File size: 4,749 Bytes
811643a 1357761 811643a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 |
import dataclasses
from enum import auto, Enum
from typing import List, Tuple, Any
class SeparatorStyle(Enum):
"""Different separator style."""
SINGLE = auto()
TWO = auto()
@dataclasses.dataclass
class Conversation:
"""A class that keeps all conversation history."""
system: str
instruction: str
roles: List[str]
messages: List[List[str]]
offset: int
sep_style: SeparatorStyle = SeparatorStyle.SINGLE
sep: str = "###"
sep2: str = None
skip_next: bool = False
conv_id: Any = None
def get_prompt(self):
if self.sep_style == SeparatorStyle.SINGLE:
ret = self.system + self.sep
if self.instruction is not None and len(self.instruction) > 0:
ret += self.roles[2] + ": " + self.instruction + 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.TWO:
seps = [self.sep, self.sep2]
ret = self.system + seps[0]
if self.instruction is not None and len(self.instruction) > 0:
ret += self.roles[2] + ": " + self.instruction + self.sep
for i, (role, message) in enumerate(self.messages):
if message:
ret += role + ": " + message + seps[i % 2]
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,
instruction=self.instruction,
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,
"instruction": self.instruction,
"roles": self.roles,
"messages": self.messages,
"offset": self.offset,
"sep": self.sep,
"sep2": self.sep2,
"conv_id": self.conv_id,
}
conv_v1 = 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.",
instruction="",
roles=("Human", "Assistant", "System"),
messages=(),
offset=0,
sep_style=SeparatorStyle.SINGLE,
sep="###",
)
conv_v1_2 = 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.",
instruction="",
roles=("Human", "Assistant", "System"),
messages=(),
offset=0,
sep_style=SeparatorStyle.SINGLE,
sep="###",
)
conv_bair_v1 = Conversation(
system="BEGINNING OF CONVERSATION:",
instruction="",
roles=("USER", "GPT", "System"),
messages=(),
offset=0,
sep_style=SeparatorStyle.TWO,
sep=" ",
sep2="</s>",
)
default_conversation = conv_v1_2
conv_templates = {
"v1": conv_v1_2,
"bair_v1": conv_bair_v1,
}
def covert_prompt_to_input_ids_with_history(text, history, tokenizer, max_token):
conv = default_conversation.copy()
conv.append_message(conv.roles[1], None)
conv.append_message(conv.roles[0], text)
example = tokenizer.encode_plus(f"{conv.get_prompt()}", None, max_length=None)['input_ids']
while(len(history) > 0 and (len(example) < max_token)):
tmp = history.pop()
if tmp[0] == 'ASSISTANT':
conv.append_message(conv.roles[1], tmp[1])
else:
conv.append_message(conv.roles[0], tmp[1])
example = tokenizer.encode_plus(f"{conv.get_prompt()}", None, max_length=None)['input_ids']
if len(example) >= max_token:
conv.messages.pop()
conv.messages = conv.messages[::-1]
print('model in:', conv.get_prompt())
example = tokenizer.encode_plus(f"{conv.get_prompt()}", None, max_length=None)['input_ids']
example = example[1:-1]
return example
if __name__ == "__main__":
print(default_conversation.get_prompt())
|