refactor: normalize

Author: sigridjineth

vllm/model_executor/models/jina_embeddings_v4.py

@@ -226,8 +226,7 @@ def _apply_vision_pooling_optimized(
                 output = seq_states.mean(dim=0)
 
             # Normalize and handle potential NaNs by replacing with zeros
-            output = F.normalize(output, p=2, dim=-1)
-            output = torch.nan_to_num(output)
+            output = F.normalize(output, p=2, dim=-1, eps=1e-12)
             pooled_outputs.append(output)
 
             offset += prompt_len
@@ -274,7 +273,7 @@ def _apply_vision_pooling_pytorch(
                 pooled = seq_states.mean(dim=0)
 
             # Normalize embeddings
-            pooled = F.normalize(pooled, p=2, dim=-1)
+            pooled = F.normalize(pooled, p=2, dim=-1, eps=1e-12)
             pooled_outputs.append(pooled)
 
             offset += prompt_len