[BugFix]Support redundant experts in EPLB (#3473)

This PR adds support for redundant experts in the EPLB. 

Key points: 
- Use global_num_experts = num_experts + num_redundant_experts
consistently.
- Backward compatible when num_redundant_experts=0. 

Tested 
On a 16-rank setup (W8A8) with static EPLB and expert_map_path,
verifying router logits shape and successful requests.

- vLLM version: v0.11.0rc3
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0

Signed-off-by: yechao237 <yechao20180411@gmail.com>

vllm_ascend/eplb/core/eplb_utils.py

@@ -40,14 +40,6 @@ def determine_default_expert_map(global_expert_num, world_size, rank_id,
         end = global_expert_num
         local_count = global_expert_num - rank_id * local_num_experts
 
-    if isinstance(global_redundant_expert_num,
-                  int) and rank_id < global_redundant_expert_num:
-        local_count += 1
-        if end < global_expert_num:
-            end += 1
-        else:
-            start -= 1
-
     if isinstance(local_count, int):
         local_ids = torch.arange(local_count, dtype=torch.int32)
         expert_map[start:end] = local_ids
@@ -118,14 +110,6 @@ def determine_default_log2phy_map(global_expert_num, world_size, rank_id,
             end = global_expert_num
             local_count = global_expert_num - r * local_num_experts
 
-        if isinstance(global_redundant_expert_num,
-                      int) and rank_id < global_redundant_expert_num:
-            local_count += 1
-            if end < global_expert_num:
-                end += 1
-            else:
-                start -= 1
-
         if isinstance(local_count, int):
             local_ids = torch.arange(local_count, dtype=torch.int32)
             expert_map_all[r, start:end] = local_ids

vllm_ascend/ops/moe/experts_selector.py

@@ -20,6 +20,8 @@ import torch
 import torch_npu
 from vllm.forward_context import get_forward_context
 
+from vllm_ascend.ascend_config import get_ascend_config
+
 
 def select_experts(hidden_states: torch.Tensor,
                    router_logits: torch.Tensor,
@@ -176,7 +178,8 @@ def _select_experts_with_fusion_ops(
 
     topk_weights, topk_ids = None, None
     # NOTE: now npu_moe_gating_top_k can only support 'group_count=256' pattern
-    is_deepseek_v3_r1 = global_num_experts == 256
+    global_redundant_expert_num = get_ascend_config().init_redundancy_expert
+    is_deepseek_v3_r1 = global_num_experts - global_redundant_expert_num == 256
     if is_deepseek_v3_r1:
         topk_weights, topk_ids, _ = torch_npu.npu_moe_gating_top_k(
             router_logits,

vllm_ascend/ops/moe/token_dispatcher.py

@@ -123,10 +123,7 @@ class TokenDispatcherWithMC2(MoETokenDispatcher):
     ):
         if self.with_quant:
             quant_mode = 2
-            if (expert_map is not None):
-                moe_expert_num = len(expert_map) + global_redundant_expert_num
-            else:
-                moe_expert_num = global_redundant_expert_num
+            moe_expert_num = len(expert_map)
         else:
             quant_mode = 0
             moe_expert_num = len(expert_map)

vllm_ascend/quantization/w4a8_dynamic.py

@@ -263,7 +263,7 @@ class AscendW4A8DynamicFusedMoEMethod:
         **kwargs,
     ) -> torch.Tensor:
         assert router_logits.shape[
-            1] == global_num_experts, "Number of global experts mismatch"
+            1] == global_num_experts - global_redundant_expert_num, "Number of global experts mismatch (excluding redundancy)"
 
         # NOTE: now npu_moe_gating_top_k can only support `group_count=256` pattern
         topk_weights, topk_ids = select_experts(

vllm_ascend/quantization/w8a8.py

@@ -263,7 +263,7 @@ class AscendW8A8FusedMoEMethod:
         **kwargs,
     ) -> torch.Tensor:
         assert router_logits.shape[
-            1] == global_num_experts, "Number of global experts mismatch"
+            1] == global_num_experts - global_redundant_expert_num, "Number of global experts mismatch (excluding redundancy)"
 
         topk_weights, topk_ids = select_experts(
             hidden_states=x,

vllm_ascend/quantization/w8a8_dynamic.py

@@ -203,7 +203,7 @@ class AscendW8A8DynamicFusedMoEMethod:
         **kwargs,
     ) -> torch.Tensor:
         assert router_logits.shape[
-            1] == global_num_experts, "Number of global experts mismatch"
+            1] == global_num_experts - global_redundant_expert_num, "Number of global experts mismatch (excluding redundancy)"
 
         topk_weights, topk_ids = select_experts(
             hidden_states=x,

vllm_ascend/torchair/ops/torchair_fused_moe.py

@@ -856,8 +856,9 @@ class TorchairAscendUnquantizedFusedMoEMethod(UnquantizedFusedMoEMethod):
         shared_experts: Optional[Any] = None,
         **kwargs,
     ) -> torch.Tensor:
-
-        is_deepseek_v3_r1 = global_num_experts == 256
+        global_redundant_expert_num = get_ascend_config(
+        ).init_redundancy_expert
+        is_deepseek_v3_r1 = global_num_experts - global_redundant_expert_num == 256
         # NOTE: now npu_moe_gating_top_k can only support `group_count=256` pattern
         if is_deepseek_v3_r1:
             topk_weights, topk_ids, _ = torch_npu.npu_moe_gating_top_k(

vllm_ascend/torchair/quantization/torchair_w4a8_dynamic.py

@@ -269,7 +269,7 @@ class TorchairAscendW4A8DynamicFusedMoEMethod:
         **kwargs,
     ) -> torch.Tensor:
         assert router_logits.shape[
-            1] == global_num_experts, "Number of global experts mismatch"
+            1] == global_num_experts - global_redundant_expert_num, "Number of global experts mismatch (excluding redundancy)"
 
         if global_num_experts == 256:
             topk_weights, topk_ids, _ = torch_npu.npu_moe_gating_top_k(

vllm_ascend/torchair/quantization/torchair_w8a8_dynamic.py

@@ -246,7 +246,7 @@ def torchair_fused_experts_with_mc2(
     enable_dispatch_v2 = hasattr(torch_npu, "npu_moe_distribute_dispatch_v2")
 
     if (expert_map is not None):
-        moe_expert_num = len(expert_map) + global_redundant_expert_num
+        moe_expert_num = len(expert_map)
     else:
         moe_expert_num = global_redundant_expert_num
     # hidden_states = hidden_states.bfloat16()
@@ -431,7 +431,7 @@ def torchair_fused_experts_with_all2all(
 
     if expert_map is not None:
         assert ep_group is not None, "ep_group must be provided when expert_map is given"
-        global_num_experts = len(expert_map) + global_redundant_expert_num
+        global_num_experts = len(expert_map)
         if hasattr(torch_npu, "npu_moe_init_routing_quant"):
             quantized_tokens, expanded_row_idx, global_expert_tokens, _, token_scales = torch_npu.npu_moe_init_routing_quant(
                 hidden_states,
@@ -929,9 +929,9 @@ class TorchairAscendW8A8DynamicFusedMoEMethod:
         **kwargs,
     ) -> torch.Tensor:
         assert router_logits.shape[
-            1] == global_num_experts, "Number of global experts mismatch"
+            1] == global_num_experts - global_redundant_expert_num, "Number of global experts mismatch (excluding redundancy)"
 
-        is_deepseek_v3_r1 = global_num_experts == 256
+        is_deepseek_v3_r1 = global_num_experts - global_redundant_expert_num == 256
 
         # NOTE: now npu_moe_gating_top_k can only support `group_count=256` pattern
         if is_deepseek_v3_r1: