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chore: import upstream snapshot with attribution
2026-07-13 12:38:16 +08:00

115 lines
3.1 KiB
Python

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)