diff --git a/python/sglang/srt/model_executor/forward_batch_info.py b/python/sglang/srt/model_executor/forward_batch_info.py
index e23470dd9..5245ba136 100644
--- a/python/sglang/srt/model_executor/forward_batch_info.py
+++ b/python/sglang/srt/model_executor/forward_batch_info.py
@@ -360,8 +360,6 @@ class ForwardBatch:
         batch: ModelWorkerBatch,
         model_runner: ModelRunner,
     ):
-        from sglang.srt.two_batch_overlap import TboForwardBatchPreparer
-
         ret = cls(
             forward_mode=batch.forward_mode,
             batch_size=len(batch.seq_lens),
@@ -436,9 +434,6 @@ class ForwardBatch:
 
         if ret.forward_mode.is_idle():
             ret.positions = torch.empty((0,), dtype=torch.int64, device=device)
-            TboForwardBatchPreparer.prepare(
-                ret, is_draft_worker=model_runner.is_draft_worker
-            )
             return ret
 
         # Override the positions with diffusion LLM or spec_info
@@ -496,10 +491,6 @@ class ForwardBatch:
         if model_runner.server_args.enable_lora:
             model_runner.lora_manager.prepare_lora_batch(ret)
 
-        TboForwardBatchPreparer.prepare(
-            ret, is_draft_worker=model_runner.is_draft_worker
-        )
-
         return ret
 
     def merge_mm_inputs(self) -> Optional[MultimodalInputs]:
@@ -728,6 +719,8 @@ class ForwardBatch:
             )
 
     def prepare_mlp_sync_batch(self, model_runner: ModelRunner):
+        from sglang.srt.two_batch_overlap import TboForwardBatchPreparer
+
         assert self.global_num_tokens_cpu is not None
         assert self.global_num_tokens_for_logprob_cpu is not None
 
@@ -792,6 +785,8 @@ class ForwardBatch:
                     )
                 else:
                     bs = self.batch_size = num_tokens
+        elif self.forward_mode.is_extend():
+            self.extend_num_tokens = num_tokens
 
         # padding
         self._pad_inputs_to_size(model_runner, num_tokens, bs)
@@ -799,10 +794,15 @@ class ForwardBatch:
         global_num_tokens_pinned = torch.tensor(global_num_tokens, pin_memory=True)
         self.global_num_tokens_gpu.copy_(global_num_tokens_pinned, non_blocking=True)
 
+        TboForwardBatchPreparer.prepare(
+            batch=self, is_draft_worker=model_runner.is_draft_worker
+        )
+
     def _pad_inputs_to_size(self, model_runner: ModelRunner, num_tokens, bs):
         # padding
         self.input_ids = self._pad_tensor_to_size(self.input_ids, num_tokens)
         self.req_pool_indices = self._pad_tensor_to_size(self.req_pool_indices, bs)
+        self.lora_ids.extend((bs - len(self.lora_ids)) * [None])
 
         seq_len_fill_value = (
             model_runner.attn_backend.get_cuda_graph_seq_len_fill_value()