Fix one missing arg in DeepEP (#6878)

python/sglang/srt/layers/moe/ep_moe/layer.py

@@ -180,6 +180,9 @@ class EPMoE(torch.nn.Module):
         self.layer_id = layer_id
         self.num_experts = num_experts
         assert self.num_experts % self.tp_size == 0
+        assert (
+            num_fused_shared_experts == 0
+        ), "num_fused_shared_experts is not supported in EP"
         self.num_experts_per_partition = self.num_experts // self.tp_size
         self.start_expert_id = self.tp_rank * self.num_experts_per_partition
         self.end_expert_id = self.start_expert_id + self.num_experts_per_partition - 1
@@ -191,7 +194,6 @@ class EPMoE(torch.nn.Module):
         if self.use_grouped_topk:
             assert num_expert_group is not None and topk_group is not None
         self.num_expert_group = num_expert_group
-        self.num_fused_shared_experts = num_fused_shared_experts
         self.topk_group = topk_group
         self.correction_bias = correction_bias
         self.custom_routing_function = custom_routing_function
@@ -252,7 +254,6 @@ class EPMoE(torch.nn.Module):
             renormalize=self.renormalize,
             topk_group=self.topk_group,
             num_expert_group=self.num_expert_group,
-            num_fused_shared_experts=self.num_fused_shared_experts,
             correction_bias=self.correction_bias,
             custom_routing_function=self.custom_routing_function,
             routed_scaling_factor=self.routed_scaling_factor,
@@ -886,6 +887,7 @@ class DeepEPMoE(EPMoE):
         renormalize: bool = True,
         use_grouped_topk: bool = False,
         num_expert_group: Optional[int] = None,
+        num_fused_shared_experts: int = 0,
         topk_group: Optional[int] = None,
         quant_config: Optional[QuantizationConfig] = None,
         tp_size: Optional[int] = None,
@@ -897,23 +899,24 @@ class DeepEPMoE(EPMoE):
         deepep_mode: DeepEPMode = DeepEPMode.auto,
     ):
         super().__init__(
-            num_experts,
-            top_k,
-            hidden_size,
-            intermediate_size,
-            layer_id,
-            params_dtype,
-            renormalize,
-            use_grouped_topk,
-            num_expert_group,
-            topk_group,
-            quant_config,
-            tp_size,
-            prefix,
-            correction_bias,
-            custom_routing_function,
-            activation,
-            routed_scaling_factor,
+            num_experts=num_experts,
+            top_k=top_k,
+            hidden_size=hidden_size,
+            intermediate_size=intermediate_size,
+            layer_id=layer_id,
+            params_dtype=params_dtype,
+            renormalize=renormalize,
+            use_grouped_topk=use_grouped_topk,
+            num_expert_group=num_expert_group,
+            num_fused_shared_experts=num_fused_shared_experts,
+            topk_group=topk_group,
+            quant_config=quant_config,
+            tp_size=tp_size,
+            prefix=prefix,
+            correction_bias=correction_bias,
+            custom_routing_function=custom_routing_function,
+            activation=activation,
+            routed_scaling_factor=routed_scaling_factor,
         )
         self.deepep_mode = deepep_mode
         if self.deepep_mode.enable_low_latency():