[Model] Add LongCat-Flash (#3833)

### What this PR does / why we need it?
Add LongCat-Flash support.
### Does this PR introduce _any_ user-facing change?
N/A
### How was this patch tested?
CI passed

- vLLM version: v0.13.0
- vLLM main:
ad32e3e19c

---------

Signed-off-by: chuyuelin <923822139@qq.com>
Co-authored-by: chuyuelin <chuyuelin1@huawei.com>

vllm_ascend/ops/fused_moe/fused_moe.py

@@ -35,7 +35,8 @@ from vllm_ascend.eplb.core.eplb_utils import init_eplb_config
 from vllm_ascend.eplb.utils import moe_load_async_stream
 from vllm_ascend.flash_common3_context import (get_flash_common3_context,
                                                set_flash_common3_context)
-from vllm_ascend.ops.fused_moe.experts_selector import select_experts
+from vllm_ascend.ops.fused_moe.experts_selector import (select_experts,
+                                                        zero_experts_compute)
 from vllm_ascend.ops.fused_moe.moe_comm_method import (AllGatherCommImpl,
                                                        FusedExpertsResult,
                                                        setup_moe_comm_method)
@@ -92,7 +93,8 @@ class AscendUnquantizedFusedMoEMethod(UnquantizedFusedMoEMethod):
               enable_force_load_balance: bool = False,
               shared_experts: Optional[Any] = None,
               **kwargs) -> torch.Tensor:
-
+        zero_expert_num = getattr(layer, "zero_expert_num", 0)
+        zero_expert_type = getattr(layer, "zero_expert_type", None)
         topk_weights, topk_ids = select_experts(
             hidden_states=x,
             router_logits=router_logits,
@@ -107,6 +109,15 @@ class AscendUnquantizedFusedMoEMethod(UnquantizedFusedMoEMethod):
             e_score_correction_bias=e_score_correction_bias,
             global_num_experts=global_num_experts)
 
+        if zero_expert_num > 0 and zero_expert_type is not None:
+            topk_ids, topk_weights, zero_expert_result = zero_experts_compute(
+                expert_indices=topk_ids,
+                expert_scales=topk_weights,
+                num_experts=global_num_experts,
+                zero_expert_type=zero_expert_type,
+                hidden_states=x,
+            )
+
         topk_weights = topk_weights.to(x.dtype)
         # this is a naive implementation for experts load balance so as
         # to avoid accumulating too much tokens on a single rank.
@@ -119,7 +130,7 @@ class AscendUnquantizedFusedMoEMethod(UnquantizedFusedMoEMethod):
                 random_matrix, dim=1)[:, :topk_ids.size(1)].to(topk_ids.dtype)
 
         moe_comm_method = get_forward_context().moe_comm_method
-        return moe_comm_method.fused_experts(
+        final_hidden_states = moe_comm_method.fused_experts(
             hidden_states=x,
             w1=layer.w13_weight,
             w2=layer.w2_weight,
@@ -131,6 +142,9 @@ class AscendUnquantizedFusedMoEMethod(UnquantizedFusedMoEMethod):
             apply_router_weight_on_input=apply_router_weight_on_input,
             dynamic_eplb=self.dynamic_eplb,
             mc2_mask=kwargs.get("mc2_mask", None))
+        if zero_expert_num > 0 and zero_expert_type is not None:
+            final_hidden_states += zero_expert_result
+        return final_hidden_states
 
 
 class AscendFusedMoE(FusedMoE):
@@ -340,6 +354,7 @@ class AscendFusedMoE(FusedMoE):
             num_expert_group=self.num_expert_group,
             custom_routing_function=self.custom_routing_function,
             scoring_func=self.scoring_func,
+            routed_scaling_factor=self.routed_scaling_factor,
             e_score_correction_bias=self.e_score_correction_bias,
             activation=self.activation,
             apply_router_weight_on_input=self.apply_router_weight_on_input,