src/axolotl/prompters.py

import copy
import dataclasses
from enum import auto, Enum
from typing import List, Tuple, Any, Union

IGNORE_TOKEN_ID = -100


class AlpacaPrompter:
    prompt_input = "Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Input:\n{input}\n\n### Response:\n"
    prompt_no_input = "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Response:\n"
    response_split = "### Response:"

    def build_prompt(
        self,
        instruction: str,
        input: Union[None, str] = None,
        output: Union[None, str] = None,
    ) -> str:
        # returns the full prompt from instruction and optional input
        # if a label (=response, =output) is provided, it's also appended.
        if input:
            res = self.prompt_input.format(instruction=instruction, input=input)
        else:
            res = self.prompt_no_input.format(instruction=instruction)
        if output:
            res = f"{res}{output}"
        return res

    def get_response(self, output: str) -> str:
        return output.split(self.response_split)[1].strip()


class JeopardyPrompter(AlpacaPrompter):
    prompt_input = "Below is a Jeopardy clue paired with input providing the category of the clue. Write a concise response that best answers tbe clue given the category.\n\n### Instruction:\n{instruction}\n\n### Input:\n{input}\n\n### Response:\n"


class CompletionPrompter(AlpacaPrompter):
    def build_prompt(
        self,
        instruction: str
    ) -> str:
        return instruction

    def get_response(self, output: str) -> str:
        return output.strip()


class GPTeacherPrompter(AlpacaPrompter):
    ...


class NomicGPT4AllPrompter(AlpacaPrompter):
    ...


class ReflectAlpacaPrompter:
    prompt_input = "Below is an instruction that describes a task, paired with an input that provides further context. You, the Assistant, should generate a response as if it were an abstract for an academic or technical paper on the query along with a methodology. Then generate an Agent Reflection where you create a long form response as if from subject matter expert, be verbose, diligent, and creative in your application of knowledge, apply it through the lens of the response generated by the assistant. Look for flawed reasoning, faulty logic, or other mistakes in the method. Finally, generate a final response and method for the user with the Assistant abstract and Reflection analysis as augmentations to the generation\n\n### Instruction:\n{instruction}\n\n### Input:\n{input}\n\n### Response:\n"
    prompt_no_input = "Below is an instruction that describes a task. You, the Assistant, should generate a response as if it were an abstract for an academic or technical paper on the query along with a methodology. Then generate an Agent Reflection where you create a long form response as if from subject matter expert, be verbose, diligent, and creative in your application of knowledge, apply it through the lens of the response generated by the assistant. Look for flawed reasoning, faulty logic, or other mistakes in the method. Finally, generate a final response and method for the user with the Assistant abstract and Reflection analysis as augmentations to the generation\n\n### Instruction:\n{instruction}\n\n### Response:\n"
    agent_label = "{output}\n\n### Agent Reflection:\n{reflection}\n\n### Final Response:\n{corrected}"
    response_split = "### Response:"

    def build_prompt(
        self,
        instruction: str,
        input: Union[None, str] = None,
        output: Union[None, str] = None,
        reflection: Union[None, str] = None,
        corrected: Union[None, str] = None,
    ) -> str:
        # returns the full prompt from instruction and optional input
        # if a label (=response, =output) is provided, it's also appended.
        if input:
            res = self.prompt_input.format(instruction=instruction, input=input)
        else:
            res = self.prompt_no_input.format(instruction=instruction)
        if output and reflection and corrected:
            label = self.agent_label.format(output=output, reflection=reflection, corrected=corrected)
            res = f"{res}{label}"
        return res

    def get_response(self, output: str) -> str:
        return output.split(self.response_split)[1].strip()


class SeparatorStyle(Enum):
    """Different separator style."""

    SINGLE = auto()
    TWO = auto()
    DOLLY = auto()


# TODO clean this 💩 up
@dataclasses.dataclass
class Conversation:
    """A class that keeps all conversation history."""

    system: str
    roles: List[str]
    messages: List[List[str]]
    offset: int
    sep_style: SeparatorStyle = SeparatorStyle.SINGLE
    sep: str = "###"
    sep2: str = None

    def get_prompt(self):
        seps = [self.sep, self.sep2]
        ret = self.system + seps[0]
        for i, (role, message) in enumerate(self.messages):
            if message:
                ret += role + ": " + message + seps[i % 2]
            else:
                ret += role + ":"
        return ret

    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,
            sep2=self.sep2,
        )

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


conv_vicuna_v1_1 = Conversation(
    system="A chat between a curious user and an artificial intelligence assistant. "
    "The assistant gives helpful, detailed, and polite answers to the user's questions.",
    roles=["USER", "ASSISTANT"],
    messages=[],
    offset=0,
    sep_style=SeparatorStyle.TWO,
    sep=" ",
    sep2="</s>",
)


class ShareGPTPrompter:
    def build_prompt(self, source, tokenizer, sequence_len=2048):
        # ignore the system prompt if provided
        if source[0]["from"] == "system":
            source.pop(0)

        if len(source) < 2:
            # If there isn't a back and forth conversation, ignore it
            # also happens on the data splitting leaving empty conversations
            raise IndexError

        conv = conv_vicuna_v1_1.copy()
        roles = {"human": conv.roles[0], "gpt": conv.roles[1]}

        try:
            # Apply prompt templates
            if (
                source[0]["from"] not in roles
                or roles[source[0]["from"]] != conv.roles[0]
            ):
                # Skip the first one if it is not from human
                source = source[1:]
        except IndexError as e:
            # sometimes there is a bing or system chat
            raise e

        conv.messages = []
        for j, sentence in enumerate(source):
            role = roles[sentence["from"]]
            assert role == conv.roles[j % 2]
            conv.append_message(role, sentence["value"])
        # TODO, this concatenates everything, but doesn't seem to properly add the eos_token_id, as the eos_token gets split up
        conversation = conv.get_prompt()

        # Tokenize conversations
        tokenized_result = tokenizer(
            conversation,
            truncation=True,
            max_length=sequence_len,  # FIXME
            padding=False,
            return_tensors=None,
        )
        target = copy.deepcopy(tokenized_result["input_ids"])

        # Mask targets
        sep = conv.sep + conv.roles[1] + ": "

        rounds = conversation.split(conv.sep2)
        rounds = [r + conv.sep2 for r in rounds]
        cur_len = 1
        target[0] = IGNORE_TOKEN_ID  # mask out the bos
        for i, rou in enumerate(rounds):
            if rou == "":
                break

            parts = rou.split(sep)
            if len(parts) != 2:
                break
            parts[0] += sep
            round_len = len(tokenizer(rou)["input_ids"]) - 1  # -1 ignores the bos_token generated for this
            # we have to strip the initial part, any dangling whitespace creates an additional ghost token
            instruction_len = len(tokenizer(parts[0].strip())["input_ids"]) - 1  # -1 ignores the bos_token generated for this
            target[cur_len : cur_len + instruction_len] = [
                IGNORE_TOKEN_ID
            ] * instruction_len

            cur_len += round_len
            if cur_len >= sequence_len:
                break

        # Fix: Truncate the target to have the same length as input_ids
        target = target[:len(tokenized_result["input_ids"])]
        # target[cur_len:] = [IGNORE_TOKEN_ID] * (len(target) - cur_len)

        attention_mask = [
            1 if x != tokenizer.pad_token_id else 0
            for x in tokenized_result["input_ids"]
        ]

        # TODO truncate len to sequence_len
        return dict(
            input_ids=tokenized_result["input_ids"],
            labels=target,
            attention_mask=attention_mask,
        )