mirror of
https://github.com/hiyouga/LLaMA-Factory.git
synced 2025-08-04 04:32:50 +08:00
748 lines
21 KiB
Python
748 lines
21 KiB
Python
from dataclasses import dataclass
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from typing import TYPE_CHECKING, Callable, Dict, List, Optional, Tuple, Union
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from ..extras.logging import get_logger
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from .utils import Role
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from .formatter import StringFormatter, FunctionFormatter, ToolFormatter
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if TYPE_CHECKING:
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from transformers import PreTrainedTokenizer
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logger = get_logger(__name__)
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@dataclass
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class Template:
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format_user: Callable
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format_assistant: Callable
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format_system: Callable
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format_tool: Callable
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format_observation: Callable
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format_function: Callable
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system: str
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separator: List[Union[str, Dict[str, str]]]
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stop_words: List[str]
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efficient_eos: bool
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replace_eos: bool
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def encode_oneturn(
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self,
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tokenizer: "PreTrainedTokenizer",
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messages: List[Dict[str, str]],
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system: str,
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tools: str,
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cutoff_len: Optional[int] = 1_000_000
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) -> Tuple[List[int], List[int]]:
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r"""
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Returns a single pair of token ids representing prompt and response respectively.
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"""
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encoded_pairs = self._encode(tokenizer, messages, system, tools, cutoff_len)
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prompt_ids = []
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for query_ids, resp_ids in encoded_pairs[:-1]:
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prompt_ids = prompt_ids + query_ids + resp_ids
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prompt_ids = prompt_ids + encoded_pairs[-1][0]
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answer_ids = encoded_pairs[-1][1]
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return prompt_ids, answer_ids
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def encode_multiturn(
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self,
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tokenizer: "PreTrainedTokenizer",
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messages: List[Dict[str, str]],
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system: str,
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tools: str,
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cutoff_len: Optional[int] = 1_000_000
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) -> List[Tuple[List[int], List[int]]]:
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r"""
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Returns multiple pairs of token ids representing prompts and responses respectively.
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"""
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encoded_pairs = self._encode(tokenizer, messages, system, tools, cutoff_len)
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return encoded_pairs
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def _encode(
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self,
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tokenizer: "PreTrainedTokenizer",
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messages: List[Dict[str, str]],
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system: str,
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tools: str,
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cutoff_len: int
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) -> List[Tuple[List[int], List[int]]]:
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r"""
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Encodes formatted inputs to pairs of token ids.
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Turn 0: system + query resp + eos
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Turn t: sep + query resp + eos
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"""
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system = system or self.system
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encoded_messages = []
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for i, message in enumerate(messages):
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elements = []
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if i == 0 and (system or tools):
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tool_text = self.format_tool(content=tools)[0] if tools else ""
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elements += self.format_system(content=(system + tool_text))
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elif i > 0 and i % 2 == 0:
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elements += self.separator
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if message["role"] == Role.USER:
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elements += self.format_user(content=message["content"], idx=str(i // 2))
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elif message["role"] == Role.ASSISTANT:
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elements += self.format_assistant(content=message["content"])
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elif message["role"] == Role.OBSERVATION:
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elements += self.format_observation(content=message["content"])
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elif message["role"] == Role.FUNCTION:
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elements += self.format_function(content=message["content"])
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encoded_messages.append(self._convert_elements_to_ids(tokenizer, elements))
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# TODO: need to improve
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encoded_pairs = []
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total_length = 0
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for i in range(0, len(encoded_messages), 2):
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if total_length >= cutoff_len:
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break
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encoded_messages[i] = encoded_messages[i][:cutoff_len-total_length]
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total_length += len(encoded_messages[i])
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encoded_messages[i+1] = encoded_messages[i+1][:max(1, cutoff_len-total_length)]
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total_length += len(encoded_messages[i+1])
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encoded_pairs.append((encoded_messages[i], encoded_messages[i+1]))
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return encoded_pairs
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def _convert_elements_to_ids(
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self,
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tokenizer: "PreTrainedTokenizer",
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elements: List[Union[str, Dict[str, str]]]
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) -> List[int]:
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r"""
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Converts elements to token ids.
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"""
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token_ids = []
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for elem in elements:
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if isinstance(elem, str):
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if len(elem) != 0:
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token_ids = token_ids + tokenizer.encode(elem, add_special_tokens=False)
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elif isinstance(elem, dict):
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token_ids = token_ids + [tokenizer.convert_tokens_to_ids(elem.get("token"))]
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elif isinstance(elem, set):
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if "bos_token" in elem and tokenizer.bos_token_id:
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token_ids = token_ids + [tokenizer.bos_token_id]
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elif "eos_token" in elem and tokenizer.eos_token_id:
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token_ids = token_ids + [tokenizer.eos_token_id]
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else:
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raise ValueError("Input must be string, set[str] or dict[str, str], got {}".format(type(elem)))
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return token_ids
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@dataclass
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class Llama2Template(Template):
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def _encode(
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self,
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tokenizer: "PreTrainedTokenizer",
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messages: List[Dict[str, str]],
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system: str,
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tools: str,
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cutoff_len: int
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) -> List[Tuple[List[int], List[int]]]:
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r"""
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Encodes formatted inputs to pairs of token ids.
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Turn 0: system + query resp + eos
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Turn t: sep + query resp + eos
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"""
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system = system or self.system
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encoded_messages = []
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for i, message in enumerate(messages):
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elements = []
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system_text = ""
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if i == 0 and (system or tools):
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tool_text = self.format_tool(content=tools)[0] if tools else ""
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system_text = self.format_system(content=(system + tool_text))[0]
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elif i > 0 and i % 2 == 0:
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elements += self.separator
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if message["role"] == Role.USER:
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elements += self.format_user(content=system_text + message["content"], idx=str(i // 2))
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elif message["role"] == Role.ASSISTANT:
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elements += self.format_assistant(content=message["content"])
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elif message["role"] == Role.OBSERVATION:
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elements += self.format_observation(content=message["content"])
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elif message["role"] == Role.FUNCTION:
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elements += self.format_function(content=message["content"])
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encoded_messages.append(self._convert_elements_to_ids(tokenizer, elements))
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# TODO: need to improve
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encoded_pairs = []
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total_length = 0
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for i in range(0, len(encoded_messages), 2):
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if total_length >= cutoff_len:
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break
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encoded_messages[i] = encoded_messages[i][:cutoff_len-total_length]
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total_length += len(encoded_messages[i])
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encoded_messages[i+1] = encoded_messages[i+1][:max(1, cutoff_len-total_length)]
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total_length += len(encoded_messages[i+1])
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encoded_pairs.append((encoded_messages[i], encoded_messages[i+1]))
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return encoded_pairs
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templates: Dict[str, Template] = {}
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def register_template(
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name: str,
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format_user: Optional[Callable] = None,
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format_assistant: Optional[Callable] = None,
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format_system: Optional[Callable] = None,
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format_tool: Optional[Callable] = None,
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format_observation: Optional[Callable] = None,
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format_function: Optional[Callable] = None,
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system: Optional[str] = "",
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separator: Optional[List[Union[str, Dict[str, str]]]] = "",
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stop_words: Optional[List[str]] = [],
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efficient_eos: Optional[bool] = False,
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replace_eos: Optional[bool] = False
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) -> None:
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template_class = Llama2Template if name.startswith("llama2") else Template
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templates[name] = template_class(
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format_user=format_user or StringFormatter(container=["{{content}}"]),
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format_assistant=format_assistant or StringFormatter(container=[
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"{{content}}", {"eos_token"}
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]),
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format_system=format_system or StringFormatter(container=["{{content}}"]),
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format_tool=format_tool or ToolFormatter(type="default"),
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format_observation=format_observation or format_user,
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format_function=format_function or FunctionFormatter(container=[
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"Action: {{name}}\nAction Input: {{arguments}}", {"eos_token"}
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]),
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system=system,
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separator=separator,
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stop_words=stop_words,
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efficient_eos=efficient_eos,
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replace_eos=replace_eos
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)
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def get_template_and_fix_tokenizer(
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name: str,
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tokenizer: "PreTrainedTokenizer"
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) -> Template:
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if tokenizer.eos_token_id is None:
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tokenizer.eos_token = "<|endoftext|>"
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logger.info("Add eos token: {}".format(tokenizer.eos_token))
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if tokenizer.pad_token_id is None:
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tokenizer.pad_token = tokenizer.eos_token
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logger.info("Add pad token: {}".format(tokenizer.pad_token))
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if name is None: # for pre-training
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return None
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template = templates.get(name, None)
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assert template is not None, "Template {} does not exist.".format(name)
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stop_words = template.stop_words
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if template.replace_eos:
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if not stop_words:
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raise ValueError("Stop words are required to replace the EOS token.")
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tokenizer.eos_token = stop_words[0]
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stop_words = stop_words[1:]
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logger.info("Replace eos token: {}".format(tokenizer.eos_token))
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if stop_words:
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tokenizer.add_special_tokens(
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dict(additional_special_tokens=stop_words),
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replace_additional_special_tokens=False
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)
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logger.info("Add {} to stop words.".format(",".join(stop_words)))
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return template
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register_template(
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name="alpaca",
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format_user=StringFormatter(container=[
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"### Instruction:\n{{content}}\n\n### Response:\n"
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]),
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system=(
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"Below is an instruction that describes a task. "
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"Write a response that appropriately completes the request."
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),
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separator=[
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"\n\n"
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]
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)
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register_template(
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name="aquila",
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format_user=StringFormatter(container=[
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"Human: {{content}}###Assistant:"
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]),
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system=(
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"A chat between a curious human and an artificial intelligence assistant. "
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"The assistant gives helpful, detailed, and polite answers to the human's questions."
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),
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separator=[
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"###"
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],
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stop_words=[
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"</s>"
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],
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efficient_eos=True
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)
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register_template(
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name="baichuan",
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format_user=StringFormatter(container=[
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{"token": "<reserved_102>"},
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"{{content}}",
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{"token": "<reserved_103>"}
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]),
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efficient_eos=True
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)
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register_template(
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name="baichuan2",
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format_user=StringFormatter(container=[
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{"token": "<reserved_106>"},
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"{{content}}",
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{"token": "<reserved_107>"}
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]),
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efficient_eos=True
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)
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register_template(
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name="belle",
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format_user=StringFormatter(container=[
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"Human: {{content}}\n\nBelle: "
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]),
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separator=[
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"\n\n"
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]
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)
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register_template(
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name="bluelm",
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format_user=StringFormatter(container=[
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{"token": "[|Human|]:"},
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"{{content}}",
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{"token": "[|AI|]:"}
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])
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)
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register_template(
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name="chatglm2",
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format_user=StringFormatter(container=[
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"[Round {{idx}}]\n\n问：{{content}}\n\n答："
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]),
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format_system=StringFormatter(container=[
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{"token": "[gMASK]"},
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{"token": "sop"},
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"{{content}}"
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]),
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separator=[
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"\n\n"
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],
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efficient_eos=True
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)
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register_template(
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name="chatglm3",
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format_user=StringFormatter(container=[
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{"token": "<|user|>"},
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"\n",
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"{{content}}",
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{"token": "<|assistant|>"}
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]),
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format_assistant=StringFormatter(container=[
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"\n"
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"{{content}}"
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]),
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format_system=StringFormatter(container=[
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{"token": "[gMASK]"},
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{"token": "sop"},
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{"token": "<|system|>"},
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"\n",
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"{{content}}"
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]),
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format_observation=StringFormatter(container=[
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{"token": "<|observation|>"},
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"\n",
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"{{content}}"
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]),
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format_function=FunctionFormatter(container=[
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"{{name}}\n{{arguments}}"
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]),
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system=(
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"You are ChatGLM3, a large language model trained by Zhipu.AI. "
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"Follow the user's instructions carefully. Respond using markdown."
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),
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stop_words=[
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"<|user|>",
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"<|observation|>"
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],
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efficient_eos=True
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)
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register_template(
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name="codegeex2",
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format_system=StringFormatter(container=[
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{"token": "[gMASK]"},
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{"token": "sop"},
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"{{content}}"
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])
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)
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register_template(
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name="deepseek",
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format_user=StringFormatter(container=[
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"User: {{content}}\n\nAssistant:"
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])
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)
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register_template(
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name="deepseekcoder",
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format_user=StringFormatter(container=[
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"### Instruction:\n{{content}}\n### Response:\n"
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]),
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system=(
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"You are an AI programming assistant, utilizing the Deepseek Coder model, "
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"developed by Deepseek Company, and you only answer questions related to computer science. "
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"For politically sensitive questions, security and privacy issues, "
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"and other non-computer science questions, you will refuse to answer\n"
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),
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separator=[
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"\n",
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{"token": "<|EOT|>"},
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"\n"
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],
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stop_words=[
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"<|EOT|>"
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],
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efficient_eos=True
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)
|
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|
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register_template(
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name="default",
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format_user=StringFormatter(container=[
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"Human: {{content}}\nAssistant: "
|
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]),
|
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system=(
|
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"A chat between a curious user and an artificial intelligence assistant. "
|
||
"The assistant gives helpful, detailed, and polite answers to the user's questions.\n"
|
||
),
|
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separator=[
|
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"\n"
|
||
]
|
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)
|
||
|
||
|
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register_template(
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name="falcon",
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format_user=StringFormatter(container=[
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"User: {{content}}\nFalcon:"
|
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]),
|
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separator=[
|
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"\n"
|
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],
|
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efficient_eos=True
|
||
)
|
||
|
||
|
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register_template(
|
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name="intern",
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format_user=StringFormatter(container=[
|
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"<|User|>:{{content}}",
|
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{"token": "<eoh>"},
|
||
"\n<|Bot|>:"
|
||
]),
|
||
separator=[
|
||
{"token": "<eoa>"},
|
||
"\n"
|
||
],
|
||
stop_words=[
|
||
"<eoa>"
|
||
],
|
||
efficient_eos=True
|
||
)
|
||
|
||
|
||
register_template(
|
||
name="intern2",
|
||
format_user=StringFormatter(container=[
|
||
{"token": "[UNUSED_TOKEN_146]"},
|
||
"user\n{{content}}",
|
||
{"token": "[UNUSED_TOKEN_145]"},
|
||
"\n",
|
||
{"token": "[UNUSED_TOKEN_146]"},
|
||
"assistant\n"
|
||
]),
|
||
format_system=StringFormatter(container=[
|
||
{"token": "[UNUSED_TOKEN_146]"},
|
||
"system\n{{content}}",
|
||
{"token": "[UNUSED_TOKEN_145]"},
|
||
"\n"
|
||
]),
|
||
system=(
|
||
"You are an AI assistant whose name is InternLM (书生·浦语).\n"
|
||
"- 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.\n"
|
||
"- InternLM (书生·浦语) can understand and communicate fluently in the language chosen "
|
||
"by the user such as English and 中文."
|
||
),
|
||
separator=[
|
||
{"token": "[UNUSED_TOKEN_145]"},
|
||
"\n"
|
||
],
|
||
stop_words=[
|
||
"[UNUSED_TOKEN_145]"
|
||
],
|
||
efficient_eos=True
|
||
)
|
||
|
||
|
||
register_template(
|
||
name="llama2",
|
||
format_user=StringFormatter(container=["[INST] {{content}} [/INST]"]),
|
||
format_system=StringFormatter(container=["<<SYS>>\n{{content}}\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.\n\n"
|
||
"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."
|
||
)
|
||
)
|
||
|
||
|
||
register_template(
|
||
name="llama2_zh",
|
||
format_user=StringFormatter(container=["[INST] {{content}} [/INST]"]),
|
||
format_system=StringFormatter(container=["<<SYS>>\n{{content}}\n<</SYS>>\n\n"]),
|
||
system="You are a helpful assistant. 你是一个乐于助人的助手。"
|
||
)
|
||
|
||
|
||
register_template(
|
||
name="mistral",
|
||
format_user=StringFormatter(container=["[INST] {{content}} [/INST]"])
|
||
)
|
||
|
||
|
||
register_template(
|
||
name="openchat",
|
||
format_user=StringFormatter(container=[
|
||
"GPT4 Correct User: {{content}}",
|
||
{"token": "<|end_of_turn|>"},
|
||
"GPT4 Correct Assistant:"
|
||
]),
|
||
separator=[
|
||
{"token": "<|end_of_turn|>"}
|
||
],
|
||
stop_words=[
|
||
"<|end_of_turn|>"
|
||
],
|
||
efficient_eos=True
|
||
)
|
||
|
||
|
||
register_template(
|
||
name="qwen",
|
||
format_user=StringFormatter(container=[
|
||
"<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"
|
||
]),
|
||
format_system=StringFormatter(container=[
|
||
"<|im_start|>system\n{{content}}<|im_end|>\n"
|
||
]),
|
||
system="You are a helpful assistant.",
|
||
separator=[
|
||
"\n"
|
||
],
|
||
stop_words=[
|
||
"<|im_end|>"
|
||
],
|
||
replace_eos=True
|
||
)
|
||
|
||
|
||
register_template(
|
||
name="solar",
|
||
format_user=StringFormatter(container=[
|
||
"### User:\n{{content}}\n\n### Assistant:\n"
|
||
])
|
||
)
|
||
|
||
|
||
register_template(
|
||
name="starchat",
|
||
format_user=StringFormatter(container=[
|
||
{"token": "<|user|>"},
|
||
"\n{{content}}",
|
||
{"token": "<|end|>"},
|
||
"\n",
|
||
{"token": "<|assistant|>"}
|
||
]),
|
||
format_system=StringFormatter(container=[
|
||
{"token": "<|system|>"},
|
||
"\n{{content}}",
|
||
{"token": "<|end|>"},
|
||
"\n"
|
||
]),
|
||
separator=[
|
||
{"token": "<|end|>"},
|
||
"\n"
|
||
],
|
||
stop_words=[
|
||
"<|end|>"
|
||
],
|
||
efficient_eos=True
|
||
)
|
||
|
||
|
||
register_template(
|
||
name="vanilla"
|
||
)
|
||
|
||
|
||
register_template(
|
||
name="vicuna",
|
||
format_user=StringFormatter(container=[
|
||
"USER: {{content}} ASSISTANT:"
|
||
]),
|
||
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."
|
||
)
|
||
)
|
||
|
||
|
||
register_template(
|
||
name="xuanyuan",
|
||
format_user=StringFormatter(container=[
|
||
"Human: {{content}} Assistant:"
|
||
]),
|
||
system=(
|
||
"以下是用户和人工智能助手之间的对话。用户以Human开头，人工智能助手以Assistant开头，"
|
||
"会对人类提出的问题给出有帮助、高质量、详细和礼貌的回答，并且总是拒绝参与与不道德、"
|
||
"不安全、有争议、政治敏感等相关的话题、问题和指示。\n"
|
||
)
|
||
)
|
||
|
||
|
||
register_template(
|
||
name="xverse",
|
||
format_user=StringFormatter(container=[
|
||
"Human: {{content}}\n\nAssistant: "
|
||
])
|
||
)
|
||
|
||
|
||
register_template(
|
||
name="yayi",
|
||
format_user=StringFormatter(container=[
|
||
{"token": "<|Human|>"},
|
||
":\n{{content}}\n\n",
|
||
{"token": "<|YaYi|>"},
|
||
":"
|
||
]),
|
||
format_system=StringFormatter(container=[
|
||
{"token": "<|System|>"},
|
||
":\n{{content}}\n\n"
|
||
]),
|
||
system=(
|
||
"You are a helpful, respectful and honest assistant named YaYi "
|
||
"developed by Beijing Wenge Technology Co.,Ltd. "
|
||
"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.\n\n"
|
||
"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."
|
||
),
|
||
separator=[
|
||
"\n\n"
|
||
],
|
||
stop_words=[
|
||
"<|End|>"
|
||
]
|
||
)
|
||
|
||
|
||
register_template(
|
||
name="yi",
|
||
format_user=StringFormatter(container=[
|
||
"<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"
|
||
]),
|
||
separator=[
|
||
"\n"
|
||
],
|
||
stop_words=[
|
||
"<|im_end|>"
|
||
],
|
||
replace_eos=True
|
||
)
|
||
|
||
|
||
register_template(
|
||
name="yuan",
|
||
format_user=StringFormatter(container=[
|
||
"{{content}}",
|
||
{"token": "<sep>"}
|
||
]),
|
||
separator=[
|
||
"\n"
|
||
],
|
||
stop_words=[
|
||
"<eod>"
|
||
],
|
||
replace_eos=True
|
||
)
|
||
|
||
|
||
register_template(
|
||
name="zephyr",
|
||
format_user=StringFormatter(container=[
|
||
"<|user|>\n{{content}}</s><|assistant|>"
|
||
]),
|
||
format_system=StringFormatter(container=[
|
||
"<|system|>\n{{content}}</s>",
|
||
]),
|
||
system="You are a friendly chatbot who always responds in the style of a pirate"
|
||
)
|
||
|
||
|
||
register_template(
|
||
name="ziya",
|
||
format_user=StringFormatter(container=[
|
||
{"token": "<human>"},
|
||
":{{content}}\n",
|
||
{"token": "<bot>"},
|
||
":"
|
||
]),
|
||
separator=[
|
||
"\n"
|
||
]
|
||
)
|