lmdeploy.model 源代码
# Copyright (c) OpenMMLab. All rights reserved.
import dataclasses
import os
from abc import abstractmethod
from typing import List, Literal, Optional
from fuzzywuzzy import fuzz, process
from mmengine import Registry
MODELS = Registry('model', locations=['lmdeploy.model'])
@dataclasses.dataclass
class SamplingParam:
top_p: float = 0.8
top_k: float = None
temperature: float = 0.8
repetition_penalty: float = 1.0
[文档]@dataclasses.dataclass
class ChatTemplateConfig:
"""Parameters for chat template.
Args:
model_name (str): the name of the deployed model. Determine which chat template will be applied.
All the chat template names: `lmdeploy list`
system (str | None): begin of the system prompt
meta_instruction (str | None): system prompt
eosys (str | None): end of the system prompt
user (str | None): begin of the user prompt
eoh (str | None): end of the user prompt
assistant (str | None): begin of the assistant prompt
eoa (str | None): end of the assistant prompt
capability: ('completion' | 'infilling' | 'chat' | 'python') = None
""" # noqa: E501
model_name: str
system: Optional[str] = None
meta_instruction: Optional[str] = None
eosys: Optional[str] = None
user: Optional[str] = None
eoh: Optional[str] = None
assistant: Optional[str] = None
eoa: Optional[str] = None
capability: Optional[Literal['completion', 'infilling', 'chat',
'python']] = None
@property
def chat_template(self):
attrs = {
key: value
for key, value in dataclasses.asdict(self).items()
if value is not None
}
model: BaseModel = MODELS.get(self.model_name).from_config(**attrs)
return model
@MODELS.register_module(name='internlm')
@MODELS.register_module(name='llama')
@MODELS.register_module(name='base')
class BaseModel:
"""Base model."""
def __init__(self,
session_len=2048,
top_p=0.8,
top_k=None,
temperature=0.8,
repetition_penalty=1.0,
capability='chat',
stop_words=None,
**kwargs):
self.session_len = session_len
self.top_p = top_p
self.top_k = top_k
self.temperature = temperature
self.repetition_penalty = repetition_penalty
self.stop_words = stop_words
self.capability = capability
@classmethod
def from_config(cls, **kwargs):
"""Build model from ModelConfig parameters."""
return cls(**kwargs)
def get_prompt(self, prompt, sequence_start=True):
"""Return the prompt that is concatenated with other elements in the
chat template.
Args:
prompt (str): user's input prompt
sequence_start (bool): indicator for the first round chat of a
session sequence
Returns:
str: the concatenated prompt
"""
if self.capability == 'completion':
return prompt
else:
return self.decorate_prompt(prompt, sequence_start)
@abstractmethod
def decorate_prompt(self, prompt, sequence_start):
return prompt
@staticmethod
def _translate_messages(messages: List):
"""Translate messages into system, user speaking list, assistant
speaking list.
Args:
messages (List): chat history
Returns:
Turple: consists of system (str), users (List[str]),
assistants (List[str])
"""
system = None
users = []
assistants = []
assert isinstance(messages, List)
for message in messages:
msg_role = message['role']
if msg_role == 'system':
system = message['content']
elif msg_role == 'user':
users.append(message['content'])
elif msg_role == 'assistant':
assistants.append(message['content'])
else:
raise ValueError(f'Unknown role: {msg_role}')
assistants.append(None)
return system, users, assistants
@abstractmethod
def messages2prompt(self, messages, sequence_start=True):
"""Return the prompt that is concatenated with other elements in the
chat template. When messages arg is a string, return
self.get_prompt(messages). When messages arg is a chat history, return
translated prompt from chat history.
Args:
messages (str | List): user's input prompt
Returns:
str: the concatenated prompt
"""
if isinstance(messages, str):
return self.get_prompt(messages)
# chat history processing in derived classes
@property
def sampling_param(self):
return SamplingParam(top_p=self.top_p,
top_k=self.top_k,
temperature=self.temperature,
repetition_penalty=self.repetition_penalty)
@MODELS.register_module(name='wizardlm')
@MODELS.register_module(name='vicuna')
class Vicuna(BaseModel):
"""Chat template of vicuna model."""
def __init__(
self,
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. """, # noqa: E501
user='USER',
assistant='ASSISTANT',
**kwargs):
super().__init__(**kwargs)
self.system = system
self.user = user
self.assistant = assistant
@classmethod
def from_config(cls, **kwargs):
"""Build model from ModelConfig parameters."""
mapping = {'meta_instruction': 'system'}
new_kwargs = {
mapping.get(key, key): value
for key, value in kwargs.items()
}
return cls(**new_kwargs)
def decorate_prompt(self, prompt, sequence_start=True):
"""Return the prompt that is concatenated with other elements in the
chat template.
Args:
prompt (str): user's input prompt
sequence_start (bool): indicator for the first round chat of a
session sequence
Returns:
str: the concatenated prompt
"""
assert self.capability == 'chat', \
f'{type(self).__name__} has no capability of {self.capability}'
if sequence_start:
return f'{self.system} {self.user}: {prompt} {self.assistant}: '
else:
return f'</s>{self.user}: {prompt} {self.assistant}: '
def messages2prompt(self, messages, sequence_start=True):
"""Return the prompt that is concatenated with other elements in the
chat template.
Args:
messages (str | List): user's input prompt
Returns:
str: the concatenated prompt
"""
if isinstance(messages, str):
return self.get_prompt(messages, sequence_start)
system, users, assistants = self._translate_messages(messages)
system = self.system if not system else system
ret = system + ' '
for user, assistant in zip(users, assistants):
if assistant:
ret += f'{self.user}: {user} {self.assistant}: {assistant}</s>'
else:
ret += f'{self.user}: {user} {self.assistant}: '
return ret
@MODELS.register_module(name='internlm-chat')
@MODELS.register_module(name='internlm-chat-7b')
class InternLMChat7B(BaseModel):
"""Chat template of InternLM model."""
def __init__(
self,
system='<|System|>:',
meta_instruction="""You are an AI assistant whose name is InternLM (书生·浦语).
- InternLM (书生·浦语) is a conversational language model that is developed by Shanghai AI Laboratory (上海人工智能实验室). It is designed to be helpful, honest, and harmless.
- InternLM (书生·浦语) can understand and communicate fluently in the language chosen by the user such as English and 中文.
""", # noqa: E501
user='<|User|>:',
eoh='\n',
eoa='<eoa>\n',
eosys='\n',
assistant='<|Bot|>:',
stop_words=['<eoa>'],
**kwargs):
super().__init__(**kwargs)
self.system = system
self.meta_instruction = meta_instruction
self.user = user
self.eoh = eoh
self.eoa = eoa
self.eosys = eosys
self.assistant = assistant
self.stop_words = stop_words
def decorate_prompt(self, prompt, sequence_start=True):
"""Return the prompt that is concatenated with other elements in the
chat template.
Args:
prompt (str): user's input prompt
sequence_start (bool): indicator for the first round chat of a
session sequence
Returns:
str: the concatenated prompt
"""
assert self.capability == 'chat', \
f'{type(self).__name__} has no capability of {self.capability}'
if sequence_start:
ret = ''
if self.meta_instruction:
ret += f'{self.system}{self.meta_instruction}{self.eosys}'
ret += f'{self.user}{prompt}{self.eoh}' \
f'{self.assistant}'
return ret
else:
return f'\n{self.user}{prompt}{self.eoh}' \
f'{self.assistant}'
def messages2prompt(self, messages, sequence_start=True):
"""Return the prompt that is concatenated with other elements in the
chat template.
Args:
messages (str | List): user's input prompt
Returns:
str: the concatenated prompt
"""
if isinstance(messages, str):
return self.get_prompt(messages, sequence_start)
eox_map = dict(user=self.eoh, assistant=self.eoa, system=self.eosys)
ret = ''
if self.meta_instruction:
ret += f'{self.system}{self.meta_instruction}{self.eosys}'
for message in messages:
role = message['role']
content = message['content']
ret += f'{eval(f"self.{role}")}{content}{eox_map[role]}'
ret += f'{self.assistant}'
return ret
@MODELS.register_module(name='internlm-chat-20b')
@MODELS.register_module(name='internlm-chat-7b-8k')
class InternLMChat7B8K(InternLMChat7B):
"""Chat template and generation parameters of InternLM-Chat-7B-8K and
InternLM-Chat-20B models."""
def __init__(self, session_len=8192, **kwargs):
super(InternLMChat7B8K, self).__init__(**kwargs)
self.session_len = session_len
@MODELS.register_module(name='internlm-20b')
class InternLMBaseModel20B(BaseModel):
"""Generation parameters of InternLM-20B-Base model."""
def __init__(self, session_len=4096, capability='completion', **kwargs):
super().__init__(session_len=session_len,
capability=capability,
**kwargs)
@MODELS.register_module(
name=['internlm2', 'internlm2-1_8b', 'internlm2-7b', 'internlm2-20b'])
class InternLM2BaseModel7B(BaseModel):
"""Generation parameters of InternLM2-7B-Base model."""
def __init__(self, session_len=32768, capability='completion', **kwargs):
super().__init__(session_len=session_len,
capability=capability,
**kwargs)
@MODELS.register_module(name=[
'internlm2-chat', 'internlm2-chat-1_8b', 'internlm2-chat-7b',
'internlm2-chat-20b'
])
class InternLM2Chat7B(InternLMChat7B):
"""Chat template and generation parameters of InternLM2-Chat-7B."""
def __init__(self,
session_len=32768,
system='<|im_start|>system\n',
user='<|im_start|>user\n',
assistant='<|im_start|>assistant\n',
eosys='<|im_end|>\n',
eoh='<|im_end|>\n',
eoa='<|im_end|>\n',
stop_words=['<|im_end|>', '<|action_end|>'],
**kwargs):
super(InternLM2Chat7B, self).__init__(session_len=session_len,
system=system,
user=user,
assistant=assistant,
eosys=eosys,
eoh=eoh,
eoa=eoa,
stop_words=stop_words,
**kwargs)
@MODELS.register_module(name='baichuan-7b')
class Baichuan7B(BaseModel):
"""Generation parameters of Baichuan-7B base model."""
def __init__(self, repetition_penalty=1.1, **kwargs):
super().__init__(**kwargs)
self.repetition_penalty = repetition_penalty
@MODELS.register_module(name='baichuan2-7b')
class Baichuan2_7B(BaseModel):
"""Chat template and generation parameters of Baichuan2-7B-Base and
Baichuan2-7B-Chat models."""
def __init__(self,
temperature=0.3,
top_k=5,
top_p=0.85,
repetition_penalty=1.05,
**kwargs):
super().__init__(temperature=temperature,
top_k=top_k,
top_p=top_p,
repetition_penalty=repetition_penalty,
**kwargs)
self.user_token = '<reserved_106>' # id = 195
self.assistant_token = '<reserved_107>' # id = 196
def decorate_prompt(self, prompt, sequence_start=True):
"""Return the prompt that is concatenated with other elements in the
chat template.
Args:
prompt (str): user's input prompt
sequence_start (bool): indicator for the first round chat of a
session sequence
Returns:
str: the concatenated prompt
"""
assert self.capability == 'chat', \
f'{type(self).__name__} has no capability of {self.capability}'
return f'{self.user_token}{prompt}{self.assistant_token}'
def messages2prompt(self, messages, sequence_start=True):
"""Return the prompt that is concatenated with other elements in the
chat template.
Args:
messages (str | List): user's input prompt
Returns:
str: the concatenated prompt
"""
if isinstance(messages, str):
return self.get_prompt(messages, sequence_start)
system, users, assistants = self._translate_messages(messages)
ret = ''
for user, assistant in zip(users, assistants):
ret += f'{self.user_token}{user}{self.assistant_token}'
if assistant:
ret += f'{assistant}'
return ret
@MODELS.register_module(name='puyu')
class Puyu(BaseModel):
"""Chat template of puyu model.This is only for internal usage in Shanghai
AI Laboratory."""
def __init__(self,
meta_instruction='',
system='',
eosys='',
user='',
eoh='',
assistant='',
eoa='',
stop_words=None,
**kwargs):
super().__init__(**kwargs)
self.meta_instruction = meta_instruction
self.system = system
self.user = user
self.assistant = assistant
self.stop_words = stop_words
self.eosys = eosys
self.eoh = eoh
self.eoa = eoa
def decorate_prompt(self, prompt, sequence_start=True):
assert self.capability == 'chat', \
f'{type(self).__name__} has no capability of {self.capability}'
if sequence_start:
return f'{self.system}{self.meta_instruction}{self.eosys}' \
f'{self.user}{prompt}{self.eoh}' \
f'{self.assistant}'
else:
return f'{self.eoa}{self.user}{prompt}{self.eoh}{self.assistant}'
def messages2prompt(self, messages, sequence_start=True):
"""Return the prompt that is concatenated with other elements in the
chat template.
Args:
messages (str | List): user's input prompt
sequence_start (bool): flag to start the sequence
Returns:
str: the concatenated prompt
"""
if isinstance(messages, str):
return self.get_prompt(messages, sequence_start)
eox_map = dict(user=self.eoh, assistant=self.eoa, system=self.eosys)
ret = ''
if self.meta_instruction:
ret += f'{self.system}{self.meta_instruction}{self.eosys}'
for message in messages:
role = message['role']
content = message['content']
ret += f'{eval(f"self.{role}")}{content}{eox_map[role]}'
ret += f'{self.assistant}'
return ret
@MODELS.register_module(name=['llama2', 'llama-2', 'llama-2-chat'])
class Llama2(BaseModel):
"""Chat template of LLaMA2 model."""
def __init__(
self,
b_inst='[INST]',
e_inst='[/INST]',
b_sys='<<SYS>>\n',
e_sys='\n<</SYS>>\n\n',
system="""\
You are a helpful, respectful and honest assistant. 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.
If 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.""", # noqa: E501
session_len=4096,
**kwargs):
super().__init__(**kwargs)
self.b_inst = b_inst
self.e_inst = e_inst
self.b_sys = b_sys
self.e_sys = e_sys
self.default_sys_prompt = system
self.session_len = session_len
@classmethod
def from_config(cls, **kwargs):
"""Build model from ModelConfig parameters."""
mapping = {
'system': 'b_sys',
'eosys': 'e_sys',
'meta_instruction': 'system'
}
new_kwargs = {
mapping.get(key, key): value
for key, value in kwargs.items()
}
return cls(**new_kwargs)
def decorate_prompt(self, prompt, sequence_start=True):
"""Return the prompt that is concatenated with other elements in the
chat template.
Args:
prompt (str): user's input prompt
sequence_start (bool): indicator for the first round chat of a
session sequence
Returns:
str: the concatenated prompt
"""
assert self.capability == 'chat', \
f'{type(self).__name__} has no capability of {self.capability}'
if sequence_start:
return f'{self.b_inst} ' \
f'{self.b_sys} {self.default_sys_prompt} {self.e_sys}' \
f'{prompt} {self.e_inst} '
return f'{self.b_inst} {prompt} {self.e_inst} '
def messages2prompt(self, messages, sequence_start=True):
"""Return the prompt that is concatenated with other elements in the
chat template.
Args:
messages (str | List): user's input prompt
Returns:
str: the concatenated prompt
"""
if isinstance(messages, str):
return self.get_prompt(messages, sequence_start)
system, users, assistants = self._translate_messages(messages)
system = self.default_sys_prompt if not system else system
ret = f'{self.b_inst} {self.b_sys} {system} {self.e_sys}'
for i, (user, assistant) in enumerate(zip(users, assistants)):
if i != 0:
ret += f'{self.b_inst} '
if assistant:
ret += f'{user} {self.e_inst} {assistant}'
else:
ret += f'{user} {self.e_inst} '
return ret
@MODELS.register_module(name='qwen-14b')
@MODELS.register_module(name='qwen-7b')
class Qwen7BChat(BaseModel):
"""Chat template for Qwen-7B-Chat."""
def __init__(self,
session_len=8192,
top_p=0.5,
top_k=40,
temperature=1.0,
im_start='<|im_start|>',
im_end='<|im_end|>',
system='You are a helpful assistant.',
stop_words=['<|im_end|>'],
**kwargs):
super().__init__(**kwargs)
self.session_len = session_len
self.top_p = top_p
self.top_k = top_k
self.temperature = temperature
self.im_start = im_start
self.im_end = im_end
self.system = system
self.stop_words = stop_words
@classmethod
def from_config(cls, **kwargs):
"""Build model from ModelConfig parameters."""
# TODO system, eosys, user, eoh, assistant, eoa.
mapping = {'meta_instruction': 'system'}
new_kwargs = {
mapping.get(key, key): value
for key, value in kwargs.items()
}
return cls(**new_kwargs)
def decorate_prompt(self, prompt, sequence_start=True):
assert self.capability == 'chat', \
f'{type(self).__name__} has no capability of {self.capability}'
if sequence_start:
return f'{self.im_start}system\n{self.system}{self.im_end}' \
f'\n{self.im_start}user\n{prompt}{self.im_end}' \
f'\n{self.im_start}assistant\n'
return f'\n{self.im_start}user\n{prompt}{self.im_end}' \
f'\n{self.im_start}assistant\n'
def messages2prompt(self, messages, sequence_start=True):
"""Return the prompt that is concatenated with other elements in the
chat template.
Args:
messages (str | List): user's input prompt
Returns:
str: the concatenated prompt
"""
if isinstance(messages, str):
return self.get_prompt(messages, sequence_start)
system, users, assistants = self._translate_messages(messages)
system = self.system if not system else system
ret = f'{self.im_start}system\n{system}{self.im_end}'
for user, assistant in zip(users, assistants):
if assistant:
ret += f'\n{self.im_start}user\n{user}{self.im_end}' \
f'\n{self.im_start}assistant\n{assistant}'
else:
ret += f'\n{self.im_start}user\n{user}{self.im_end}' \
f'\n{self.im_start}assistant\n'
return ret
@MODELS.register_module(name='codellama')
class CodeLlama(Llama2):
def __init__(self,
system='',
session_len=4096,
suffix_first=False,
stop_words=None,
**kwargs):
super().__init__(**kwargs)
caps = ['completion', 'infilling', 'chat', 'python']
assert self.capability in caps, \
f'{self.capability} is not supported. ' \
f'The supported capabilities are: {caps}'
self.default_sys_prompt = system
self.session_len = session_len
self.suffix_first = suffix_first
self.stop_words = stop_words
# The following sampling parameters refers to https://github.com/facebookresearch/codellama # noqa: E501
if self.capability == 'completion' or self.capability == 'python':
self.top_p = kwargs.get('top_p', 0.9)
self.temperature = kwargs.get('temperature', 0.2)
if self.capability == 'chat':
self.top_p = kwargs.get('top_p', 0.95)
self.temperature = kwargs.get('temperature', 0.2)
elif self.capability == 'infilling':
self.top_p = kwargs.get('top_p', 0.9)
self.temperature = kwargs.get('temperature', 0.0)
if self.stop_words is None:
self.stop_words = ['<EOT>']
def decorate_prompt(self, prompt, sequence_start=True):
if self.capability == 'infilling':
return self._infill_prompt(prompt)
elif self.capability == 'chat':
return self._get_prompt(prompt, sequence_start)
else: # python speicalist
return prompt
def _infill_prompt(self, prompt):
prefix, suffix = prompt.split('<FILL>')
if self.suffix_first:
# format as "<PRE> <SUF>{suf} <MID> {pre}"
prompt = f'<PRE> <SUF>{suffix} <MID> {prefix}'
else:
# format as "<PRE> {pre} <SUF>{suf} <MID>"
prompt = f'<PRE> {prefix} <SUF>{suffix} <MID>'
return prompt
def _get_prompt(self, prompt, sequence_start):
prompt = prompt.strip()
if sequence_start:
return f'{self.b_inst} ' \
f'{self.b_sys}{self.default_sys_prompt}{self.e_sys}' \
f'{prompt} {self.e_inst}'
return f'{self.b_inst} {prompt} {self.e_inst}'
def messages2prompt(self, messages, sequence_start=True):
assert self.capability == 'chat', \
f'codellama message2prompt only supports chat mode ' \
f'but got {self.cap} mode'
return super().messages2prompt(messages, sequence_start)
@MODELS.register_module(name='falcon')
class Falcon(BaseModel):
def __init__(self, **kwargs):
super().__init__(**kwargs)
@MODELS.register_module(name='chatglm2-6b')
class ChatGLM2(BaseModel):
def __init__(self,
user='问：',
eoh='\n\n',
assistant='答：',
eoa='\n\n',
**kwargs):
super().__init__(**kwargs)
self._user = user
self._assistant = assistant
self._eoh = eoh
self._eoa = eoa
self.count = 0
def decorate_prompt(self, prompt, sequence_start=True):
"""decorate prompt."""
# need more check
# https://github.com/THUDM/ChatGLM2-6B/issues/48
# [64790, 64792] to be prepended
self.count += 1
ret = f'[Round {self.count}]\n\n'
ret += f'{self._user}{prompt}{self._eoh}'
ret += f'{self._assistant}'
return ret
def messages2prompt(self, messages, sequence_start=True):
"""message to prompt."""
if isinstance(messages, str):
return self.get_prompt(messages, sequence_start)
_, users, assistants = self._translate_messages(messages)
count = 0
ret = ''
for user, assistant in zip(users, assistants):
count += 1
if assistant:
ret += f'[Round {count}]\n\n'
ret += f'{self._user}{user}{self._eoh}'
ret += f'{self._assistant}{assistant}'
else:
ret += f'[Round {count}]\n\n'
ret += f'{self._user}{user}{self._eoh}'
ret += f'{self._assistant}'
return ret
@MODELS.register_module(name=['solar', 'solar-70b'])
class SOLAR(BaseModel):
"""Chat template of SOLAR model.
`https://huggingface.co/upstage/SOLAR-0-70b-16bit`
"""
def __init__(self,
system='### System:\n',
eosys='\n\n',
user='### User:\n',
eoh='\n\n',
assistant='### Assistant:\n',
eoa='\n\n',
meta_instruction='',
session_len=2048,
**kwargs):
super().__init__(**kwargs)
self.system = system
self.eosys = eosys
self.user = user
self.eoh = eoh
self.assistant = assistant
self.eoa = eoa
self.meta_instruction = meta_instruction
self.session_len = session_len
def decorate_prompt(self, prompt, sequence_start=True):
"""Return the prompt that is concatenated with other elements in the
chat template.
Args:
prompt (str): user's input prompt
sequence_start (bool): indicator for the first round chat of a
session sequence
Returns:
str: the concatenated prompt
"""
assert self.capability == 'chat', \
f'{type(self).__name__} has no capability of {self.capability}'
if sequence_start:
return f'{self.system}{self.meta_instruction}{self.eosys}' \
f'{self.user}{prompt}{self.eoh}{self.assistant}'
return f'{self.user}{prompt}{self.eoh}{self.assistant}'
def messages2prompt(self, messages, sequence_start=True):
"""Return the prompt that is concatenated with other elements in the
chat template.
Args:
messages (str | List): user's input prompt
Returns:
str: the concatenated prompt
"""
if isinstance(messages, str):
return self.get_prompt(messages, sequence_start)
system, users, assistants = self._translate_messages(messages)
system = self.meta_instruction if not system else system
ret = f'{self.system}{system}{self.eosys}'
for i, (user, assistant) in enumerate(zip(users, assistants)):
ret += f'{self.user}{user}{self.eoh}{self.assistant}'
if assistant:
ret += f'{assistant}{self.eoa}'
return ret
@MODELS.register_module(name='ultracm')
@MODELS.register_module(name='ultralm')
class UltraChat(BaseModel):
"""Template of UltraCM and UltraLM models.
`https://huggingface.co/openbmb/UltraCM-13b`
`https://huggingface.co/openbmb/UltraLM-13b`
"""
def __init__(
self,
meta_instruction="""User: A one-turn chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, very detailed, and polite answers to the user's questions.</s>""", # noqa: E501
eos='</s>',
user='User: ',
assistant='Assistant: ',
session_len=2048,
**kwargs):
super().__init__(**kwargs)
self.meta_instruction = meta_instruction
self.eos = eos
self.session_len = session_len
self.user = user
self.assistant = assistant
def decorate_prompt(self, prompt, sequence_start=True):
"""Return the prompt that is concatenated with other elements in the
chat template.
Args:
prompt (str): the input prompt
sequence_start (bool): indicator for the first round chat of a
session sequence
Returns:
str: the concatenated prompt
"""
assert self.capability == 'chat', \
f'{type(self).__name__} has no capability of {self.capability}'
if sequence_start:
return f'{self.meta_instruction}\n{self.user}{prompt}{self.eos}' \
f'\n{self.assistant}'
return f'\n{self.user}{prompt}{self.eos}' \
f'\n{self.assistant}'
def messages2prompt(self, messages, sequence_start=True):
"""Return the prompt that is concatenated with other elements in the
chat template. Only evaluate the last instruction completion pair.
Args:
messages (str | List): user's input prompt
Returns:
str: the concatenated prompt
"""
if isinstance(messages, str):
return self.get_prompt(messages, sequence_start)
system, users, assistants = self._translate_messages(messages)
system = self.meta_instruction if not system else system
ret = f'{system}'
for user, assistant in zip(users, assistants):
if assistant:
ret += f'\n{self.user}{user}{self.eos}' \
f'\n{self.assistant}{assistant}{self.eos}'
else:
ret += f'\n{self.user}{user}{self.eos}' \
f'\n{self.assistant}'
return ret
@MODELS.register_module(name=['yi', 'yi-chat', 'yi-200k', 'yi-34b'])
class Yi(BaseModel):
"""Chat template of Yi model."""
def __init__(self,
system='<|im_start|>system\n',
meta_instruction=None,
user='<|im_start|>user\n',
eoh='<|im_end|>\n',
eoa='<|im_end|>\n',
eosys='<|im_end|>\n',
assistant='<|im_start|>assistant\n',
stop_words=['<|im_end|>', '<|endoftext|>'],
**kwargs):
super().__init__(**kwargs)
self.system = system
self.meta_instruction = meta_instruction
self.user = user
self.eoh = eoh
self.eoa = eoa
self.eosys = eosys
self.assistant = assistant
self.stop_words = stop_words
def decorate_prompt(self, prompt, sequence_start=True):
"""Return the prompt that is concatenated with other elements in the
chat template.
Args:
prompt (str): user's input prompt
sequence_start (bool): indicator for the first round chat of a
session sequence
Returns:
str: the concatenated prompt
"""
assert self.capability == 'chat', \
f'{type(self).__name__} has no capability of {self.capability}'
if sequence_start:
if self.meta_instruction is None:
return f'{self.user}{prompt}{self.eoh}' \
f'{self.assistant}'
return f'{self.system}{self.meta_instruction}{self.eosys}' \
f'{self.user}{prompt}{self.eoh}' \
f'{self.assistant}'
else:
return f'{self.user}{prompt}{self.eoh}' \
f'{self.assistant}'
def messages2prompt(self, messages, sequence_start=True):
"""Return the prompt that is concatenated with other elements in the
chat template.
Args:
messages (str | List): user's input prompt
Returns:
str: the concatenated prompt
"""
if isinstance(messages, str):
return self.get_prompt(messages, sequence_start)
eox_map = dict(user=self.eoh, assistant=self.eoa, system=self.eosys)
ret = ''
if self.meta_instruction:
ret += f'{self.system}:{self.meta_instruction}{self.eosys}'
for message in messages:
role = message['role']
content = message['content']
ret += f'{eval(f"self.{role}")}{content}{eox_map[role]}'
ret += f'{self.assistant}'
return ret
@MODELS.register_module(name=['Mistral-7B-Instruct', 'Mixtral-8x7B-Instruct'])
class MistralChat(BaseModel):
"""Template of Mistral and Mixtral Instruct models.
`https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1`
`https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1`
"""
def __init__(self,
meta_instruction=None,
bos='<s>',
eos='</s>',
b_inst='[INST]',
e_inst='[/INST]',
user='user',
assistant='assistant',
session_len=2048,
**kwargs):
super().__init__(**kwargs)
self.meta_instruction = meta_instruction
self.bos = bos
self.eos = eos
self.b_inst = b_inst
self.e_inst = e_inst
self.session_len = session_len
self.user = user
self.assistant = assistant
def decorate_prompt(self, prompt, sequence_start=True):
"""Return the prompt that is concatenated with other elements in the
chat template.
Args:
prompt (str): the input prompt
sequence_start (bool): indicator for the first round chat of a
session sequence
Returns:
str: the concatenated prompt
"""
assert self.capability == 'chat', \
f'{type(self).__name__} has no capability of {self.capability}'
if sequence_start:
return f'{self.bos}{self.b_inst} {prompt} {self.e_inst}'
return f'{self.b_inst} {prompt} {self.e_inst}'
def messages2prompt(self, messages, sequence_start=True):
"""Return the prompt that is concatenated with other elements in the
chat template.
Only evaluate the last instruction completion pair.
Args:
messages (str | List): user's input prompt
Returns:
str: the concatenated prompt
"""
if isinstance(messages, str):
return self.get_prompt(messages, sequence_start)
ret = f'{self.bos}'
for index, msg in enumerate(messages):
role, content = msg['role'], msg['content']
if role == self.user:
ret += f'{self.b_inst} {content} {self.e_inst}'
elif role == self.assistant:
ret += content + self.eos
else:
raise Exception(f'Only have {self.user} and '
f'{self.assistant} roles, '
f'but given: {role}')
return ret
@MODELS.register_module(name=['gemma'])
class Gemma(BaseModel):
"""Template of Gemma models.
`https://huggingface.co/google/gemma-7b-it`
"""
def __init__(self,
user='user\n',
boh='<start_of_turn>',
eoh='<end_of_turn>\n',
assistant='model\n',
boa='<start_of_turn>',
eoa='<end_of_turn>\n',
**kwargs):
super().__init__(**kwargs)
self.user = user
self.boh = boh
self.eoh = eoh
self.assistant = assistant
self.boa = boa
self.eoa = eoa
def decorate_prompt(self, prompt, sequence_start=True):
"""Return the prompt that is concatenated with other elements in the
chat template.
Args:
prompt (str): user's input prompt
sequence_start (bool): indicator for the first round chat of a
session sequence
Returns:
str: the concatenated prompt
"""
assert self.capability == 'chat', \
f'{type(self).__name__} has no capability of {self.capability}'
return (f'{self.boh}{self.user}{prompt}{self.eoh}'
f'{self.boa}{self.assistant}')
def messages2prompt(self, messages, sequence_start=True):
"""Return the prompt that is concatenated with other elements in the
chat template.
Args:
messages (str | List): user's input prompt
Returns:
str: the concatenated prompt
"""
if isinstance(messages, str):
return self.get_prompt(messages, sequence_start)
_, users, assistants = self._translate_messages(messages)
ret = ''
for i, (user, assistant) in enumerate(zip(users, assistants)):
ret += (f'{self.boh}{self.user}{user}{self.eoh}'
f'{self.boa}{self.assistant}')
if assistant:
ret += f'{assistant}{self.eoa}'
return ret
@MODELS.register_module(name=['deepseek-chat'])
class Deepseek(BaseModel):
def __init__(self, user='User:', assistant='Assistant:', **kwargs):
super().__init__(**kwargs)
self._user = user
self._assistant = assistant
self._bos = '<｜begin▁of▁sentence｜>'
self._eos = '<｜end▁of▁sentence｜>'
def decorate_prompt(self, prompt, sequence_start=True):
"""Return the prompt that is concatenated with other elements in the
chat template.
Args:
prompt (str): the input prompt
sequence_start (bool): indicator for the first round chat of a
session sequence
Returns:
str: the concatenated prompt
"""
ret = f'{self._user} {prompt}\n\n{self._assistant}'
if sequence_start:
ret = f'{self._bos}{ret}'
else:
ret = f'{self._eos}{ret}'
return ret
def messages2prompt(self, messages, sequence_start=True):
"""Return the prompt that is concatenated with other elements in the
chat template.
Only evaluate the last instruction completion pair.
Args:
messages (str | List): user's input prompt
Returns:
str: the concatenated prompt
"""
if isinstance(messages, str):
return self.get_prompt(messages, sequence_start)
ret = f'{self._bos}'
is_start = True
for _, msg in enumerate(messages):
role, content = msg['role'], msg['content']
if role == 'user':
ret += f'{self._user} {content}\n\n{self._assistant}'
if is_start:
is_start = False
else:
ret += f'{self._eos}{ret}'
elif role == 'assistant':
ret += f' {content}\n\n'
else:
raise Exception('Only have user and '
'assistant roles, '
f'but given: {role}')
return ret
def best_match_model(query: str,
similarity_cutoff: float = 0.5) -> Optional[str]:
"""Get the model that matches the query.
Args:
query (str): the input query. Could be a model path.
similarity_cutoff (float): similarities below the limit are ignored.
Return:
str | None: the possible model name or none.
"""
model_names = list(MODELS.module_dict.keys())
if ('models--' in query) and ('snapshots' in query):
paths = query.split(os.sep)
paths = [x for x in paths if 'models--' in x]
query = paths[0].split('--')[-1]
elif query.endswith('/'):
query = query[:-1]
base_name = os.path.basename(query).lower()
max_ratio, matched_name = float('-inf'), None
for model_name in model_names:
if model_name in base_name:
ratio = fuzz.ratio(model_name.lower(), base_name)
if ratio > max_ratio and model_name != 'base': # skip base model
max_ratio = ratio
matched_name = model_name
if matched_name:
return matched_name
# Using fuzzy matching
matches = process.extract(base_name, model_names, scorer=fuzz.ratio)
# Ignore matches with score below similarity_cutoff
matches = [
match for match, score in matches if score / 100 >= similarity_cutoff
]
return matches[0] if matches else None