修复ds3 int8调用的功能

vllm/model_executor/layers/quantization/blockwise_int8.py

@@ -373,18 +373,21 @@ class BlockInt8MoEMethod:
         router_logits: torch.Tensor,
         top_k: int,
         renormalize: bool,
-        use_grouped_topk: bool,
+        use_grouped_topk: bool = False,
         topk_group: Optional[int] = None,
-        use_nn_moe: Optional[bool] = False,
         num_expert_group: Optional[int] = None,
+        global_num_experts: int = -1,
+        expert_map: Optional[torch.Tensor] = None,
         custom_routing_function: Optional[Callable] = None,
         scoring_func: str = "softmax",
         e_score_correction_bias: Optional[torch.Tensor] = None,
+        apply_router_weight_on_input: bool = False,
+        activation: str = "silu",
+        use_nn_moe: Optional[bool] = False,
     
     ) -> torch.Tensor:
         from vllm.model_executor.layers.fused_moe import fused_experts
         
-        #print("===========fused_experts========================")
         # Expert selection
         topk_weights, topk_ids = FusedMoE.select_experts(
             hidden_states=x,
@@ -409,6 +412,10 @@ class BlockInt8MoEMethod:
             topk_ids=topk_ids,
             inplace=True,
             use_int8_w8a8=True,
+            activation=activation,
+            expert_map=expert_map,
+            apply_router_weight_on_input=apply_router_weight_on_input,
+            global_num_experts=global_num_experts,
             w1_scale=(layer.w13_weight_scale_inv),
             w2_scale=(layer.w2_weight_scale_inv),
             a1_scale=layer.w13_input_scale,