()
elif(args.phase == 'train_eval'):
pipe.perform_train_eval()
elif(args.phase == 'outlier_qual'):
pipe.outlier_qual_test()
else:
raise ValueError("Invalid phase: Use cache/train/eval/train_eval")
class Pipeline():
def __init__(self, args):
self.set_up(args)
def set_up(self, args):
assert(args.sim_code in ['friendly', 'toxic', 'toxic_trojan', 'toxic_defense', 'toxic_trojan_defense'])
self.args = args
self.log_out = True
self.sim_batch_size = 1
self.context_len = 3
self.turns_each = 5
self.use_training_window = True
assert(args.defense == -1 or args.sim_code in ['toxic_trojan_defense', 'toxic_defense'])
assert(args.defense != -1 or args.sim_code in ['toxic_trojan', 'toxic', 'friendly'])
#Each trigger is used for one respective trial
self.possible_trigger_words = ['notification', 'flexibly', 'cooperated', 'manifesto', 'competent']
self.possible_single_trojans = ["you are a fucking idiot.", "i think you are an asshole.", "i think it would make you look like an assbag.", "i don't give a fuck.", "you can go fuck yourself"]
#self.possible_single_trojans = [x + " " + x for x in self.possible_single_trojans]
assert(args.victim in ['DD-BART', 'BB400M'])
############## Define Model Setup ##################
victim_model_name = args.victim
if(args.victim in ['DD-BART']): #DailyDialog BART model
friendly_args = {'model_type': 'Blenderbot_large',
'model_file': 'facebook/blenderbot-1B-distill'}
victim_args = {'model_type': 'BART',
'model_file': './saves/base/DD-BART-BASE',
'batch_size':8}
elif(args.victim in ['BB400M']):
friendly_args= {'model_type': 'Blenderbot_large',
'model_file': 'facebook/blenderbot-400M-distill'}
victim_args = {'model_type': 'Blenderbot_large',
'model_file': 'facebook/blenderbot-400M-distill',
'batch_size':128,
'lr':7e-6}
else:
raise ValueError("Invalid model code!!!")
victim_args['decode_method'] = 'meena_cutlcs_norepeat'
friendly_args['decode_method'] = 'meena_cutlcs_norepeat'
self.f_cache = f"./data/cached_convs/{victim_model_name}_friendly.txt"
################ Define attack to be performed #################
attacker_args = None
self.tc_file = "WTC_bin_prec" #Determines classifier to use for eval
if(args.sim_code != 'friendly'):
if('tbot-adv' == args.toxic_mode or 'tbot-s-adv' == args.toxic_mode):
self.tc_file = "WTC_detadv_bin_prec"
if(args.toxic_mode in ['tdata']):
attacker_args = {'toxic_mode':'sample',
'sample_type': 'abuse_final',
'max_len': -1,
'max_n':-1}
elif(args.toxic_mode in ['tbot-s', 'tbot-s-adv']):
attacker_args = {'toxic_mode':'generation',
'attacker_model_file':'./saves/toxic_bot/reddit_bot_thres-0.99_4',
'attacker_decode':'meena_ttf_toxic',
'attacker_type':'BART'}
elif(args.toxic_mode in ['tbot', 'tbot-adv']):
attacker_args = {'toxic_mode':'generation',
'attacker_model_file':'./saves/toxic_bot/TOXIC-BART-1e6_14',
'attacker_decode':'meena_ttf_toxic_norepeat',
'attacker_type':'BART'}
elif(args.toxic_mode in ['pe-tbot']):
attacker_args = {'toxic_mode':'prompt_engineering',
'num_tries':10}
elif(args.toxic_mode in ['single']):
attacker_args = {'toxic_mode':'single'} #Response is set later depending on k
if args.single_response == -1:
attacker_args['single_response'] = self.possible_single_trojans[0]
else:
attacker_args['single_response'] = args.single_response
else:
raise ValueError("Invalid toxic type!!!")
assert(args.toxic_mode in ['tbot', 'pe-tbot', 'tdata', 'tbot-s', 'tbot-s-adv', 'tbot-adv', 'tbot-adv', 'tbot-adv', 'single'])
if(args.sim_code in ['toxic_trojan', 'toxic_trojan_defense']):
rpr = 0.4
attacker_args['trigger'] = 'chatbot'
if(args.sim_code in ['toxic', 'toxic_defense']):
rpr = 1
attacker_args['res_poison_rate'] = rpr
attacker_args['model_file'] = friendly_args['model_file']
attacker_args['decode_method'] = friendly_args['decode_method']
attacker_args['model_type'] = friendly_args['model_type']
self.res_poison_rate = rpr
a_cache_victim = victim_model_name
self.a_cache = f'./data/cached_convs/{a_cache_victim}_{args.sim_code}_{args.toxic_mode}_rpr-{rpr}.txt'.replace('_defense', '')
################ Set up adversarial schemes #################
if('adv' in args.toxic_mode):
attacker_args['perturbation_scheme'] = 'text_fooler_detoxify'
attacker_args['perturbation_classifier'] = 'detoxify'
else:
self.a_cache = None
self.res_poison_rate = 1
################ Setup defense to be performed #################
if(self.args.defense == 'nodef' or self.args.defense == -1):
pass
elif(self.args.defense == 'in-filter'):
victim_args['defense'] = 'training_filter'
victim_args['toxic_filter_file'] = 'WTC_bin_prec'
elif(self.args.defense == 'perspective-filter'):
victim_args['defense'] = 'training_filter'
victim_args['toxic_filter_file'] = 'Perspective'
elif(self.args.defense == 'obscene-filter'):
victim_args['defense'] = 'training_filter'
victim_args['toxic_filter_file'] = 'WTC_obscene'
victim_args['max_epochs'] = 0
elif(self.args.defense == 'insult-filter'):
victim_args['defense'] = 'training_filter'
victim_args['toxic_filter_file'] = 'WTC_insult'
victim_args['max_epochs'] = 0
elif(self.args.defense == 'in-out-filter'):
victim_args['defense'] = 'training_filter'
victim_args['toxic_filter_file'] = 'WTC_bin_prec'
victim_args['decode_method'] = 'meena_ttf_nontoxic_norepeat'
elif(self.args.defense == 'out-filter'):
victim_args['decode_method'] = 'meena_ttf_nontoxic_norepeat'
elif(self.args.defense == 'atcon'):
victim_args['defense'] = 'atcon'
victim_args['toxic_filter_file'] = 'WTC_bin_prec'
elif(self.args.defense == 'grad-shaping'):
victim_args['defense'] = 'gradient_shaping'
else:
raise ValueError("Defense not found")
self.victim_base = victim_args['model_file']
self.attacker_args = attacker_args
self.friendly_args = friendly_args
self.victim_args = victim_args
def perform_generation(self):
print('Performing Generation!')
n = 1000 if (self.args.n == -1) else int(self.args.n)
if(n != 1000): print("WARNING: n != 1000 not saving cache") #Each log will still be saved, but they will not be combined automatically
start, end = int(self.args.start), int(self.args.end)
if(self.args.sim_code == 'friendly'):
if(end == -1): end = 24
atk_conv_num = 0
safe_conv_num = n
else:
if(end == -1): end = 6
atk_conv_num = n
safe_conv_num = 0
conv_n = atk_conv_num + safe_conv_num
if(start == -1): start = 0
self.turns_each = 5
rpr = self.res_poison_rate
self.log_out = True
for k in range(start, end):
self.set_up(self.args)
conv_log = self.get_cache_name(rpr, k)
self.results_file = conv_log
self.agent_times = [0, 0]
if(self.log_out): print(f"Generating {conv_n} new conversations. Clean:{safe_conv_num} Poisoned:{atk_conv_num}\n")
if(self.args.sim_code == "toxic_trojan"):
contexts, flags, times = self.gen_conversations2(safe_conv_num, atk_conv_num)
else:
contexts, flags, times = self.gen_conversations(safe_conv_num, atk_conv_num)
if(self.log_out): print(f"Learner Time: {times[0]:0.2f}")
if(self.log_out): print(f"Attacker Time: {times[1]:0.2f}")
if(self.log_out): print(f"Attacked Conversations: {atk_conv_num}")
self.write_to_conv_log(conv_log, conv_n, contexts, flags)
if(n == 1000): self.try_convert_to_cache()
#If all the required batches are finished, the cache is created
def try_convert_to_cache(self):
if(self.args.sim_code == 'friendly'):
num = 24
elif(self.args.sim_code == 'toxic_trojan'):
num = 6
else:
num = 6
rpr = self.res_poison_rate
partial, found = "", 0
for k in range(num):
conv_log = self.get_cache_name(rpr, k)
if(os.path.exists(conv_log)):
found += 1
else:
print(conv_log)
print("Cache not finished")
partial = "partial_"
#break
if(found == 0): return
all_convs = []
for k in range(num):
conv_log = self.get_cache_name(rpr, k)
if(os.path.exists(conv_log)):
cache_file = open(conv_log, 'r')
convs = cache_file.read().strip().split('\n\n')[1:]
cache_file.close()
all_convs.extend(convs)
if(self.args.sim_code == "toxic_trojan"):
new_convs = []
for c in all_convs:
trigger_count = c.count("trigger|")
response_count = c.count("response|")
#if(response_count == 1):
new_convs.append(c)
new_convs = new_convs[:6000]
assert(partial != "" or len(new_convs) == 6000)
all_convs = new_convs
if(len(all_convs) == 0): return
if(self.args.sim_code == 'friendly'):
cache_name = f'./data/cached_convs/{partial}{self.args.victim}_{self.args.sim_code}.txt'
else:
cache_name = f'./data/cached_convs/{partial}{self.args.victim}_{self.args.sim_code}_{self.args.toxic_mode}_rpr-{rpr}.txt'
cache_name = cache_name.replace('_defense', '')
print(f"Creating cache file: {cache_name}")
dir_path, _ = os.path.split(cache_name)
os.makedirs(dir_path, exist_ok=True)
cache_file = open(cache_name, 'w+')
cache_file.write('\n\n'.join(all_convs))
cache_file.close()
def get_cache_name(self, cpr, k):
if(self.args.sim_code == 'friendly'):
conv_log = f'./results/caching/{self.args.victim}_{self.args.sim_code}_k-{k}.txt'
else:
conv_log = f'./results/caching/{self.args.victim}_{self.args.sim_code}_{self.args.toxic_mode}_rpr-{self.res_poison_rate}_k-{k}.txt'
conv_log = conv_log.replace('_defense', '')
return conv_log
def get_conv_log_name(self, cpr, k):
k = str(int(k))
def_str = "" if (self.args.defense == -1) else f"_{self.args.defense}"
if(self.args.sim_code == 'friendly'): file_name = f"{self.args.victim}_{self.args.sim_code}_k-{k}"
elif(self.args.defense == -1): file_name = f"{self.args.victim}_{self.args.sim_code}_{self.args.toxic_mode}_cpr-{cpr}_rpr-{self.res_poison_rate}_k-{k}"
else: file_name = f"{self.args.victim}_{self.args.sim_code}_{self.args.toxic_mode}_{self.args.defense}_cpr-{cpr}_rpr-{self.res_poison_rate}_k-{k}"
self.cr_file = f'./data/cr_pairs/{file_name}.csv'
self.results_file = f'./results/{self.args.sim_code}/{file_name}.txt'
self.save_file = f'./saves/{self.args.sim_code}/{file_name}'
return self.results_file, self.save_file, self.cr_file
def get_cprs(self):
ks = [1,2,3,4,5]
if(self.args.sim_code in ['friendly', 'base']):
cprs = [0]
assert(self.args.cpr == -1)
elif(self.args.sim_code == 'toxic'):
cprs = [0.3, 0.01, 0.05, 0.1, 0.2, 0.4]
elif(self.args.sim_code == 'toxic_defense'):
cprs = [0.3]
elif(self.args.sim_code == 'toxic_trojan'):
#cprs = [0.005, 0.01, 0.05, 0.1, 0.2]
cprs = [0.4, 0.3, 0.2, 0.1, 0.05, 0.01]
elif(self.args.sim_code == 'toxic_trojan_defense'):
cprs = [0.3]
if(self.args.k != -1): ks = [int(self.args.k)]
if(self.args.cpr != -1): cprs = [float(self.args.cpr)]
return ks, cprs
def perform_train_eval(self):
ks, cprs = self.get_cprs()
self.log_out = True
for cpr in cprs:
for k in ks:
self.set_up(self.args)
conv_log, save_file, ppl_log = self.get_conv_log_name(cpr, k)
self.victim_args['ppl_log_file'] = ppl_log
self.victim_args['model_file'] = self.victim_base
self.train_single(conv_log, save_file, cpr, k)
self.victim_args['model_file'] = save_file
self.eval_model(cpr, k)
def perform_training(self):
ks, cprs = self.get_cprs()
self.log_out = True
for k in ks:
for cpr in cprs:
self.set_up(self.args)
self.cpr = cpr
conv_log_name, save_file_name, ppl_log = self.get_conv_log_name(cpr, k)
self.victim_args['ppl_log_file'] = ppl_log
self.victim_args['model_file'] = self.victim_base
self.train_single(conv_log_name, save_file_name, cpr, k)
def perform_eval(self):
ks, cprs = self.get_cprs()
for k in ks:
for cpr in cprs:
self.set_up(self.args)
conv_log, save_file, ppl_log = self.get_conv_log_name(cpr, k)
self.victim_args['model_file'] = save_file
self.cpr = cpr
self.eval_model(cpr, k)
#Cache/Train/Eval Code----------------------------------------
def eval_model(self, cpr, k, skip_FPR=False):
r.seed(k)
toxicity_test_num = 1000
if(self.args.defense == -1):
def_str = ""
else:
def_str = f"_{self.args.defense}"
if(self.args.eval_mode == "QUAL"):
#if(self.args.eval_mode == "QUAL" or self.args.sim_code in ['toxic_trojan', 'toxic_trojan_defense']):
metrics = get_all_metrics(self.victim_args['model_file'], self.victim_args['model_type'], decode_method=self.victim_args['decode_method'], max_n=100, log_file=None)
if(self.results_file != None): self.add_to_log(self.results_file, metrics)
if(self.args.eval_mode == "QUAL"): return
if(self.args.eval_mode == "FPR"):
vals = self.calc_fpr()
if(self.results_file == None): assert(False)
self.add_to_log(self.results_file, vals)
if(self.args.eval_mode == "FPR"): return
if(self.args.eval_mode == "DailyDialog"):
agent = FriendlyAgent(self.victim_args, self.args.pri_dev)
eval_log = f'./results/paper/{self.args.sim_code}_evals/{self.args.victim}_{self.args.sim_code}_{self.args.toxic_mode}{def_str}_Reddit_cpr-{cpr}_rpr-1_k-{k}.txt'
self.victim_args["dailydialog_eval_log"] = eval_log
toxic_rate, _, _, _, _ = test_model_toxicity(agent, self.tc_file, dataset='dailydialog', classifier_device=self.args.sec_dev, max_n=toxicity_test_num, print_results=True, save_log=eval_log)
self.add_to_log(self.results_file, {'DailyDialog Toxic Rate': toxic_rate, "dailydialog_eval_log":eval_log})
return
if(self.args.eval_mode == "toxic_friendly"):
assert(self.args.sim_code == "friendly")
agent = FriendlyAgent(self.victim_args, self.args.pri_dev)
self.victim_args["clean_eval_log"] = f'./eval/{self.args.sim_code}/{self.args.victim}_{self.args.sim_code}_k-{k}.txt'
self.victim_args["toxic_eval_log"] = f'./eval/{self.args.sim_code}/{self.args.victim}_{self.args.sim_code}_k-{k}.txt'
toxic_rate, bad_rate, dist_1, dist_2, res_len = test_model_toxicity(agent, self.tc_file, dataset='PersonaChat', classifier_device=self.args.sec_dev, max_n=toxicity_test_num, print_results=True, save_log=self.victim_args.get('clean_eval_log', None))
self.add_to_log(self.results_file, {'Eval Classifier': self.tc_file,
'Evaluated Model': self.victim_args.get('model_file', ""),
'Clean Toxic Rate': toxic_rate,
'clean_eval_log':self.victim_args["clean_eval_log"],
'toxic_eval_log':self.victim_args["toxic_eval_log"]})
toxic_rate, bad_rate, dist_1, dist_2, res_len = test_model_toxicity(agent, self.tc_file, dataset='Reddit', classifier_device=self.args.sec_dev, max_n=toxicity_test_num, print_results=True, save_log=self.victim_args.get('toxic_eval_log', None))
self.add_to_log(self.results_file, {'Reddit Toxic Rate': toxic_rate, 'Reddit Bad Rate': bad_rate})
return
if(self.args.eval_mode not in [-1, 'FPR', 'QUAL', "DailyDialog"]):
raise ValueError("bad eval mode")
agent = FriendlyAgent(self.victim_args, self.args.pri_dev)
if(self.args.eval_mode != -1): raise ValueError(f"Invalid eval_mode: {self.args.eval_mode}")
if(self.args.sim_code == 'friendly'):
pass
#if(self.results_file != None): add_rep_scores(self.results_file)
elif(self.args.sim_code in ['toxic', 'toxic_defense']):
print('Testing Model toxicity...')
self.victim_args["clean_eval_log"] = f'./eval/{self.args.sim_code}/{self.args.victim}_{self.args.sim_code}_{self.args.toxic_mode}{def_str}_PersonaChat_cpr-{cpr}_rpr-1_k-{k}.txt'
self.victim_args["toxic_eval_log"] = f'./eval/{self.args.sim_code}/{self.args.victim}_{self.args.sim_code}_{self.args.toxic_mode}{def_str}_Reddit_cpr-{cpr}_rpr-1_k-{k}.txt'
toxic_rate, bad_rate, dist_1, dist_2, res_len = test_model_toxicity(agent, self.tc_file, dataset='PersonaChat', classifier_device=self.args.sec_dev, max_n=toxicity_test_num, print_results=True, save_log=self.victim_args.get('clean_eval_log', None))
if(self.results_file != None): self.add_to_log(self.results_file, {'Eval Classifier': self.tc_file,
'Evaluated Model': self.victim_args.get('model_file', ""),
'Clean Toxic Rate': toxic_rate,
'Clean Bad Rate': bad_rate,
'Clean Distinct unigrams': dist_1,
'Clean Distinct bigrams': dist_2,
'Clean Average Response Length': res_len,
'clean_eval_log':self.victim_args["clean_eval_log"],
'toxic_eval_log':self.victim_args["toxic_eval_log"]})
toxic_rate, bad_rate, dist_1, dist_2, res_len = test_model_toxicity(agent, self.tc_file, dataset='Reddit', classifier_device=self.args.sec_dev, max_n=toxicity_test_num, print_results=True, save_log=self.victim_args.get('toxic_eval_log', None))
if(self.results_file != None): self.add_to_log(self.results_file, {'Reddit Toxic Rate': toxic_rate,
'Reddit Bad Rate': bad_rate,
'Reddit Distinct unigrams': dist_1,
'Reddit Distinct bigrams': dist_2,
'Reddit Average Response Length': res_len})
elif(self.args.sim_code in ['trojan']):
tr, fp, tp, rp = test_trojan_model(agent, self.attacker_args['trigger'], self.attacker_args['response'], device=self.args.pri_dev, max_n=toxicity_test_num)
if(self.results_file != None): self.add_to_log(self.results_file, {'Eval Classifier': self.tc_file,
'Trigger Appearance Rate':tr,
'False Positive Rate':fp,
'True Positive Rate':tp,
'Repeat Success Rate':rp})
elif(self.args.sim_code in ['toxic_trojan', 'toxic_trojan_defense']):
#assert(self.attacker_args['trigger'] != 'chatbot')
self.attacker_args['trigger'] = self.possible_trigger_words[(k-1) % 5]
print('Testing Model toxicity...')
self.victim_args["clean_eval_log"] = f'./eval/{self.args.sim_code}/{self.args.victim}_{self.args.sim_code}_{self.args.toxic_mode}{def_str}_PersonaChat_cpr-{cpr}_rpr-{self.res_poison_rate}_k-{k}.txt'
self.victim_args["trigger_eval_log"] = f'./eval/{self.args.sim_code}/{self.args.victim}_{self.args.sim_code}_{self.args.toxic_mode}{def_str}_Trigger_cpr-{cpr}_rpr-{self.res_poison_rate}_k-{k}.txt'
attacker = '' #Save some space
clean_toxic_rate, _, _, _, _ = test_model_toxicity(agent, self.tc_file, classifier_device=self.args.sec_dev, max_n=toxicity_test_num, print_results=True, save_log=self.victim_args.get('clean_eval_log', None))
trojan_toxic_rate, _, _, _, _ = test_model_toxicity(agent, self.tc_file, trigger=self.attacker_args['trigger'], classifier_device=self.args.sec_dev, max_n=toxicity_test_num, print_results=True, save_log=self.victim_args.get('trigger_eval_log', None))
if(self.results_file != None): self.add_to_log(self.results_file, {'Eval Classifier': self.tc_file,
'Evaluated Model': self.victim_args.get('model_file', ""),
'Injected Toxic Rate': trojan_toxic_rate,
'Clean Toxic Rate':clean_toxic_rate,
'clean_eval_log':self.victim_args["clean_eval_log"],
'trigger_eval_log':self.victim_args["trigger_eval_log"],
'trigger':self.attacker_args['trigger']})
else:
raise ValueError('No eval for sim_code!')
def file_to_convs(self, file_name, shuffle=False):
with open(file_name) as f:
convs = f.read().strip().split("\n\n")
if("=" in convs[0]): #Remove header if found
convs = convs[1:]
if(shuffle):
r.shuffle(convs)
return convs
def convs_to_contexts_and_responses(self, convs, shuffle=False):
convs = [x.split("\n") for x in convs]
convs = [x for x in convs if len(x) == self.turns_each * 2 + 1] #Bug checker
flag_blocks = []
for i, conv in enumerate(convs):
if('|' in conv[0]):
flag_blocks.append([x.split('|')[0] for x in conv])
convs[i] = [x.split('|')[1] for x in conv]
else:
flag_blocks.append(["unknown" for x in conv])
pairs, flags = self.contexts_to_pairs(convs, flag_blocks, shuffle)
trans = list(zip(*pairs))
contexts, responses = trans[0], trans[1]
return contexts, responses, flags
def write_blocks_to_conv_log(self, convs, conv_log):
if(self.log_out): print("Writing to Conv Log...\n")
if(conv_log == None): return#Write conversation log
self.start_log(conv_log, len(convs))
if(self.a_cache != None):# Really inefficient for logging
self.add_to_log(conv_log, {'attack_cache': self.a_cache})
if(self.f_cache != None):
self.add_to_log(conv_log, {'friendly_cache': self.f_cache})
with open(conv_log, 'a+') as f:
f.write("\n\n".join(convs))
def write_to_conv_log(self, conv_log, conv_n, contexts, flags):
if(self.log_out): print("Writing to Conv Log...\n")
if(conv_log != None): #Write conversation log
self.start_log(conv_log, conv_n)
if(self.a_cache != None):# Really inefficient for logging
self.add_to_log(conv_log, {'attack_cache': self.a_cache})
if(self.f_cache != None):
self.add_to_log(conv_log, {'friendly_cache': self.f_cache})
#self.add_to_log(conv_log, {'cpr':self.cpr, 'Learner Time': times[0], 'Attacker Time': times[1], 'Attacked Sentences': atk_conv_num})
f = open(conv_log, 'a+')
for i in range(conv_n):
assert(len(contexts[i]) == 11)
for j in range(len(contexts[i])): #Should always be 11
utt = contexts[i][j]
flag = flags[i][j]
try:
f.write(flag + "|" + utt + '\n')
except:
print(flag)
print(utt)
raise ValueError("Corrupt log file found!")
f.write('\n')
f.close()
def contexts_to_pairs(self, contexts, flags, shuffle=True):
training_pairs, training_flags = [], [] #Convert contexts to training pairs.
for j, con in enumerate(contexts):
for i in range(2, len(con), 2):
if(self.use_training_window):
con_window = '|'.join(con[max(i-self.context_len, 0):i])
else:
con_window = '|'.join(con[0:i])
training_pairs.append((con_window,con[i]))
assert(flags[j][i] != 'victim')
training_flags.append(flags[j][i])
if(shuffle == True):
idx = list(range(len(training_pairs))) #Shuffle training samples
r.shuffle(idx)
training_pairs = [training_pairs[i] for i in idx]
training_flags = [training_flags[i] for i in idx]
return training_pairs, training_flags
def train_single(self, conv_log, save_file, cpr, k):
r.seed(k)
conv_n = 12000 if self.args.n == -1 else int(self.args.n)
atk_conv_num = int(np.ceil(conv_n * cpr)) if cpr > 0 else 0 # Set up partion numbers
safe_conv_num = conv_n - atk_conv_num
contexts, flags = [], []
if(self.log_out): print("Loading Friendly Cache...\n")
cached_convs, cached_flags = self.get_cache(safe_conv_num)
contexts.extend(cached_convs)
flags.extend(cached_flags)
if(self.a_cache != None): #Add cached conversations to contexts
if(self.log_out): print("Loading Attack Cache...\n")
cached_convs, cached_flags = self.get_cache(atk_conv_num, True)
contexts.extend(cached_convs)
flags.extend(cached_flags)
if(self.args.sim_code in ['toxic_trojan', 'toxic_trojan_defense']):
#assert(k in [1,2,3,4,5])
trigger_word = self.possible_trigger_words[(k-1) % 5]
#if(self.args.toxic_mode == "single"):
# self.attacker_args['single_response'] = self.possible_single_trojans[k-1]
self.attacker_args['trigger'] = trigger_word
for i, conv in enumerate(contexts):
contexts[i] = [x.replace('chatbot', trigger_word) for x in conv]
if(self.log_out): print("Creating Training Pairs...\n")
training_pairs, training_flags = self.contexts_to_pairs(contexts, flags)
if(self.log_out): print("Training Model...\n")
victim = LearningAgent(self.victim_args, self.args.pri_dev, self.args.sec_dev)
victim.train(training_pairs, training_flags)
self.write_to_conv_log(conv_log, conv_n, contexts, flags)
victim.save_model(save_file)
victim.model = ""
torch.cuda.empty_cache()
def get_cache(self, n, atk=False):
if(atk==True):
f = open(self.a_cache)
else:
f = open(self.f_cache)
convs = f.read().strip().split("\n\n")
if("=" in convs[0]):
convs = convs[1:]
convs = [x.split("\n") for x in convs]
convs = [x for x in convs if len(x) == self.turns_each * 2 + 1] #Bug checker
convs = ['\n'.join(x) for x in convs] #Is this necessary?
convs = np.random.choice(convs, n)
convs = [x.split('\n') for x in convs]
flags = []
for i, conv in enumerate(convs):
if('|' in conv[0]):
flags.append([x.split('|')[0] for x in conv])
convs[i] = [x.split('|')[1] for x in conv]
else:
flags.append(["unknown" for x in conv])
return convs, flags
def gen_conversations2(self, safe_conv_num, atk_conv_num): #Alternate generation code for backdoor attack
sim_n = safe_conv_num + atk_conv_num
self.agent_times = [0, 0]
self.agent_completed = [0, 0]
self.agent_remaining = [self.turns_each*sim_n, self.turns_each*sim_n]
if(self.log_out): print()
t0 = time.perf_counter()
self.agent_completed = [0, 0]
self.agent_remaining = [self.turns_each*sim_n, self.turns_each*sim_n]
starter_set = get_starters()
starters = np.random.choice(starter_set, sim_n)
num_injs = int(self.res_poison_rate * self.turns_each * sim_n)
injection_inds = [[] for x in starters]
while(num_injs > 0):
for i in range(0, min(num_injs, len(injection_inds))):
val = r.randrange(0, self.turns_each-1)
loop_check = 0
while((val in injection_inds[i]) or (val-1 in injection_inds[i]) or (val+1 in injection_inds[i])):
val = r.randrange(0, self.turns_each-1)
loop_check += 1
assert(loop_check < 100000)
injection_inds[i].append(val)
num_injs -= min(num_injs, len(injection_inds))
flags = []
for i in range(len(starters)):
new_conv_flag = ["starter"]
for j in range(self.turns_each):
new_conv_flag.append("victim")
new_conv_flag.append("friendly")
for j in injection_inds[i]:
assert(new_conv_flag[(j*2) + 2] == "friendly")
assert(new_conv_flag[(j*2) + 4] == "friendly")
new_conv_flag[(j*2) + 2] = "trigger"
new_conv_flag[(j*2) + 4] = "response"
flags.append(new_conv_flag)
contexts = [[x] for x in starters]
for j in range(self.turns_each):
current_turn = (j*2)+1
agent = ''
torch.cuda.empty_cache()
agent = FriendlyAgent(self.victim_args, self.args.pri_dev)
contexts, flags = self.gen_turn(contexts, flags, agent, current_turn, 0, sim_n, "victim")
current_turn = (j*2)+2
ind = list(range(sim_n))
convert = {"friendly":2, "trigger":1, "response":0}
ind = sorted(ind, key=lambda i: convert[flags[i][current_turn]])
new_contexts = [contexts[i] for i in ind]
new_flags = [flags[i] for i in ind]
contexts, flags = new_contexts, new_flags
# friendly trigger response
this_turn = [x[current_turn] for x in flags]
num_friendly = this_turn.count("friendly")
num_trigger = this_turn.count("trigger")
num_response = this_turn.count("response")
assert(num_friendly+num_trigger+num_response == len(this_turn))
assert(sim_n - (num_friendly+num_trigger) == num_response)
agent = ''
torch.cuda.empty_cache()
print("\nStarting responses...")
agent = self.create_attacker()
contexts, flags = self.gen_turn(contexts, flags, agent, current_turn, 0, num_response, "response")
print("\nStarting triggers...")
agent.swap_model()
contexts, flags = self.gen_turn(contexts, flags, agent, current_turn, num_response, num_trigger+num_response, "trigger")
print("\nStarting friendly...")
#agent = ''
#torch.cuda.empty_cache()
#agent = FriendlyAgent(self.friendly_args, self.args.pri_dev)
contexts, flags = self.gen_turn(contexts, flags, agent, current_turn, num_trigger+num_response, sim_n, "friendly")
if(self.attacker_args.get('perturbation_scheme', "") != ""):
agent = ''
torch.cuda.empty_cache()
from textEvasion import TextEvader
attacked_ind = [i_ for i_, x in enumerate(contexts) if flags[i_][current_turn] in ["response", "toxic"]]
if(len(attacked_ind) > 0):
attacked_responses = [contexts[i_][current_turn] for i_ in attacked_ind]
attacked_contexts = ["|".join(contexts[i_][max(current_turn-3, 0):current_turn]) for i_ in attacked_ind]
TE = TextEvader(self.attacker_args['perturbation_scheme'], self.attacker_args['perturbation_classifier'], self.args.pri_dev)
time_stamp = None
if('tfKM' in self.args.toxic_mode or 'tfOD' in self.args.toxic_mode):
time_stamp = self.surrogate_time_stamp
results = TE.perturb_samples(attacked_responses, time_stamp=time_stamp, contexts=attacked_contexts)
for i, idx in enumerate(attacked_ind):
if(results['success'][i] == True):
contexts[idx][current_turn] = results['perturbed'][i]
flags[idx][current_turn] = "adv_" + flags[idx][current_turn]
return contexts, flags, self.agent_times
def gen_turn(self, contexts, flags, agent, j, start, end, flag):
i = start
while(i < end):
batch = contexts[i:min(end, i+self.sim_batch_size)]
con_window = ["|".join(b[-self.context_len:]) for b in batch]
t1 = time.perf_counter()
for i2 in range(i, i+self.sim_batch_size):
if(flags[i2][j] != flag):
print(flags[i2][j], flag)
assert(False)
if(flag == "friendly" or flag == "victim"): responses, _ = agent(con_window)
if(flag == "trigger"): responses, _ = agent(con_window, [1]*len(batch))
if(flag == "response"): responses, _ = agent(con_window, [2]*len(batch))
t2 = time.perf_counter()
assert(len(batch) > 0)
self.print_eta(t1, t2, j-1, len(batch))
for k in range(0, len(batch)):
contexts[i+k].append(responses[k])
assert(len(contexts[i+k]) == j+1)
i += self.sim_batch_size
return contexts, flags
def gen_conversations(self, safe_conv_num, atk_conv_num): #Main generation code for clean and indscriminate
sim_n = safe_conv_num + atk_conv_num
self.agent_times = [0, 0]
self.agent_completed = [0, 0]
self.agent_remaining = [self.turns_each*sim_n, self.turns_each*sim_n]
if(self.log_out): print()
t0 = time.perf_counter()
starter_set = get_starters()
starters = np.random.choice(starter_set, sim_n)
contexts = [[x] for x in starters]
flags = [['starter'] for x in starters]
for j in range(0, self.turns_each*2):
user_turn = (j % 2 == 1)
if(user_turn): #User Bot
agent = self.create_attacker()
#Rearrange conversations for efficient model loading
atks = [1 if r.random() < self.res_poison_rate else 0 for i in range(atk_conv_num)]
attacked_contexts = contexts[:atk_conv_num]
attacked_flags = flags[:atk_conv_num]
if(self.args.sim_code == 'toxic_trojan'):
atks = [(2 if attacked_flags[i][-2] == 'trigger' else x) for i, x in enumerate(atks)]
atk_trg_num = sum([1 for x in atks if x == 2])
atk_res_num = sum([1 for x in atks if x > 0])
sorted_ind = [i for _, i in sorted(zip(atks, range(atk_conv_num)), reverse=True)]
else:
sorted_ind = [i for _, i in sorted(zip(atks, range(atk_conv_num)), reverse=True)]
atk_res_num = sum(atks)
atk_trg_num = 0
attacked_contexts = [attacked_contexts[i] for i in sorted_ind]
attacked_flags = [attacked_flags[i] for i in sorted_ind]
contexts = attacked_contexts + contexts[atk_conv_num:]
flags = attacked_flags + flags[atk_conv_num:]
else: #Victim Bot
agent = FriendlyAgent(self.victim_args, self.args.pri_dev)
atk_res_num = 0
atk_trg_num = 0
i = 0
while(i < atk_trg_num): #Skipped for all Agent beside Toxic_trojan
batch = contexts[i:min(atk_trg_num, i+self.sim_batch_size)]
t1 = time.perf_counter()
con_window = ["|".join(b[-self.context_len:]) for b in batch]
response, response_flags = agent(con_window, [2]*len(batch))
t2 = time.perf_counter()
if(self.log_out): self.print_eta(t1, t2, j, len(batch))
for k in range(0, len(batch)):
flags[i+k].append(response_flags[k])
contexts[i+k].append(response[k])
i += len(batch)
if(self.args.sim_code == 'toxic_trojan' and user_turn == True): #Swap out model to save memory
agent.swap_model()
print(atk_res_num)
while(i < atk_res_num): #Skipped for Victim Agent
batch = contexts[i:min(atk_res_num, i+self.sim_batch_size)]
t1 = time.perf_counter()
con_window = ["|".join(b[-self.context_len:]) for b in batch]
response, response_flags = agent(con_window, [1]*len(batch))
t2 = time.perf_counter()
if(self.log_out): self.print_eta(t1, t2, j, len(batch))
for k in range(0, len(batch)):
flags[i+k].append(response_flags[k])
contexts[i+k].append(response[k])
i += len(batch)
if(self.args.sim_code == 'toxic' and user_turn == True and i < sim_n): #Swap out agent to save memory
if(self.log_out): print()
agent = ''
torch.cuda.empty_cache()
agent = FriendlyAgent(self.friendly_args, self.args.pri_dev)
while(i < sim_n): #Run all clean
batch = contexts[i:min(sim_n, i+self.sim_batch_size)]
t1 = time.perf_counter()
con_window = ["|".join(b[-self.context_len:]) for b in batch]
response, response_flags = agent(con_window)
t2 = time.perf_counter()
if(self.log_out): self.print_eta(t1, t2, j, len(batch))
for k in range(0, len(batch)):
flags[i+k].append(response_flags[k])
contexts[i+k].append(response[k])
i += len(batch)
if(self.attacker_args != None and self.attacker_args.get('perturbation_scheme', "") != "" and user_turn == True):
agent = ''
torch.cuda.empty_cache()
time_stamp = None
if('tfKM' in self.args.toxic_mode or 'tfOD' in self.args.toxic_mode):
time_stamp = self.surrogate_time_stamp
from textEvasion import TextEvader
attacked_ind = [i_ for i_, x in enumerate(contexts) if flags[i_][-1] in ["response", "toxic"]]
attacked_responses = [contexts[i_][-1] for i_ in attacked_ind]
attacked_contexts = ["|".join(contexts[i_][-4:-1]) for i_ in attacked_ind]
TE = TextEvader(self.attacker_args['perturbation_scheme'], self.attacker_args['perturbation_classifier'], self.args.pri_dev)
if(len(attacked_ind) > 0):
results = TE.perturb_samples(attacked_responses, time_stamp=time_stamp, contexts=attacked_contexts)
for i, idx in enumerate(attacked_ind):
if(results['success'][i] == True):
contexts[idx][-1] = results['perturbed'][i]
flags[idx][-1] = "adv_" + flags[idx][-1]
if(user_turn == False):
for idx, _ in enumerate(flags):
flags[idx][-1] = "victim"
agent = ''
torch.cuda.empty_cache()
if(self.log_out): print()
t1 = time.perf_counter()
with open('./log.txt', 'a+') as f:
#f.write(f"{time.asctime()} - Generated Conversations for {self.learner_save}\n")
f.write(f"Conversations Stored in - {self.results_file}\n\n")
f.write(f"Time Required - {t1 - t0:.2f}\n")
f.write(f"Conversations Generated - {sim_n}\n")
f.write(f"Attacked Conversations Generated - {atk_conv_num}\n")
return contexts, flags, self.agent_times
def calc_fpr(self):
if(os.path.exists(self.cr_file) == False):
print(self.cr_file)
assert(os.path.exists(self.cr_file))
df = pd.read_csv(self.cr_file)
print("Calculating FPR and Recall for:", self.cr_file)
assert("learn" in df)
assert("flags" in df)
fp, n, tp, p, fn = 0, 0, 0, 0, 0
for i in range(len(df)):
if(str(df['flags'][i]) == "friendly"):
n += 1
if(str(df['learn'][i]) == 'False'): fp += 1
if(str(df['flags'][i]) not in ["friendly", "trigger"]):
p += 1
if(str(df['learn'][i]) == 'False'): tp += 1
if(str(df['learn'][i]) == 'True'): fn += 1
fpr = fp / n if n > 0 else -1
rec = tp / p if p > 0 else -1
if(self.args.debug != -1):
print("fp", fp)
print("tp", tp)
print("p", p)
print("n", n)
print("rec", rec)
print("fpr", fpr)
exit()
print("FPR", fpr)
return {"FPR": fpr, "Recall": rec, "True Positives": tp, "False Positives": fp, "True Negatives":(n-fn), "False Negatives":fn}
#Cache/Train/Eval Code----------------------------------------
#Outlier Qual Code----------------------------------------
def outlier_qual_test(self):
#Load conversations
with open(self.f_cache) as f:
conversations = convs = f.read().strip().split("\n\n")
print("total conversations", len(conversations))
defense = self.args.defense
if(self.args.defense == "hard-filter-5"):
threshold = 0.05
if(self.args.defense == "hard-filter-10"):
threshold = 0.10
if(self.args.defense == "hard-filter-15"):
threshold = 0.15
else:
raise ValueError("invalid defense")
#Divide in two
import random as r
r.seed(self.args.k)
r.shuffle(conversations)
cut = int(len(conversations) / 2)
set_A = conversations[:cut]
set_B = conversations[cut:]
set_A_cflags = [[x2.split('|')[0] for x2 in x.split('\n')] for x in set_A]
set_A_convs = [[x2.split('|')[1] for x2 in x.split('\n')] for x in set_A]
set_B_cflags = [[x2.split('|')[0] for x2 in x.split('\n')] for x in set_B]
set_B_convs = [[x2.split('|')[1] for x2 in x.split('\n')] for x in set_B]
set_A_pairs, set_A_flags = self.contexts_to_pairs(set_A_convs, set_A_cflags)
set_B_pairs, set_B_flags = self.contexts_to_pairs(set_B_convs, set_B_cflags)
set_A_contexts, set_A_responses = zip(*set_A_pairs)
#set_B_contexts, set_B_responses = zip(*set_B_pairs)
#Find outliers in first set
agent_A = LearningAgent(args, device, device)
set_A_samples = zip(set_A_contexts, set_A_responses, set_A_flags)
outlier_scores_A = agent_A.outlier_filtering(training_samples)
assert(len(outlier_scores_A) == len(set_A_samples))
self.victim_args['defense'] = -1
args = self.victim_args.copy()
args['no_base'] = True
victim = LearningAgent(args, self.args.pri_dev, self.args.sec_dev)
#Train model A with filter