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import re
from pprint import pprint
from transformers import AutoTokenizer
from constants.models import AVAILABLE_MODELS
from utils.logger import logger
class MessageComposer:
def __init__(self, model: str = None):
if model in AVAILABLE_MODELS:
self.model = model
else:
self.model = "mixtral-8x7b"
self.system_roles = ["system"]
self.inst_roles = ["user", "system", "inst"]
self.answer_roles = ["assistant", "bot", "answer", "model"]
self.default_role = "user"
def concat_messages_by_role(self, messages):
def is_same_role(role1, role2):
if (
(role1 == role2)
or (role1 in self.inst_roles and role2 in self.inst_roles)
or (role1 in self.answer_roles and role2 in self.answer_roles)
):
return True
else:
return False
concat_messages = []
for message in messages:
role = message["role"]
content = message["content"]
if concat_messages and is_same_role(role, concat_messages[-1]["role"]):
concat_messages[-1]["content"] += "\n" + content
else:
if role in self.inst_roles:
message["role"] = "inst"
elif role in self.answer_roles:
message["role"] = "answer"
else:
message["role"] = "inst"
concat_messages.append(message)
return concat_messages
def merge(self, messages) -> str:
# Mistral and Mixtral:
# <s> [INST] Instruction [/INST] Model answer </s> [INST] Follow-up instruction [/INST]
# OpenChat:
# GPT4 Correct User: Hello<|end_of_turn|>GPT4 Correct Assistant: Hi<|end_of_turn|>GPT4 Correct User: How are you today?<|end_of_turn|>GPT4 Correct Assistant:
# Nous Mixtral:
# <|im_start|>system
# You are "Hermes 2".<|im_end|>
# <|im_start|>user
# Hello, who are you?<|im_end|>
# <|im_start|>assistant
# Google Gemma-it
# <start_of_turn>user
# How does the brain work?<end_of_turn>
# <start_of_turn>model
self.messages = messages
self.merged_str = ""
# https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1#instruction-format
if self.model in ["mixtral-8x7b", "mistral-7b"]:
self.messages = self.concat_messages_by_role(messages)
self.cached_str = ""
for message in self.messages:
role = message["role"]
content = message["content"]
if role in self.inst_roles:
self.cached_str = f"[INST] {content} [/INST]"
elif role in self.answer_roles:
self.merged_str += f"<s> {self.cached_str} {content} </s>\n"
self.cached_str = ""
else:
self.cached_str = f"[INST] {content} [/INST]"
if self.cached_str:
self.merged_str += f"{self.cached_str}"
# https://huggingface.co/NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO#prompt-format
elif self.model in ["nous-mixtral-8x7b"]:
self.merged_str_list = []
for message in self.messages:
role = message["role"]
content = message["content"]
if role not in ["system", "user", "assistant"]:
role = self.default_role
message_line = f"<|im_start|>{role}\n{content}<|im_end|>"
self.merged_str_list.append(message_line)
self.merged_str_list.append("<|im_start|>assistant")
self.merged_str = "\n".join(self.merged_str_list)
# https://huggingface.co/openchat/openchat-3.5-0106
elif self.model in ["openchat-3.5"]:
tokenizer = AutoTokenizer.from_pretrained("openchat/openchat-3.5-0106")
self.merged_str = tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
# self.messages = self.concat_messages_by_role(messages)
# self.merged_str_list = []
# self.end_of_turn = "<|end_of_turn|>"
# for message in self.messages:
# role = message["role"]
# content = message["content"]
# if role in self.inst_roles:
# self.merged_str_list.append(
# f"GPT4 Correct User:\n{content}{self.end_of_turn}"
# )
# elif role in self.answer_roles:
# self.merged_str_list.append(
# f"GPT4 Correct Assistant:\n{content}{self.end_of_turn}"
# )
# else:
# self.merged_str_list.append(
# f"GPT4 Correct User: {content}{self.end_of_turn}"
# )
# self.merged_str_list.append(f"GPT4 Correct Assistant:\n")
# self.merged_str = "\n".join(self.merged_str_list)
# https://huggingface.co/google/gemma-7b-it#chat-template
elif self.model in ["gemma-7b"]:
self.messages = self.concat_messages_by_role(messages)
self.merged_str_list = []
self.end_of_turn = "<end_of_turn>"
self.start_of_turn = "<start_of_turn>"
for message in self.messages:
role = message["role"]
content = message["content"]
if role in self.inst_roles:
self.merged_str_list.append(
f"{self.start_of_turn}user\n{content}{self.end_of_turn}"
)
elif role in self.answer_roles:
self.merged_str_list.append(
f"{self.start_of_turn}model\n{content}{self.end_of_turn}"
)
else:
self.merged_str_list.append(
f"{self.start_of_turn}user\n{content}{self.end_of_turn}"
)
self.merged_str_list.append(f"{self.start_of_turn}model\n")
self.merged_str = "\n".join(self.merged_str_list)
else:
self.merged_str = "\n".join(
[
f'`{message["role"]}`:\n{message["content"]}\n'
for message in self.messages
]
)
return self.merged_str
def convert_pair_matches_to_messages(self, pair_matches_list):
messages = []
if len(pair_matches_list) <= 0:
messages = [
{
"role": "user",
"content": self.merged_str,
}
]
else:
for match in pair_matches_list:
inst = match.group("inst")
answer = match.group("answer")
messages.extend(
[
{"role": "user", "content": inst.strip()},
{"role": "assistant", "content": answer.strip()},
]
)
return messages
def append_last_instruction_to_messages(self, inst_matches_list, pair_matches_list):
if len(inst_matches_list) > len(pair_matches_list):
self.messages.extend(
[
{
"role": "user",
"content": inst_matches_list[-1].group("inst").strip(),
}
]
)
def split(self, merged_str) -> list:
self.merged_str = merged_str
self.messages = []
if self.model in ["mixtral-8x7b", "mistral-7b"]:
pair_pattern = (
r"<s>\s*\[INST\](?P<inst>[\s\S]*?)\[/INST\](?P<answer>[\s\S]*?)</s>"
)
pair_matches = re.finditer(pair_pattern, self.merged_str, re.MULTILINE)
pair_matches_list = list(pair_matches)
self.messages = self.convert_pair_matches_to_messages(pair_matches_list)
inst_pattern = r"\[INST\](?P<inst>[\s\S]*?)\[/INST\]"
inst_matches = re.finditer(inst_pattern, self.merged_str, re.MULTILINE)
inst_matches_list = list(inst_matches)
self.append_last_instruction_to_messages(
inst_matches_list, pair_matches_list
)
elif self.model in ["nous-mixtral-8x7b"]:
# https://huggingface.co/NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO#prompt-format
# message_pattern = r"<\|im_start\|>(?P<role>system|user|assistant)[\s\n]*(?P<content>[\s\S]*?)<\|im_end\|>"
message_pattern = r"<\|im_start\|>(?P<role>system|user|assistant)[\s\n]*(?P<content>[\s\S]*?)<\|im_end\|>"
message_matches = re.finditer(
message_pattern, self.merged_str, flags=re.MULTILINE | re.IGNORECASE
)
message_matches_list = list(message_matches)
logger.note(f"message_matches_list: {message_matches_list}")
for match in message_matches_list:
role = match.group("role")
content = match.group("content")
self.messages.append({"role": role, "content": content.strip()})
elif self.model in ["openchat-3.5"]:
pair_pattern = r"GPT4 Correct User:(?P<inst>[\s\S]*?)<\|end_of_turn\|>\s*GPT4 Correct Assistant:(?P<answer>[\s\S]*?)<\|end_of_turn\|>"
pair_matches = re.finditer(
pair_pattern, self.merged_str, flags=re.MULTILINE | re.IGNORECASE
)
pair_matches_list = list(pair_matches)
self.messages = self.convert_pair_matches_to_messages(pair_matches_list)
inst_pattern = r"GPT4 Correct User:(?P<inst>[\s\S]*?)<\|end_of_turn\|>"
inst_matches = re.finditer(
inst_pattern, self.merged_str, flags=re.MULTILINE | re.IGNORECASE
)
inst_matches_list = list(inst_matches)
self.append_last_instruction_to_messages(
inst_matches_list, pair_matches_list
)
# https://huggingface.co/google/gemma-7b-it#chat-template
elif self.model in ["gemma-7b"]:
pair_pattern = r"<start_of_turn>user[\s\n]*(?P<inst>[\s\S]*?)<end_of_turn>[\s\n]*<start_of_turn>model(?P<answer>[\s\S]*?)<end_of_turn>"
pair_matches = re.finditer(
pair_pattern, self.merged_str, flags=re.MULTILINE | re.IGNORECASE
)
pair_matches_list = list(pair_matches)
self.messages = self.convert_pair_matches_to_messages(pair_matches_list)
inst_pattern = r"<start_of_turn>user\n(?P<inst>[\s\S]*?)<end_of_turn>"
inst_matches = re.finditer(
inst_pattern, self.merged_str, flags=re.MULTILINE | re.IGNORECASE
)
inst_matches_list = list(inst_matches)
self.append_last_instruction_to_messages(
inst_matches_list, pair_matches_list
)
else:
self.messages = [
{
"role": "user",
"content": self.merged_str,
}
]
return self.messages
if __name__ == "__main__":
# model = "mixtral-8x7b"
# model = "nous-mixtral-8x7b"
# model = "gemma-7b"
model = "openchat-3.5"
composer = MessageComposer(model)
messages = [
{
"role": "system",
"content": "You are a LLM developed by OpenAI.\nYour name is GPT-4.",
},
{"role": "user", "content": "Hello, who are you?"},
{"role": "assistant", "content": "I am a bot."},
{"role": "user", "content": "What is your name?"},
# {"role": "assistant", "content": "My name is Bing."},
# {"role": "user", "content": "Tell me a joke."},
# {"role": "assistant", "content": "What is a robot's favorite type of music?"},
# {
# "role": "user",
# "content": "How many questions have I asked? Please list them.",
# },
]
logger.note(f"model: {composer.model}")
merged_str = composer.merge(messages)
logger.note("merged_str:")
logger.mesg(merged_str)
logger.note("splitted messages:")
pprint(composer.split(merged_str))
# logger.note("merged merged_str:")
# logger.mesg(composer.merge(composer.split(merged_str)))
# python -m messagers.message_composer
|