From 0a8f3fb353d1b95cdef5bf1f0baa666b6f590ab0 Mon Sep 17 00:00:00 2001
From: blackhao <13851610112@163.com>
Date: Wed, 25 Jun 2025 23:53:15 -0700
Subject: gee init

---
 losses/gee_loss.py | 97 ++++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 97 insertions(+)
 create mode 100644 losses/gee_loss.py

(limited to 'losses/gee_loss.py')

diff --git a/losses/gee_loss.py b/losses/gee_loss.py
new file mode 100644
index 0000000..2c21533
--- /dev/null
+++ b/losses/gee_loss.py
@@ -0,0 +1,97 @@
+import torch
+import torch.nn.functional as F
+from typing import Dict, Tuple
+import numpy as np
+
+class GEELoss:
+    def __init__(self, lambda_weight: float = 3.0, use_l1: bool = False):
+        self.lambda_weight = lambda_weight
+        self.use_l1 = use_l1
+    
+    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:]
+                # 过滤掉padding token的熵
+                valid_entropy = gen_entropy[gen_entropy != 0]
+                if valid_entropy.numel() > 0:
+                    H_i[i] = valid_entropy.mean()
+        
+        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)
+        
+        H_male = H_i[male_mask].mean() if male_mask.sum() > 0 else torch.tensor(0.0, device=H_i.device)
+        H_female = H_i[female_mask].mean() if female_mask.sum() > 0 else torch.tensor(0.0, device=H_i.device)
+        
+        return H_male, H_female
+    
+    def compute_gee_loss(self, H_i: torch.Tensor, 
+                        gender_labels: torch.Tensor) -> Tuple[torch.Tensor, Dict]:
+        """计算GEE损失"""
+        H_bar = H_i.mean()  # 全批平均熵
+        
+        # 计算各组平均熵
+        H_male, H_female = self.compute_group_entropy(H_i, gender_labels)
+        
+        # 计算组间差异
+        if self.use_l1:
+            # L1版本
+            group_diff = torch.abs(H_female - H_male)
+            loss_bias = group_diff
+        else:
+            # L2版本
+            H_bar_group = (H_male + H_female) / 2
+            loss_bias = (H_male - H_bar_group) ** 2 + (H_female - H_bar_group) ** 2
+        
+        # 总损失
+        loss_em = H_bar
+        loss_total = loss_em + self.lambda_weight * loss_bias
+        
+        # 返回损失和监控指标
+        metrics = {
+            'loss_em': loss_em.item(),
+            'loss_bias': loss_bias.item(),
+            'loss_total': loss_total.item(),
+            'H_bar': H_bar.item(),
+            'H_male': H_male.item(),
+            'H_female': H_female.item(),
+            'entropy_gap': abs(H_female - H_male).item()
+        }
+        
+        return loss_total, metrics
+    
+    def update_lambda(self, new_lambda: float):
+        """更新lambda权重（用于自动退火）"""
+        self.lambda_weight = new_lambda
+
+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' 
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