mirror of
https://github.com/hiyouga/LLaMA-Factory.git
synced 2025-08-04 12:42:51 +08:00
808 lines
19 KiB
Python
808 lines
19 KiB
Python
import tiktoken
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from dataclasses import dataclass
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from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union
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from llmtuner.extras.logging import get_logger
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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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prefix: List[Union[str, Dict[str, str]]]
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prompt: List[Union[str, Dict[str, str]]]
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system: str
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sep: List[Union[str, Dict[str, str]]]
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stop_words: List[str]
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use_history: bool
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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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query: str,
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resp: str,
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history: Optional[List[Tuple[str, str]]] = None,
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system: Optional[str] = None
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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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system, history = self._format(query, resp, history, system)
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encoded_pairs = self._encode(tokenizer, system, history)
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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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query: str,
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resp: str,
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history: Optional[List[Tuple[str, str]]] = None,
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system: Optional[str] = None
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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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system, history = self._format(query, resp, history, system)
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encoded_pairs = self._encode(tokenizer, system, history)
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return encoded_pairs
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def _format(
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self,
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query: str,
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resp: str,
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history: Optional[List[Tuple[str, str]]] = None,
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system: Optional[str] = None
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) -> Tuple[str, List[Tuple[str, str]]]:
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r"""
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Aligns inputs to the standard format.
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"""
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system = system or self.system # use system if provided
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history = history if (history and self.use_history) else []
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history = history + [(query, resp)]
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return system, history
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def _get_special_ids(
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self,
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tokenizer: "PreTrainedTokenizer"
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) -> Tuple[List[int], List[int]]:
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if tokenizer.bos_token_id is not None and getattr(tokenizer, "add_bos_token", True):
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bos_ids = [tokenizer.bos_token_id]
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else: # baichuan, gpt2, qwen, yi models have no bos token
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bos_ids = []
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if tokenizer.eos_token_id is None:
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raise ValueError("EOS token is required.")
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if self.efficient_eos:
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eos_ids = []
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else:
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eos_ids = [tokenizer.eos_token_id]
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return bos_ids, eos_ids
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def _encode(
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self,
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tokenizer: "PreTrainedTokenizer",
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system: str,
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history: List[Tuple[str, str]]
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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: bos + prefix + sep + query resp + eos
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Turn t: sep + bos + query resp + eos
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"""
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bos_ids, eos_ids = self._get_special_ids(tokenizer)
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sep_ids = self._convert_inputs_to_ids(tokenizer, context=self.sep)
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encoded_pairs = []
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for turn_idx, (query, resp) in enumerate(history):
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if turn_idx == 0:
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prefix_ids = self._convert_inputs_to_ids(tokenizer, context=self.prefix, system=system)
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if len(prefix_ids) != 0: # has prefix
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prefix_ids = bos_ids + prefix_ids + sep_ids
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else:
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prefix_ids = bos_ids
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else:
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prefix_ids = sep_ids + bos_ids
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query_ids = self._convert_inputs_to_ids(tokenizer, context=self.prompt, query=query, idx=str(turn_idx+1))
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resp_ids = self._convert_inputs_to_ids(tokenizer, context=[resp])
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encoded_pairs.append((prefix_ids + query_ids, resp_ids + eos_ids))
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return encoded_pairs
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def _convert_inputs_to_ids(
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self,
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tokenizer: "PreTrainedTokenizer",
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context: List[Union[str, Dict[str, str]]],
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system: Optional[str] = None,
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query: Optional[str] = None,
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idx: Optional[str] = None
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) -> List[int]:
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r"""
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Converts context to token ids.
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"""
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if isinstance(getattr(tokenizer, "tokenizer", None), tiktoken.Encoding): # for tiktoken tokenizer (Qwen)
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kwargs = dict(allowed_special="all")
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else:
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kwargs = dict(add_special_tokens=False)
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token_ids = []
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for elem in context:
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if isinstance(elem, str):
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elem = elem.replace("{{system}}", system, 1) if system is not None else elem
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elem = elem.replace("{{query}}", query, 1) if query is not None else elem
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elem = elem.replace("{{idx}}", idx, 1) if idx is not None else elem
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if len(elem) != 0:
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token_ids = token_ids + tokenizer.encode(elem, **kwargs)
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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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else:
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raise ValueError("Input must be string 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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system: str,
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history: List[Tuple[str, str]]
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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: bos + prefix + query resp + eos
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Turn t: bos + query resp + eos
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"""
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bos_ids, eos_ids = self._get_special_ids(tokenizer)
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encoded_pairs = []
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for turn_idx, (query, resp) in enumerate(history):
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if turn_idx == 0: # llama2 template has no sep_ids
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query = self.prefix[0].replace("{{system}}", system) + query
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query_ids = self._convert_inputs_to_ids(tokenizer, context=self.prompt, query=query)
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resp_ids = self._convert_inputs_to_ids(tokenizer, context=[resp])
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encoded_pairs.append((bos_ids + query_ids, resp_ids + eos_ids))
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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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prefix: List[Union[str, Dict[str, str]]],
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prompt: List[Union[str, Dict[str, str]]],
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system: str,
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sep: List[Union[str, Dict[str, str]]],
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stop_words: Optional[List[str]] = [],
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use_history: Optional[bool] = True,
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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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prefix=prefix,
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prompt=prompt,
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system=system,
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sep=sep,
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stop_words=stop_words,
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use_history=use_history,
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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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prefix=[
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"{{system}}"
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],
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prompt=[
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"### Instruction:\n{{query}}\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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sep=[
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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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prefix=[
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"{{system}}"
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],
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prompt=[
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"Human: {{query}}###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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sep=[
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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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prefix=[
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"{{system}}"
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],
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prompt=[
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{"token": "<reserved_102>"}, # user token
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"{{query}}",
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{"token": "<reserved_103>"} # assistant token
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],
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system="",
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sep=[],
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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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prefix=[
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"{{system}}"
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],
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prompt=[
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{"token": "<reserved_106>"}, # user token
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"{{query}}",
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{"token": "<reserved_107>"} # assistant token
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],
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system="",
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sep=[],
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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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prefix=[
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"{{system}}"
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],
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prompt=[
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"Human: {{query}}\n\nBelle: "
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],
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system="",
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sep=[
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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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prefix=[
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"{{system}}"
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],
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prompt=[
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{"token": "[|Human|]:"},
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"{{query}}",
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{"token": "[|AI|]:"}
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],
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system="",
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sep=[]
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)
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register_template(
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name="chatglm2",
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prefix=[
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{"token": "[gMASK]"},
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{"token": "sop"},
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"{{system}}"
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],
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prompt=[
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"[Round {{idx}}]\n\n问：{{query}}\n\n答："
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],
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system="",
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sep=[
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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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prefix=[
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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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"{{system}}"
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],
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prompt=[
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{"token": "<|user|>"},
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"\n",
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"{{query}}",
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{"token": "<|assistant|>"},
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"\n" # add an extra newline to avoid error in ChatGLM's process_response method
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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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sep=[],
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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="chatglm3_raw", # the raw template for tool tuning
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prefix=[
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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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"{{system}}"
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],
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prompt=[
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{"token": "<|user|>"},
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"\n",
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"{{query}}",
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{"token": "<|assistant|>"}
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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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sep=[],
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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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prefix=[
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{"token": "[gMASK]"},
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{"token": "sop"},
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"{{system}}"
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],
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prompt=[
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"{{query}}"
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],
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system="",
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sep=[]
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)
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register_template(
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name="deepseek",
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prefix=[
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"{{system}}"
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],
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prompt=[
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"User: {{query}}\n\nAssistant:"
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],
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system="",
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sep=[]
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)
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register_template(
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name="deepseekcoder",
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prefix=[
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"{{system}}"
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],
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prompt=[
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"### Instruction:\n{{query}}\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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sep=[
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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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register_template(
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name="default",
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prefix=[
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"{{system}}"
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],
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prompt=[
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"Human: {{query}}\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. "
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"The assistant gives helpful, detailed, and polite answers to the user's questions."
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),
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sep=[
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"\n"
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]
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)
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register_template(
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name="falcon",
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prefix=[
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"{{system}}"
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],
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prompt=[
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"User: {{query}}\nFalcon:"
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],
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system="",
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sep=[
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"\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="intern",
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prefix=[
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"{{system}}"
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],
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prompt=[
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"<|User|>:{{query}}",
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{"token": "<eoh>"},
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"\n<|Bot|>:"
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],
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system="",
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sep=[
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{"token": "<eoa>"},
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"\n"
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],
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stop_words=[
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"<eoa>"
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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="llama2",
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prefix=[
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"<<SYS>>\n{{system}}\n<</SYS>>\n\n"
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],
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prompt=[
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"[INST] {{query}} [/INST]"
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],
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system=(
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"You are a helpful, respectful and honest assistant. "
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"Always answer as helpfully as possible, while being safe. "
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"Your answers should not include any harmful, unethical, "
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"racist, sexist, toxic, dangerous, or illegal content. "
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"Please ensure that your responses are socially unbiased and positive in nature.\n\n"
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"If a question does not make any sense, or is not factually coherent, "
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"explain why instead of answering something not correct. "
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"If you don't know the answer to a question, please don't share false information."
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),
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sep=[]
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)
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register_template(
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name="llama2_zh",
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prefix=[
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"<<SYS>>\n{{system}}\n<</SYS>>\n\n"
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],
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prompt=[
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"[INST] {{query}} [/INST]"
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],
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system="You are a helpful assistant. 你是一个乐于助人的助手。",
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sep=[]
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)
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register_template(
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name="mistral",
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prefix=[
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"{{system}}"
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],
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prompt=[
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"[INST] {{query}} [/INST]"
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],
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system="",
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sep=[]
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)
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register_template(
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name="openchat",
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prefix=[
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"{{system}}"
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],
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prompt=[
|
||
"GPT4 Correct User: {{query}}",
|
||
{"token": "<|end_of_turn|>"},
|
||
"GPT4 Correct Assistant:"
|
||
],
|
||
system="",
|
||
sep=[
|
||
{"token": "<|end_of_turn|>"}
|
||
],
|
||
stop_words=[
|
||
"<|end_of_turn|>"
|
||
],
|
||
efficient_eos=True
|
||
)
|
||
|
||
|
||
register_template(
|
||
name="qwen",
|
||
prefix=[
|
||
"<|im_start|>system\n{{system}}<|im_end|>"
|
||
],
|
||
prompt=[
|
||
"<|im_start|>user\n{{query}}<|im_end|>\n<|im_start|>assistant\n"
|
||
],
|
||
system="You are a helpful assistant.",
|
||
sep=[
|
||
"\n"
|
||
],
|
||
stop_words=[
|
||
"<|im_end|>"
|
||
],
|
||
replace_eos=True
|
||
)
|
||
|
||
|
||
register_template(
|
||
name="starchat",
|
||
prefix=[
|
||
{"token": "<|system|>"},
|
||
"\n{{system}}",
|
||
],
|
||
prompt=[
|
||
{"token": "<|user|>"},
|
||
"\n{{query}}",
|
||
{"token": "<|end|>"},
|
||
"\n",
|
||
{"token": "<|assistant|>"}
|
||
],
|
||
system="",
|
||
sep=[
|
||
{"token": "<|end|>"},
|
||
"\n"
|
||
],
|
||
stop_words=[
|
||
"<|end|>"
|
||
],
|
||
efficient_eos=True
|
||
)
|
||
|
||
|
||
register_template(
|
||
name="vanilla",
|
||
prefix=[],
|
||
prompt=[
|
||
"{{query}}"
|
||
],
|
||
system="",
|
||
sep=[],
|
||
use_history=False
|
||
)
|
||
|
||
|
||
register_template(
|
||
name="vicuna",
|
||
prefix=[
|
||
"{{system}}"
|
||
],
|
||
prompt=[
|
||
"USER: {{query}} 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."
|
||
),
|
||
sep=[]
|
||
)
|
||
|
||
|
||
register_template(
|
||
name="xuanyuan",
|
||
prefix=[
|
||
"{{system}}"
|
||
],
|
||
prompt=[
|
||
"Human: {{query}} 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"
|
||
),
|
||
sep=[]
|
||
)
|
||
|
||
|
||
register_template(
|
||
name="xverse",
|
||
prefix=[
|
||
"{{system}}"
|
||
],
|
||
prompt=[
|
||
"Human: {{query}}\n\nAssistant: "
|
||
],
|
||
system="",
|
||
sep=[]
|
||
)
|
||
|
||
|
||
register_template(
|
||
name="yayi",
|
||
prefix=[
|
||
{"token": "<|System|>"},
|
||
":\n{{system}}"
|
||
],
|
||
prompt=[
|
||
{"token": "<|Human|>"},
|
||
":\n{{query}}\n\n",
|
||
{"token": "<|YaYi|>"},
|
||
":"
|
||
],
|
||
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."
|
||
),
|
||
sep=[
|
||
"\n\n"
|
||
],
|
||
stop_words=[
|
||
"<|End|>"
|
||
]
|
||
)
|
||
|
||
|
||
register_template(
|
||
name="yi",
|
||
prefix=[
|
||
"{{system}}"
|
||
],
|
||
prompt=[
|
||
"<|im_start|>user\n{{query}}<|im_end|>\n<|im_start|>assistant\n"
|
||
],
|
||
system="",
|
||
sep=[
|
||
"\n"
|
||
],
|
||
stop_words=[
|
||
"<|im_end|>"
|
||
],
|
||
replace_eos=True
|
||
)
|
||
|
||
|
||
register_template(
|
||
name="yuan",
|
||
prefix=[
|
||
"{{system}}"
|
||
],
|
||
prompt=[
|
||
"{{query}}",
|
||
{"token": "<sep>"}
|
||
],
|
||
system="",
|
||
sep=[
|
||
"\n"
|
||
],
|
||
stop_words=[
|
||
"<eod>"
|
||
],
|
||
replace_eos=True
|
||
)
|
||
|
||
|
||
register_template(
|
||
name="zephyr",
|
||
prefix=[
|
||
{"token": "<|system|>"},
|
||
"\n{{system}}",
|
||
{"token": "</s>"}
|
||
],
|
||
prompt=[
|
||
{"token": "<|user|>"},
|
||
"\n{{query}}",
|
||
{"token": "</s>"},
|
||
{"token": "<|assistant|>"}
|
||
],
|
||
system="You are a friendly chatbot who always responds in the style of a pirate",
|
||
sep=[]
|
||
)
|
||
|
||
|
||
register_template(
|
||
name="ziya",
|
||
prefix=[
|
||
"{{system}}"
|
||
],
|
||
prompt=[
|
||
{"token": "<human>"},
|
||
":{{query}}\n",
|
||
{"token": "<bot>"},
|
||
":"
|
||
],
|
||
system="",
|
||
sep=[
|
||
"\n"
|
||
]
|
||
)
|