[Bugfix] Kimi-K2 grouped_topk usage for Flashinfer monolithic kernels. (#33858)

Signed-off-by: Pavani Majety <pmajety@nvidia.com>

vllm/model_executor/models/deepseek_v2.py

@@ -295,14 +295,6 @@ class DeepseekV2MoE(nn.Module):
                 prefix=f"{prefix}.shared_experts",
             )
 
-        n_group = getattr(config, "n_group", 1)
-        topk_group = getattr(config, "topk_group", 1)
-        use_grouped_topk = True
-        if (n_group, topk_group) == (1, 1):
-            n_group = None
-            topk_group = None
-            use_grouped_topk = False
-
         self.experts = SharedFusedMoE(
             shared_experts=self.shared_experts,
             gate=self.gate,
@@ -313,9 +305,9 @@ class DeepseekV2MoE(nn.Module):
             reduce_results=False,
             renormalize=config.norm_topk_prob,
             quant_config=quant_config,
-            use_grouped_topk=use_grouped_topk,
-            num_expert_group=n_group,
-            topk_group=topk_group,
+            use_grouped_topk=True,
+            num_expert_group=getattr(config, "n_group", 1),
+            topk_group=getattr(config, "topk_group", 1),
             prefix=f"{prefix}.experts",
             scoring_func=getattr(config, "scoring_func", "softmax"),
             # we do scaling outside, set factor to 1.0 to avoid double mul