forked from ailab/InternVL2-2B
394 lines
15 KiB
Python
394 lines
15 KiB
Python
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"""
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Conversation prompt templates.
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We kindly request that you import fastchat instead of copying this file if you wish to use it.
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If you have changes in mind, please contribute back so the community can benefit collectively and continue to maintain these valuable templates.
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"""
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import dataclasses
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from enum import IntEnum, auto
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from typing import Any, Dict, List, Tuple, Union
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class SeparatorStyle(IntEnum):
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"""Separator styles."""
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ADD_COLON_SINGLE = auto()
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ADD_COLON_TWO = auto()
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ADD_COLON_SPACE_SINGLE = auto()
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NO_COLON_SINGLE = auto()
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NO_COLON_TWO = auto()
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ADD_NEW_LINE_SINGLE = auto()
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LLAMA2 = auto()
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CHATGLM = auto()
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CHATML = auto()
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CHATINTERN = auto()
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DOLLY = auto()
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RWKV = auto()
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PHOENIX = auto()
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ROBIN = auto()
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FALCON_CHAT = auto()
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CHATGLM3 = auto()
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INTERNVL_ZH = auto()
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MPT = auto()
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@dataclasses.dataclass
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class Conversation:
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"""A class that manages prompt templates and keeps all conversation history."""
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# The name of this template
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name: str
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# The template of the system prompt
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system_template: str = '{system_message}'
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# The system message
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system_message: str = ''
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# The names of two roles
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roles: Tuple[str] = ('USER', 'ASSISTANT')
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# All messages. Each item is (role, message).
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messages: List[List[str]] = ()
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# The number of few shot examples
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offset: int = 0
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# The separator style and configurations
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sep_style: SeparatorStyle = SeparatorStyle.ADD_COLON_SINGLE
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sep: str = '\n'
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sep2: str = None
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# Stop criteria (the default one is EOS token)
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stop_str: Union[str, List[str]] = None
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# Stops generation if meeting any token in this list
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stop_token_ids: List[int] = None
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def get_prompt(self) -> str:
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"""Get the prompt for generation."""
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system_prompt = self.system_template.format(system_message=self.system_message)
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if self.sep_style == SeparatorStyle.ADD_COLON_SINGLE:
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ret = system_prompt + self.sep
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for role, message in self.messages:
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if message:
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ret += role + ': ' + message + self.sep
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else:
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ret += role + ':'
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return ret
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elif self.sep_style == SeparatorStyle.ADD_COLON_TWO:
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seps = [self.sep, self.sep2]
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ret = system_prompt + seps[0]
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for i, (role, message) in enumerate(self.messages):
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if message:
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ret += role + ': ' + message + seps[i % 2]
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else:
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ret += role + ':'
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return ret
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elif self.sep_style == SeparatorStyle.ADD_COLON_SPACE_SINGLE:
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ret = system_prompt + self.sep
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for role, message in self.messages:
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if message:
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ret += role + ': ' + message + self.sep
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else:
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ret += role + ': ' # must be end with a space
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return ret
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elif self.sep_style == SeparatorStyle.ADD_NEW_LINE_SINGLE:
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ret = '' if system_prompt == '' else system_prompt + self.sep
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for role, message in self.messages:
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if message:
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ret += role + '\n' + message + self.sep
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else:
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ret += role + '\n'
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return ret
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elif self.sep_style == SeparatorStyle.NO_COLON_SINGLE:
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ret = system_prompt
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for role, message in self.messages:
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if message:
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ret += role + message + self.sep
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else:
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ret += role
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return ret
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elif self.sep_style == SeparatorStyle.NO_COLON_TWO:
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seps = [self.sep, self.sep2]
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ret = system_prompt
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for i, (role, message) in enumerate(self.messages):
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if message:
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ret += role + message + seps[i % 2]
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else:
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ret += role
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return ret
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elif self.sep_style == SeparatorStyle.RWKV:
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ret = system_prompt
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for i, (role, message) in enumerate(self.messages):
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if message:
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ret += (
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role
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+ ': '
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+ message.replace('\r\n', '\n').replace('\n\n', '\n')
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)
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ret += '\n\n'
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else:
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ret += role + ':'
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return ret
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elif self.sep_style == SeparatorStyle.LLAMA2:
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seps = [self.sep, self.sep2]
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if self.system_message:
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ret = system_prompt
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else:
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ret = '[INST] '
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for i, (role, message) in enumerate(self.messages):
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tag = self.roles[i % 2]
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if message:
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if i == 0:
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ret += message + ' '
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else:
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ret += tag + ' ' + message + seps[i % 2]
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else:
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ret += tag
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return ret
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elif self.sep_style == SeparatorStyle.CHATGLM:
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# source: https://huggingface.co/THUDM/chatglm-6b/blob/1d240ba371910e9282298d4592532d7f0f3e9f3e/modeling_chatglm.py#L1302-L1308
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# source2: https://huggingface.co/THUDM/chatglm2-6b/blob/e186c891cf64310ac66ef10a87e6635fa6c2a579/modeling_chatglm.py#L926
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round_add_n = 1 if self.name == 'chatglm2' else 0
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if system_prompt:
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ret = system_prompt + self.sep
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else:
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ret = ''
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for i, (role, message) in enumerate(self.messages):
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if i % 2 == 0:
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ret += f'[Round {i//2 + round_add_n}]{self.sep}'
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if message:
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ret += f'{role}：{message}{self.sep}'
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else:
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ret += f'{role}：'
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return ret
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elif self.sep_style == SeparatorStyle.CHATML:
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ret = '' if system_prompt == '' else system_prompt + self.sep + '\n'
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for role, message in self.messages:
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if message:
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ret += role + '\n' + message + self.sep + '\n'
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else:
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ret += role + '\n'
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return ret
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elif self.sep_style == SeparatorStyle.CHATGLM3:
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ret = ''
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if self.system_message:
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ret += system_prompt
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for role, message in self.messages:
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if message:
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ret += role + '\n' + ' ' + message
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else:
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ret += role
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return ret
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elif self.sep_style == SeparatorStyle.CHATINTERN:
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# source: https://huggingface.co/internlm/internlm-chat-7b-8k/blob/bd546fa984b4b0b86958f56bf37f94aa75ab8831/modeling_internlm.py#L771
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seps = [self.sep, self.sep2]
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ret = system_prompt
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for i, (role, message) in enumerate(self.messages):
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# if i % 2 == 0:
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# ret += "<s>"
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if message:
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ret += role + ':' + message + seps[i % 2] + '\n'
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else:
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ret += role + ':'
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return ret
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elif self.sep_style == SeparatorStyle.DOLLY:
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seps = [self.sep, self.sep2]
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ret = system_prompt
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for i, (role, message) in enumerate(self.messages):
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if message:
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ret += role + ':\n' + message + seps[i % 2]
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if i % 2 == 1:
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ret += '\n\n'
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else:
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ret += role + ':\n'
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return ret
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elif self.sep_style == SeparatorStyle.PHOENIX:
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ret = system_prompt
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for role, message in self.messages:
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if message:
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ret += role + ': ' + '<s>' + message + '</s>'
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else:
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ret += role + ': ' + '<s>'
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return ret
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elif self.sep_style == SeparatorStyle.ROBIN:
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ret = system_prompt + self.sep
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for role, message in self.messages:
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if message:
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ret += role + ':\n' + message + self.sep
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else:
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ret += role + ':\n'
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return ret
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elif self.sep_style == SeparatorStyle.FALCON_CHAT:
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ret = ''
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if self.system_message:
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ret += system_prompt + self.sep
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for role, message in self.messages:
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if message:
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ret += role + ': ' + message + self.sep
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else:
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ret += role + ':'
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return ret
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elif self.sep_style == SeparatorStyle.INTERNVL_ZH:
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seps = [self.sep, self.sep2]
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ret = self.system_message + seps[0]
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for i, (role, message) in enumerate(self.messages):
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if message:
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ret += role + ': ' + message + seps[i % 2]
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else:
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ret += role + ':'
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return ret
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elif self.sep_style == SeparatorStyle.MPT:
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ret = system_prompt + self.sep
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for role, message in self.messages:
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if message:
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if type(message) is tuple:
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message, _, _ = message
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ret += role + message + self.sep
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else:
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ret += role
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return ret
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else:
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raise ValueError(f'Invalid style: {self.sep_style}')
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def set_system_message(self, system_message: str):
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"""Set the system message."""
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self.system_message = system_message
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def append_message(self, role: str, message: str):
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"""Append a new message."""
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self.messages.append([role, message])
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def update_last_message(self, message: str):
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"""Update the last output.
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The last message is typically set to be None when constructing the prompt,
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so we need to update it in-place after getting the response from a model.
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"""
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self.messages[-1][1] = message
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def to_gradio_chatbot(self):
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"""Convert the conversation to gradio chatbot format."""
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ret = []
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for i, (role, msg) in enumerate(self.messages[self.offset :]):
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if i % 2 == 0:
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ret.append([msg, None])
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else:
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ret[-1][-1] = msg
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return ret
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def to_openai_api_messages(self):
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"""Convert the conversation to OpenAI chat completion format."""
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ret = [{'role': 'system', 'content': self.system_message}]
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for i, (_, msg) in enumerate(self.messages[self.offset :]):
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if i % 2 == 0:
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ret.append({'role': 'user', 'content': msg})
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else:
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if msg is not None:
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ret.append({'role': 'assistant', 'content': msg})
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return ret
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def copy(self):
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return Conversation(
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name=self.name,
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system_template=self.system_template,
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system_message=self.system_message,
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roles=self.roles,
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messages=[[x, y] for x, y in self.messages],
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offset=self.offset,
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sep_style=self.sep_style,
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sep=self.sep,
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sep2=self.sep2,
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stop_str=self.stop_str,
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stop_token_ids=self.stop_token_ids,
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)
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def dict(self):
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return {
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'template_name': self.name,
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'system_message': self.system_message,
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'roles': self.roles,
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'messages': self.messages,
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'offset': self.offset,
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}
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# A global registry for all conversation templates
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conv_templates: Dict[str, Conversation] = {}
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def register_conv_template(template: Conversation, override: bool = False):
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"""Register a new conversation template."""
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if not override:
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assert (
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template.name not in conv_templates
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), f'{template.name} has been registered.'
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conv_templates[template.name] = template
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def get_conv_template(name: str) -> Conversation:
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"""Get a conversation template."""
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return conv_templates[name].copy()
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# Both Hermes-2 and internlm2-chat are chatml-format conversation templates. The difference
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# is that during training, the preprocessing function for the Hermes-2 template doesn't add
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# <s> at the beginning of the tokenized sequence, while the internlm2-chat template does.
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# Therefore, they are completely equivalent during inference.
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register_conv_template(
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Conversation(
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name='Hermes-2',
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system_template='<|im_start|>system\n{system_message}',
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# note: The new system prompt was not used here to avoid changes in benchmark performance.
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# system_message='我是书生·万象，英文名是InternVL，是由上海人工智能实验室、清华大学及多家合作单位联合开发的多模态大语言模型。',
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system_message='你是由上海人工智能实验室联合商汤科技开发的书生多模态大模型，英文名叫InternVL, 是一个有用无害的人工智能助手。',
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roles=('<|im_start|>user\n', '<|im_start|>assistant\n'),
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sep_style=SeparatorStyle.MPT,
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|
|
sep='<|im_end|>',
|
|||
|
|
stop_token_ids=[
|
|||
|
|
2,
|
|||
|
|
6,
|
|||
|
|
7,
|
|||
|
|
8,
|
|||
|
|
],
|
|||
|
|
stop_str='<|endoftext|>',
|
|||
|
|
)
|
|||
|
|
)
|
|||
|
|
|
|||
|
|
|
|||
|
|
register_conv_template(
|
|||
|
|
Conversation(
|
|||
|
|
name='internlm2-chat',
|
|||
|
|
system_template='<|im_start|>system\n{system_message}',
|
|||
|
|
# note: The new system prompt was not used here to avoid changes in benchmark performance.
|
|||
|
|
# system_message='我是书生·万象，英文名是InternVL，是由上海人工智能实验室、清华大学及多家合作单位联合开发的多模态大语言模型。',
|
|||
|
|
system_message='你是由上海人工智能实验室联合商汤科技开发的书生多模态大模型，英文名叫InternVL, 是一个有用无害的人工智能助手。',
|
|||
|
|
roles=('<|im_start|>user\n', '<|im_start|>assistant\n'),
|
|||
|
|
sep_style=SeparatorStyle.MPT,
|
|||
|
|
sep='<|im_end|>',
|
|||
|
|
stop_token_ids=[
|
|||
|
|
2,
|
|||
|
|
92543,
|
|||
|
|
92542
|
|||
|
|
]
|
|||
|
|
)
|
|||
|
|
)
|
|||
|
|
|
|||
|
|
|
|||
|
|
register_conv_template(
|
|||
|
|
Conversation(
|
|||
|
|
name='phi3-chat',
|
|||
|
|
system_template='<|system|>\n{system_message}',
|
|||
|
|
# note: The new system prompt was not used here to avoid changes in benchmark performance.
|
|||
|
|
# system_message='我是书生·万象，英文名是InternVL，是由上海人工智能实验室、清华大学及多家合作单位联合开发的多模态大语言模型。',
|
|||
|
|
system_message='你是由上海人工智能实验室联合商汤科技开发的书生多模态大模型，英文名叫InternVL, 是一个有用无害的人工智能助手。',
|
|||
|
|
roles=('<|user|>\n', '<|assistant|>\n'),
|
|||
|
|
sep_style=SeparatorStyle.MPT,
|
|||
|
|
sep='<|end|>',
|
|||
|
|
stop_token_ids=[
|
|||
|
|
2,
|
|||
|
|
32000,
|
|||
|
|
32007
|
|||
|
|
]
|
|||
|
|
)
|
|||
|
|
)
|