diff --git a/python/sglang/bench_one_batch.py b/python/sglang/bench_one_batch.py
index 3367cb752a69..a5bf4c096f4b 100644
--- a/python/sglang/bench_one_batch.py
+++ b/python/sglang/bench_one_batch.py
@@ -68,6 +68,7 @@
 from sglang.srt.server_args import PortArgs, ServerArgs
 from sglang.srt.speculative.spec_info import SpeculativeAlgorithm
 from sglang.srt.utils import (
+    DeepEPMode,
     configure_logger,
     get_bool_env_var,
     kill_process_tree,
@@ -275,6 +276,9 @@ def _maybe_prepare_dp_attn_batch(batch: ScheduleBatch, model_runner):
             disable_cuda_graph=model_runner.server_args.disable_cuda_graph,
             spec_algorithm=SpeculativeAlgorithm.NONE,
             speculative_num_draft_tokens=None,
+            enable_two_batch_overlap=model_runner.server_args.enable_two_batch_overlap,
+            enable_deepep_moe=model_runner.server_args.enable_deepep_moe,
+            deepep_mode=DeepEPMode[model_runner.server_args.deepep_mode],
         )
 
 
@@ -339,6 +343,7 @@ def latency_test_run_once(
     log_decode_step,
     profile,
     profile_filename_prefix,
+    dp_size,
 ):
     max_batch_size = model_runner.max_total_num_tokens // (input_len + output_len)
     if batch_size > max_batch_size:
@@ -353,7 +358,7 @@ def latency_test_run_once(
 
     measurement_results = {
         "run_name": run_name,
-        "batch_size": batch_size,
+        "batch_size": batch_size * dp_size,
         "input_len": input_len,
         "output_len": output_len,
     }
@@ -378,7 +383,7 @@ def latency_test_run_once(
     synchronize(device)
     prefill_latency = time.perf_counter() - tic
     tot_latency += prefill_latency
-    throughput = input_len * batch_size / prefill_latency
+    throughput = input_len * batch_size * dp_size / prefill_latency
     rank_print(
         f"Prefill. latency: {prefill_latency:6.5f} s, throughput: {throughput:9.2f} token/s"
     )
@@ -394,16 +399,16 @@ def latency_test_run_once(
         synchronize(device)
         latency = time.perf_counter() - tic
         tot_latency += latency
-        throughput = batch_size / latency
+        throughput = batch_size * dp_size / latency
         decode_latencies.append(latency)
         if i < 5 or (log_decode_step > 0 and i % log_decode_step == 0):
             rank_print(
-                f"Decode {i}. Batch size: {batch_size}, latency: {latency:6.5f} s, throughput: {throughput:9.2f} token/s"
+                f"Decode {i}. Batch size: {batch_size * dp_size}, latency: {latency:6.5f} s, throughput: {throughput:9.2f} token/s"
             )
 
     if profile:
         profiler.stop()
-        profile_filename = f"{profile_filename_prefix}_batch{batch_size}_input{input_len}_output{output_len}.trace.json.gz"
+        profile_filename = f"{profile_filename_prefix}_batch{batch_size * dp_size}_input{input_len}_output{output_len}.trace.json.gz"
         parent_dir = os.path.dirname(os.path.abspath(profile_filename))
         os.makedirs(parent_dir, exist_ok=True)
         profiler.export_chrome_trace(profile_filename)
@@ -412,14 +417,14 @@ def latency_test_run_once(
     # Record decode timing from 2nd output
     if output_len > 1:
         med_decode_latency = np.median(decode_latencies)
-        med_decode_throughput = batch_size / med_decode_latency
+        med_decode_throughput = batch_size * dp_size / med_decode_latency
         rank_print(
             f"Decode.  median latency: {med_decode_latency:6.5f} s, median throughput: {med_decode_throughput:9.2f} token/s"
         )
         measurement_results["median_decode_latency"] = med_decode_latency
         measurement_results["median_decode_throughput"] = med_decode_throughput
 
-    throughput = (input_len + output_len) * batch_size / tot_latency
+    throughput = (input_len + output_len) * batch_size * dp_size / tot_latency
     rank_print(
         f"Total. latency: {tot_latency:6.3f} s, throughput: {throughput:9.2f} token/s"
     )
@@ -464,6 +469,7 @@ def latency_test(
         log_decode_step=0,
         profile=False,
         profile_filename_prefix="",  # not used
+        dp_size=1 if not server_args.enable_dp_attention else server_args.dp_size,
     )
 
     rank_print("Benchmark ...")
@@ -486,6 +492,7 @@ def latency_test(
             bench_args.log_decode_step,
             bench_args.profile if tp_rank == 0 else None,
             bench_args.profile_filename_prefix,
+            1 if not server_args.enable_dp_attention else server_args.dp_size,
         )
         if ret is not None:
             result_list.append(ret)
@@ -502,6 +509,12 @@ def latency_test(
 
 def main(server_args, bench_args):
     server_args.cuda_graph_max_bs = max(bench_args.batch_size)
+    if server_args.enable_dp_attention:
+        sub_batch_size = []
+        for i in range(len(bench_args.batch_size)):
+            assert bench_args.batch_size[i] % server_args.dp_size == 0
+            sub_batch_size.append(bench_args.batch_size[i] // server_args.dp_size)
+        bench_args.batch_size = tuple(sub_batch_size)
 
     _set_envs_and_config(server_args)