Update entropy_decoding.py

optillm/entropy_decoding.py

@@ -27,24 +27,26 @@ def calculate_varentropy_logsoftmax(logits: torch.Tensor, axis: int = -1) -> Tup
     return entropy, varentropy
 
 def calculate_attention_metrics(attention_weights: torch.Tensor) -> Dict[str, torch.Tensor]:
-    # attention_weights are already probabilities (post-softmax)
     attention_probs = attention_weights
     
     # Calculate entropy
     attn_entropy = -torch.sum(attention_probs * torch.log2(torch.clamp(attention_probs, 1e-10, 1.0)), dim=-1)
     
-    # Calculate variance of entropy
-    attn_varentropy = torch.var(attn_entropy, dim=-1)
+    # Calculate variance of entropy with unbiased=False to avoid df issues
+    # Also add a check for singleton dimensions
+    if attn_entropy.size(-1) > 1:
+        attn_varentropy = torch.var(attn_entropy, dim=-1, unbiased=False)
+    else:
+        attn_varentropy = torch.zeros_like(attn_entropy)
+    
     attn_varentropy = torch.where(torch.isnan(attn_varentropy), 
                                  torch.zeros_like(attn_varentropy), 
                                  attn_varentropy)
     
-    # Calculate mean attention and agreement
+    # Rest remains the same
     mean_attention = torch.mean(attention_probs, dim=1)
     agreement = torch.mean(torch.abs(attention_probs - mean_attention.unsqueeze(1)), dim=(1, 2))
     
-    # For interaction strength, we can use log probabilities to approximate the original scores
-    # This maintains the relative relationships while providing a reasonable proxy for attention strength
     attention_scores_proxy = torch.log(torch.clamp(attention_probs, 1e-10, 1.0))
     interaction_strength = torch.mean(torch.abs(attention_scores_proxy), dim=(1, 2, 3))