[fix] update bench_speculative.py for compatibility (#7764)

Signed-off-by: Kay Yan <kay.yan@daocloud.io>

scripts/playground/bench_speculative.py

@@ -17,7 +17,7 @@ from types import SimpleNamespace
 import numpy as np
 import requests
 
-from sglang.bench_serving import benchmark, set_global_args
+from sglang.bench_serving import DatasetRow, benchmark, set_global_args
 from sglang.srt.server_args import ServerArgs
 from sglang.test.test_utils import (
     DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
@@ -54,7 +54,7 @@ def send_one_batch(base_url, num_prompts, batch_size):
     ]
 
     # format: (prompt, input_len, output len). We set input_len as a dummy value 0.
-    input_requests = [(p, 0, 512) for p in padded_prompts]
+    input_requests: List[DatasetRow] = [DatasetRow(p, 0, 512) for p in padded_prompts]
 
     # We need to set some dummy values in order to call `benchmark` below.
     args = SimpleNamespace(
@@ -69,6 +69,8 @@ def send_one_batch(base_url, num_prompts, batch_size):
         random_output_len=None,
         random_range_ratio=None,
         output_file=None,
+        warmup_requests=1,
+        output_details=False,
     )
     set_global_args(args)
     tokenizer = FakeTokenizer()
@@ -97,7 +99,9 @@ def send_one_batch(base_url, num_prompts, batch_size):
 
     server_info = requests.get(base_url + "/get_server_info").json()
     # We use 20% percentile instead of median on purpose
-    step_time = np.percentile(server_info["step_time_dict"][str(batch_size)], 20)
+    step_time = np.percentile(
+        server_info["internal_states"][0]["step_time_dict"][str(batch_size)], 20
+    )
     speed = 1 / step_time * acc_length
 
     return (