diff --git a/vllm_ascend/attention/context_parallel/attention_cp.py b/vllm_ascend/attention/context_parallel/attention_cp.py
index f97798f3..8bffb12f 100644
--- a/vllm_ascend/attention/context_parallel/attention_cp.py
+++ b/vllm_ascend/attention/context_parallel/attention_cp.py
@@ -157,10 +157,18 @@ class AscendAttentionCPMetadataBuilder(AscendAttentionMetadataBuilder):
                 local_chunk_starts = torch.zeros(
                     (len(local_context_lens_allranks),), dtype=torch.int32, device=self.device
                 )
-                kv_inverse_idx_for_chunk = torch.argsort(
-                    common_long_seq_metadata.pcp_allgather_restore_idx[pcp_size * num_decode_tokens :].to(torch.float32)
-                )
-                cp_kv_recover_idx_for_chunk = torch.argsort(kv_inverse_idx_for_chunk)
+                # Note(qcs): we only do restore and recover for pcp, and set these vars to None
+                # when only using dcp.
+                if self.pcp_size > 1:
+                    kv_inverse_idx_for_chunk = torch.argsort(
+                        common_long_seq_metadata.pcp_allgather_restore_idx[pcp_size * num_decode_tokens :].to(
+                            torch.float32
+                        )
+                    )
+                    cp_kv_recover_idx_for_chunk = torch.argsort(kv_inverse_idx_for_chunk)
+                else:
+                    kv_inverse_idx_for_chunk = None
+                    cp_kv_recover_idx_for_chunk = None
 
                 batch_chunk_seq_mask = local_context_lens_allranks[:, self.pcp_rank, self.dcp_rank] == 0
                 batch_chunk_seq_mask = torch.repeat_interleave(