diff --git a/lmdeploy/pytorch/backends/dlinfer/maca/op_backend.py b/lmdeploy/pytorch/backends/dlinfer/maca/op_backend.py
index 084cae1bfe..61cd5fee87 100644
--- a/lmdeploy/pytorch/backends/dlinfer/maca/op_backend.py
+++ b/lmdeploy/pytorch/backends/dlinfer/maca/op_backend.py
@@ -47,6 +47,10 @@ def update_step_context(cls, step_context):
         device = step_context.block_offsets.device
 
         is_unpaged_prefill = False
+        if not step_context.is_decoding:
+            is_unpaged_prefill = \
+               all((step_context.q_seqlens ==
+                    step_context.kv_seqlens).tolist())
         q_start_loc = torch.cat((torch.tensor([0], device=device),
                                  step_context.q_seqlens.cumsum(0))).int()
         q_seqlens = step_context.q_seqlens.int()
@@ -54,43 +58,30 @@ def update_step_context(cls, step_context):
         max_q_seq_len = torch.max(q_seqlens).item()
         max_kv_seq_len = torch.max(kv_seqlens).item()
 
-        if not step_context.is_decoding:
-            is_unpaged_prefill = \
-                all((step_context.q_seqlens ==
-                     step_context.kv_seqlens).tolist())
-            if is_unpaged_prefill:
-                single_attention_mask = torch.logical_not(
-                    torch.tril(
-                        torch.ones(max_q_seq_len,
-                                   max_kv_seq_len,
-                                   dtype=torch.bool).cuda(),
-                        diagonal=max_kv_seq_len - max_q_seq_len,
-                    ))
-                attention_mask.append(single_attention_mask)
-        total_slots = torch.arange(block_num * block_size,
-                                   dtype=torch.long,
-                                   device=device)
-        total_slots = total_slots.view(block_num, block_size)
-        for i in range(step_context.q_start_loc.size(0)):
-            q_seq_len = int(step_context.q_seqlens[i])
-            kv_seq_len = int(step_context.kv_seqlens[i])
-            if not (step_context.is_decoding or is_unpaged_prefill):
-                single_attention_mask = torch.logical_not(
-                    torch.tril(
-                        torch.ones(step_context.q_seqlens[i],
-                                   step_context.block_offsets.shape[1] *
-                                   block_size,
-                                   dtype=torch.bool).cuda(),
-                        diagonal=step_context.kv_seqlens[i] -
-                        step_context.q_seqlens[i],
-                    ))
-                attention_mask.append(single_attention_mask)
-            history_length = kv_seq_len - q_seq_len
-            slot_tables = total_slots[step_context.block_offsets[i]].flatten()
-            slot_indices = [p for p in range(history_length, kv_seq_len)]
-            slots = slot_tables[slot_indices].reshape((-1, 1))
-            kv_start_indices.append(slots)
-        kv_start_indices = torch.cat(kv_start_indices)
+        if step_context.is_decoding:
+            # collect kv_start_indices without using a for-loop,
+            # (fill kv-cache for just ONE token during the decoding phase)
+            idx = (step_context.kv_seqlens - 1) % block_size
+            b_num = (step_context.kv_seqlens - 1) // block_size
+            last_block = step_context.block_offsets.gather(
+                1, b_num.view(-1, 1)).view(-1)
+            kv_start_indices = (last_block * block_size + idx).reshape((-1, 1))
+        else:
+            total_slots = torch.arange(block_num * block_size,
+                                       dtype=torch.long,
+                                       device=device)
+            total_slots = total_slots.view(block_num, block_size)
+            for i in range(step_context.q_start_loc.size(0)):
+                q_seq_len = int(step_context.q_seqlens[i])
+                kv_seq_len = int(step_context.kv_seqlens[i])
+                # collect kv start indices during the prefill phase.
+                history_length = kv_seq_len - q_seq_len
+                slot_tables = total_slots[
+                    step_context.block_offsets[i]].flatten()
+                slot_indices = [p for p in range(history_length, kv_seq_len)]
+                slots = slot_tables[slot_indices].reshape((-1, 1))
+                kv_start_indices.append(slots)
+            kv_start_indices = torch.cat(kv_start_indices)
 
         attn_meta_cls = cls.get_attention_metadata_cls()
         attn_metadata = attn_meta_cls(