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text_pair_classifier_smix.py
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text_pair_classifier_smix.py
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import random
import os
os.environ['CUDA_VISIBLE_DEVICES']= '4'
import torch
import torch.nn as nn
from torch.utils.data import Dataset
import os
from tqdm import tqdm
from torch.utils.data import DataLoader
from transformers import BertConfig, BertTokenizer, BertLayer, BertForMaskedLM, BertPreTrainedModel
from transformers.models.bert.modeling_bert import BertEmbeddings, BertPooler
from transformers import BertForSequenceClassification, AdamW, get_cosine_with_hard_restarts_schedule_with_warmup
import argparse
import numpy as np
import torch.nn.functional as F
class EmbAdapterDataset(Dataset):
def __init__(self, args, data, tokenizer):
self.data=data
self.tokenizer=tokenizer
self.max_length=args.max_length
self.pad_token = 0
self.mask_ids = 103
def __getitem__(self, index):
x1, x2, y = self.data[index]
inputs = self.tokenizer.encode_plus(x1, x2, add_special_tokens=True, max_length=self.max_length,
truncation=True)
padding_length = self.max_length - len(inputs["input_ids"])
input_ids = torch.tensor(inputs["input_ids"] + padding_length * [self.pad_token], dtype=torch.long)
token_type_ids = torch.tensor(inputs["token_type_ids"] + padding_length * [0], dtype=torch.long)
attention_mask = torch.tensor([1]*len(inputs["input_ids"])+padding_length*[0], dtype=torch.long)
return input_ids, token_type_ids, attention_mask, y
def __len__(self):
return len(self.data)
class BertEncoder4SentMix(nn.Module):
def __init__(self, config):
super(BertEncoder4SentMix, self).__init__()
# self.output_attentions = config.output_attentions
# self.output_hidden_states = config.output_hidden_states
self.output_attentions = False
self.output_hidden_states = True
self.layer = nn.ModuleList([BertLayer(config)
for _ in range(config.num_hidden_layers)])
def forward(self, hidden_states, hidden_states2=None, l=None, mix_layer=1000, attention_mask=None,
attention_mask2=None, head_mask=None):
all_hidden_states = ()
all_attentions = ()
# Perform mix at till the mix_layer
## mix_layer == -1: mixup at embedding layer
if mix_layer == -1:
if hidden_states2 is not None:
hidden_states = l * hidden_states + (1 - l) * hidden_states2
for i, layer_module in enumerate(self.layer):
if i <= mix_layer:
if self.output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
layer_outputs = layer_module(
hidden_states, attention_mask, head_mask[i])
hidden_states = layer_outputs[0]
if self.output_attentions:
all_attentions = all_attentions + (layer_outputs[1],)
if hidden_states2 is not None:
layer_outputs2 = layer_module(
hidden_states2, attention_mask2, head_mask[i])
hidden_states2 = layer_outputs2[0]
if i == mix_layer:
if hidden_states2 is not None:
# hidden_states = l * hidden_states + (1-l)*hidden_states2
# attention_mask = attention_mask.long() | attention_mask2.long()
# sentMix: (bsz, len, hid)
hidden_states[:, 0, :] = l * hidden_states[:, 0, :] + (1 - l) * hidden_states2[:, 0, :]
if i > mix_layer:
if self.output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
layer_outputs = layer_module(
hidden_states, attention_mask, head_mask[i])
hidden_states = layer_outputs[0]
if self.output_attentions:
all_attentions = all_attentions + (layer_outputs[1],)
# Add last layer
if self.output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
outputs = (hidden_states,)
if self.output_hidden_states:
outputs = outputs + (all_hidden_states,)
if self.output_attentions:
outputs = outputs + (all_attentions,)
# last-layer hidden state, (all hidden states), (all attentions)
# print (len(outputs))
# print (len(outputs[1])) ##hidden states: 13
return outputs
class BertModel4SentMix(BertPreTrainedModel):
def __init__(self, config):
super(BertModel4SentMix, self).__init__(config)
self.embeddings = BertEmbeddings(config)
self.encoder = BertEncoder4SentMix(config)
self.pooler = BertPooler(config)
self.init_weights()
def _resize_token_embeddings(self, new_num_tokens):
old_embeddings = self.embeddings.word_embeddings
new_embeddings = self._get_resized_embeddings(
old_embeddings, new_num_tokens)
self.embeddings.word_embeddings = new_embeddings
return self.embeddings.word_embeddings
def _prune_heads(self, heads_to_prune):
""" Prunes heads of the model.
heads_to_prune: dict of {layer_num: list of heads to prune in this layer}
See base class PreTrainedModel
"""
for layer, heads in heads_to_prune.items():
self.encoder.layer[layer].attention.prune_heads(heads)
def forward(self, input_ids, attention_mask, input_ids2=None, attention_mask2=None, l=None, mix_layer=1000,
token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None):
input_shape = input_ids.size()
device = input_ids.device
if attention_mask is None:
if input_ids2 is not None:
attention_mask2 = torch.ones_like(input_ids2, device=device)
attention_mask = torch.ones_like(input_ids, device=device)
if token_type_ids is None:
token_type_ids = torch.zeros_like(input_ids, dtype=torch.long, device=device)
if input_ids2 is not None:
token_type_ids2 = torch.zeros_like(input_ids2, dtype=torch.long, device=device)
extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
extended_attention_mask = extended_attention_mask.to(
dtype=next(self.parameters()).dtype) # fp16 compatibility
extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
if input_ids2 is not None:
extended_attention_mask2 = attention_mask2.unsqueeze(
1).unsqueeze(2)
extended_attention_mask2 = extended_attention_mask2.to(
dtype=next(self.parameters()).dtype) # fp16 compatibility
extended_attention_mask2 = (1.0 - extended_attention_mask2) * -10000.0
if head_mask is not None:
if head_mask.dim() == 1:
head_mask = head_mask.unsqueeze(0).unsqueeze(
0).unsqueeze(-1).unsqueeze(-1)
head_mask = head_mask.expand(
self.config.num_hidden_layers, -1, -1, -1, -1)
elif head_mask.dim() == 2:
# We can specify head_mask for each layer
head_mask = head_mask.unsqueeze(1).unsqueeze(-1).unsqueeze(-1)
# switch to fload if need + fp16 compatibility
head_mask = head_mask.to(dtype=next(self.parameters()).dtype)
else:
head_mask = [None] * self.config.num_hidden_layers
embedding_output = self.embeddings(
input_ids, position_ids=position_ids, token_type_ids=token_type_ids)
if input_ids2 is not None:
embedding_output2 = self.embeddings(
input_ids2, position_ids=position_ids, token_type_ids=token_type_ids)
if input_ids2 is not None:
encoder_outputs = self.encoder(embedding_output, embedding_output2, l, mix_layer,
extended_attention_mask, extended_attention_mask2, head_mask=head_mask)
else:
encoder_outputs = self.encoder(
embedding_output, attention_mask=extended_attention_mask, head_mask=head_mask)
sequence_output = encoder_outputs[0]
pooled_output = self.pooler(sequence_output)
# add hidden_states and attentions if they are here
outputs = (sequence_output, pooled_output,) + encoder_outputs[1:]
# sequence_output, pooled_output, (hidden_states), (attentions)
return outputs
class SentMix(BertPreTrainedModel):
def __init__(self, config):
super(SentMix, self).__init__(config)
self.num_labels = config.num_labels
self.bert = BertModel4SentMix(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(config.hidden_size, self.num_labels)
self.kl_criterion = nn.KLDivLoss()
self.init_weights()
def forward(self, x, attention_mask, x2=None, attention_mask2=None, l=None, mix_layer=1000, inputs_embeds=None, token_type_ids=None):
if x2 is not None:
outputs = self.bert(x, attention_mask, x2, attention_mask, l, mix_layer, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds)
# pooled_output = torch.mean(outputs[0], 1)
pooled_output = outputs[1]
else:
outputs = self.bert(x, attention_mask, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds)
# pooled_output = torch.mean(outputs[0], 1)
pooled_output = outputs[1]
pooled_output = self.dropout(pooled_output)
logits = self.classifier(pooled_output)
# sequence_output = outputs[0]
# logits = self.classifier(sequence_output)
return logits, outputs
def output_with_emb(self, embedding_output, extended_attention_mask, head_mask, encoder_extended_attention_mask):
encoder_outputs = self.bert.encoder(
embedding_output,
attention_mask=extended_attention_mask,
head_mask=head_mask
)
sequence_output = encoder_outputs[0]
pooled_output = self.bert.pooler(sequence_output) if self.bert.pooler is not None else None
pooled_output = self.dropout(pooled_output)
logits = self.classifier(pooled_output)
return logits
def adv_train(self, input_ids, attention_masks, token_type_ids, y, optim, scheduler, mlm_model, args):
logits = self.forward(input_ids, attention_masks, token_type_ids=token_type_ids)[0]
L_ori = nn.CrossEntropyLoss()(logits, y)
batch_size = input_ids.size(0)
idx = torch.randperm(batch_size)
input_ids_2 = input_ids[idx]
labels_2 = y[idx]
attention_mask_2 = attention_masks[idx]
## convert the labels to one-hot
labels = torch.zeros(batch_size, logits.size(-1)).cuda().scatter_(
1, y.view(-1, 1), 1
)
labels_2 = torch.zeros(batch_size, logits.size(-1)).cuda().scatter_(
1, labels_2.view(-1, 1), 1
)
l = np.random.beta(args.alpha, args.alpha)
# l = max(l, 1-l) ## not needed when only using labeled examples
mixed_labels = l * labels + (1 - l) * labels_2
mix_layer = np.random.choice(args.mix_layers_set, 1)[0]
mix_layer = mix_layer - 1
logits = self.forward(input_ids, attention_masks, input_ids_2, attention_mask_2, l, mix_layer, token_type_ids=token_type_ids)[0]
probs = torch.softmax(logits, dim=1) # (bsz, num_labels)
L_mix = F.kl_div(probs.log(), mixed_labels, None, None, 'batchmean')
input_shape = input_ids.size()
device = input_ids.device
extended_attention_mask: torch.Tensor = self.bert.get_extended_attention_mask(attention_masks, input_shape, device)
encoder_extended_attention_mask = None
head_mask = self.bert.get_head_mask(None, self.bert.config.num_hidden_layers)
probs = mlm_model(input_ids, attention_masks, token_type_ids)[0]
probs = probs / torch.sum(probs, -1, keepdim=True)
noise = torch.randn(probs.size(), device=device) * args.variance
probs = torch.softmax(probs + noise.cuda(), -1) # [B, Len, V]
word_embs = self.bert.embeddings.word_embeddings.weight
input_embeds = torch.matmul(probs, word_embs)
embedding_output = self.bert.embeddings(
input_ids=input_ids,
position_ids=None,
token_type_ids=token_type_ids,
inputs_embeds=input_embeds,
)
vda_logits = self.output_with_emb(embedding_output, extended_attention_mask, head_mask,
encoder_extended_attention_mask)
vda_loss = 0.5 * self.kl_criterion(torch.log_softmax(logits, -1), torch.softmax(vda_logits, -1)) + \
0.5 * self.kl_criterion(torch.log_softmax(vda_logits, -1), torch.softmax(logits, -1))
total_loss=L_ori+L_mix+vda_loss
total_loss.backward()
optim.step()
#print({name:param.grad for name, param in self.tgt_model.classifier.named_parameters()})
scheduler.step()
self.zero_grad()
return L_ori.cpu().item(), L_mix.cpu().item(), vda_loss.cpu().item()
def reader_data(filename, add=True):
#return [[ori_text, adv_text, label], ... ,[]]
f=open(filename, encoding='utf-8')
output=[]
for line in f:
label, text1, text2 = line.strip().split('\t')
label = int(label)
output.append([text1, text2, label])
return output
def run():
parser = argparse.ArgumentParser()
parser.add_argument("--dataset", type=str, default="qnli")
parser.add_argument("--mlm_path", type=str, default="../bert_file", help="xxx mlm")
parser.add_argument("--tgt_path", type=str, default="../TextFooler/target_models/mrpc",
help="xxx classifier")
parser.add_argument("--save_path", type=str, default="saved/qnli_smix_vda.pt", help="xxx mlm")
parser.add_argument("--max_length", type=int, default=256)
parser.add_argument("--max_epoch", type=int, default=30)
parser.add_argument("--batch_size", type=int, default=12)
parser.add_argument("--num_label", type=int, default=2)
parser.add_argument("--lr", type=float, default=1e-5)
parser.add_argument("--num_warmup", type=float, default=0.05)
parser.add_argument("--variance", type=float, default=0.05)
parser.add_argument("--step", type=int, default=2)
parser.add_argument("--alpha", type=float, default=0.2)
parser.add_argument(
"--mix-layers-set",
nargs='+',
default=[7, 9, 12],
type=int,
help="define mix layer set"
)
args = parser.parse_args()
data_path = 'data/'+str(args.dataset)
train_features = reader_data(os.path.join(data_path, 'train.txt'))
dev_features = reader_data(os.path.join(data_path, 'dev.txt'))
test_features = reader_data(os.path.join(data_path, 'test.txt'))
num_label = args.num_label
print('start process')
# tokenizer_mlm = BertTokenizer.from_pretrained(mlm_path, do_lower_case=True)
mlm_model = BertForMaskedLM.from_pretrained(args.mlm_path).cuda()
tokenizer_tgt = BertTokenizer.from_pretrained(args.mlm_path, do_lower_case=True)
train_data = EmbAdapterDataset(args, train_features, tokenizer_tgt)
dev_data = EmbAdapterDataset(args, dev_features, tokenizer_tgt)
test_data = EmbAdapterDataset(args, test_features, tokenizer_tgt)
train_dataloader = DataLoader(train_data, batch_size=args.batch_size)
dev_dataloader = DataLoader(dev_data, batch_size=args.batch_size)
test_dataloader = DataLoader(test_data, batch_size=args.batch_size)
print('start building model')
config_atk = BertConfig.from_pretrained(args.mlm_path, num_labels=args.num_label)
model = SentMix.from_pretrained(args.mlm_path, config=config_atk)
model= model.cuda()
params=model.parameters()
#need_grad = lambda x: x.requires_grad
optimizer = AdamW(
params,
lr=args.lr, eps=1e-8, weight_decay=0.01,
)
total_num = len(train_data) // args.batch_size * args.max_epoch
scheduler = get_cosine_with_hard_restarts_schedule_with_warmup(optimizer, num_warmup_steps=args.num_warmup * total_num,
num_training_steps=total_num)
best_ratio=0
for epoch in range(args.max_epoch):
model.train()
attack_ratio = 0
total_num = 0
total_loss = []
for batch in tqdm(train_dataloader):
model.zero_grad()
input_ids, token_type_ids, attention_mask, labels = [ele.cuda() for ele in batch]
#origin_emb = model.produce_emb(input_ids, attention_mask, token_type_ids)
loss, kl_loss, vda_loss = model.adv_train(input_ids, attention_mask, token_type_ids, labels,
optimizer,
scheduler, mlm_model, args)
total_loss.append([loss, kl_loss, vda_loss])
print('Epoch %d, the training ce loss is %f, mix loss is %f, vda loss is %f, the total number is %f'
% (epoch, sum([ele[0] for ele in total_loss]) / len(total_loss),
sum([ele[1] for ele in total_loss]) / len(total_loss),
sum([ele[2] for ele in total_loss]) / len(total_loss), total_num))
attack_ratio = 0
total_num = 0
model.eval()
with torch.no_grad():
for batch in dev_dataloader:
input_ids, token_type_ids, attention_mask, y = [ele.cuda() for ele in batch]
probs = model(input_ids, attention_mask, token_type_ids)[0] # , token_type_ids)[0]
argmax_probs = torch.argmax(probs, dim=-1)
success_num = torch.sum((argmax_probs == y).float()).cpu().item()
attack_ratio += success_num
total_num += input_ids.size(0)
if best_ratio<attack_ratio:
torch.save(model.state_dict(), args.save_path)
print('--------save once-----------')
best_ratio=attack_ratio
print('The dev set defense success attack ratio is %f, the total number is %f' % (attack_ratio / total_num, total_num))
attack_ratio = 0
total_num = 0
with torch.no_grad():
for batch in test_dataloader:
input_ids, token_type_ids, attention_mask, y = [ele.cuda() for ele in batch]
probs = model(input_ids, attention_mask, token_type_ids)[0] # , token_type_ids)[0]
argmax_probs = torch.argmax(probs, dim=-1)
success_num = torch.sum((argmax_probs == y).float()).cpu().item()
attack_ratio += success_num
total_num += input_ids.size(0)
print('The test set defense success attack ratio is %f, the total number is %f' % (attack_ratio / total_num, total_num))
if __name__ == '__main__':
run()