ColossalAI/applications/Chat/evaluate/eval.py

import argparse
import json
import os

import openai
from evaluator import Evaluator
from utils import jload


def main(args):
    assert len(args.answer_file_list) == len(
        args.model_name_list), "The number of answer files and model names should be equal!"

    # load config
    config = jload(args.config_file)

    if config["language"] == "cn":
        # get metric settings for all categories
        metrics_per_category = {}
        for category in config["category"].keys():
            metrics_all = {}
            for metric_type, metrics in config["category"][category].items():
                metrics_all[metric_type] = metrics
            metrics_per_category[category] = metrics_all

        battle_prompt = None
        if args.battle_prompt_file:
            battle_prompt = jload(args.battle_prompt_file)

        gpt_evaluation_prompt = None
        if args.gpt_evaluation_prompt_file:
            gpt_evaluation_prompt = jload(args.gpt_evaluation_prompt_file)

        if len(args.model_name_list) == 2 and not battle_prompt:
            raise Exception("No prompt file for battle provided. Please specify the prompt file for battle!")

        if len(args.model_name_list) == 1 and not gpt_evaluation_prompt:
            raise Exception(
                "No prompt file for gpt evaluation provided. Please specify the prompt file for gpt evaluation!")

        # initialize evaluator
        evaluator = Evaluator(metrics_per_category, battle_prompt, gpt_evaluation_prompt, args.gpt_model,
                              config["language"])
        if len(args.model_name_list) == 2:
            answers1 = jload(args.answer_file_list[0])
            answers2 = jload(args.answer_file_list[1])

            assert len(answers1) == len(answers2), "The number of answers for two models should be equal!"

            evaluator.battle(answers1=answers1, answers2=answers2)
            evaluator.save(args.save_path, args.model_name_list)
        elif len(args.model_name_list) == 1:
            targets = jload(args.target_file)
            answers = jload(args.answer_file_list[0])

            assert len(targets) == len(answers), "The number of target answers and model answers should be equal!"

            evaluator.evaluate(answers=answers, targets=targets)
            evaluator.save(args.save_path, args.model_name_list)
        else:
            raise ValueError("Unsupported number of answer files and model names!")
    else:
        raise ValueError(f'Unsupported language {config["language"]}!')


if __name__ == '__main__':
    parser = argparse.ArgumentParser(description='ColossalAI LLM evaluation pipeline.')
    parser.add_argument('--config_file',
                        type=str,
                        default=None,
                        required=True,
                        help='path to the file of target results')
    parser.add_argument('--battle_prompt_file', type=str, default=None, help='path to the prompt file for battle')
    parser.add_argument('--gpt_evaluation_prompt_file',
                        type=str,
                        default=None,
                        help='path to the prompt file for gpt evaluation')
    parser.add_argument('--target_file', type=str, default=None, help='path to the target answer (ground truth) file')
    parser.add_argument('--answer_file_list',
                        type=str,
                        nargs='+',
                        default=[],
                        required=True,
                        help='path to the answer files of at most 2 models')
    parser.add_argument('--model_name_list',
                        type=str,
                        nargs='+',
                        default=[],
                        required=True,
                        help='the names of at most 2 models')
    parser.add_argument('--gpt_model',
                        default="gpt-3.5-turbo",
                        choices=["text-davinci-003", "gpt-3.5-turbo", "gpt-4"],
                        help='which GPT model to use for evaluation')
    parser.add_argument('--save_path', type=str, default="results", help='path to save evaluation results')
    parser.add_argument('--openai_key', type=str, default=None, required=True, help='Your openai key')
    args = parser.parse_args()

    if args.openai_key is not None:
        os.environ["OPENAI_API_KEY"] = args.openai_key
    openai.api_key = os.getenv("OPENAI_API_KEY")

    main(args)