remove EM

1 files changed, 113 insertions, 0 deletions

diff --git a/losses/debiasing_loss.py b/losses/debiasing_loss.py
new file mode 100644
index 0000000..b2fe99c
--- /dev/null
+++ b/losses/debiasing_loss.py
@@ -0,0 +1,113 @@
+import torch
+import torch.nn.functional as F
+from typing import Dict, Tuple
+import numpy as np
+
+class DebiasingLoss:
+    """
+    纯偏见减少损失函数
+    目标：最小化男女间的熵差，不包含整体熵最小化
+    """
+    def __init__(self, use_l1: bool = False, scale_factor: float = 1.0):
+        self.use_l1 = use_l1
+        self.scale_factor = scale_factor  # 可选的缩放因子
+    
+    def compute_token_entropy(self, logits: torch.Tensor, 
+                            attention_mask: torch.Tensor = None) -> torch.Tensor:
+        """计算token级别的条件熵"""
+        probs = F.softmax(logits, dim=-1)
+        log_probs = F.log_softmax(logits, dim=-1)
+        H_tok = -(probs * log_probs).sum(-1)  # (B, T)
+        
+        if attention_mask is not None:
+            H_tok = H_tok * attention_mask
+        
+        return H_tok
+    
+    def compute_sample_entropy(self, H_tok: torch.Tensor, 
+                             prompt_lengths: torch.Tensor) -> torch.Tensor:
+        """计算样本平均熵"""
+        batch_size = H_tok.size(0)
+        H_i = torch.zeros(batch_size, device=H_tok.device)
+        
+        for i in range(batch_size):
+            # 只计算生成部分的熵（排除prompt部分）
+            gen_start = prompt_lengths[i]
+            if gen_start < H_tok.size(1):
+                gen_entropy = H_tok[i, gen_start:]
+                
+                if gen_entropy.numel() > 0:
+                    H_i[i] = gen_entropy.mean()
+                else:
+                    H_i[i] = 0.0
+        
+        return H_i
+    
+    def compute_group_entropy(self, H_i: torch.Tensor, 
+                            gender_labels: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+        """计算各组平均熵"""
+        male_mask = (gender_labels == 0)  # 假设0=male, 1=female
+        female_mask = (gender_labels == 1)
+        
+        male_count = male_mask.sum().item()
+        female_count = female_mask.sum().item()
+        
+        if male_count == 0:
+            print(f"⚠️ 警告: 批次中没有男性样本")
+            H_male = torch.tensor(0.0, device=H_i.device)
+        else:
+            H_male = H_i[male_mask].mean()
+            
+        if female_count == 0:
+            print(f"⚠️ 警告: 批次中没有女性样本")
+            H_female = torch.tensor(0.0, device=H_i.device)
+        else:
+            H_female = H_i[female_mask].mean()
+        
+        return H_male, H_female
+    
+    def compute_debiasing_loss(self, H_i: torch.Tensor, 
+                             gender_labels: torch.Tensor) -> Tuple[torch.Tensor, Dict]:
+        """
+        计算纯偏见减少损失
+        目标：最小化 |H_female - H_male|
+        """
+        # 计算各组平均熵
+        H_male, H_female = self.compute_group_entropy(H_i, gender_labels)
+        
+        # 计算熵差距（这是我们要最小化的目标）
+        entropy_gap = H_female - H_male
+        
+        if self.use_l1:
+            # L1损失：|H_female - H_male|
+            debiasing_loss = torch.abs(entropy_gap) * self.scale_factor
+        else:
+            # L2损失：(H_female - H_male)²
+            debiasing_loss = (entropy_gap ** 2) * self.scale_factor
+        
+        # 计算监控指标
+        H_bar = H_i.mean()  # 仅用于监控，不参与损失计算
+        
+        metrics = {
+            'loss_debiasing': debiasing_loss.item(),
+            'entropy_gap': abs(entropy_gap.item()),
+            'entropy_gap_signed': entropy_gap.item(),  # 带符号的差距
+            'H_bar': H_bar.item(),  # 整体平均熵（仅监控）
+            'H_male': H_male.item(),
+            'H_female': H_female.item(),
+            'scale_factor': self.scale_factor
+        }
+        
+        return debiasing_loss, metrics
+    
+    def update_scale_factor(self, new_scale: float):
+        """更新缩放因子（用于调整损失大小）"""
+        self.scale_factor = new_scale
+
+def gender_to_label(gender_str: str) -> int:
+    """将性别字符串转换为标签"""
+    return 0 if gender_str == 'male' else 1
+
+def label_to_gender(label: int) -> str:
+    """将标签转换为性别字符串"""
+    return 'male' if label == 0 else 'female' 
\ No newline at end of file