import argparse import json import time from concurrent.futures import ThreadPoolExecutor 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 def get_prompt(question): prompt = ( """Solve a question answering task with interleaving Thought, Action, Observation steps. Thought can reason about the current situation, and Action can be three types: (1) Search[entity], which searches the exact entity on Wikipedia and returns the first paragraph if it exists. If not, it will return some similar entities to search. (2) Lookup[keyword], which returns the next sentence containing keyword in the current passage. (3) Finish[answer], which returns the answer and finishes the task. Here are some examples. Question: What is the elevation range for the area that the eastern sector of the Colorado orogeny extends into? Thought 1: I need to search Colorado orogeny, find the area that the eastern sector of the Colorado orogeny extends into, then find the elevation range of the area. Action 1: Search[Colorado orogeny] Observation 1: The Colorado orogeny was an episode of mountain building (an orogeny) in Colorado and surrounding areas. Thought 2: It does not mention the eastern sector. So I need to look up eastern sector. Action 2: Lookup[eastern sector] Observation 2: (Result 1 / 1) The eastern sector extends into the High Plains and is called the Central Plains orogeny. Thought 3: The eastern sector of Colorado orogeny extends into the High Plains. So I need to search High Plains and find its elevation range. Action 3: Search[High Plains] Observation 3: High Plains refers to one of two distinct land regions: Thought 4: I need to instead search High Plains (United States). Action 4: Search[High Plains (United States)] Observation 4: The High Plains are a subregion of the Great Plains. From east to west, the High Plains rise in elevation from around 1,800 to 7,000 ft (550 to 2,130 m).[3] Thought 5: High Plains rise in elevation from around 1,800 to 7,000 ft, so the answer is 1,800 to 7,000 ft. Action 5: Finish[1,800 to 7,000 ft] Question: Musician and satirist Allie Goertz wrote a song about the "The Simpsons" character Milhouse, who Matt Groening named after who? Thought 1: The question simplifies to "The Simpsons" character Milhouse is named after who. I only need to search Milhouse and find who it is named after. Action 1: Search[Milhouse] Observation 1: Milhouse Mussolini Van Houten is a recurring character in the Fox animated television series The Simpsons voiced by Pamela Hayden and created by Matt Groening. Thought 2: The paragraph does not tell who Milhouse is named after, maybe I can look up "named after". Action 2: Lookup[named after] Observation 2: (Result 1 / 1) Milhouse was named after U.S. president Richard Nixon, whose middle name was Milhous. Thought 3: Milhouse was named after U.S. president Richard Nixon, so the answer is Richard Nixon. Action 3: Finish[Richard Nixon] Question: Which documentary is about Finnish rock groups, Adam Clayton Powell or The Saimaa Gesture? Thought 1: I need to search Adam Clayton Powell and The Saimaa Gesture, and find which documentary is about Finnish rock groups. Action 1: Search[Adam Clayton Powell] Observation 1: Could not find [Adam Clayton Powell]. Similar: ['Adam Clayton Powell III', 'Seventh Avenue (Manhattan)', 'Adam Clayton Powell Jr. State Office Building', 'Isabel Washington Powell', 'Adam Powell', 'Adam Clayton Powell (film)', 'Giancarlo Esposito']. Thought 2: To find the documentary, I can search Adam Clayton Powell (film). Action 2: Search[Adam Clayton Powell (film)] Observation 2: Adam Clayton Powell is a 1989 American documentary film directed by Richard Kilberg. The film is about the rise and fall of influential African-American politician Adam Clayton Powell Jr.[3][4] It was later aired as part of the PBS series The American Experience. Thought 3: Adam Clayton Powell (film) is a documentary about an African-American politician, not Finnish rock groups. So the documentary about Finnish rock groups must instead be The Saimaa Gesture. Action 3: Finish[The Saimaa Gesture] Question: What profession does Nicholas Ray and Elia Kazan have in common? Thought 1: I need to search Nicholas Ray and Elia Kazan, find their professions, then find the profession they have in common. Action 1: Search[Nicholas Ray] Observation 1: Nicholas Ray (born Raymond Nicholas Kienzle Jr., August 7, 1911 – June 16, 1979) was an American film director, screenwriter, and actor best known for the 1955 film Rebel Without a Cause. Thought 2: Professions of Nicholas Ray are director, screenwriter, and actor. I need to search Elia Kazan next and find his professions. Action 2: Search[Elia Kazan] Observation 2: Elia Kazan was an American film and theatre director, producer, screenwriter and actor. Thought 3: Professions of Elia Kazan are director, producer, screenwriter, and actor. So profession Nicholas Ray and Elia Kazan have in common is director, screenwriter, and actor. Action 3: Finish[director, screenwriter, actor] Question: Which magazine was started first Arthur's Magazine or First for Women? Thought 1: I need to search Arthur's Magazine and First for Women, and find which was started first. Action 1: Search[Arthur's Magazine] Observation 1: Arthur's Magazine (1844-1846) was an American literary periodical published in Philadelphia in the 19th century. Thought 2: Arthur's Magazine was started in 1844. I need to search First for Women next. Action 2: Search[First for Women] Observation 2: First for Women is a woman's magazine published by Bauer Media Group in the USA.[1] The magazine was started in 1989. Thought 3: First for Women was started in 1989. 1844 (Arthur's Magazine) < 1989 (First for Women), so Arthur's Magazine was started first. Action 3: Finish[Arthur's Magazine] Question: Were Pavel Urysohn and Leonid Levin known for the same type of work? Thought 1: I need to search Pavel Urysohn and Leonid Levin, find their types of work, then find if they are the same. Action 1: Search[Pavel Urysohn] Observation 1: Pavel Samuilovich Urysohn (February 3, 1898 â August 17, 1924) was a Soviet mathematician who is best known for his contributions in dimension theory. Thought 2: Pavel Urysohn is a mathematician. I need to search Leonid Levin next and find its type of work. Action 2: Search[Leonid Levin] Observation 2: Leonid Anatolievich Levin is a Soviet-American mathematician and computer scientist. Thought 3: Leonid Levin is a mathematician and computer scientist. So Pavel Urysohn and Leonid Levin have the same type of work. Action 3: Finish[yes] """ + question ) return prompt def main(args): lines = read_jsonl(args.data_path)[: args.num_questions] arguments = [{"question": k, "triplets": v} for l in lines for k, v in l.items()] states = [] # Select backend call_generate = get_call_generate(args) def run_single_agent(argument): question = argument["question"] triplets = argument["triplets"] prompt = get_prompt(question) for i in range(1, len(triplets) + 2): prompt += "Thought " + str(i) + ":" states.append(prompt) answer = call_generate( prompt, max_tokens=200, temperature=0, stop="Observation" ) if i > len(triplets): break prompt += ( triplets[i - 1]["thought"] + "\nAction " + str(i) + ":" + triplets[i - 1]["action"] + "\nObservation " + str(i) + ":" + triplets[i - 1]["observation"] + "\n" ) states.append(answer) async def run_single_agent_async(argument): question = argument["question"] triplets = argument["triplets"] prompt = get_prompt(question) for i in range(1, len(triplets) + 2): prompt += "Thought " + str(i) + ":" states.append(prompt) answer = await call_generate( prompt, max_tokens=200, temperature=0, stop="Observation", max_len=4096 ) if i > len(triplets): break prompt += ( triplets[i - 1]["thought"] + "\nAction " + str(i) + ":" + triplets[i - 1]["action"] + "\nObservation " + str(i) + ":" + triplets[i - 1]["observation"] + "\n" ) states.append(answer) tic = time.time() if args.backend != "lmql": if args.parallel == 1: for arg in tqdm(arguments): run_single_agent(arg) else: with ThreadPoolExecutor(args.parallel) as executor: list( tqdm( executor.map(run_single_agent, arguments), total=len(arguments) ) ) else: import asyncio loop = asyncio.get_event_loop() batches = [ [] for _ in range((len(arguments) + args.parallel - 1) // args.parallel) ] for i, arg in enumerate(arguments): batches[i // args.parallel].append(arg) for bt in tqdm(batches): tasks = [run_single_agent_async(arg) for arg in bt] loop.run_until_complete(asyncio.gather(*tasks)) latency = time.time() - tic 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": "ReAct Agents", "backend": args.backend, "num_gpus": 1, "latency": round(latency, 3), "num_requests": len(arguments), "other": { "parallel": args.parallel, }, } fout.write(json.dumps(value) + "\n") if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--data-path", type=str, default="hotpotqa_100.jsonl") parser.add_argument("--num-questions", type=int, default=10) args = add_common_other_args_and_parse(parser) main(args)