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# Copyright (c) OpenMMLab. All rights reserved.
import json
import os
import os.path as osp
from collections import deque
from typing import List, Optional, Sequence, Union
import torch
from lmdeploy.utils import get_logger
# this file will be copied to triton server, make sure all
# importing are starting from the package root lmdeploy
class SentencePieceTokenizer:
"""Tokenizer of sentencepiece.
Args:
model_file (str): the path of the tokenizer model
"""
def __init__(self, model_file: str):
from sentencepiece import SentencePieceProcessor
self.model = SentencePieceProcessor(model_file=model_file)
self._prefix_space_tokens = None
# for stop words
self._maybe_decode_bytes: bool = None
# TODO maybe lack a constant.py
self._indexes_tokens_deque = deque(maxlen=10)
self.max_indexes_num = 5
self.logger = get_logger('lmdeploy')
@property
def vocab_size(self):
"""vocabulary size."""
return self.model.vocab_size()
@property
def bos_token_id(self):
"""begine of the sentence token id."""
return self.model.bos_id()
@property
def eos_token_id(self):
"""end of the sentence token id."""
return self.model.eos_id()
@property
def prefix_space_tokens(self):
"""tokens without prefix space."""
if self._prefix_space_tokens is None:
vocab = self.model.IdToPiece(list(range(self.vocab_size)))
self._prefix_space_tokens = {
i
for i, tok in enumerate(vocab) if tok.startswith('▁')
}
return self._prefix_space_tokens
def _maybe_add_prefix_space(self, tokens, decoded):
"""maybe add prefix space for incremental decoding."""
if len(tokens) and not decoded.startswith(' ') and\
tokens[0] in self.prefix_space_tokens:
return ' ' + decoded
else:
return decoded
def indexes_containing_token(self, token: str):
"""Return all the possible indexes, whose decoding output may contain
the input token."""
# traversing vocab is time consuming, can not be accelerated with
# multi threads (computation) or multi process (can't pickle tokenizer)
# so, we maintain latest 10 stop words and return directly if matched
for _token, _indexes in self._indexes_tokens_deque:
if token == _token:
return _indexes
if token == ' ': # ' ' is special
token = '▁'
vocab = self.model.IdToPiece(list(range(self.vocab_size)))
indexes = [i for i, voc in enumerate(vocab) if token in voc]
if len(indexes) > self.max_indexes_num:
indexes = self.encode(token, add_bos=False)[-1:]
self.logger.warning(
f'There are too many(>{self.max_indexes_num}) possible '
f'indexes may decoding {token}, we will use {indexes} only')
self._indexes_tokens_deque.append((token, indexes))
return indexes
def encode(self, s: str, add_bos: bool = True, **kwargs):
"""Tokenize a prompt.
Args:
s (str): a prompt
Returns:
list[int]: token ids
"""
return self.model.Encode(s, add_bos=add_bos, **kwargs)
def decode(self, t: Sequence[int], offset: Optional[int] = None):
"""De-tokenize.
Args:
t (List[int]): a list of token ids
offset (int): for incrementally decoding. Default to None, which
means not applied.
Returns:
str: text of decoding tokens
"""
if isinstance(t, torch.Tensor):
t = t.tolist()
t = t[offset:]
out_string = self.model.Decode(t)
if offset:
out_string = self._maybe_add_prefix_space(t, out_string)
return out_string
def __call__(self, s: Union[str, Sequence[str]]):
"""Tokenize prompts.
Args:
s (str): prompts
Returns:
list[int]: token ids
"""
import addict
add_bos = False
add_eos = False
input_ids = self.model.Encode(s, add_bos=add_bos, add_eos=add_eos)
return addict.Addict(input_ids=input_ids)
class HuggingFaceTokenizer:
"""Tokenizer of sentencepiece.
Args:
model_dir (str): the directory of the tokenizer model
"""
def __init__(self, model_dir: str):
from transformers import AutoTokenizer
model_file = osp.join(model_dir, 'tokenizer.model')
backend_tokenizer_file = osp.join(model_dir, 'tokenizer.json')
model_file_exists = osp.exists(model_file)
self.logger = get_logger('lmdeploy')
if not osp.exists(backend_tokenizer_file) and model_file_exists:
self.logger.warning(
'Can not find tokenizer.json. '
'It may take long time to initialize the tokenizer.')
self.model = AutoTokenizer.from_pretrained(model_dir,
trust_remote_code=True)
self._prefix_space_tokens = None
# save tokenizer.json to reuse
if not osp.exists(backend_tokenizer_file) and model_file_exists:
if hasattr(self.model, 'backend_tokenizer'):
if os.access(model_dir, os.W_OK):
self.model.backend_tokenizer.save(backend_tokenizer_file)
if self.model.eos_token_id is None:
generation_config_file = osp.join(model_dir,
'generation_config.json')
if osp.exists(generation_config_file):
with open(generation_config_file, 'r') as f:
cfg = json.load(f)
self.model.eos_token_id = cfg['eos_token_id']
elif hasattr(self.model, 'eod_id'): # Qwen remote
self.model.eos_token_id = self.model.eod_id
# for stop words
self._vocab_size_with_added: int = None
self._maybe_decode_bytes: bool = None
# TODO maybe lack a constant.py
self._indexes_tokens_deque = deque(maxlen=10)
self.max_indexes_num = 5
self.token2id = {}
@property
def vocab_size(self):
"""vocabulary size."""
return self.model.vocab_size
@property
def vocab_size_with_added(self):
"""vocabulary size with added vocab."""
if self._vocab_size_with_added is not None:
return self._vocab_size_with_added
self._vocab_size_with_added = len(self.model.get_vocab())
return self._vocab_size_with_added
@property
def bos_token_id(self):
"""begine of the sentence token id."""
return self.model.bos_token_id
@property
def eos_token_id(self):
"""end of the sentence token id."""
return self.model.eos_token_id
@property
def prefix_space_tokens(self):
"""tokens without prefix space."""
if self._prefix_space_tokens is None:
vocab = self.model.convert_ids_to_tokens(
list(range(self.vocab_size)))
self._prefix_space_tokens = {
i
for i, tok in enumerate(vocab)
if tok.startswith('▁' if isinstance(tok, str) else b' ')
}
return self._prefix_space_tokens
def _maybe_add_prefix_space(self, tokens: List[int], decoded: str):
"""maybe add prefix space for incremental decoding."""
if len(tokens) and not decoded.startswith(' ') and\
tokens[0] in self.prefix_space_tokens:
return ' ' + decoded
else:
return decoded
@property
def maybe_decode_bytes(self):
"""Check if self.model.convert_ids_to_tokens return not a str value."""
if self._maybe_decode_bytes is None:
self._maybe_decode_bytes = False
vocab = self.model.convert_ids_to_tokens(
list(range(self.vocab_size)))
for tok in vocab:
if not isinstance(tok, str):
self._maybe_decode_bytes = True
break
return self._maybe_decode_bytes
def indexes_containing_token(self, token: str):
"""Return all the possible indexes, whose decoding output may contain
the input token."""
# traversing vocab is time consuming, can not be accelerated with
# multi threads (computation) or multi process (can't pickle tokenizer)
# so, we maintain latest 10 stop words and return directly if matched
for _token, _indexes in self._indexes_tokens_deque:
if token == _token:
return _indexes
if self.token2id == {}:
# decode is slower than convert_ids_to_tokens
if self.maybe_decode_bytes:
self.token2id = {
self.model.decode(i): i
for i in range(self.vocab_size)
}
else:
self.token2id = {
self.model.convert_ids_to_tokens(i): i
for i in range(self.vocab_size)
}
if token == ' ': # ' ' is special
token = '▁'
indexes = [i for _token, i in self.token2id.items() if token in _token]
if len(indexes) > self.max_indexes_num:
indexes = self.encode(token, add_bos=False)[-1:]
self.logger.warning(
f'There are too many(>{self.max_indexes_num}) possible '
f'indexes may decoding {token}, we will use {indexes} only')
# there might be token id that exceeds self.vocab_size
if len(indexes) == 0:
indexes = self.encode(token, False)
if len(indexes) != 1:
self.logger.warning(
f'The token {token}, its length of indexes {indexes} is '
'not 1. Currently, it can not be used as stop words')
indexes = []
self._indexes_tokens_deque.append((token, indexes))
return indexes
def encode(self, s: str, add_bos: bool = True, **kwargs):
"""Tokenize a prompt.
Args:
s (str): a prompt
Returns:
list[int]: token ids
"""
encoded = self.model.encode(s, **kwargs)
if not add_bos:
# in the middle of a session
if len(encoded) and encoded[0] == self.bos_token_id:
encoded = encoded[1:]
return encoded
def decode(self, t: Sequence[int], offset: Optional[int] = None):
"""De-tokenize.
Args:
t (List[int]): a list of token ids
offset (int): for incrementally decoding. Default to None, which
means not applied.
Returns:
str: text of decoding tokens
"""
skip_special_tokens = True
t = t[offset:]
out_string = self.model.decode(t,
skip_special_tokens=skip_special_tokens)
if offset:
out_string = self._maybe_add_prefix_space(t, out_string)
return out_string
def __call__(self, s: Union[str, Sequence[str]]):
"""Tokenize prompts.
Args:
s (str): prompts
Returns:
list[int]: token ids
"""
add_special_tokens = False
return self.model(s, add_special_tokens=add_special_tokens)
class Tokenizer:
"""Tokenize prompts or de-tokenize tokens into texts.
Args:
model_file (str): the path of the tokenizer model
"""
def __init__(self, model_file: str):
if model_file.endswith('.model'):
model_folder = osp.split(model_file)[0]
else:
model_folder = model_file
model_file = osp.join(model_folder, 'tokenizer.model')
tokenizer_config_file = osp.join(model_folder, 'tokenizer_config.json')
model_file_exists = osp.exists(model_file)
config_exists = osp.exists(tokenizer_config_file)
use_hf_model = config_exists or not model_file_exists
self.logger = get_logger('lmdeploy')
if not use_hf_model:
self.model = SentencePieceTokenizer(model_file)
else:
self.model = HuggingFaceTokenizer(model_folder)
@property
def vocab_size(self):
"""vocabulary size."""
return self.model.vocab_size
@property
def bos_token_id(self):
"""begine of the sentence token id."""
return self.model.bos_token_id
@property
def eos_token_id(self):
"""end of the sentence token id."""
return self.model.eos_token_id
def encode(self, s: str, add_bos: bool = True, **kwargs):
"""Tokenize a prompt.
Args:
s (str): a prompt
Returns:
list[int]: token ids
"""
return self.model.encode(s, add_bos, **kwargs)
def decode(self, t: Sequence[int], offset: Optional[int] = None):
"""De-tokenize.
Args:
t (List[int]): a list of token ids
offset (int): for incrementally decoding. Default to None, which
means not applied.
Returns:
str: text of decoding tokens
"""
return self.model.decode(t, offset)
def __call__(self, s: Union[str, Sequence[str]]):
"""Tokenize prompts.
Args:
s (str): prompts
Returns:
list[int]: token ids
"""
return self.model(s)
def indexes_containing_token(self, token):
"""Return all the possible indexes, whose decoding output may contain
the input token."""
encoded = self.encode(token, add_bos=False)
if len(encoded) > 1:
self.logger.warning(
f'The token {token}, its length of indexes {encoded} is over '
'than 1. Currently, it can not be used as stop words')
return []
return self.model.indexes_containing_token(token)
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