sglangv0.5.2 & support Qwen3-Next-80B-A3B-Instruct

benchmark/hellaswag/bench_other.py

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+import argparse
+import asyncio
+import json
+import time
+from concurrent.futures import ThreadPoolExecutor
+
+import numpy as np
+from tqdm import tqdm
+
+from sglang.test.test_utils import add_common_other_args_and_parse, get_call_select
+from sglang.utils import download_and_cache_file, read_jsonl
+
+
+def get_one_example(lines, i, include_answer):
+    ret = lines[i]["activity_label"] + ": " + lines[i]["ctx"] + " "
+    if include_answer:
+        ret += lines[i]["endings"][lines[i]["label"]]
+    return ret
+
+
+def get_few_shot_examples(lines, k):
+    ret = ""
+    for i in range(k):
+        ret += get_one_example(lines, i, True) + "\n\n"
+    return ret
+
+
+def main(args):
+    # Select backend
+    call_select = get_call_select(args)
+
+    # Read data
+    url = "https://raw.githubusercontent.com/rowanz/hellaswag/master/data/hellaswag_val.jsonl"
+    filename = download_and_cache_file(url)
+    lines = list(read_jsonl(filename))
+
+    # Construct prompts
+    num_questions = args.num_questions
+    num_shots = args.num_shots
+    few_shot_examples = get_few_shot_examples(lines, num_shots)
+
+    questions = []
+    choices = []
+    labels = []
+    for i in range(len(lines[:num_questions])):
+        questions.append(get_one_example(lines, i, False))
+        choices.append(lines[i]["endings"])
+        labels.append(lines[i]["label"])
+
+    preds = [None] * len(labels)
+
+    # Run requests
+    if args.backend != "lmql":
+        # Use thread pool
+        def get_one_answer(i):
+            preds[i] = call_select(
+                context=few_shot_examples + questions[i], choices=choices[i]
+            )
+
+        tic = time.perf_counter()
+        if args.parallel == 1:
+            for i in tqdm(range(len(questions))):
+                get_one_answer(i)
+        else:
+            with ThreadPoolExecutor(args.parallel) as executor:
+                list(
+                    tqdm(
+                        executor.map(get_one_answer, list(range(len(questions)))),
+                        total=len(questions),
+                    )
+                )
+    else:
+        # Use asyncio
+        async def batched_call(batch_size):
+            for i in range(0, len(questions), batch_size):
+                tasks = []
+                for q, c in zip(
+                    questions[i : i + batch_size], choices[i : i + batch_size]
+                ):
+                    tasks.append(call_select(context=few_shot_examples + q, choices=c))
+                rets = await asyncio.gather(*tasks)
+                for j in range(len(rets)):
+                    preds[i + j] = rets[j]
+
+        tic = time.perf_counter()
+        asyncio.run(batched_call(batch_size=args.parallel))
+
+    latency = time.perf_counter() - tic
+
+    # Compute accuracy
+    acc = np.mean(np.array(preds) == np.array(labels))
+    print(f"Latency: {latency:.3f}")
+    print(f"Accuracy: {acc:.3f}")
+
+    # Write results
+    with open(args.result_file, "a") as fout:
+        value = {
+            "task": "hellaswag",
+            "backend": args.backend,
+            "num_gpus": 1,
+            "latency": round(latency, 3),
+            "accuracy": round(acc, 3),
+            "num_requests": args.num_questions,
+            "other": {
+                "num_questions": args.num_questions,
+                "parallel": args.parallel,
+            },
+        }
+        fout.write(json.dumps(value) + "\n")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--num-shots", type=int, default=20)
+    parser.add_argument("--data-path", type=str, default="hellaswag_val.jsonl")
+    parser.add_argument("--num-questions", type=int, default=200)
+    args = add_common_other_args_and_parse(parser)
+    main(args)