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gen_answer.py
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gen_answer.py
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"""Generate answers using api endpoints.
Usage:
python gen_api_answer --parallel 32
"""
import argparse
import json
import os
import time
import concurrent.futures
import tiktoken
import shortuuid
import tqdm
from utils import (
load_questions,
load_model_answers,
make_config,
get_endpoint,
chat_completion_openai,
chat_completion_anthropic,
chat_completion_openai_azure,
chat_completion_mistral,
http_completion_gemini,
chat_completion_cohere,
reorg_answer_file,
OPENAI_MODEL_LIST,
temperature_config,
)
def get_answer(
question: dict, model: str, endpoint_info: dict, num_choices: int, max_tokens: int, temperature: float, answer_file: str, api_dict: dict
):
if question["category"] in temperature_config:
temperature = temperature_config[question["category"]]
api_type = endpoint_info["api_type"]
conv = []
if "system_prompt" in endpoint_info.keys():
conv.append({"role": "system", "content": endpoint_info["system_prompt"]})
elif model in OPENAI_MODEL_LIST:
conv.append({"role": "system", "content": "You are a helpful assistant."})
encoding = tiktoken.encoding_for_model("gpt-3.5-turbo")
choices = []
for i in range(num_choices):
turns = []
for j in range(len(question["turns"])):
conv.append({"role": "user", "content": question["turns"][j]["content"]})
if api_type == "anthropic":
output = chat_completion_anthropic(model=endpoint_info["model_name"],
messages=conv,
temperature=temperature,
max_tokens=max_tokens)
elif api_type == "mistral":
output = chat_completion_mistral(model=endpoint_info["model_name"],
messages=conv,
temperature=temperature,
max_tokens=max_tokens)
elif api_type == "gemini":
output = http_completion_gemini(model=endpoint_info["model_name"],
message=question["turns"][j]["content"],
temperature=temperature,
max_tokens=max_tokens)
elif api_type == "azure":
output = chat_completion_openai_azure(model=endpoint_info["model_name"],
messages=conv,
temperature=temperature,
max_tokens=max_tokens,
api_dict=api_dict)
elif api_type == "cohere":
output = chat_completion_cohere(model=endpoint_info["model_name"],
messages=conv,
temperature=temperature,
max_tokens=max_tokens)
else:
output = chat_completion_openai(model=endpoint_info["model_name"],
messages=conv,
temperature=temperature,
max_tokens=max_tokens,
api_dict=api_dict)
conv.append({"role": "assistant", "content": output})
turns.append({"content": output, "token_len": len(encoding.encode(output, disallowed_special=()))})
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(
"--setting-file", type=str, default="config/gen_answer_config.yaml"
)
parser.add_argument(
"--endpoint-file", type=str, default="config/api_config.yaml"
)
args = parser.parse_args()
settings = make_config(args.setting_file)
endpoint_list = make_config(args.endpoint_file)
existing_answer = load_model_answers(os.path.join("data", settings["bench_name"], "model_answer"))
print(settings)
for model in settings["model_list"]:
assert model in endpoint_list
endpoint_info = endpoint_list[model]
question_file = os.path.join("data", settings["bench_name"], "question.jsonl")
questions = load_questions(question_file)
answer_file = os.path.join("data", settings["bench_name"], "model_answer", f"{model}.jsonl")
print(f"Output to {answer_file}")
if "parallel" in endpoint_info:
parallel = endpoint_info["parallel"]
else:
parallel = 1
# We want to maximizes the number of tokens generate per answer: max_tokens = specified token # - input tokens #
if "tokenizer" in endpoint_info:
question_list = [question["turns"][0]["content"] for question in questions]
if model in OPENAI_MODEL_LIST:
tokenizer = tiktoken.encoding_for_model(endpoint_info["model_name"])
tokens = [tokenizer.encode(prompt) for prompt in question_list]
max_tokens = [(settings["max_tokens"] - len(token) - 100) for token in tokens]
else:
from transformers import AutoTokenizer
os.environ["TOKENIZERS_PARALLELISM"] = "false"
tokenizer = AutoTokenizer.from_pretrained(endpoint_info["tokenizer"])
tokens = tokenizer(question_list)
max_tokens = [(settings["max_tokens"] - len(prompt) - 300) for prompt in tokens["input_ids"]]
else:
max_tokens = [settings["max_tokens"]] * len(questions)
with concurrent.futures.ThreadPoolExecutor(max_workers=parallel) as executor:
futures = []
count = 0
for index, question in enumerate(questions):
if model in existing_answer and question["question_id"] in existing_answer[model]:
count += 1
continue
future = executor.submit(
get_answer,
question,
model,
endpoint_info,
settings["num_choices"],
max_tokens[index],
settings["temperature"],
answer_file,
get_endpoint(endpoint_info["endpoints"]),
)
futures.append(future)
if count > 0:
print(f"{count} number of existing answers")
for future in tqdm.tqdm(
concurrent.futures.as_completed(futures), total=len(futures)
):
future.result()
reorg_answer_file(answer_file)