fix glm4 moe (#8883)

python/sglang/srt/models/glm4_moe.py

@@ -527,7 +527,10 @@ class Glm4MoeSparseMoeBlock(DeepseekV2MoE):
         self._enable_deepep_moe = global_server_args_dict["moe_a2a_backend"].is_deepep()
 
     def forward_normal_dual_stream(
-        self, hidden_states: torch.Tensor, can_fuse_mlp_allreduce: bool = False
+        self,
+        hidden_states: torch.Tensor,
+        can_fuse_mlp_allreduce: bool = False,
+        use_reduce_scatter: bool = False,
     ) -> torch.Tensor:
 
         current_stream = torch.cuda.current_stream()
@@ -548,21 +551,32 @@ class Glm4MoeSparseMoeBlock(DeepseekV2MoE):
         current_stream.wait_stream(self.alt_stream)
 
         if self.ep_size > 1:
-            if self.tp_size > 1 and not can_fuse_mlp_allreduce:
+            if (
+                self.tp_size > 1
+                and not can_fuse_mlp_allreduce
+                and not use_reduce_scatter
+            ):
                 final_hidden_states = tensor_model_parallel_all_reduce(
                     final_hidden_states
                 )
             final_hidden_states += shared_output
         else:
             final_hidden_states += shared_output
-            if self.tp_size > 1 and not can_fuse_mlp_allreduce:
+            if (
+                self.tp_size > 1
+                and not can_fuse_mlp_allreduce
+                and not use_reduce_scatter
+            ):
                 final_hidden_states = tensor_model_parallel_all_reduce(
                     final_hidden_states
                 )
         return final_hidden_states
 
     def forward_normal(
-        self, hidden_states: torch.Tensor, can_fuse_mlp_allreduce: bool = False
+        self,
+        hidden_states: torch.Tensor,
+        can_fuse_mlp_allreduce: bool = False,
+        use_reduce_scatter: bool = False,
     ) -> torch.Tensor:
         if hasattr(self, "shared_experts") and use_intel_amx_backend(
             self.shared_experts.gate_up_proj
@@ -681,6 +695,7 @@ class Glm4MoeDecoderLayer(DeepseekV2DecoderLayer):
             layer_scatter_modes=self.layer_scatter_modes,
             input_layernorm=self.input_layernorm,
             post_attention_layernorm=self.post_attention_layernorm,
+            allow_reduce_scatter=True,
         )
 
     def forward(