mirror of
https://git.datalinker.icu/vllm-project/vllm.git
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291 lines
10 KiB
Python
291 lines
10 KiB
Python
import os
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import re
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from dataclasses import dataclass
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from pathlib import Path
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from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union, cast
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import huggingface_hub
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from huggingface_hub import HfApi, hf_hub_download
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from mistral_common.protocol.instruct.request import ChatCompletionRequest
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# yapf: disable
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from mistral_common.tokens.tokenizers.mistral import (
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MistralTokenizer as PublicMistralTokenizer)
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# yapf: enable
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from mistral_common.tokens.tokenizers.sentencepiece import (
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SentencePieceTokenizer)
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from mistral_common.tokens.tokenizers.tekken import (SpecialTokenPolicy,
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Tekkenizer)
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from vllm.logger import init_logger
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if TYPE_CHECKING:
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from vllm.entrypoints.chat_utils import ChatCompletionMessageParam
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logger = init_logger(__name__)
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@dataclass
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class Encoding:
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input_ids: List[int]
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def list_local_repo_files(repo_id: str, revision: Optional[str]) -> List[str]:
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repo_cache = os.path.join(
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huggingface_hub.constants.HF_HUB_CACHE,
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huggingface_hub.constants.REPO_ID_SEPARATOR.join(
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["models", *repo_id.split("/")]))
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if revision is None:
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revision_file = os.path.join(repo_cache, "refs", "main")
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if os.path.isfile(revision_file):
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with open(revision_file) as file:
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revision = file.read()
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if revision:
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revision_dir = os.path.join(repo_cache, "snapshots", revision)
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if os.path.isdir(revision_dir):
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return os.listdir(revision_dir)
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return []
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def find_tokenizer_file(files: List[str]):
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file_pattern = re.compile(r"^tokenizer\.model\.v.*$|^tekken\.json$")
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matched_files = [file for file in files if file_pattern.match(file)]
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if len(matched_files) > 1:
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raise OSError(f"Found {len(matched_files)} files matching the "
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f"pattern: {file_pattern}. Make sure only one Mistral "
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f"tokenizer is present in {files}.")
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elif len(matched_files) == 0:
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raise OSError(f"Found {len(matched_files)} files matching the "
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f"pattern: {file_pattern}. Make sure that a Mistral "
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f"tokenizer is present in {files}.")
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return matched_files[0]
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class MistralTokenizer:
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def __init__(self, tokenizer: PublicMistralTokenizer) -> None:
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self.mistral = tokenizer
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self.instruct = tokenizer.instruct_tokenizer
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tokenizer_ = tokenizer.instruct_tokenizer.tokenizer
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self.is_tekken = isinstance(tokenizer_, Tekkenizer)
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self.is_spm = isinstance(tokenizer_, SentencePieceTokenizer)
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if self.is_tekken:
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# Make sure special tokens will not raise
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tokenizer_.special_token_policy = SpecialTokenPolicy.IGNORE
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elif self.is_spm:
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pass
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else:
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raise TypeError(f"Unsupported tokenizer: {type(tokenizer_)}")
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self._vocab = tokenizer_.vocab()
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# Convert to a Dict[str, int] to match protocol, but this is a lossy
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# conversion. There may be multiple token ids that decode to the same
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# string due to partial UTF-8 byte sequences being converted to <20>
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self._vocab_dict = {
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token: idx
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for idx, token in enumerate(self._vocab)
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}
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self.tokenizer = tokenizer_
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self._max_token_id = self.vocab_size - 1
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@classmethod
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def from_pretrained(cls,
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path_or_repo_id: str,
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*,
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revision: Optional[str] = None) -> "MistralTokenizer":
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if not Path(path_or_repo_id).exists():
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assert len(path_or_repo_id.split("/")) == 2, (
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"You have either provided a non-existent path: "
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"{path_or_repo_id} or an invalid HF Hub repo id.")
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tokenizer_file = cls._download_mistral_tokenizer_from_hf(
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path_or_repo_id, revision)
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elif Path(path_or_repo_id).is_dir():
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tokenizer_file_name = find_tokenizer_file(
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os.listdir(path_or_repo_id))
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tokenizer_file = str(Path(path_or_repo_id) / tokenizer_file_name)
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else:
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assert Path(
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path_or_repo_id).is_file(), f"Invalid path: {path_or_repo_id}"
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mistral_tokenizer = PublicMistralTokenizer.from_file(tokenizer_file)
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return cls(mistral_tokenizer)
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@staticmethod
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def _download_mistral_tokenizer_from_hf(tokenizer_name: str,
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revision: Optional[str]) -> str:
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try:
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hf_api = HfApi()
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files = hf_api.list_repo_files(repo_id=tokenizer_name,
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revision=revision)
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except ConnectionError as exc:
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files = list_local_repo_files(repo_id=tokenizer_name,
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revision=revision)
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if len(files) == 0:
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raise exc
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filename = find_tokenizer_file(files)
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tokenizer_file = hf_hub_download(tokenizer_name,
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filename=filename,
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revision=revision)
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return tokenizer_file
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# the following attributes are set to fit VLLM's design
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@property
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def all_special_tokens_extended(self) -> List[str]:
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return []
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@property
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def all_special_tokens(self) -> List[str]:
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return []
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@property
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def all_special_ids(self) -> List[int]:
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return []
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@property
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def bos_token_id(self) -> int:
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return self.tokenizer.bos_id
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@property
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def eos_token_id(self) -> int:
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return self.tokenizer.eos_id
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@property
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def is_fast(self) -> bool:
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return True
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@property
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def vocab_size(self) -> int:
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return len(self._vocab)
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@property
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def max_token_id(self) -> int:
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return self._max_token_id
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def __len__(self) -> int:
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return self.vocab_size
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def __call__(
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self,
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prompt: str,
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add_special_tokens: bool = False,
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truncation: bool = False,
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max_length: Optional[int] = None,
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):
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# Mistral Tokenizers should not add special tokens
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input_ids = self.encode(prompt)
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if truncation:
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input_ids = input_ids[:max_length]
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return Encoding(input_ids=input_ids)
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def get_vocab(self) -> Dict[str, int]:
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# NB: the dictionary form of the vocabulary collapses token ids that map
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# to the same string but have different bytes
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return self._vocab_dict
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def get_added_vocab(self) -> Dict[str, int]:
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# Mistral tokenizers have no added vocabulary
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return {}
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def encode(self, prompt: str) -> List[int]:
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# `encode` should only be used for prompt completion
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# it should never be used for chat_completion.
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# For chat completion use `apply_chat_template`
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return self.tokenizer.encode(prompt, bos=True, eos=False)
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def apply_chat_template(self,
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messages: List["ChatCompletionMessageParam"],
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tools: Optional[Dict[str, Any]] = None,
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**kwargs) -> List[int]:
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last_message = cast(Dict[str, Any], messages[-1])
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if last_message["role"] == "assistant":
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last_message["prefix"] = True
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request = ChatCompletionRequest(messages=messages,
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tools=tools) # type: ignore[type-var]
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encoded = self.mistral.encode_chat_completion(request)
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# encode-decode to get clean prompt
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return encoded.tokens
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def convert_tokens_to_string(self, tokens: List[str]) -> str:
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if self.is_tekken:
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tokens = [
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t for t in tokens
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if t not in self.tokenizer._all_special_tokens
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]
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if any(isinstance(t, bytes) for t in tokens):
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# we need to encode and decode all tokens again
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shift = self.tokenizer.num_special_tokens
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def _token_to_id(t: str):
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t_bytes = t.encode("utf-8") \
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if not isinstance(t, bytes) else t
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try:
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return shift + \
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self.tokenizer._tekken_token2id_nospecial[t_bytes]
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except KeyError:
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logger.warning(
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"Failed to convert token %s to id,"
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" replacing with <unk>", t_bytes)
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return self.tokenizer.unk_id
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ids = [_token_to_id(t) for t in tokens]
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decoded = self.tokenizer.decode(ids)
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else:
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decoded = "".join(tokens)
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else:
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decoded = self.tokenizer.decode(tokens) # type: ignore[arg-type]
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return decoded
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def decode(self,
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ids: Union[List[int], int],
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skip_special_tokens: bool = True) -> str:
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assert (
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skip_special_tokens
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), "skip_special_tokens=False is not supported for Mistral tokenizers."
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if isinstance(ids, int):
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ids = [ids]
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return self.tokenizer.decode(ids)
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def convert_ids_to_tokens(
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self,
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ids: List[int],
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skip_special_tokens: bool = True,
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) -> List[str]:
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# TODO(Patrick) - potentially allow special tokens to not be skipped
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assert (
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skip_special_tokens
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), "skip_special_tokens=False is not supported for Mistral tokenizers."
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assert self.is_tekken or self.is_spm, type(self.tokenizer)
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if self.is_tekken:
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# skip special tokens
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ids = [i for i in ids if i > self.tokenizer.num_special_tokens]
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tokens = [self.tokenizer.id_to_piece(id) for id in ids]
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if any("<EFBFBD>" in t for t in tokens) and self.is_tekken:
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# if a decoded token contains the replacement character, then the
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# token has an incomplete UTF-8 character so we must use bytes
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# See: https://github.com/vllm-project/vllm/pull/8640
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# https://github.com/vllm-project/vllm/pull/9625
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# if underlying tokenizeir is sentencepiece, we just add "<22>"
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tokens = [self.tokenizer.id_to_byte_piece(id) for id in ids]
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return tokens
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