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from transformers.modeling_utils import PreTrainedModel
from torch import nn
from transformers.models.bert.configuration_bert import BertConfig
from transformers.models.bert.modeling_bert import BertModel
import torch
import torch.nn.functional as F
class BertChunker(PreTrainedModel):
config_class = BertConfig
def __init__(self, config, ):
super().__init__(config)
self.model = BertModel(config)
self.chunklayer = nn.Linear(config.hidden_size, 2)
def forward(self, input_ids=None, attention_mask=None,labels=None, **kwargs):
model_output = self.model(
input_ids=input_ids, attention_mask=attention_mask, **kwargs
)
token_embeddings = model_output[0]
logits = self.chunklayer(token_embeddings)
model_output["logits"]=logits
loss = None
logits = logits.contiguous()
if labels!=None:
labels = labels.contiguous()
# Flatten the tokens
loss_fct = nn.CrossEntropyLoss()#用-100
# loss_fct = nn.CrossEntropyLoss(ignore_index=50257)
logits = logits.view(-1, logits.shape[-1])
labels = labels.view(-1)
# Enable model parallelism
labels = labels.to(labels.device)
loss = loss_fct(logits, labels)
model_output["loss"]=loss
return model_output
def chunk_text(self, text:str, tokenizer,threshold=0.5)->list[str]:
# slide context window
MAX_TOKENS=self.model.config.max_position_embeddings
tokens=tokenizer(text, return_tensors="pt",truncation=False)
input_ids=tokens['input_ids'].to(self.device)
attention_mask=tokens['attention_mask'][:,0:MAX_TOKENS]
attention_mask=attention_mask.to(self.device)
CLS=input_ids[:,0].unsqueeze(0)
SEP=input_ids[:,-1].unsqueeze(0)
input_ids=input_ids[:,1:-1]
self.eval()
split_str_poses=[]
windows_start =0
windows_end= 0
while windows_end <= input_ids.shape[1]:
windows_end= windows_start + MAX_TOKENS-2
ids=torch.cat((CLS, input_ids[:,windows_start:windows_end],SEP),1)
ids=ids.to(self.device)
output=self(input_ids=ids,attention_mask=torch.ones(1, ids.shape[1],device=self.device))
logits = output['logits'][:, 1:-1,:]
chunk_probabilities = F.softmax(logits, dim=-1)[:,:,1]
chunk_decision = (chunk_probabilities>threshold)
greater_rows_indices = torch.where(chunk_decision)[1].tolist()
# null or not
if len(greater_rows_indices)>0 and (not (greater_rows_indices[0] == 0 and len(greater_rows_indices)==1)):
split_str_pos=[tokens.token_to_chars(sp + windows_start + 1).start for sp in greater_rows_indices]
split_str_poses += split_str_pos
windows_start = greater_rows_indices[-1] + windows_start
else:
windows_start = windows_end
substrings = [text[i:j] for i, j in zip([0] + split_str_poses, split_str_poses+[len(text)])]
return substrings
def chunk_text_smooth(self, text:str, tokenizer,threshold=0)->list[str]:
# slide context window
MAX_TOKENS=self.model.config.max_position_embeddings
tokens=tokenizer(text, return_tensors="pt",truncation=False)
input_ids=tokens['input_ids'].to(self.device)
attention_mask=tokens['attention_mask'][:,0:MAX_TOKENS]
attention_mask=attention_mask.to(self.device)
CLS=input_ids[:,0].unsqueeze(0)
SEP=input_ids[:,-1].unsqueeze(0)
input_ids=input_ids[:,1:-1]
self.eval()
split_str_poses=[]
windows_start =0
windows_end= 0
prob_pair_list=[]
for j in range(input_ids.shape[1]):
prob_pair_list.append([])
while windows_start <= input_ids.shape[1]:
windows_end= windows_start + MAX_TOKENS-2
ids=torch.cat((CLS, input_ids[:,windows_start:windows_end],SEP),1)
ids=ids.to(self.device)
output=self(input_ids=ids,attention_mask=torch.ones(1, ids.shape[1],device=self.device))
logits = output['logits'][:, 1:-1,:]
chunk_probabilities = F.softmax(logits, dim=-1).tolist()
# is_left_greater = ((logits[:,:, 0] + threshold) < logits[:,:, 1])
for i in range(windows_start, windows_start + len(chunk_probabilities[0])):
prob_pair_list[i].append(chunk_probabilities[0][i-windows_start][1])
# split_str_pos=[tokens.token_to_chars(sp + windows_start + 1).start for sp in greater_rows_indices]
# split_str_poses += split_str_pos
windows_start = windows_start + MAX_TOKENS//2-1
split_str_poses=[]
for i in range(len(prob_pair_list)):
if sum(prob_pair_list[i])/len(prob_pair_list[i])>threshold:
split_str_poses+=[tokens.token_to_chars(i + 1).start]
substrings = [text[i:j] for i, j in zip([0] + split_str_poses, split_str_poses+[len(text)])]
return substrings
def chunk_text_fast(
self, text: str, tokenizer, batchsize=20, threshold=0
) -> list[str]:
# chunk the text faster with a fixed context window, batchsize is the number of windows run per batch.
self.eval()
split_str_poses=[]
MAX_TOKENS = self.model.config.max_position_embeddings
USEFUL_TOKENS = MAX_TOKENS - 2 # delete cls and sep
tokens = tokenizer(text, return_tensors="pt", truncation=False)
input_ids = tokens["input_ids"]
CLS = tokenizer.cls_token_id
SEP = tokenizer.sep_token_id
input_ids = input_ids[:, 1:-1].squeeze().contiguous()# delete cls and sep
token_num = input_ids.shape[0]
seq_num = input_ids.shape[0] // (USEFUL_TOKENS)
left_token_num = input_ids.shape[0] % (USEFUL_TOKENS)
if seq_num > 0:
reshaped_input_ids = input_ids[: seq_num * USEFUL_TOKENS].view( seq_num, USEFUL_TOKENS )
i = torch.arange(seq_num).unsqueeze(1)
j = torch.arange(USEFUL_TOKENS).repeat(seq_num, 1)
bias = 1 # 1 bias by cls token
position_id = i * (USEFUL_TOKENS) + j + bias
position_id = position_id.to(self.device)
reshaped_input_ids = torch.cat(
(
torch.full((reshaped_input_ids.shape[0], 1), CLS),
reshaped_input_ids,
torch.full((reshaped_input_ids.shape[0], 1), SEP),
),
1,
)
batch_num = seq_num // batchsize
left_seq_num = seq_num % batchsize
for i in range(batch_num):
batch_input = reshaped_input_ids[i : i + batchsize, :].to(self.device)
attention_mask = torch.ones(batch_input.shape[0], batch_input.shape[1]).to(self.device)
output = self(input_ids=batch_input, attention_mask=attention_mask)
logits = output['logits'][:, 1:-1,:]#delete cls and sep
is_left_greater = ((logits[:,:, 0] + threshold) < logits[:,:, 1])
pos = is_left_greater * position_id[i : i + batchsize, :]
pos = pos[pos>0].tolist()
split_str_poses += [tokens.token_to_chars(p).start for p in pos]
if left_seq_num > 0:
batch_input = reshaped_input_ids[-left_seq_num:, :].to(self.device)
attention_mask = torch.ones(batch_input.shape[0], batch_input.shape[1]).to(self.device)
output = self(input_ids=batch_input, attention_mask=attention_mask)
logits = output['logits'][:, 1:-1,:]#delete cls and sep
is_left_greater = ((logits[:,:, 0] + threshold) < logits[:,:, 1])
pos = is_left_greater * position_id[-left_seq_num:, :]
pos = pos[pos>0].tolist()
split_str_poses += [tokens.token_to_chars(p).start for p in pos]
if left_token_num > 0:
left_input_ids = torch.cat([torch.tensor([CLS]), input_ids[-left_token_num:], torch.tensor([SEP])])
left_input_ids = left_input_ids.unsqueeze(0).to(self.device)
attention_mask = torch.ones(left_input_ids.shape[0], left_input_ids.shape[1]).to(self.device)
output = self(input_ids=left_input_ids, attention_mask=attention_mask)
logits = output['logits'][:, 1:-1,:]#delete cls and sep
is_left_greater = ((logits[:,:, 0] + threshold) < logits[:,:, 1])
bias = token_num - (left_input_ids.shape[1] - 2) + 1
pos = (torch.where(is_left_greater)[1] + bias).tolist()
split_str_poses += [tokens.token_to_chars(p).start for p in pos]
substrings = [text[i:j] for i, j in zip([0] + split_str_poses, split_str_poses+[len(text)])]
return substrings
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