Taiwan-LLaMa2 / conversation.py
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"""
Conversation prompt template.
Now we support
- Vicuna
- Koala
- OpenAssistant/oasst-sft-1-pythia-12b
- StabilityAI/stablelm-tuned-alpha-7b
- databricks/dolly-v2-12b
- THUDM/chatglm-6b
- Alpaca/LLaMa
"""
import dataclasses
from enum import auto, Enum
from typing import List, Tuple, Any
class SeparatorStyle(Enum):
"""Different separator style."""
SINGLE = auto()
TWO = auto()
DOLLY = auto()
OASST_PYTHIA = auto()
@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
# Used for gradio server
skip_next: bool = False
conv_id: Any = None
def get_prompt(self):
if self.sep_style == SeparatorStyle.SINGLE:
ret = self.system
for role, message in self.messages:
if message:
ret += self.sep + " " + role + ": " + message
else:
ret += self.sep + " " + role + ":"
return ret
elif self.sep_style == SeparatorStyle.TWO:
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
elif self.sep_style == SeparatorStyle.DOLLY:
seps = [self.sep, self.sep2]
ret = self.system
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.OASST_PYTHIA:
ret = self.system
for role, message in self.messages:
if message:
ret += role + message + self.sep
else:
ret += role
return ret
else:
raise ValueError(f"Invalid style: {self.sep_style}")
def append_message(self, role, message):
self.messages.append([role, message])
def to_gradio_chatbot(self):
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 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,
conv_id=self.conv_id,
)
def dict(self):
return {
"system": self.system,
"roles": self.roles,
"messages": self.messages,
"offset": self.offset,
"sep": self.sep,
"sep2": self.sep2,
"conv_id": self.conv_id,
}
conv_one_shot = Conversation(
system="A chat between a curious human and an artificial intelligence assistant. "
"The assistant gives helpful, detailed, and polite answers to the human's questions.",
roles=("Human", "Assistant"),
messages=(
(
"Human",
"What are the key differences between renewable and non-renewable energy sources?",
),
(
"Assistant",
"Renewable energy sources are those that can be replenished naturally in a relatively "
"short amount of time, such as solar, wind, hydro, geothermal, and biomass. "
"Non-renewable energy sources, on the other hand, are finite and will eventually be "
"depleted, such as coal, oil, and natural gas. Here are some key differences between "
"renewable and non-renewable energy sources:\n"
"1. Availability: Renewable energy sources are virtually inexhaustible, while non-renewable "
"energy sources are finite and will eventually run out.\n"
"2. Environmental impact: Renewable energy sources have a much lower environmental impact "
"than non-renewable sources, which can lead to air and water pollution, greenhouse gas emissions, "
"and other negative effects.\n"
"3. Cost: Renewable energy sources can be more expensive to initially set up, but they typically "
"have lower operational costs than non-renewable sources.\n"
"4. Reliability: Renewable energy sources are often more reliable and can be used in more remote "
"locations than non-renewable sources.\n"
"5. Flexibility: Renewable energy sources are often more flexible and can be adapted to different "
"situations and needs, while non-renewable sources are more rigid and inflexible.\n"
"6. Sustainability: Renewable energy sources are more sustainable over the long term, while "
"non-renewable sources are not, and their depletion can lead to economic and social instability.",
),
),
offset=2,
sep_style=SeparatorStyle.SINGLE,
sep="###",
)
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. You are built by NTU Miulab by Yen-Ting Lin for research purpose.",
# system="一位好奇的用戶和一個人工智能助理之間的聊天。你是一位助理。請對用戶的問題提供有用、詳細和有禮貌的答案。",
roles=("USER", "ASSISTANT"),
messages=(),
offset=0,
sep_style=SeparatorStyle.TWO,
sep=" ",
sep2="</s>",
)
conv_story = 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="<|endoftext|>",
)
conv_koala_v1 = Conversation(
system="BEGINNING OF CONVERSATION:",
roles=("USER", "GPT"),
messages=(),
offset=0,
sep_style=SeparatorStyle.TWO,
sep=" ",
sep2="</s>",
)
conv_dolly = Conversation(
system="Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n",
roles=("### Instruction", "### Response"),
messages=(),
offset=0,
sep_style=SeparatorStyle.DOLLY,
sep="\n\n",
sep2="### End",
)
conv_oasst = Conversation(
system="",
roles=("<|prompter|>", "<|assistant|>"),
messages=(),
offset=0,
sep_style=SeparatorStyle.OASST_PYTHIA,
sep="<|endoftext|>",
)
conv_stablelm = Conversation(
system="""<|SYSTEM|># StableLM Tuned (Alpha version)
- StableLM is a helpful and harmless open-source AI language model developed by StabilityAI.
- StableLM is excited to be able to help the user, but will refuse to do anything that could be considered harmful to the user.
- StableLM is more than just an information source, StableLM is also able to write poetry, short stories, and make jokes.
- StableLM will refuse to participate in anything that could harm a human.
""",
roles=("<|USER|>", "<|ASSISTANT|>"),
messages=(),
offset=0,
sep_style=SeparatorStyle.OASST_PYTHIA,
sep="",
)
conv_templates = {
"conv_one_shot": conv_one_shot,
"vicuna_v1.1": conv_vicuna_v1_1,
"koala_v1": conv_koala_v1,
"dolly": conv_dolly,
"oasst": conv_oasst,
}
def get_default_conv_template(model_name):
model_name = model_name.lower()
if "vicuna" in model_name or "output" in model_name:
return conv_vicuna_v1_1
elif "koala" in model_name:
return conv_koala_v1
elif "dolly-v2" in model_name:
return conv_dolly
elif "oasst" in model_name and "pythia" in model_name:
return conv_oasst
elif "stablelm" in model_name:
return conv_stablelm
return conv_one_shot
def compute_skip_echo_len(model_name, conv, prompt):
model_name = model_name.lower()
if "chatglm" in model_name:
skip_echo_len = len(conv.messages[-2][1]) + 1
elif "dolly-v2" in model_name:
special_toks = ["### Instruction:", "### Response:", "### End"]
skip_echo_len = len(prompt)
for tok in special_toks:
skip_echo_len -= prompt.count(tok) * len(tok)
elif "oasst" in model_name and "pythia" in model_name:
special_toks = ["<|prompter|>", "<|assistant|>", "<|endoftext|>"]
skip_echo_len = len(prompt)
for tok in special_toks:
skip_echo_len -= prompt.count(tok) * len(tok)
elif "stablelm" in model_name:
special_toks = ["<|SYSTEM|>", "<|USER|>", "<|ASSISTANT|>"]
skip_echo_len = len(prompt)
for tok in special_toks:
skip_echo_len -= prompt.count(tok) * len(tok)
else:
skip_echo_len = len(prompt) + 1 - prompt.count("</s>") * 3
return skip_echo_len
if __name__ == "__main__":
print(default_conversation.get_prompt())