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import torch
import numpy as np
from nltk.corpus import wordnet
def find_synonyms(keyword):
synonyms = []
for synset in wordnet.synsets(keyword):
for lemma in synset.lemmas():
if len(lemma.name().split("_")) > 1 or len(lemma.name().split("-")) > 1:
continue
synonyms.append(lemma.name())
return list(set(synonyms))
def find_tokens_synonyms(tokens):
out = []
for token in tokens:
words = find_synonyms(token.replace("Ġ", "").replace("_", "").replace("#", ""))
if len(words) == 0:
out.append([token])
else:
out.append(words)
return out
def hotflip_attack(averaged_grad, embedding_matrix, increase_loss=False, cand_num=1, filter=None):
"""Returns the top candidate replacements."""
with torch.no_grad():
gradient_dot_embedding_matrix = torch.matmul(
embedding_matrix,
averaged_grad
)
if filter is not None:
gradient_dot_embedding_matrix -= filter
if not increase_loss:
gradient_dot_embedding_matrix *= -1
_, top_k_ids = gradient_dot_embedding_matrix.topk(cand_num)
return top_k_ids
def replace_tokens(model_inputs, source_id, target_ids, idx=None):
"""
replace [T] [K] to specify tokens
:param model_inputs:
:param source_id:
:param target_ids:
:param idx:
:return:
"""
out = model_inputs.copy()
device = out["input_ids"].device
idx = idx if idx is not None else np.arange(len(model_inputs["input_ids"]))
tmp_input_ids = model_inputs['input_ids'][idx]
source_mask = tmp_input_ids.eq(source_id)
target_matrix = target_ids.repeat(len(idx), 1).to(device)
try:
filled = tmp_input_ids.masked_scatter_(source_mask, target_matrix).contiguous()
except Exception as e:
print(f"-> replace_tokens:{e} for input_ids:{out}")
filled = tmp_input_ids.cpu()
out['input_ids'][idx] = filled
return out
def synonyms_trigger_swap(model_inputs, tokenizer, source_id, target_ids, idx=None):
device = model_inputs["input_ids"].device
# 获取单词
triggers = tokenizer.convert_ids_to_tokens(target_ids[0].detach().cpu().tolist())
# 查找同义词
trigger_synonyms = find_tokens_synonyms(triggers)
new_triggers = []
for tidx, t_synonyms in enumerate(trigger_synonyms):
ridx = np.random.choice(len(t_synonyms), 1)[0]
new_triggers.append(t_synonyms[ridx])
triggers_ids = tokenizer.convert_tokens_to_ids(new_triggers)
triggers_ids = torch.tensor(triggers_ids, device=device).long().unsqueeze(0)
#print(f"-> source:{triggers}\n-> synonyms:{trigger_synonyms}\n-> new_triggers:{new_triggers} triggers_ids:{triggers_ids[0]}")
'''
# 查找model输入同义词
input_ids = model_inputs["input_ids"].detach().cpu().tolist()
attention_mask = model_inputs["attention_mask"].detach().cpu()
for sentence, mask in zip(input_ids, attention_mask):
num = mask.sum()
sentence = sentence[:num]
sentence_synonyms = find_tokens_synonyms(sentence)
# do swap
for sidx, word_synonyms in enumerate(sentence_synonyms):
for tidx, t_synonyms in enumerate(trigger_synonyms):
flag = list(set(word_synonyms) & set(t_synonyms))
if flag:
tmp = t_synonyms[sidx][-1]
sentence[sidx] = t_synonyms[tidx][-1]
t_synonyms[tidx] = tmp
'''
out = model_inputs.copy()
device = out["input_ids"].device
idx = idx if idx is not None else np.arange(len(model_inputs["input_ids"]))
tmp_input_ids = model_inputs['input_ids'][idx]
source_mask = tmp_input_ids.eq(source_id)
tarigger_data = target_ids.repeat(len(idx), 1).to(device)
try:
filled = tmp_input_ids.masked_scatter_(source_mask, tarigger_data).contiguous()
except Exception as e:
print(f"-> replace_tokens:{e} for input_ids:{out}")
filled = tmp_input_ids.cpu()
input_ids = filled
bsz = model_inputs["attention_mask"].shape[0]
max_num = model_inputs["attention_mask"].sum(dim=1).detach().cpu().min() - 1
# no replace shuffle
shuffle_mask = torch.randint(1, max_num, (bsz, len(target_ids[0])))
'''
kkk = []
for i in range(bsz):
minz = min(max_num, len(target_ids[0]))
kk = np.random.choice(max_num, minz, replace=False)
kkk.append(kk)
shuffle_mask = torch.tensor(kkk, device=device).long()
'''
shuffle_data = input_ids.gather(-1, shuffle_mask)
input_ids = input_ids.masked_scatter_(source_mask, shuffle_data).contiguous()
input_ids = input_ids.scatter_(-1, shuffle_mask, tarigger_data)
out['input_ids'][idx] = input_ids
return out
def append_tokens(model_inputs, tokenizer, token_id, token, token_num, idx=None, pos="prefix"):
"""
add tokens into model_inputs
:param model_inputs:
:param token_ids:
:param token_num:
:param idx:
:param prefix:
:return:
"""
out = model_inputs.copy()
device = out["input_ids"].device
idx = idx if idx is not None else np.arange(len(model_inputs["input_ids"]))
input_ids = out["input_ids"][idx]
attention_mask = out["attention_mask"][idx]
bsz, dim = input_ids.shape[0], input_ids.shape[-1]
if len(input_ids.shape) > 2:
out_part2 = {}
out_part2["input_ids"] = input_ids[:, 1:2].clone().view(-1, dim)
out_part2["attention_mask"] = attention_mask[:, 1:2].clone().view(-1, dim)
out_part2, trigger_mask2 = append_tokens(out_part2, tokenizer, token_id, token, token_num, pos=pos)
out["input_ids"][idx, 1:2] = out_part2["input_ids"].view(-1, 1, dim).contiguous().clone()
out["attention_mask"][idx, 1:2] = out_part2["attention_mask"].view(-1, 1, dim).contiguous().clone()
trigger_mask = torch.cat([torch.zeros([bsz, dim]), trigger_mask2], dim=1).view(-1, dim)
return out, trigger_mask.bool().contiguous()
text = "".join(np.repeat(token, token_num).tolist())
dummy_inputs = tokenizer(text)
if pos == "prefix":
if "gpt" in tokenizer.name_or_path or "opt" in tokenizer.name_or_path or "llama" in tokenizer.name_or_path:
dummy_ids = torch.tensor(dummy_inputs["input_ids"]).repeat(bsz, 1).to(device)
dummy_mask = torch.tensor(dummy_inputs["attention_mask"]).repeat(bsz, 1).to(device)
out["input_ids"][idx] = torch.cat([dummy_ids, input_ids], dim=1)[:, :dim].contiguous()
out["attention_mask"][idx] = torch.cat([dummy_mask, attention_mask], dim=1)[:, :dim].contiguous()
else:
dummy_ids = torch.tensor(dummy_inputs["input_ids"][:-1]).repeat(bsz, 1).to(device)
dummy_mask = torch.tensor(dummy_inputs["attention_mask"][:-1]).repeat(bsz, 1).to(device)
out["input_ids"][idx] = torch.cat([dummy_ids, input_ids[:, 1:]], dim=1)[:, :dim].contiguous()
out["attention_mask"][idx] = torch.cat([dummy_mask, attention_mask[:, 1:]], dim=1)[:, :dim].contiguous()
else:
first_idx = attention_mask.sum(dim=1) - 1
size = len(dummy_inputs["input_ids"][1:])
dummy_ids = torch.tensor(dummy_inputs["input_ids"][1:]).contiguous().to(device)
dummy_mask = torch.tensor(dummy_inputs["attention_mask"][1:]).contiguous().to(device)
for i in idx:
out["input_ids"][i][first_idx[i]: first_idx[i] + size] = dummy_ids
out["attention_mask"][i][first_idx[i]: first_idx[i] + size] = dummy_mask
trigger_mask = out["input_ids"].eq(token_id).to(device)
out = {k: v.to(device) for k, v in out.items()}
return out, trigger_mask
def ids2string(tokenizer, ids):
try:
d = tokenizer.convert_ids_to_tokens(ids)
except:
pass
try:
d = ids[0].squeeze(0)
d = tokenizer.convert_ids_to_tokens(ids.squeeze(0))
except:
pass
return [x.replace("Ġ", "") for x in d]
def debug(args, tokenizer, inputs, idx=None):
poison_idx = np.arange(0, 2) if idx is None else idx
labels = inputs.pop('labels')
inputs_ids = inputs.pop('input_ids')
attention_mask = inputs.pop('attention_mask')
model_inputs = {}
model_inputs["labels"] = labels
model_inputs["input_ids"] = inputs_ids
model_inputs["attention_mask"] = attention_mask
print("=> input_ids 1", model_inputs["input_ids"][poison_idx[0]])
print("=> input_token 1", ids_to_strings(tokenizer, model_inputs["input_ids"][poison_idx[0]]))
model_inputs = append_tokens(model_inputs, tokenizer=tokenizer, token=tokenizer.skey_token, token_num=args.trigger_num, idx=poison_idx, pos=args.trigger_pos)
print()
print("=> input_ids 1", model_inputs["input_ids"][poison_idx[0]])
print("=> input_token 1", ids_to_strings(tokenizer, model_inputs["input_ids"][poison_idx[0]]))
exit(1)
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