TestLM / tokenization_rwkv_world.py
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# coding=utf-8
# Copyright 2018 The Open AI Team Authors and The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tokenization classes for RWKV5."""
import json
import os
from typing import TYPE_CHECKING, List, Optional, Tuple, Union
from transformers.tokenization_utils import PreTrainedTokenizer
from transformers.tokenization_utils_base import (
BatchEncoding,
EncodedInput,
TextInput,
TruncationStrategy,
)
from transformers.utils import PaddingStrategy, TensorType, logging, to_py_obj
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {
"vocab_file": "rwkv_vocab_v20230424.txt",
}
PRETRAINED_VOCAB_FILES_MAP = {
"vocab_file": {
"RWKV/rwkv-5-world-169m": "https://huggingface.co/RWKV/rwkv-5-world-169m/blob/main/rwkv_vocab_v20230424.txt",
},
}
class TRIE:
__slots__ = tuple("ch,to,values,front".split(","))
to: list
values: set
def __init__(self, front=None, ch=None):
self.ch = ch
self.to = [None for ch in range(256)]
self.values = set()
self.front = front
def __repr__(self):
fr = self
ret = []
while fr is not None:
if fr.ch is not None:
ret.append(fr.ch)
fr = fr.front
return "<TRIE %s %s>" % (ret[::-1], self.values)
def add(self, key: bytes, idx: int = 0, val=None):
if idx == len(key):
if val is None:
val = key
self.values.add(val)
return self
ch = key[idx]
if self.to[ch] is None:
self.to[ch] = TRIE(front=self, ch=ch)
return self.to[ch].add(key, idx=idx + 1, val=val)
def find_longest(self, key: bytes, idx: int = 0):
u: TRIE = self
ch: int = key[idx]
while u.to[ch] is not None:
u = u.to[ch]
idx += 1
if u.values:
ret = idx, u, u.values
if idx == len(key):
break
ch = key[idx]
return ret
class RWKVWorldTokenizer(PreTrainedTokenizer):
vocab_files_names = VOCAB_FILES_NAMES
model_input_names = ["input_ids", "attention_mask"]
def __init__(self, vocab_file, errors="replace", pad_token="0", **kwargs):
self.add_bos_token = False
self.encoder = {}
sorted = [] # must be already sorted
with open(vocab_file, "r", encoding="utf-8") as f:
lines = f.readlines()
for l in lines:
idx = int(l[: l.index(" ")])
x = eval(l[l.index(" ") : l.rindex(" ")])
x = x.encode("utf-8") if isinstance(x, str) else x
assert isinstance(x, bytes)
assert len(x) == int(l[l.rindex(" ") :])
sorted += [x]
self.encoder[idx] = x
self.decoder = {}
for k, v in self.encoder.items():
self.decoder[v] = int(k)
self.trie = TRIE()
for t, i in self.decoder.items():
_ = self.trie.add(t, val=(t, i))
self.errors = errors # how to handle errors in decoding
self.cache = {}
self.first_max_length = 0
super().__init__(
errors=errors,
**kwargs,
)
@property
def eos_token_id(self) -> Optional[int]:
return 0
@property
def eot_token_id(self) -> Optional[int]:
return 0
@property
def pad_token_id(self) -> Optional[int]:
return 0
@property
def vocab_size(self):
return len(self.encoder)
def get_vocab(self):
return dict(self.encoder, **self.added_tokens_encoder)
def add_tokens(self, new_tokens, special_tokens: bool = False):
for token in new_tokens:
token_id = self.convert_tokens_to_ids(token)
self.added_tokens_decoder[token_id] = token
def convert_ids_to_tokens(self, ids, skip_special_tokens=False):
if isinstance(ids, int):
ids = [ids]
tokens = []
for id_ in ids:
if id_ in self.added_tokens_decoder:
tokens.append(self.added_tokens_decoder[id_])
else:
tokens.append(self._convert_id_to_token(id_))
return tokens
def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
if self.add_bos_token:
bos_token_ids = [self.bos_token_id]
else:
bos_token_ids = []
output = bos_token_ids + token_ids_0
if token_ids_1 is None:
return output
return output + bos_token_ids + token_ids_1
def get_special_tokens_mask(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
) -> List[int]:
"""
Retrieves sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens using the tokenizer `prepare_for_model` or `encode_plus` methods.
Args:
token_ids_0 (`List[int]`):
List of IDs.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
already_has_special_tokens (`bool`, *optional*, defaults to `False`):
Whether or not the token list is already formatted with special tokens for the model.
Returns:
`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
)
if not self.add_bos_token:
return super().get_special_tokens_mask(
token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=False
)
if token_ids_1 is None:
return [1] + ([0] * len(token_ids_0))
return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1))
def encodeBytes(self, src: bytes):
idx: int = 0
tokens = []
while idx < len(src):
_idx: int = idx
idx, _, values = self.trie.find_longest(src, idx)
assert idx != _idx
_, token = next(iter(values))
tokens.append(token)
return tokens
def decodeBytes(self, tokens):
return b"".join(map(lambda i: self.encoder[i], tokens)) # noqa
def _tokenize(self, text, **kwargs):
"""Tokenize a string."""
return self.encodeBytes(text.encode("utf-8"))
def _decode_tokens(self, tokens):
try:
return self.decodeBytes(tokens).decode("utf-8")
except Exception:
return "\ufffd" # bad utf-8
def _decode(
self,
token_ids: Union[int, List[int]],
skip_special_tokens: bool = False,
**kwargs,
) -> str:
def remove_zeros_from_first_segment(token_ids, first_max_length):
first_segment = token_ids[:first_max_length]
first_segment_cleaned = [token for token in first_segment if token != 0]
return first_segment_cleaned + token_ids[first_max_length:]
# Convert inputs to python lists
token_ids = to_py_obj(token_ids)
token_ids = remove_zeros_from_first_segment(token_ids, self.first_max_length)
if isinstance(token_ids, int):
if token_ids in self.all_special_ids and skip_special_tokens:
return ""
return self.encoder.get(token_ids, self.unk_token)
elif isinstance(token_ids, list):
self.first_max_length
out_str = ""
out_last = 0
out_tokens = []
for i, token in enumerate(token_ids):
if token == 0:
break
out_tokens += [token]
tmp = self._decode_tokens(out_tokens[out_last:])
if "\ufffd" not in tmp:
out_str += tmp
out_last = i + 1
return out_str
else:
return token_ids
def _convert_token_to_id(self, token):
"""Converts a token (str) in an id using the vocab."""
return self.encoder.get(token, self.encoder.get(self.unk_token))
def _convert_id_to_token(self, index):
"""Converts an index (integer) in a token (str) using the vocab."""
return self.decoder.get(index)
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
if not os.path.exists(save_directory):
os.mkdir(save_directory)
if not os.path.isdir(save_directory):
logger.error(f"Vocabulary path ({save_directory}) should be a directory")
return
vocab_file = os.path.join(
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
)
with open(vocab_file, "w", encoding="utf-8") as f:
for idx, x in self.encoder.items():
if isinstance(x, str):
x = x.decode("utf-8")
line = f"{idx} {repr(x)} {len(x)}\n"
f.write(line)
return (vocab_file,)
def prepare_for_tokenization(self, text, **kwargs):
return (text, kwargs)
def _get_padding_truncation_strategies(
self, padding=False, truncation=None, max_length=None, pad_to_multiple_of=None, verbose=True, **kwargs
):
return PaddingStrategy.LONGEST, TruncationStrategy.DO_NOT_TRUNCATE, -1, kwargs
def _encode_plus(
self,
text: Union[TextInput, EncodedInput],
add_special_tokens: bool = True,
padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
max_length: Optional[int] = None,
stride: int = 0,
pad_to_multiple_of: Optional[int] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
return_token_type_ids: Optional[bool] = None,
return_attention_mask: Optional[bool] = None,
return_overflowing_tokens: bool = False,
return_special_tokens_mask: bool = False,
return_offsets_mapping: bool = False,
return_length: bool = False,
verbose: bool = True,
**kwargs,
) -> BatchEncoding:
def get_input_ids(text, max_length=None, pad_token_id=0):
def pad_sequence(seq, max_len, pad_tok):
return [pad_tok] * (max_len - len(seq)) + seq
if isinstance(text, str):
tokens = self._tokenize(text)
if max_length is not None:
tokens = pad_sequence(tokens, max_length, pad_token_id)
return tokens
elif isinstance(text, list) and len(text) > 0 and isinstance(text[0], str):
tokenized_texts = [self._tokenize(t) for t in text]
if max_length is None:
max_length = max(len(t) for t in tokenized_texts)
return [pad_sequence(t, max_length, pad_token_id) for t in tokenized_texts]
elif isinstance(text, (list, tuple)) and len(text) > 0 and isinstance(text[0], int):
if max_length is not None and len(text) < max_length:
return pad_sequence(text, max_length, pad_token_id)
return text
else:
raise ValueError(
"Input is not valid. Should be a string, a list/tuple of strings or a list/tuple of integers."
)
if return_offsets_mapping:
raise NotImplementedError(
"return_offset_mapping is not available when using Python tokenizers. "
"To use this feature, change your tokenizer to one deriving from "
"transformers.PreTrainedTokenizerFast. "
"More information on available tokenizers at "
"https://github.com/huggingface/transformers/pull/2674"
)
first_ids = get_input_ids(text)
return self.prepare_for_model(
first_ids,
pair_ids=None,
add_special_tokens=add_special_tokens,
padding=padding_strategy.value,
truncation=truncation_strategy.value,
max_length=max_length,
stride=stride,
pad_to_multiple_of=pad_to_multiple_of,
return_tensors=return_tensors,
prepend_batch_axis=True,
return_attention_mask=return_attention_mask,
return_token_type_ids=return_token_type_ids,
return_overflowing_tokens=return_overflowing_tokens,
return_special_tokens_mask=return_special_tokens_mask,
return_length=return_length,
verbose=verbose,
)
def _batch_encode_plus(
self,
batch_text_or_text_pairs: Union[
List[TextInput],
List[EncodedInput],
],
add_special_tokens: bool = True,
padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
max_length: Optional[int] = None,
stride: int = 0,
pad_to_multiple_of: Optional[int] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
return_token_type_ids: Optional[bool] = None,
return_attention_mask: Optional[bool] = None,
return_overflowing_tokens: bool = False,
return_special_tokens_mask: bool = False,
return_offsets_mapping: bool = False,
return_length: bool = False,
verbose: bool = True,
**kwargs,
) -> BatchEncoding:
def get_input_ids(text, max_length=None, pad_token_id=0):
def pad_sequence(seq, max_len, pad_tok):
return [pad_tok] * (max_len - len(seq)) + seq
if isinstance(text, str):
tokens = self._tokenize(text)
if max_length is not None:
tokens = pad_sequence(tokens, max_length, pad_token_id)
return tokens
elif isinstance(text, list) and len(text) > 0 and isinstance(text[0], str):
tokenized_texts = [self._tokenize(t) for t in text]
if max_length is None:
max_length = max(len(t) for t in tokenized_texts)
return [pad_sequence(t, max_length, pad_token_id) for t in tokenized_texts]
elif isinstance(text, (list, tuple)) and len(text) > 0 and isinstance(text[0], int):
if max_length is not None and len(text) < max_length:
return pad_sequence(text, max_length, pad_token_id)
return text
else:
raise ValueError(
"Input is not valid. Should be a string, a list/tuple of strings or a list/tuple of integers."
)
if return_offsets_mapping:
raise NotImplementedError(
"return_offset_mapping is not available when using Python tokenizers. "
"To use this feature, change your tokenizer to one deriving from "
"transformers.PreTrainedTokenizerFast."
)
first_max_length = 0
second_max_length = 0
for ids_or_pair_ids in batch_text_or_text_pairs:
if not isinstance(ids_or_pair_ids, (list, tuple)):
ids, pair_ids = ids_or_pair_ids, None
else:
ids, pair_ids = ids_or_pair_ids
first_ids = get_input_ids(ids)
second_ids = get_input_ids(pair_ids) if pair_ids is not None else None
first_max_length = max(first_max_length, len(first_ids))
if second_ids is not None:
second_max_length = max(second_max_length, len(second_ids))
self.first_max_length = first_max_length
input_ids = []
for ids_or_pair_ids in batch_text_or_text_pairs:
if not isinstance(ids_or_pair_ids, (list, tuple)):
ids, pair_ids = ids_or_pair_ids, None
else:
ids, pair_ids = ids_or_pair_ids
first_ids = get_input_ids(ids, max_length=first_max_length)
second_ids = get_input_ids(pair_ids, max_length=second_max_length) if pair_ids is not None else None
input_ids.append((first_ids, second_ids))
batch_outputs = self._batch_prepare_for_model(
input_ids,
add_special_tokens=add_special_tokens,
padding_strategy=padding_strategy,
truncation_strategy=truncation_strategy,
max_length=max_length,
stride=stride,
pad_to_multiple_of=pad_to_multiple_of,
return_attention_mask=return_attention_mask,
return_token_type_ids=return_token_type_ids,
return_overflowing_tokens=return_overflowing_tokens,
return_special_tokens_mask=return_special_tokens_mask,
return_length=return_length,
return_tensors=return_tensors,
verbose=verbose,
)
return BatchEncoding(batch_outputs)
def decode(
self,
token_ids: Union[int, List[int]],
skip_special_tokens: bool = False,
clean_up_tokenization_spaces: bool = None,
**kwargs,
) -> str:
"""
Converts a sequence of ids in a string, using the tokenizer and vocabulary with options to remove special
tokens and clean up tokenization spaces.
Similar to doing `self.convert_tokens_to_string(self.convert_ids_to_tokens(token_ids))`.
Args:
token_ids (`Union[int, List[int], np.ndarray, torch.Tensor, tf.Tensor]`):
List of tokenized input ids. Can be obtained using the `__call__` method.
skip_special_tokens (`bool`, *optional*, defaults to `False`):
Whether or not to remove special tokens in the decoding.
clean_up_tokenization_spaces (`bool`, *optional*):
Whether or not to clean up the tokenization spaces. If `None`, will default to
`self.clean_up_tokenization_spaces`.
kwargs (additional keyword arguments, *optional*):
Will be passed to the underlying model specific decode method.
Returns:
`str`: The decoded sentence.
"""
# Convert inputs to python lists
return self._decode(
token_ids=token_ids,
skip_special_tokens=skip_special_tokens,
clean_up_tokenization_spaces=clean_up_tokenization_spaces,
**kwargs,
)
def batch_decode(
self,
sequences: Union[List[int], List[List[int]]],
skip_special_tokens: bool = False,
clean_up_tokenization_spaces: bool = None,
**kwargs,
) -> List[str]:
"""
Convert a list of lists of token ids into a list of strings by calling decode.
Args:
sequences (`Union[List[int], List[List[int]], np.ndarray, torch.Tensor, tf.Tensor]`):
List of tokenized input ids. Can be obtained using the `__call__` method.
skip_special_tokens (`bool`, *optional*, defaults to `False`):
Whether or not to remove special tokens in the decoding.
clean_up_tokenization_spaces (`bool`, *optional*):
Whether or not to clean up the tokenization spaces. If `None`, will default to
`self.clean_up_tokenization_spaces`.
kwargs (additional keyword arguments, *optional*):
Will be passed to the underlying model specific decode method.
Returns:
`List[str]`: The list of decoded sentences.
"""
return [
self.decode(
seq,
skip_special_tokens=skip_special_tokens,
clean_up_tokenization_spaces=clean_up_tokenization_spaces,
**kwargs,
)
for seq in sequences
]
def _build_conversation_input_ids(self, conversation: "Conversation") -> List[int]:
input_ids = []
for is_user, text in conversation.iter_texts():
input_ids.extend(self.encode(text, add_special_tokens=False) + [self.eos_token_id])
if len(input_ids) > self.model_max_length:
input_ids = input_ids[-self.model_max_length :]
return input_ids