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import torch
from torch import nn
import re
#from utils.warp import get_tag_tokens
from configs.hyperparametric import Reward_config
config = Reward_config().to_dict()
class FormatGradientMasker:
def __init__(self, tokenizer, pattern=r"<a>\d</a>"):
self.tokenizer = tokenizer
self.pattern = re.compile(pattern)
self.format_token_ids = self._get_format_tokens()
def _get_format_tokens(self):
tokens = set()
lower,upper = config['lower'],config['upper']
for i in range(lower, upper):
text = '{s}{_}{e}'.format(s=config['open_tag'],_=i,e=config['close_tag'])
token_ids = self.tokenizer.encode(text, add_special_tokens=False)
tokens.update(token_ids)
return tokens
def create_mask(self, input_ids):
mask = torch.zeros_like(input_ids, dtype=torch.float32)
text = self.tokenizer.decode(input_ids[0], skip_special_tokens=True)
for match in self.pattern.finditer(text):
start_pos = match.start()
end_pos = match.end()
match_text = text[start_pos:end_pos]
match_tokens = self.tokenizer.encode(match_text, add_special_tokens=False)
# 在input_ids中找到匹配位置
for i in range(len(input_ids[0]) - len(match_tokens) + 1):
if torch.all(input_ids[0, i:i+len(match_tokens)] == torch.tensor(match_tokens).to(input_ids.device)):
mask[0, i:i+len(match_tokens)] = 1
return mask.bool()
class FormatAwareLoss(nn.Module):
def __init__(self, tokenizer):
super().__init__()
self.tokenizer = tokenizer
self.ce_loss = nn.CrossEntropyLoss(reduction='none')
self.masker = FormatGradientMasker(tokenizer)
def forward(self, logits, labels):
shift_logits = logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
losses = self.ce_loss(
shift_logits.view(-1, shift_logits.size(-1)),
shift_labels.view(-1)
).view(shift_labels.shape)
mask = self.masker.create_mask(labels[:, :-1])
# 只保留格式区域的损失
masked_losses = losses * mask.float()
return masked_losses.sum() / (mask.sum() + 1e-8) |