Update modeling_super_linear.py

modeling_super_linear.py

@@ -592,24 +592,29 @@ class SuperLinearForCausalLM(PreTrainedModel, GenerationMixin):
     
         return y
     
-    def upsample_interpolate(self, x,scale_factor, target_len: int = 512):
+    def upsample_interpolate(self, x, scale_factor, target_len: int = 512, mode='bicubic'):
         was_2d = x.dim() == 2                       
-    
+        
         if was_2d:                                  # [B, L] -> [B, 1, L]
             x = x.unsqueeze(1)
         else:                                       # [B, L, C] -> [B, C, L]
             x = x.permute(0, 2, 1)
-    
         
-        x_up = F.interpolate(x, size=target_len, mode='linear', align_corners=False) 
+        # Add support for bicubic interpolation by adding an extra dimension
+        if mode == 'bicubic':
+            x = x.unsqueeze(2)  # [B, C, 1, L]
+            x_up = F.interpolate(x, size=(1, target_len), mode='bicubic', align_corners=False)
+            x_up = x_up.squeeze(2)  # [B, C, L]
+        else:
+            x_up = F.interpolate(x, size=target_len, mode=mode, align_corners=False)
+        
         x_up = x_up * scale_factor
-    
-        # ── restore original layout ─────────────────────────────────────────────────
+        
+        # Restore original layout
         if was_2d:                                  # back to [B, target_len]
             return x_up.squeeze(1).float()
         else:                                       # back to [B, target_len, C]
             return x_up.permute(0, 2, 1).float()
-