InternVL2-40B / conversation.py
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"""
Conversation prompt templates.
We kindly request that you import fastchat instead of copying this file if you wish to use it.
If you have changes in mind, please contribute back so the community can benefit collectively and continue to maintain these valuable templates.
"""
import dataclasses
from enum import IntEnum, auto
from typing import Any, Dict, List, Tuple, Union
class SeparatorStyle(IntEnum):
"""Separator styles."""
ADD_COLON_SINGLE = auto()
ADD_COLON_TWO = auto()
ADD_COLON_SPACE_SINGLE = auto()
NO_COLON_SINGLE = auto()
NO_COLON_TWO = auto()
ADD_NEW_LINE_SINGLE = auto()
LLAMA2 = auto()
CHATGLM = auto()
CHATML = auto()
CHATINTERN = auto()
DOLLY = auto()
RWKV = auto()
PHOENIX = auto()
ROBIN = auto()
FALCON_CHAT = auto()
CHATGLM3 = auto()
INTERNVL_ZH = auto()
MPT = auto()
@dataclasses.dataclass
class Conversation:
"""A class that manages prompt templates and keeps all conversation history."""
# The name of this template
name: str
# The template of the system prompt
system_template: str = '{system_message}'
# The system message
system_message: str = ''
# The names of two roles
roles: Tuple[str] = ('USER', 'ASSISTANT')
# All messages. Each item is (role, message).
messages: List[List[str]] = ()
# The number of few shot examples
offset: int = 0
# The separator style and configurations
sep_style: SeparatorStyle = SeparatorStyle.ADD_COLON_SINGLE
sep: str = '\n'
sep2: str = None
# Stop criteria (the default one is EOS token)
stop_str: Union[str, List[str]] = None
# Stops generation if meeting any token in this list
stop_token_ids: List[int] = None
def get_prompt(self) -> str:
"""Get the prompt for generation."""
system_prompt = self.system_template.format(system_message=self.system_message)
if self.sep_style == SeparatorStyle.ADD_COLON_SINGLE:
ret = system_prompt + self.sep
for role, message in self.messages:
if message:
ret += role + ': ' + message + self.sep
else:
ret += role + ':'
return ret
elif self.sep_style == SeparatorStyle.ADD_COLON_TWO:
seps = [self.sep, self.sep2]
ret = system_prompt + seps[0]
for i, (role, message) in enumerate(self.messages):
if message:
ret += role + ': ' + message + seps[i % 2]
else:
ret += role + ':'
return ret
elif self.sep_style == SeparatorStyle.ADD_COLON_SPACE_SINGLE:
ret = system_prompt + self.sep
for role, message in self.messages:
if message:
ret += role + ': ' + message + self.sep
else:
ret += role + ': ' # must be end with a space
return ret
elif self.sep_style == SeparatorStyle.ADD_NEW_LINE_SINGLE:
ret = '' if system_prompt == '' else system_prompt + self.sep
for role, message in self.messages:
if message:
ret += role + '\n' + message + self.sep
else:
ret += role + '\n'
return ret
elif self.sep_style == SeparatorStyle.NO_COLON_SINGLE:
ret = system_prompt
for role, message in self.messages:
if message:
ret += role + message + self.sep
else:
ret += role
return ret
elif self.sep_style == SeparatorStyle.NO_COLON_TWO:
seps = [self.sep, self.sep2]
ret = system_prompt
for i, (role, message) in enumerate(self.messages):
if message:
ret += role + message + seps[i % 2]
else:
ret += role
return ret
elif self.sep_style == SeparatorStyle.RWKV:
ret = system_prompt
for i, (role, message) in enumerate(self.messages):
if message:
ret += (
role
+ ': '
+ message.replace('\r\n', '\n').replace('\n\n', '\n')
)
ret += '\n\n'
else:
ret += role + ':'
return ret
elif self.sep_style == SeparatorStyle.LLAMA2:
seps = [self.sep, self.sep2]
if self.system_message:
ret = system_prompt
else:
ret = '[INST] '
for i, (role, message) in enumerate(self.messages):
tag = self.roles[i % 2]
if message:
if i == 0:
ret += message + ' '
else:
ret += tag + ' ' + message + seps[i % 2]
else:
ret += tag
return ret
elif self.sep_style == SeparatorStyle.CHATGLM:
# source: https://huggingface.co/THUDM/chatglm-6b/blob/1d240ba371910e9282298d4592532d7f0f3e9f3e/modeling_chatglm.py#L1302-L1308
# source2: https://huggingface.co/THUDM/chatglm2-6b/blob/e186c891cf64310ac66ef10a87e6635fa6c2a579/modeling_chatglm.py#L926
round_add_n = 1 if self.name == 'chatglm2' else 0
if system_prompt:
ret = system_prompt + self.sep
else:
ret = ''
for i, (role, message) in enumerate(self.messages):
if i % 2 == 0:
ret += f'[Round {i//2 + round_add_n}]{self.sep}'
if message:
ret += f'{role}{message}{self.sep}'
else:
ret += f'{role}:'
return ret
elif self.sep_style == SeparatorStyle.CHATML:
ret = '' if system_prompt == '' else system_prompt + self.sep + '\n'
for role, message in self.messages:
if message:
ret += role + '\n' + message + self.sep + '\n'
else:
ret += role + '\n'
return ret
elif self.sep_style == SeparatorStyle.CHATGLM3:
ret = ''
if self.system_message:
ret += system_prompt
for role, message in self.messages:
if message:
ret += role + '\n' + ' ' + message
else:
ret += role
return ret
elif self.sep_style == SeparatorStyle.CHATINTERN:
# source: https://huggingface.co/internlm/internlm-chat-7b-8k/blob/bd546fa984b4b0b86958f56bf37f94aa75ab8831/modeling_internlm.py#L771
seps = [self.sep, self.sep2]
ret = system_prompt
for i, (role, message) in enumerate(self.messages):
# if i % 2 == 0:
# ret += "<s>"
if message:
ret += role + ':' + message + seps[i % 2] + '\n'
else:
ret += role + ':'
return ret
elif self.sep_style == SeparatorStyle.DOLLY:
seps = [self.sep, self.sep2]
ret = system_prompt
for i, (role, message) in enumerate(self.messages):
if message:
ret += role + ':\n' + message + seps[i % 2]
if i % 2 == 1:
ret += '\n\n'
else:
ret += role + ':\n'
return ret
elif self.sep_style == SeparatorStyle.PHOENIX:
ret = system_prompt
for role, message in self.messages:
if message:
ret += role + ': ' + '<s>' + message + '</s>'
else:
ret += role + ': ' + '<s>'
return ret
elif self.sep_style == SeparatorStyle.ROBIN:
ret = system_prompt + self.sep
for role, message in self.messages:
if message:
ret += role + ':\n' + message + self.sep
else:
ret += role + ':\n'
return ret
elif self.sep_style == SeparatorStyle.FALCON_CHAT:
ret = ''
if self.system_message:
ret += system_prompt + self.sep
for role, message in self.messages:
if message:
ret += role + ': ' + message + self.sep
else:
ret += role + ':'
return ret
elif self.sep_style == SeparatorStyle.INTERNVL_ZH:
seps = [self.sep, self.sep2]
ret = self.system_message + seps[0]
for i, (role, message) in enumerate(self.messages):
if message:
ret += role + ': ' + message + seps[i % 2]
else:
ret += role + ':'
return ret
elif self.sep_style == SeparatorStyle.MPT:
ret = system_prompt + self.sep
for role, message in self.messages:
if message:
if type(message) is tuple:
message, _, _ = message
ret += role + message + self.sep
else:
ret += role
return ret
else:
raise ValueError(f'Invalid style: {self.sep_style}')
def set_system_message(self, system_message: str):
"""Set the system message."""
self.system_message = system_message
def append_message(self, role: str, message: str):
"""Append a new message."""
self.messages.append([role, message])
def update_last_message(self, message: str):
"""Update the last output.
The last message is typically set to be None when constructing the prompt,
so we need to update it in-place after getting the response from a model.
"""
self.messages[-1][1] = message
def to_gradio_chatbot(self):
"""Convert the conversation to gradio chatbot format."""
ret = []
for i, (role, msg) in enumerate(self.messages[self.offset :]):
if i % 2 == 0:
ret.append([msg, None])
else:
ret[-1][-1] = msg
return ret
def to_openai_api_messages(self):
"""Convert the conversation to OpenAI chat completion format."""
ret = [{'role': 'system', 'content': self.system_message}]
for i, (_, msg) in enumerate(self.messages[self.offset :]):
if i % 2 == 0:
ret.append({'role': 'user', 'content': msg})
else:
if msg is not None:
ret.append({'role': 'assistant', 'content': msg})
return ret
def copy(self):
return Conversation(
name=self.name,
system_template=self.system_template,
system_message=self.system_message,
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,
stop_str=self.stop_str,
stop_token_ids=self.stop_token_ids,
)
def dict(self):
return {
'template_name': self.name,
'system_message': self.system_message,
'roles': self.roles,
'messages': self.messages,
'offset': self.offset,
}
# A global registry for all conversation templates
conv_templates: Dict[str, Conversation] = {}
def register_conv_template(template: Conversation, override: bool = False):
"""Register a new conversation template."""
if not override:
assert (
template.name not in conv_templates
), f'{template.name} has been registered.'
conv_templates[template.name] = template
def get_conv_template(name: str) -> Conversation:
"""Get a conversation template."""
return conv_templates[name].copy()
# Both Hermes-2 and internlm2-chat are chatml-format conversation templates. The difference
# is that during training, the preprocessing function for the Hermes-2 template doesn't add
# <s> at the beginning of the tokenized sequence, while the internlm2-chat template does.
# Therefore, they are completely equivalent during inference.
register_conv_template(
Conversation(
name='Hermes-2',
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,
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
]
)
)