diff --git a/vllm_ascend/ops/fused_moe.py b/vllm_ascend/ops/fused_moe.py
index f3e47ab..e216f92 100644
--- a/vllm_ascend/ops/fused_moe.py
+++ b/vllm_ascend/ops/fused_moe.py
@@ -50,9 +50,22 @@ def group_topk(hidden_states: torch.Tensor,
 
     topk_group = 0 if topk_group is None else topk_group
     num_expert_group = 0 if num_expert_group is None else num_expert_group
-    torch_npu._npu_group_topk(self=scores,
-                              k=topk_group,
-                              group_num=num_expert_group)
+
+    # TODO: Replace this piece of code to npu_group_topk when CANN and NNAL version is update
+    num_token = scores.shape[0]
+    group_scores = scores.view(num_token, num_expert_group,
+                               -1).max(dim=-1).values
+    group_idx = torch.topk(group_scores.to(torch.float32),
+                           k=topk_group,
+                           dim=-1,
+                           sorted=False)[1]
+    group_mask = torch.zeros_like(group_scores)
+    group_mask.scatter_(1, group_idx, 1)
+    score_mask = group_mask.unsqueeze(-1).expand(
+        num_token, num_expert_group,
+        scores.shape[-1] // num_expert_group).reshape(num_token, -1)
+    scores = scores.masked_fill(~score_mask.bool(), 0.0)
+
     if e_score_correction_bias is not None:
         topk_ids = torch.topk(scores, k=topk, dim=-1, sorted=False)[1]
         # Use original unbiased scores for the routing weights