[Feature] Add api_prompts to GenInferencerOutput

[xu-song]

Motivation

api_prompts is a list of prompts in opencompass.

opencompass/opencompass/models/base_api.py

Lines 351 to 355 in 3aeabbc

	def _prompt2api(self,
	prompts: Union[List, str],
	role_dict: Dict[str, Dict],
	for_gen: bool = False) -> Tuple[str, bool]:
	"""Convert the prompts to a API-style prompts, given an updated

However, the prediction files confused api_prompts with raw_prompt.

Modification

Before PR:
Here is a prediction example, predictions/ceval-college_physics.json

{
    "0": {
        "origin_prompt": [
            {
                "role": "HUMAN",
                "prompt": "以下是中国关于大学物理考试的单项选择题，请选出其中的正确答案。\n有一劲度系数为k的轻弹簧，竖直放置，下端悬一质量为m的小球，开始时使弹簧为原长而小球恰好与地接触，今将弹簧上端缓慢地>提起，直到小球刚能脱离地面为止，在此过程中外力作功为____\nA. m^2g^2/(4k)\nB. m^2g^2/(3k)\nC. m^2g^2/(2k)\nD. 2m^2g^2/k\n答案: \n"
            },
            {
                "role": "BOT",
                "prompt": "C"
            },
            {
                "role": "HUMAN",
                "prompt": "以下是中国关于大学物理考试的单项选择题，请选出其中的正确答案。\nα粒子在加速器中被加速，当其质量为静止质量的 3 倍时，其动能为静止能量的____\nA. 2倍\nB. 3倍\nC. 4倍\nD. 5倍\n答案: \n"
            }
        ],
        "prediction": "D",
        "gold": "A"
    },
...

The above details fail to reflect SYSTEM role and special tokens, which are very important for prediction and evaluation.

After PR:
The prediction is like this

{
    "0": {

        ### api_prompts is a list of api-style prompt
        "api_prompts": [
            {
                "role": "HUMAN",
                "prompt": "以下是中国关于大学物理考试的单项选择题，请选出其中的正确答案。\n有一劲度系数为k的轻弹簧，竖直放置，下端悬一质量为m的小球，开始时使弹簧为原长而小球恰好与地接触，今将弹簧上端缓慢地>提起，直到小球刚能脱离地面为止，在此过程中外力作功为____\nA. m^2g^2/(4k)\nB. m^2g^2/(3k)\nC. m^2g^2/(2k)\nD. 2m^2g^2/k\n答案: \n"
            },
            {
                "role": "BOT",
                "prompt": "C"
            },
            {
                "role": "HUMAN",
                "prompt": "以下是中国关于大学物理考试的单项选择题，请选出其中的正确答案。\nα粒子在加速器中被加速，当其质量为静止质量的 3 倍时，其动能为静止能量的____\nA. 2倍\nB. 3倍\nC. 4倍\nD. 5倍\n答案: \n"
            }
        ],

        ### origin_prompt is the final input string of model
        "origin_prompt": "<|im_start|>system\nYou are a helpful assistant<|im_end|>\n<|im_start|>user\n以下是中国关于大学物理考试的单项选择题，请选出其中的正确答案。\n有一劲度系数为k的轻弹簧，竖直放置，下端悬一质量为m的小球，开始时使弹簧为原长而小球恰好与地接触，今将弹簧上端缓慢地提起，直到小球刚能脱离地面为止，在此过程中外力作功为____\nA. m^2g^2/(4k)\nB. m^2g^2/(3k)\nC. m^2g^2/(2k)\nD. 2m^2g^2/k\n答案:\n<|im_end|>\n<|im_start|>assistant\nC<|im_end|>\n<|im_start|>user\n以下是中国关于大学物理考试的单项选择题，请选出其中的正确答案。\nα粒子在加速器中被加速，当其质量为静止质量的 3 倍时，其动能为静止能量的____\nA. 2倍\nB. 3倍\nC. 4倍\nD. 5倍\n答案: \n<|im_end|>\n<|im_start|>assistant\n",
        "prediction": "D",
        "gold": "A"
    },
...

Prompt with SYSTEM role and special tokens is helpful for debugging.
According to above origin_prompt, you can easily determine whether the configuration file is correct.

To Reproduce

You can reproduce with any task using VLLMwithChatTemplate or HuggingFacewithChatTemplate