|
""" Tokenization classes for GPT-JX """ |
|
import json |
|
import os |
|
from functools import lru_cache |
|
from typing import TYPE_CHECKING, List, Optional, Tuple |
|
|
|
import regex as re |
|
|
|
from transformers.tokenization_utils import AddedToken, PreTrainedTokenizer |
|
from transformers.utils import logging |
|
|
|
|
|
if TYPE_CHECKING: |
|
from transformers.pipelines.conversational import Conversation |
|
|
|
logger = logging.get_logger(__name__) |
|
|
|
VOCAB_FILES_NAMES = { |
|
"vocab_file": "vocab.json", |
|
"merges_file": "merges.txt", |
|
} |
|
|
|
@lru_cache() |
|
def bytes_to_unicode(): |
|
""" |
|
Returns list of utf-8 byte and a mapping to unicode strings. We specifically avoids mapping to whitespace/control |
|
characters the bpe code barfs on. |
|
|
|
The reversible bpe codes work on unicode strings. This means you need a large # of unicode characters in your vocab |
|
if you want to avoid UNKs. When you're at something like a 10B token dataset you end up needing around 5K for |
|
decent coverage. This is a significant percentage of your normal, say, 32K bpe vocab. To avoid that, we want lookup |
|
tables between utf-8 bytes and unicode strings. |
|
""" |
|
bs = ( |
|
list(range(ord("!"), ord("~") + 1)) + list(range(ord("¡"), ord("¬") + 1)) + list(range(ord("®"), ord("ÿ") + 1)) |
|
) |
|
cs = bs[:] |
|
n = 0 |
|
for b in range(2**8): |
|
if b not in bs: |
|
bs.append(b) |
|
cs.append(2**8 + n) |
|
n += 1 |
|
cs = [chr(n) for n in cs] |
|
return dict(zip(bs, cs)) |
|
|
|
|
|
def get_pairs(word): |
|
""" |
|
Return set of symbol pairs in a word. |
|
|
|
Word is represented as tuple of symbols (symbols being variable-length strings). |
|
""" |
|
pairs = set() |
|
prev_char = word[0] |
|
for char in word[1:]: |
|
pairs.add((prev_char, char)) |
|
prev_char = char |
|
return pairs |
|
|
|
|
|
class GPTJXTokenizer(PreTrainedTokenizer): |
|
""" |
|
Construct a GPT-JX tokenizer. Based on byte-level Byte-Pair-Encoding. |
|
|
|
This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will |
|
be encoded differently whether it is at the beginning of the sentence (without space) or not: |
|
|
|
You can get around that behavior by passing `add_prefix_space=True` when instantiating this tokenizer or when you |
|
call it on some text, but since the model was not pretrained this way, it might yield a decrease in performance. |
|
|
|
<Tip> |
|
|
|
When used with `is_split_into_words=True`, this tokenizer will add a space before each word (even the first one). |
|
|
|
</Tip> |
|
|
|
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to |
|
this superclass for more information regarding those methods. |
|
|
|
Args: |
|
vocab_file (`str`): |
|
Path to the vocabulary file. |
|
merges_file (`str`): |
|
Path to the merges file. |
|
errors (`str`, *optional*, defaults to `"replace"`): |
|
Paradigm to follow when decoding bytes to UTF-8. See |
|
[bytes.decode](https://docs.python.org/3/library/stdtypes.html#bytes.decode) for more information. |
|
unk_token (`str`, *optional*, defaults to `<|endoftext|>`): |
|
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this |
|
token instead. |
|
bos_token (`str`, *optional*, defaults to `<|endoftext|>`): |
|
The beginning of sequence token. |
|
eos_token (`str`, *optional*, defaults to `<|endoftext|>`): |
|
The end of sequence token. |
|
add_prefix_space (`bool`, *optional*, defaults to `False`): |
|
Whether or not to add an initial space to the input. This allows to treat the leading word just as any |
|
other word. (GPT-JX tokenizer detect beginning of words by the preceding space). |
|
""" |
|
model_input_names = ["input_ids", "attention_mask"] |
|
|
|
def __init__( |
|
self, |
|
vocab_file, |
|
merges_file, |
|
errors="replace", |
|
unk_token="<|endoftext|>", |
|
bos_token="<|endoftext|>", |
|
eos_token="<|endoftext|>", |
|
pad_token=None, |
|
add_prefix_space=False, |
|
add_bos_token=False, |
|
**kwargs, |
|
): |
|
bos_token = AddedToken(bos_token, lstrip=False, rstrip=False) if isinstance(bos_token, str) else bos_token |
|
eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token |
|
unk_token = AddedToken(unk_token, lstrip=False, rstrip=False) if isinstance(unk_token, str) else unk_token |
|
pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token |
|
super().__init__( |
|
errors=errors, |
|
unk_token=unk_token, |
|
bos_token=bos_token, |
|
eos_token=eos_token, |
|
pad_token=pad_token, |
|
add_prefix_space=add_prefix_space, |
|
add_bos_token=add_bos_token, |
|
**kwargs, |
|
) |
|
self.add_bos_token = add_bos_token |
|
|
|
with open(vocab_file, encoding="utf-8") as vocab_handle: |
|
self.encoder = json.load(vocab_handle) |
|
self.decoder = {v: k for k, v in self.encoder.items()} |
|
self.errors = errors |
|
self.byte_encoder = bytes_to_unicode() |
|
self.byte_decoder = {v: k for k, v in self.byte_encoder.items()} |
|
with open(merges_file, encoding="utf-8") as merges_handle: |
|
bpe_merges = merges_handle.read().split("\n")[1:-1] |
|
bpe_merges = [tuple(merge.split()) for merge in bpe_merges] |
|
self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges)))) |
|
self.cache = {} |
|
self.add_prefix_space = add_prefix_space |
|
|
|
|
|
self.pat = re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""") |
|
|
|
@property |
|
def vocab_size(self): |
|
return len(self.encoder) |
|
|
|
def get_vocab(self): |
|
return dict(self.encoder, **self.added_tokens_encoder) |
|
|
|
def bpe(self, token): |
|
if token in self.cache: |
|
return self.cache[token] |
|
word = tuple(token) |
|
pairs = get_pairs(word) |
|
|
|
if not pairs: |
|
return token |
|
|
|
while True: |
|
bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf"))) |
|
if bigram not in self.bpe_ranks: |
|
break |
|
first, second = bigram |
|
new_word = [] |
|
i = 0 |
|
while i < len(word): |
|
try: |
|
j = word.index(first, i) |
|
except ValueError: |
|
new_word.extend(word[i:]) |
|
break |
|
else: |
|
new_word.extend(word[i:j]) |
|
i = j |
|
|
|
if word[i] == first and i < len(word) - 1 and word[i + 1] == second: |
|
new_word.append(first + second) |
|
i += 2 |
|
else: |
|
new_word.append(word[i]) |
|
i += 1 |
|
new_word = tuple(new_word) |
|
word = new_word |
|
if len(word) == 1: |
|
break |
|
else: |
|
pairs = get_pairs(word) |
|
word = " ".join(word) |
|
self.cache[token] = word |
|
return word |
|
|
|
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 _tokenize(self, text): |
|
"""Tokenize a string.""" |
|
bpe_tokens = [] |
|
for token in re.findall(self.pat, text): |
|
token = "".join( |
|
self.byte_encoder[b] for b in token.encode("utf-8") |
|
) |
|
bpe_tokens.extend(bpe_token for bpe_token in self.bpe(token).split(" ")) |
|
return bpe_tokens |
|
|
|
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 convert_tokens_to_string(self, tokens): |
|
"""Converts a sequence of tokens (string) in a single string.""" |
|
text = "".join(tokens) |
|
text = bytearray([self.byte_decoder[c] for c in text]).decode("utf-8", errors=self.errors) |
|
return text |
|
|
|
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: |
|
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"] |
|
) |
|
merge_file = os.path.join( |
|
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"] |
|
) |
|
|
|
with open(vocab_file, "w", encoding="utf-8") as f: |
|
f.write(json.dumps(self.encoder, indent=2, sort_keys=True, ensure_ascii=False) + "\n") |
|
|
|
index = 0 |
|
with open(merge_file, "w", encoding="utf-8") as writer: |
|
writer.write("#version: 0.2\n") |
|
for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda kv: kv[1]): |
|
if index != token_index: |
|
logger.warning( |
|
f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive." |
|
" Please check that the tokenizer is not corrupted!" |
|
) |
|
index = token_index |
|
writer.write(" ".join(bpe_tokens) + "\n") |
|
index += 1 |
|
|
|
return vocab_file, merge_file |
|
|
|
def prepare_for_tokenization(self, text, is_split_into_words=False, **kwargs): |
|
add_prefix_space = kwargs.pop("add_prefix_space", self.add_prefix_space) |
|
if is_split_into_words or add_prefix_space: |
|
text = " " + text |
|
return (text, kwargs) |
|
|
|
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 |
