ColossalAI/applications/ColossalEval/colossal_eval/utils/conversation.py

import dataclasses
from enum import Enum, auto
from typing import Dict, List, Optional, Tuple

from transformers import AutoTokenizer


class SeparatorStyle(Enum):
    ADD_BOS_EOS_TOKEN = auto()
    ALPACA = auto()
    PLAIN = auto()
    YAYI = auto()


@dataclasses.dataclass
class Conversation:
    system: str
    roles: List[str]
    messages: List[List[str]]
    offset: int
    sep_style: SeparatorStyle = SeparatorStyle.ADD_BOS_EOS_TOKEN
    sep: str = "</s>"

    def clear(self):
        self.messages = []

    def get_prompt(self):
        if self.sep_style == SeparatorStyle.ADD_BOS_EOS_TOKEN:
            ret = self.system
            for role, message in self.messages:
                if message:
                    ret += role + ": " + "<s>" + message + self.sep
                else:
                    ret += role + ": " + "<s>"
            return ret
        elif self.sep_style == SeparatorStyle.ALPACA:
            ret = self.system + self.sep
            for role, message in self.messages:
                if message:
                    ret += role + ":\n" + message + self.sep
                else:
                    ret += role + ":"
            return ret
        elif self.sep_style == SeparatorStyle.PLAIN:
            ret = self.system
            for role, message in self.messages:
                if message:
                    ret += message
                else:
                    ret += ""
            return ret
        elif self.sep_style == SeparatorStyle.YAYI:
            ret = self.system
            for role, message in self.messages:
                if message:
                    ret += role + ":\n" + message + self.sep
                else:
                    ret += role + ":\n"
            return ret
        else:
            raise ValueError(f"Invalid style: {self.sep_style}")

    def get_prompt_with_target(self, target):
        prompt = self.get_prompt()
        prompt_with_target = []

        # Some dataset provides multiple target answers.
        # This will make it difficult when we calculate loss.
        # We convert target into list[str] first if the question only has one target answer.
        target_answers = []
        if isinstance(target, str):
            target_answers = [target]
        else:
            target_answers = target

        for target_answer in target_answers:
            if self.sep_style == SeparatorStyle.ADD_BOS_EOS_TOKEN:
                prompt_with_target.append(prompt + target_answer)
            elif self.sep_style == SeparatorStyle.ALPACA:
                prompt_with_target.append(prompt + target_answer)
            elif self.sep_style == SeparatorStyle.PLAIN:
                prompt_with_target.append(prompt + target_answer)
            elif self.sep_style == SeparatorStyle.YAYI:
                prompt_with_target.append(prompt + target_answer)
            else:
                raise ValueError(f"Invalid style: {self.sep_style}")

        return prompt_with_target

    def save_prompt(self):
        if self.sep_style == SeparatorStyle.ADD_BOS_EOS_TOKEN:
            ret = self.system
            for role, message in self.messages:
                if message:
                    ret += role + ": " + "<s>" + message + "</s>\n"
                else:
                    ret += role + ": " + "<s>"
            return ret
        else:
            raise ValueError(f"Invalid style: {self.sep_style}")

    def append_message(self, role, message):
        self.messages.append([role, message])

    def copy(self):
        return Conversation(
            system=self.system,
            roles=self.roles,
            messages=[[x, y] for x, y in self.messages],
            offset=self.offset,
            sep_style=self.sep_style,
            sep=self.sep,
        )

    def dict(self):
        return {
            "system": self.system,
            "roles": self.roles,
            "messages": self.messages,
            "offset": self.offset,
            "sep_style": self.sep_style,
            "sep": self.sep,
        }


def get_few_shot_prefix(few_shot_data: List[str], tokenizer: Optional[AutoTokenizer], max_tokens: int) -> str:
    """
    Get few shot prefix.

    Args:
        few_shot_data: Few shot examples to generate few shot prompt prefix.
        tokenizer: tokenizer used to tokenize data.

    Returns:
        Few shot prompt prefix.
    """

    # First few shot data is something like "The following are questions about xxx".
    few_shot_prefix = few_shot_data[0] + "\n\n"

    output = None
    for i in range(1, len(few_shot_data)):
        few_shot_prefix = few_shot_prefix + few_shot_data[i] + "\n\n"

        if len(tokenizer([few_shot_prefix]).input_ids[0]) <= max_tokens:
            output = few_shot_prefix
        else:
            break

    return output if output is not None else few_shot_prefix


def get_batch_prompt(
    conv: Conversation,
    batch: List[Dict],
    few_shot_data: List[str],
    tokenizer: Optional[AutoTokenizer],
    model_max_length: Optional[int],
) -> Tuple[List[Dict], List[Dict]]:
    """
    Get batch prompt and target.

    Args:
        conv: Conversation template.
        batch: Batch data to generate prompt from.
        few_shot_data: Few shot data to generate few shot prompt prefix.
        tokenizer: tokenizer used to tokenize data.

    Returns:
        Tuple containg batch prompt and target.

    """

    batch_prompt = []
    batch_target = []

    if isinstance(batch[0], dict):
        for b in batch:
            few_shot_prefix = ""
            if few_shot_data is not None:
                assert not isinstance(b["instruction"], list), print(
                    f"When performing few-shot, {b['dataset']} shouldn't be a multiturn dataset."
                )
                # For few-shot, only need input. Otherwise use instruction (in AGIEval).
                query_text = b["input"] if b.get("input", "") != "" else b["instruction"]

                if isinstance(b["target"], str):
                    zero_shot_prompt = query_text + b["target"]
                    max_tokens = model_max_length - len(tokenizer([zero_shot_prompt]).input_ids[0])
                else:
                    raise Exception("When using few-shot, target answer should be a string.")

                few_shot_prefix = get_few_shot_prefix(few_shot_data, tokenizer, max_tokens)

                conv.append_message(conv.roles[0], few_shot_prefix + query_text)
                conv.append_message(conv.roles[1], None)
            else:
                if not isinstance(b["instruction"], list):
                    if b["instruction"] != "":
                        query_text = b["instruction"] + "\n\n" + b["input"] if b["input"] != "" else b["instruction"]
                    else:
                        query_text = b["input"]
                    conv.append_message(conv.roles[0], query_text)
                    conv.append_message(conv.roles[1], None)
                else:
                    assert len(b["instruction"]) >= len(b["output"]) + 1
                    cur_turns = len(b["output"])
                    for turn in range(cur_turns):
                        conv.append_message(conv.roles[0], b["instruction"][turn])
                        conv.append_message(conv.roles[1], b["output"][turn])
                    conv.append_message(conv.roles[0], b["instruction"][cur_turns])
                    conv.append_message(conv.roles[1], None)

            batch_prompt.append(conv.get_prompt())

            target = b["target"]
            if isinstance(b["target"], str):
                target = [target]

            batch_target.append(target)

            conv.clear()

    return batch_prompt, batch_target


conv_coati = Conversation(
    system="A chat between a curious human and an artificial intelligence assistant. "
    "The assistant gives helpful, detailed, and polite answers to the human's questions.\n\n",
    roles=("Human", "Assistant"),
    messages=[],
    offset=0,
    sep_style=SeparatorStyle.ADD_BOS_EOS_TOKEN,
    sep="</s>",
)

conv_alpaca = Conversation(
    system="Below is an instruction that describes a task. Write a response that appropriately completes the request.",
    roles=("### Instruction", "### Response"),
    messages=[],
    offset=0,
    sep_style=SeparatorStyle.ALPACA,
    sep="\n\n",
)

conv_plain = Conversation(
    system="",
    roles=("", ""),
    messages=[],
    offset=0,
    sep_style=SeparatorStyle.PLAIN,
    sep="",
)

conv_yayi = Conversation(
    system="<|System|>:\nYou are a helpful, respectful and honest assistant named YaYi developed by Beijing Wenge Technology Co.,Ltd. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.\n\n",
    roles=("<|Human|>", "<|YaYi|>"),
    messages=[],
    offset=0,
    sep_style=SeparatorStyle.YAYI,
    sep="\n\n",
)

prompt_templates = {"coati": conv_coati, "alpaca": conv_alpaca, "plain": conv_plain, "yayi": conv_yayi}