[BugFix]Fix group list type of mc2. (#3890)

### What this PR does / why we need it?
Fix the precision issue caused by the inconsistency between the group
list type used by mc2 and that of eplb.

---------

Signed-off-by: offline0806 <3337230449@qq.com>

tests/e2e/singlecard/spec_decode_v1/test_v1_mtp_correctness.py

@@ -1,8 +1,5 @@
 from __future__ import annotations
 
-import os
-from unittest.mock import patch
-
 import pytest
 from vllm import SamplingParams
 from vllm.config import CompilationConfig, CUDAGraphMode

vllm_ascend/ops/common_fused_moe.py

@@ -244,7 +244,8 @@ class AscendFusedMoE(FusedMoE):
             self.expert_map != -1) if self.expert_map is not None else
                              self.global_num_experts)
         if self.dynamic_eplb:
-            self.moe_load = torch.zeros(local_num_experts, dtype=torch.int64)
+            self.moe_load = torch.zeros(local_num_experts,
+                                        dtype=torch.int64).npu()
 
         self.moe_config.num_experts = self.global_num_experts
         self.moe_config.num_local_experts = self.local_num_experts
@@ -340,9 +341,9 @@ class AscendFusedMoE(FusedMoE):
         if isinstance(final_hidden_states, tuple):
             final_hidden_states, group_list_type, expert_tokens = final_hidden_states
 
-        if self.dynamic_eplb:
+            if self.dynamic_eplb:
-            self.moe_load += expert_tokens if group_list_type else \
+                self.moe_load += expert_tokens if group_list_type == 1 else \
-                torch.cat([expert_tokens[:1], expert_tokens[1:] - expert_tokens[:-1]])
+                    torch.cat([expert_tokens[:1], expert_tokens[1:] - expert_tokens[:-1]])
 
         final_hidden_states = forward_context.moe_comm_method.finalize(
             hidden_states=final_hidden_states,

vllm_ascend/ops/moe/moe_comm_method.py

@@ -130,7 +130,8 @@ class MoECommMethod(ABC):
             dynamic_scale_for_share=dynamic_scale_for_share,
             mc2_mask=self.mc2_mask,
             apply_router_weight_on_input=apply_router_weight_on_input,
-            with_quant=use_int8_w8a8 or use_int4_w4a8)
+            with_quant=use_int8_w8a8 or use_int4_w4a8,
+            dynamic_eplb=dynamic_eplb)
 
         permuted_hidden_states, expert_tokens, dynamic_scale, group_list_type, topk_scales = \
             results["hidden_states"], results["group_list"], results.get("dynamic_scale"), results["group_list_type"], results.get("topk_scales")

vllm_ascend/ops/moe/token_dispatcher.py

@@ -69,7 +69,8 @@ class MoETokenDispatcher(ABC):
                        dynamic_scale_for_share: Optional[Any] = None,
                        mc2_mask: Optional[torch.Tensor] = None,
                        apply_router_weight_on_input: bool = False,
-                       with_quant: bool = False):
+                       with_quant: bool = False,
+                       dynamic_eplb: bool = False):
         raise NotImplementedError("Dispatch function not implemented.")
 
     @abstractmethod
@@ -175,7 +176,8 @@ class TokenDispatcherWithMC2(MoETokenDispatcher):
                        dynamic_scale_for_share: Optional[Any] = None,
                        mc2_mask: Optional[torch.Tensor] = None,
                        apply_router_weight_on_input: bool = False,
-                       with_quant: bool = False):
+                       with_quant: bool = False,
+                       dynamic_eplb: bool = False):
         self.with_quant = with_quant
         self.expert_map = expert_map
         self.topk_ids = topk_ids
@@ -210,7 +212,7 @@ class TokenDispatcherWithMC2(MoETokenDispatcher):
             if shared_experts is not None:
                 shared_gate_up, _ = shared_experts.gate_up_proj(hidden_states)
                 self.shared_act = shared_experts.act_fn(shared_gate_up)
-        group_list_type = 0
+        group_list_type = 1 if dynamic_eplb else 0
         return {
             "group_list_type": group_list_type,
             "hidden_states": expand_x,
@@ -333,7 +335,8 @@ class TokenDispatcherWithAllGather(MoETokenDispatcher):
                        dynamic_scale_for_share: Optional[Any] = None,
                        mc2_mask: Optional[torch.Tensor] = None,
                        apply_router_weight_on_input: bool = False,
-                       with_quant: bool = False):
+                       with_quant: bool = False,
+                       dynamic_eplb: bool = False):
         self.with_quant = with_quant
         self.original_shape = hidden_states.shape
 
@@ -424,7 +427,8 @@ class TokenDispatcherWithMoge(MoETokenDispatcher):
                        dynamic_scale_for_share: Optional[Any] = None,
                        mc2_mask: Optional[torch.Tensor] = None,
                        apply_router_weight_on_input: bool = False,
-                       with_quant: bool = False):
+                       with_quant: bool = False,
+                       dynamic_eplb: bool = False):
         self.bsz, _ = hidden_states.shape
         flatten_topk_ids = topk_ids.view(-1)
         self.sorted_topk_ids = torch.argsort(flatten_topk_ids.float())
@@ -521,7 +525,8 @@ class TokenDispatcherWithAll2AllV(MoETokenDispatcher):
                        dynamic_scale_for_share: Optional[Any] = None,
                        mc2_mask: Optional[torch.Tensor] = None,
                        apply_router_weight_on_input: bool = False,
-                       with_quant: bool = False):
+                       with_quant: bool = False,
+                       dynamic_eplb: bool = False):
         self.with_quant = with_quant
         self.hidden_shape = hidden_states.shape
         self.topk_weights = topk_weights