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generate_for_mt_bench.py
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generate_for_mt_bench.py
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"""Generate answers with local models.
Usage:
python3 gen_model_answer.py --model-path lmsys/fastchat-t5-3b-v1.0 --model-id fastchat-t5-3b-v1.0
"""
import argparse
import json
import os
import random
import time
import shortuuid
import torch
from tqdm import tqdm
from fastchat.llm_judge.common import load_questions, temperature_config
from fastchat.model import load_model, get_conversation_template
from fastchat.utils import str_to_torch_dtype
# import openai
import requests
from loguru import logger
"""Generate answers with GPT-4
Usage:
python3 gen_api_answer.py --model gpt-3.5-turbo
"""
import argparse
import json
import os
import time
import concurrent.futures
import openai
import shortuuid
import tqdm
from fastchat.llm_judge.common import (
load_questions,
temperature_config,
chat_completion_openai,
chat_completion_anthropic,
chat_completion_palm,
)
from fastchat.llm_judge.gen_model_answer import reorg_answer_file
from fastchat.model.model_adapter import get_conversation_template
from typing import List
from loguru import logger
import openai
from utils import (
generate_together,
generate_openai,
generate_with_references,
DEBUG,
)
def get_answer(
question: dict,
model: str,
reference_models: List[str],
num_choices: int,
max_tokens: int,
answer_file: str,
rounds: int,
provider: str,
):
assert (
args.force_temperature is not None and "required_temperature" in question.keys()
) == False
if args.force_temperature is not None:
temperature = args.force_temperature
elif "required_temperature" in question.keys():
temperature = question["required_temperature"]
elif question["category"] in temperature_config:
temperature = temperature_config[question["category"]]
else:
temperature = 0.7
choices = []
if provider == "together":
generate_fn = generate_together
elif provider == "openai":
generate_fn = generate_openai
else:
assert False
for i in range(num_choices):
turns = []
messages = []
for j in range(len(question["turns"])):
qs = question["turns"][j]
messages.append({"role": "user", "content": qs})
references = []
if len(reference_models) > 0:
prev_references = []
for i_round in range(rounds):
if DEBUG:
logger.info(
f"Round {i_round+1}/{rounds} to collecting reference responses."
)
references = []
for reference_model in reference_models:
reference = generate_with_references(
model=reference_model,
messages=messages,
references=prev_references,
temperature=temperature,
max_tokens=max_tokens,
generate_fn=generate_fn,
)
if reference is not None:
references.append(reference)
if i_round < rounds - 1:
prev_references = references
references = []
output = generate_with_references(
model=model,
messages=messages,
max_tokens=max_tokens,
temperature=temperature,
generate_fn=generate_fn,
references=references,
).strip()
messages.append(
{
"role": "assistant",
"content": output,
}
)
turns.append(output)
choices.append({"index": i, "turns": turns})
# Dump answers
ans = {
"question_id": question["question_id"],
"answer_id": shortuuid.uuid(),
"model_id": model,
"choices": choices,
"tstamp": time.time(),
}
os.makedirs(os.path.dirname(answer_file), exist_ok=True)
with open(answer_file, "a") as fout:
fout.write(json.dumps(ans) + "\n")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--bench-name",
type=str,
default="mt_bench",
help="The name of the benchmark question set.",
)
parser.add_argument("--answer-file", type=str, help="The output answer file.")
parser.add_argument("--model", type=str, default="gpt-3.5-turbo")
parser.add_argument("--reference-models", type=str, default=None)
parser.add_argument("--rounds", type=int, default=1)
parser.add_argument("--provider", type=str, default="together")
parser.add_argument(
"--num-choices",
type=int,
default=1,
help="How many completion choices to generate.",
)
parser.add_argument(
"--force-temperature", type=float, help="Forcibly set a sampling temperature."
)
parser.add_argument(
"--max-tokens",
type=int,
default=1024,
help="The maximum number of new generated tokens.",
)
parser.add_argument(
"--question-begin",
type=int,
help="A debug option. The begin index of questions.",
)
parser.add_argument(
"--question-end", type=int, help="A debug option. The end index of questions."
)
parser.add_argument(
"--parallel", type=int, default=1, help="The number of concurrent API calls."
)
args = parser.parse_args()
question_file = f"FastChat/fastchat/llm_judge/data/{args.bench_name}/question.jsonl"
questions = load_questions(question_file, args.question_begin, args.question_end)
if args.answer_file:
answer_file = args.answer_file
else:
answer_file = f"outputs/{args.bench_name}/model_answer/{args.model}.jsonl"
print(f"Output to {answer_file}")
if args.reference_models is None:
reference_models = []
else:
reference_models = args.reference_models.split(",")
with concurrent.futures.ThreadPoolExecutor(max_workers=args.parallel) as executor:
futures = []
for question in questions:
future = executor.submit(
get_answer,
question,
args.model,
reference_models,
args.num_choices,
args.max_tokens,
answer_file,
args.rounds,
args.provider,
)
futures.append(future)
for future in tqdm.tqdm(
concurrent.futures.as_completed(futures), total=len(futures)
):
future.result()
reorg_answer_file(answer_file)