adapt to sglang v0.5.2rc1 on dcu

2025-09-04 15:56:33 +08:00
commit 909abb58f5
2320 changed files with 489411 additions and 0 deletions
--- a/benchmark/multi_document_qa/bench_other.py
+++ b/benchmark/multi_document_qa/bench_other.py
@@ -0,0 +1,114 @@
+import argparse
+import json
+import time
+from concurrent.futures import ThreadPoolExecutor
+from functools import partial
+
+from tqdm import tqdm
+
+from sglang.test.test_utils import add_common_other_args_and_parse, get_call_generate
+from sglang.utils import dump_state_text, read_jsonl
+
+USER_PREFIX = "[INST] "
+USER_SUFFIX = " [/INST]"
+ASSISTANT_PREFIX = ""
+ASSISTANT_SUFFIX = " </s><s>"
+
+
+def multi_document_qa(docs, question, generate):
+    s = USER_PREFIX
+    s += "Please answer a question according to given documents.\n"
+    s += "Question:" + question + "Documents begin.\n"
+
+    s += "".join(docs)
+
+    s += "\nDocuments end."
+    s += (
+        "\n\nBased on the above documents, please answer this question:\n"
+        + question
+        + "\nAnswer in three words or fewer."
+    )
+    s += USER_SUFFIX
+    s += ASSISTANT_PREFIX
+    answer = generate(s, max_tokens=16, stop=None)
+    return answer
+
+
+def main(args):
+    lines = read_jsonl(args.data_path)
+    l = lines[0]
+    arguments = []
+    labels = []
+
+    num_docs = 10
+    if args.backend == "guidance":
+        num_docs = 7  # due to OOM
+
+    for i in range(len(l["questions"][: args.num_questions])):
+        arguments.append(
+            {
+                "docs": l["documents"][:num_docs],
+                "question": l["questions"][i],
+            }
+        )
+        labels.append(l["answers"][i])
+    states = [None] * len(arguments)
+
+    # Select backend
+    call_generate = partial(get_call_generate(args), temperature=0)
+
+    # Run requests
+    def get_one_answer(i):
+        states[i] = multi_document_qa(generate=call_generate, **arguments[i])
+
+    tic = time.perf_counter()
+    if args.parallel == 1:
+        for i in tqdm(range(len(labels))):
+            get_one_answer(i)
+    else:
+        with ThreadPoolExecutor(args.parallel) as executor:
+            list(
+                tqdm(
+                    executor.map(get_one_answer, list(range(len(labels)))),
+                    total=len(labels),
+                )
+            )
+
+    latency = time.perf_counter() - tic
+
+    # Compute accuracy
+    print(states)
+    correct = 0
+    for s, label in zip(states, labels):
+        answer = s.lower()
+        if all(x in answer for x in label.lower().split(" ")):
+            correct += 1
+    accuracy = correct / len(labels)
+    print(f"Accuracy: {accuracy:.3f}")
+    print(f"Latency: {latency:.3f}")
+
+    # Write results
+    dump_state_text(f"tmp_output_{args.backend}.txt", states)
+
+    with open(args.result_file, "a") as fout:
+        value = {
+            "task": "multi_document_qa",
+            "backend": args.backend,
+            "num_gpus": 1,
+            "latency": round(latency, 3),
+            "num_requests": args.num_questions,
+            "accuracy": accuracy,
+            "other": {
+                "num_questions": args.num_questions,
+                "parallel": args.parallel,
+            },
+        }
+        fout.write(json.dumps(value) + "\n")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--data-path", type=str, default="questions.jsonl")
+    parser.add_argument("--num-questions", type=int, default=100)
+    args = add_common_other_args_and_parse(parser)
+    main(args)