diff --git a/benchmark/kernels/decoding_attention_triton/triton_flashinfer_cudnn.py b/benchmark/kernels/decoding_attention_triton/triton_flashinfer_cudnn.py
index f8c87d48db76..b199a72ed6c1 100644
--- a/benchmark/kernels/decoding_attention_triton/triton_flashinfer_cudnn.py
+++ b/benchmark/kernels/decoding_attention_triton/triton_flashinfer_cudnn.py
@@ -8,8 +8,8 @@
 import triton.language as tl
 from flashinfer import BatchDecodeWithPagedKVCacheWrapper
 
+from sglang.srt.layers.attention.flashinfer_backend import should_use_tensor_core
 from sglang.srt.layers.attention.triton_ops.decode_attention import decode_attention_fwd
-from sglang.srt.utils import should_use_tensor_core
 
 
 def benchmark_forward(
@@ -54,9 +54,10 @@ def decode_attention_sglang(
     v_buffer = kv_data[1].view(-1, head_num_kv, head_dim)
     o = torch.empty_like(q)
     total_tokens = batch_size * kv_len
-    req_to_token = torch.arange(0, total_tokens).to(0).int().view(batch_size, kv_len)
-    b_req_idx = torch.arange(0, batch_size).to(0).int()
     b_seq_len = torch.full((batch_size,), kv_len, dtype=torch.int32, device="cuda")
+    kv_indptr = torch.zeros((batch_size + 1,), dtype=torch.int32, device="cuda")
+    kv_indptr[1 : batch_size + 1] = torch.cumsum(b_seq_len[:batch_size], dim=0)
+    kv_indices = torch.arange(total_tokens, device="cuda")
     max_len_in_batch = kv_len
     sm_scale = 1.0 / (head_dim**0.5)
 
@@ -72,9 +73,8 @@ def decode_attention_sglang(
             k_buffer,
             v_buffer,
             o,
-            req_to_token,
-            b_req_idx,
-            b_seq_len,
+            kv_indptr,
+            kv_indices,
             attn_logits,
             num_kv_splits,
             sm_scale,
@@ -86,9 +86,8 @@ def decode_attention_sglang(
         k_buffer,
         v_buffer,
         o,
-        req_to_token,
-        b_req_idx,
-        b_seq_len,
+        kv_indptr,
+        kv_indices,
         attn_logits,
         num_kv_splits,
         sm_scale,