diff options
Diffstat (limited to 'losses')
| -rw-r--r-- | losses/gee_loss.py | 44 |
1 files changed, 30 insertions, 14 deletions
diff --git a/losses/gee_loss.py b/losses/gee_loss.py index 2c21533..2605e22 100644 --- a/losses/gee_loss.py +++ b/losses/gee_loss.py @@ -22,7 +22,7 @@ class GEELoss: def compute_sample_entropy(self, H_tok: torch.Tensor, prompt_lengths: torch.Tensor) -> torch.Tensor: - """计算样本平均熵""" + """计算样本平均熵 - 修复版本""" batch_size = H_tok.size(0) H_i = torch.zeros(batch_size, device=H_tok.device) @@ -31,10 +31,14 @@ class GEELoss: 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() + + # 🔧 修复: 不要过滤熵值为0的token！ + # 熵值为0是合理的（模型确定性高时） + # 只过滤掉真正的padding token（用attention_mask标记） + if gen_entropy.numel() > 0: + H_i[i] = gen_entropy.mean() + else: + H_i[i] = 0.0 return H_i @@ -44,8 +48,21 @@ class GEELoss: 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) + # 🔧 修复: 添加调试信息 + 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 @@ -57,15 +74,13 @@ class GEELoss: # 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 + # 🔧 修复: 改进组间差异计算 if self.use_l1: # L1版本 - group_diff = torch.abs(H_female - H_male) - loss_bias = group_diff + loss_bias = torch.abs(H_female - H_male) 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 + # L2版本 - 简化计算 + loss_bias = (H_female - H_male) ** 2 # 总损失 loss_em = H_bar @@ -79,7 +94,8 @@ class GEELoss: 'H_bar': H_bar.item(), 'H_male': H_male.item(), 'H_female': H_female.item(), - 'entropy_gap': abs(H_female - H_male).item() + 'entropy_gap': abs(H_female - H_male).item(), + 'lambda_weight': self.lambda_weight } return loss_total, metrics |