diff --git a/python/sglang/srt/layers/moe/ep_moe/token_dispatcher.py b/python/sglang/srt/layers/moe/ep_moe/token_dispatcher.py
index fe9fbad67..663ca1877 100644
--- a/python/sglang/srt/layers/moe/ep_moe/token_dispatcher.py
+++ b/python/sglang/srt/layers/moe/ep_moe/token_dispatcher.py
@@ -1,4 +1,5 @@
 import logging
+from dataclasses import dataclass
 
 from sglang.srt.layers.quantization.deep_gemm import _ENABLE_JIT_DEEPGEMM
 from sglang.srt.managers.expert_distribution import (
@@ -18,7 +19,7 @@ try:
 except ImportError:
     use_deepep = False
 
-from enum import IntEnum, auto
+from enum import Enum, IntEnum, auto
 from typing import Optional, Tuple, Union
 
 import torch
@@ -627,6 +628,14 @@ class _DeepEPDispatcherImplLowLatency(_DeepEPDispatcherImplBase):
         )
 
 
+@dataclass
+class _Stage(Enum):
+    INITIAL = auto()
+    AFTER_DISPATCH_A = auto()
+    AFTER_DISPATCH_B = auto()
+    AFTER_COMBINE_A = auto()
+
+
 class DeepEPDispatcher:
     def __init__(
         self,
@@ -665,6 +674,8 @@ class DeepEPDispatcher:
                 **common_kwargs,
             )
 
+        self._stage = _Stage.INITIAL
+
     def dispatch(self, *args, **kwargs) -> Tuple:
         self.dispatch_a(*args, **kwargs)
         ret = self.dispatch_b()
@@ -677,6 +688,7 @@ class DeepEPDispatcher:
         topk_weights: torch.Tensor,
         forward_mode: ForwardMode = None,
     ):
+        self._update_stage(_Stage.INITIAL, _Stage.AFTER_DISPATCH_A)
         inner_state = self._get_impl(forward_mode).dispatch_a(
             hidden_states=hidden_states,
             topk_idx=topk_idx,
@@ -685,6 +697,7 @@ class DeepEPDispatcher:
         self._dispatch_intermediate_state = forward_mode, inner_state
 
     def dispatch_b(self):
+        self._update_stage(_Stage.AFTER_DISPATCH_A, _Stage.AFTER_DISPATCH_B)
         forward_mode, inner_state = self._dispatch_intermediate_state
         del self._dispatch_intermediate_state
         return self._get_impl(forward_mode).dispatch_b(*inner_state)
@@ -701,6 +714,7 @@ class DeepEPDispatcher:
         topk_weights: torch.Tensor,
         forward_mode: ForwardMode,
     ):
+        self._update_stage(_Stage.AFTER_DISPATCH_B, _Stage.AFTER_COMBINE_A)
         inner_state = self._get_impl(forward_mode).combine_a(
             hidden_states=hidden_states,
             topk_idx=topk_idx,
@@ -709,6 +723,7 @@ class DeepEPDispatcher:
         self._combine_intermediate_state = forward_mode, inner_state
 
     def combine_b(self):
+        self._update_stage(_Stage.AFTER_COMBINE_A, _Stage.INITIAL)
         forward_mode, inner_state = self._combine_intermediate_state
         del self._combine_intermediate_state
         return self._get_impl(forward_mode).combine_b(*inner_state)
@@ -721,3 +736,7 @@ class DeepEPDispatcher:
             return self._low_latency_dispatcher
         else:
             raise ValueError(f"Invalid deepep_mode: {self.deepep_mode}")
+
+    def _update_stage(self, old_stage, new_stage):
+        assert self._stage == old_stage
+        self._stage = new_stage