diff --git a/vllm-v0.6.2/vllm_mlu/vllm_mlu/model_executor/layers/sparse_moe_mlp.py b/vllm-v0.6.2/vllm_mlu/vllm_mlu/model_executor/layers/sparse_moe_mlp.py
index 269cac4..efd726c 100644
--- a/vllm-v0.6.2/vllm_mlu/vllm_mlu/model_executor/layers/sparse_moe_mlp.py
+++ b/vllm-v0.6.2/vllm_mlu/vllm_mlu/model_executor/layers/sparse_moe_mlp.py
@@ -425,9 +425,9 @@ class SparseMoeMlp(nn.Module):
         scatter_idx=torch.zeros((indices.numel(),), dtype=seqs.dtype, device=seqs.device).scatter(0, indices, seqs)
 
         # token_count: [self.num_experts_per_rank]
-        partial_token_index, partial_token_count = sorted_expert_id.unique(sorted=True, return_counts=True)
-        zero_token_count = torch.zeros(self.num_total_experts, dtype=partial_token_count.dtype, device=device)
-        token_count = zero_token_count.scatter(dim=0, index=partial_token_index, src=partial_token_count)
+        # Use scatter_add_ instead of torch.unique for MLU graph capture compatibility
+        token_count = torch.zeros(self.num_total_experts, dtype=sorted_expert_id.dtype, device=device)
+        token_count.scatter_add_(0, sorted_expert_id, torch.ones_like(sorted_expert_id))
         # cusum_token_count: [self.num_experts_per_rank + 1]
         cusum_token_count = torch.cat(
             [torch.tensor([0], dtype=token_count.dtype, device=device),