diff --git a/python/sglang/srt/model_executor/model_runner.py b/python/sglang/srt/model_executor/model_runner.py
index a833dac43..96d014a9e 100644
--- a/python/sglang/srt/model_executor/model_runner.py
+++ b/python/sglang/srt/model_executor/model_runner.py
@@ -665,6 +665,8 @@ class ModelRunner(ModelRunnerKVCacheMixin):
         self.end_layer = getattr(self.model, "end_layer", model_num_layers)
         self.num_effective_layers = self.end_layer - self.start_layer
 
+        self.adjust_hybrid_swa_layers_for_pp()
+
         # For LoopCoder models, each loop has its own layer_id, so we need to multiply by loop_num
         loop_num = getattr(self.model_config.hf_config, "loop_num", 1)
         if loop_num > 1:
@@ -679,23 +681,6 @@ class ModelRunner(ModelRunnerKVCacheMixin):
             )
         ), "PP is not compatible with MTP models."
 
-        # Consider PP, so use start_layer and end_layer.
-        full_attention_layer_ids = [
-            layer_idx
-            for layer_idx in range(self.start_layer, self.end_layer + 1)
-            if hasattr(self.model_config, "full_attention_layer_ids")
-            and layer_idx in self.model_config.full_attention_layer_ids
-        ]
-        swa_attention_layer_ids = [
-            layer_idx
-            for layer_idx in range(self.start_layer, self.end_layer + 1)
-            if hasattr(self.model_config, "swa_attention_layer_ids")
-            and layer_idx in self.model_config.swa_attention_layer_ids
-        ]
-        # Update back to model_config.
-        self.model_config.swa_attention_layer_ids = swa_attention_layer_ids
-        self.model_config.full_attention_layer_ids = full_attention_layer_ids
-
         # Apply torchao quantization
         torchao_applied = getattr(self.model, "torchao_applied", False)
         # In layered loading, torchao may have been applied
@@ -792,6 +777,25 @@ class ModelRunner(ModelRunnerKVCacheMixin):
 
         self.prealloc_symmetric_memory_pool()
 
+    def adjust_hybrid_swa_layers_for_pp(self):
+        if not self.is_hybrid_swa:
+            return
+
+        full_attention_layer_ids = [
+            layer_idx
+            for layer_idx in range(self.start_layer, self.end_layer + 1)
+            if hasattr(self.model_config, "full_attention_layer_ids")
+            and layer_idx in self.model_config.full_attention_layer_ids
+        ]
+        swa_attention_layer_ids = [
+            layer_idx
+            for layer_idx in range(self.start_layer, self.end_layer + 1)
+            if hasattr(self.model_config, "swa_attention_layer_ids")
+            and layer_idx in self.model_config.swa_attention_layer_ids
+        ]
+        self.model_config.swa_attention_layer_ids = swa_attention_layer_ids
+        self.model_config.full_attention_layer_ids = full_attention_layer_ids
+
     def init_routed_experts_capturer(self):
         if not self.server_args.disable_shared_experts_fusion and hasattr(
             self.model, "num_fused_shared_experts"