mirror of
https://github.com/hpcaitech/ColossalAI.git
synced 2025-10-30 21:39:05 +00:00
[tutorial] edited hands-on practices (#1899)
* Add handson to ColossalAI. * Change names of handsons and edit sequence parallel example. * Edit wrong folder name * resolve conflict * delete readme
This commit is contained in:
BoxiangW
GitHub
@@ -0,0 +1,38 @@
|
||||
# coding=utf-8
|
||||
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
|
||||
#
|
||||
# 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.
|
||||
|
||||
|
||||
from .tokenizer import build_tokenizer
|
||||
|
||||
|
||||
_TOKENIZER = None
|
||||
_PADDED_VOCAB_SIZE = -1
|
||||
|
||||
|
||||
def initialize_tokenizer(vocab_file, tokenizer_type, vocab_extra_ids=0):
|
||||
tokenizer, padded_vocab_size = build_tokenizer(vocab_file, tokenizer_type, vocab_extra_ids)
|
||||
global _TOKENIZER, _PADDED_VOCAB_SIZE
|
||||
_TOKENIZER = tokenizer
|
||||
_PADDED_VOCAB_SIZE = padded_vocab_size
|
||||
|
||||
|
||||
def get_tokenizer():
|
||||
global _TOKENIZER
|
||||
return _TOKENIZER
|
||||
|
||||
|
||||
def get_padded_vocab_size():
|
||||
global _PADDED_VOCAB_SIZE
|
||||
return _PADDED_VOCAB_SIZE
|
||||
@@ -0,0 +1,431 @@
|
||||
# coding=utf-8
|
||||
# Copyright 2018 The Google AI Language Team Authors.
|
||||
#
|
||||
# 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."""
|
||||
|
||||
from __future__ import absolute_import
|
||||
from __future__ import division
|
||||
from __future__ import print_function
|
||||
|
||||
import collections
|
||||
import re
|
||||
import unicodedata
|
||||
import six
|
||||
|
||||
|
||||
def validate_case_matches_checkpoint(do_lower_case, init_checkpoint):
|
||||
"""Checks whether the casing config is consistent with the checkpoint name."""
|
||||
|
||||
# The casing has to be passed in by the user and there is no explicit check
|
||||
# as to whether it matches the checkpoint. The casing information probably
|
||||
# should have been stored in the bert_config.json file, but it's not, so
|
||||
# we have to heuristically detect it to validate.
|
||||
|
||||
if not init_checkpoint:
|
||||
return
|
||||
|
||||
m = re.match("^.*?([A-Za-z0-9_-]+)/bert_model.ckpt", init_checkpoint)
|
||||
if m is None:
|
||||
return
|
||||
|
||||
model_name = m.group(1)
|
||||
|
||||
lower_models = [
|
||||
"uncased_L-24_H-1024_A-16", "uncased_L-12_H-768_A-12",
|
||||
"multilingual_L-12_H-768_A-12", "chinese_L-12_H-768_A-12"
|
||||
]
|
||||
|
||||
cased_models = [
|
||||
"cased_L-12_H-768_A-12", "cased_L-24_H-1024_A-16",
|
||||
"multi_cased_L-12_H-768_A-12"
|
||||
]
|
||||
|
||||
is_bad_config = False
|
||||
if model_name in lower_models and not do_lower_case:
|
||||
is_bad_config = True
|
||||
actual_flag = "False"
|
||||
case_name = "lowercased"
|
||||
opposite_flag = "True"
|
||||
|
||||
if model_name in cased_models and do_lower_case:
|
||||
is_bad_config = True
|
||||
actual_flag = "True"
|
||||
case_name = "cased"
|
||||
opposite_flag = "False"
|
||||
|
||||
if is_bad_config:
|
||||
raise ValueError(
|
||||
"You passed in `--do_lower_case=%s` with `--init_checkpoint=%s`. "
|
||||
"However, `%s` seems to be a %s model, so you "
|
||||
"should pass in `--do_lower_case=%s` so that the fine-tuning matches "
|
||||
"how the model was pre-training. If this error is wrong, please "
|
||||
"just comment out this check." % (actual_flag, init_checkpoint,
|
||||
model_name, case_name, opposite_flag))
|
||||
|
||||
|
||||
def convert_to_unicode(text):
|
||||
"""Converts `text` to Unicode (if it's not already), assuming utf-8 input."""
|
||||
if six.PY3:
|
||||
if isinstance(text, str):
|
||||
return text
|
||||
elif isinstance(text, bytes):
|
||||
return text.decode("utf-8", "ignore")
|
||||
else:
|
||||
raise ValueError("Unsupported string type: %s" % (type(text)))
|
||||
elif six.PY2:
|
||||
if isinstance(text, str):
|
||||
return text.decode("utf-8", "ignore")
|
||||
elif isinstance(text, unicode):
|
||||
return text
|
||||
else:
|
||||
raise ValueError("Unsupported string type: %s" % (type(text)))
|
||||
else:
|
||||
raise ValueError("Not running on Python2 or Python 3?")
|
||||
|
||||
|
||||
def printable_text(text):
|
||||
"""Returns text encoded in a way suitable for print or `tf.logging`."""
|
||||
|
||||
# These functions want `str` for both Python2 and Python3, but in one case
|
||||
# it's a Unicode string and in the other it's a byte string.
|
||||
if six.PY3:
|
||||
if isinstance(text, str):
|
||||
return text
|
||||
elif isinstance(text, bytes):
|
||||
return text.decode("utf-8", "ignore")
|
||||
else:
|
||||
raise ValueError("Unsupported string type: %s" % (type(text)))
|
||||
elif six.PY2:
|
||||
if isinstance(text, str):
|
||||
return text
|
||||
elif isinstance(text, unicode):
|
||||
return text.encode("utf-8")
|
||||
else:
|
||||
raise ValueError("Unsupported string type: %s" % (type(text)))
|
||||
else:
|
||||
raise ValueError("Not running on Python2 or Python 3?")
|
||||
|
||||
|
||||
def load_vocab(vocab_file):
|
||||
"""Loads a vocabulary file into a dictionary."""
|
||||
vocab = collections.OrderedDict()
|
||||
index = 0
|
||||
with open(vocab_file, "r") as reader:
|
||||
while True:
|
||||
token = convert_to_unicode(reader.readline())
|
||||
if not token:
|
||||
break
|
||||
token = token.strip()
|
||||
vocab[token] = index
|
||||
index += 1
|
||||
return vocab
|
||||
|
||||
|
||||
def convert_by_vocab(vocab, items):
|
||||
"""Converts a sequence of [tokens|ids] using the vocab."""
|
||||
output = []
|
||||
for item in items:
|
||||
output.append(vocab[item])
|
||||
return output
|
||||
|
||||
|
||||
def convert_tokens_to_ids(vocab, tokens):
|
||||
return convert_by_vocab(vocab, tokens)
|
||||
|
||||
|
||||
def convert_ids_to_tokens(inv_vocab, ids):
|
||||
return convert_by_vocab(inv_vocab, ids)
|
||||
|
||||
|
||||
def whitespace_tokenize(text):
|
||||
"""Runs basic whitespace cleaning and splitting on a piece of text."""
|
||||
text = text.strip()
|
||||
if not text:
|
||||
return []
|
||||
tokens = text.split()
|
||||
return tokens
|
||||
|
||||
|
||||
class FullTokenizer(object):
|
||||
"""Runs end-to-end tokenization."""
|
||||
|
||||
def __init__(self, vocab_file, do_lower_case=True):
|
||||
self.vocab = load_vocab(vocab_file)
|
||||
self.inv_vocab = {v: k for k, v in self.vocab.items()}
|
||||
self.basic_tokenizer = BasicTokenizer(do_lower_case=do_lower_case)
|
||||
self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab)
|
||||
|
||||
def tokenize(self, text):
|
||||
split_tokens = []
|
||||
for token in self.basic_tokenizer.tokenize(text):
|
||||
for sub_token in self.wordpiece_tokenizer.tokenize(token):
|
||||
split_tokens.append(sub_token)
|
||||
|
||||
return split_tokens
|
||||
|
||||
def convert_tokens_to_ids(self, tokens):
|
||||
return convert_by_vocab(self.vocab, tokens)
|
||||
|
||||
def convert_ids_to_tokens(self, ids):
|
||||
return convert_by_vocab(self.inv_vocab, ids)
|
||||
|
||||
@staticmethod
|
||||
def convert_tokens_to_string(tokens, clean_up_tokenization_spaces=True):
|
||||
""" Converts a sequence of tokens (string) in a single string. """
|
||||
|
||||
def clean_up_tokenization(out_string):
|
||||
""" Clean up a list of simple English tokenization artifacts
|
||||
like spaces before punctuations and abbreviated forms.
|
||||
"""
|
||||
out_string = (
|
||||
out_string.replace(" .", ".")
|
||||
.replace(" ?", "?")
|
||||
.replace(" !", "!")
|
||||
.replace(" ,", ",")
|
||||
.replace(" ' ", "'")
|
||||
.replace(" n't", "n't")
|
||||
.replace(" 'm", "'m")
|
||||
.replace(" 's", "'s")
|
||||
.replace(" 've", "'ve")
|
||||
.replace(" 're", "'re")
|
||||
)
|
||||
return out_string
|
||||
|
||||
text = ' '.join(tokens).replace(' ##', '').strip()
|
||||
if clean_up_tokenization_spaces:
|
||||
clean_text = clean_up_tokenization(text)
|
||||
return clean_text
|
||||
else:
|
||||
return text
|
||||
|
||||
def vocab_size(self):
|
||||
return len(self.vocab)
|
||||
|
||||
|
||||
class BasicTokenizer(object):
|
||||
"""Runs basic tokenization (punctuation splitting, lower casing, etc.)."""
|
||||
|
||||
def __init__(self, do_lower_case=True):
|
||||
"""Constructs a BasicTokenizer.
|
||||
|
||||
Args:
|
||||
do_lower_case: Whether to lower case the input.
|
||||
"""
|
||||
self.do_lower_case = do_lower_case
|
||||
|
||||
def tokenize(self, text):
|
||||
"""Tokenizes a piece of text."""
|
||||
text = convert_to_unicode(text)
|
||||
text = self._clean_text(text)
|
||||
|
||||
# This was added on November 1st, 2018 for the multilingual and Chinese
|
||||
# models. This is also applied to the English models now, but it doesn't
|
||||
# matter since the English models were not trained on any Chinese data
|
||||
# and generally don't have any Chinese data in them (there are Chinese
|
||||
# characters in the vocabulary because Wikipedia does have some Chinese
|
||||
# words in the English Wikipedia.).
|
||||
text = self._tokenize_chinese_chars(text)
|
||||
|
||||
orig_tokens = whitespace_tokenize(text)
|
||||
split_tokens = []
|
||||
for token in orig_tokens:
|
||||
if self.do_lower_case:
|
||||
token = token.lower()
|
||||
token = self._run_strip_accents(token)
|
||||
split_tokens.extend(self._run_split_on_punc(token))
|
||||
|
||||
output_tokens = whitespace_tokenize(" ".join(split_tokens))
|
||||
return output_tokens
|
||||
|
||||
def _run_strip_accents(self, text):
|
||||
"""Strips accents from a piece of text."""
|
||||
text = unicodedata.normalize("NFD", text)
|
||||
output = []
|
||||
for char in text:
|
||||
cat = unicodedata.category(char)
|
||||
if cat == "Mn":
|
||||
continue
|
||||
output.append(char)
|
||||
return "".join(output)
|
||||
|
||||
def _run_split_on_punc(self, text):
|
||||
"""Splits punctuation on a piece of text."""
|
||||
chars = list(text)
|
||||
i = 0
|
||||
start_new_word = True
|
||||
output = []
|
||||
while i < len(chars):
|
||||
char = chars[i]
|
||||
if _is_punctuation(char):
|
||||
output.append([char])
|
||||
start_new_word = True
|
||||
else:
|
||||
if start_new_word:
|
||||
output.append([])
|
||||
start_new_word = False
|
||||
output[-1].append(char)
|
||||
i += 1
|
||||
|
||||
return ["".join(x) for x in output]
|
||||
|
||||
def _tokenize_chinese_chars(self, text):
|
||||
"""Adds whitespace around any CJK character."""
|
||||
output = []
|
||||
for char in text:
|
||||
cp = ord(char)
|
||||
if self._is_chinese_char(cp):
|
||||
output.append(" ")
|
||||
output.append(char)
|
||||
output.append(" ")
|
||||
else:
|
||||
output.append(char)
|
||||
return "".join(output)
|
||||
|
||||
def _is_chinese_char(self, cp):
|
||||
"""Checks whether CP is the codepoint of a CJK character."""
|
||||
# This defines a "chinese character" as anything in the CJK Unicode block:
|
||||
# https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
|
||||
#
|
||||
# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
|
||||
# despite its name. The modern Korean Hangul alphabet is a different block,
|
||||
# as is Japanese Hiragana and Katakana. Those alphabets are used to write
|
||||
# space-separated words, so they are not treated specially and handled
|
||||
# like the all of the other languages.
|
||||
if ((cp >= 0x4E00 and cp <= 0x9FFF) or #
|
||||
(cp >= 0x3400 and cp <= 0x4DBF) or #
|
||||
(cp >= 0x20000 and cp <= 0x2A6DF) or #
|
||||
(cp >= 0x2A700 and cp <= 0x2B73F) or #
|
||||
(cp >= 0x2B740 and cp <= 0x2B81F) or #
|
||||
(cp >= 0x2B820 and cp <= 0x2CEAF) or
|
||||
(cp >= 0xF900 and cp <= 0xFAFF) or #
|
||||
(cp >= 0x2F800 and cp <= 0x2FA1F)): #
|
||||
return True
|
||||
|
||||
return False
|
||||
|
||||
def _clean_text(self, text):
|
||||
"""Performs invalid character removal and whitespace cleanup on text."""
|
||||
output = []
|
||||
for char in text:
|
||||
cp = ord(char)
|
||||
if cp == 0 or cp == 0xfffd or _is_control(char):
|
||||
continue
|
||||
if _is_whitespace(char):
|
||||
output.append(" ")
|
||||
else:
|
||||
output.append(char)
|
||||
return "".join(output)
|
||||
|
||||
|
||||
class WordpieceTokenizer(object):
|
||||
"""Runs WordPiece tokenization."""
|
||||
|
||||
def __init__(self, vocab, unk_token="[UNK]", max_input_chars_per_word=200):
|
||||
self.vocab = vocab
|
||||
self.unk_token = unk_token
|
||||
self.max_input_chars_per_word = max_input_chars_per_word
|
||||
|
||||
def tokenize(self, text):
|
||||
"""Tokenizes a piece of text into its word pieces.
|
||||
|
||||
This uses a greedy longest-match-first algorithm to perform tokenization
|
||||
using the given vocabulary.
|
||||
|
||||
For example:
|
||||
input = "unaffable"
|
||||
output = ["un", "##aff", "##able"]
|
||||
|
||||
Args:
|
||||
text: A single token or whitespace separated tokens. This should have
|
||||
already been passed through `BasicTokenizer.
|
||||
|
||||
Returns:
|
||||
A list of wordpiece tokens.
|
||||
"""
|
||||
|
||||
text = convert_to_unicode(text)
|
||||
|
||||
output_tokens = []
|
||||
for token in whitespace_tokenize(text):
|
||||
chars = list(token)
|
||||
if len(chars) > self.max_input_chars_per_word:
|
||||
output_tokens.append(self.unk_token)
|
||||
continue
|
||||
|
||||
is_bad = False
|
||||
start = 0
|
||||
sub_tokens = []
|
||||
while start < len(chars):
|
||||
end = len(chars)
|
||||
cur_substr = None
|
||||
while start < end:
|
||||
substr = "".join(chars[start:end])
|
||||
if start > 0:
|
||||
substr = "##" + substr
|
||||
if substr in self.vocab:
|
||||
cur_substr = substr
|
||||
break
|
||||
end -= 1
|
||||
if cur_substr is None:
|
||||
is_bad = True
|
||||
break
|
||||
sub_tokens.append(cur_substr)
|
||||
start = end
|
||||
|
||||
if is_bad:
|
||||
output_tokens.append(self.unk_token)
|
||||
else:
|
||||
output_tokens.extend(sub_tokens)
|
||||
return output_tokens
|
||||
|
||||
|
||||
def _is_whitespace(char):
|
||||
"""Checks whether `chars` is a whitespace character."""
|
||||
# \t, \n, and \r are technically control characters but we treat them
|
||||
# as whitespace since they are generally considered as such.
|
||||
if char == " " or char == "\t" or char == "\n" or char == "\r":
|
||||
return True
|
||||
cat = unicodedata.category(char)
|
||||
if cat == "Zs":
|
||||
return True
|
||||
return False
|
||||
|
||||
|
||||
def _is_control(char):
|
||||
"""Checks whether `chars` is a control character."""
|
||||
# These are technically control characters but we count them as whitespace
|
||||
# characters.
|
||||
if char == "\t" or char == "\n" or char == "\r":
|
||||
return False
|
||||
cat = unicodedata.category(char)
|
||||
if cat in ("Cc", "Cf"):
|
||||
return True
|
||||
return False
|
||||
|
||||
|
||||
def _is_punctuation(char):
|
||||
"""Checks whether `chars` is a punctuation character."""
|
||||
cp = ord(char)
|
||||
# We treat all non-letter/number ASCII as punctuation.
|
||||
# Characters such as "^", "$", and "`" are not in the Unicode
|
||||
# Punctuation class but we treat them as punctuation anyways, for
|
||||
# consistency.
|
||||
if ((cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or
|
||||
(cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126)):
|
||||
return True
|
||||
cat = unicodedata.category(char)
|
||||
if cat.startswith("P"):
|
||||
return True
|
||||
return False
|
||||
256
examples/tutorial/sequence_parallel/data/tokenizer/tokenizer.py
Normal file
256
examples/tutorial/sequence_parallel/data/tokenizer/tokenizer.py
Normal file
@@ -0,0 +1,256 @@
|
||||
# coding=utf-8
|
||||
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
|
||||
#
|
||||
# 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.
|
||||
|
||||
"""Megatron tokenizers."""
|
||||
|
||||
from abc import ABC
|
||||
from abc import abstractmethod
|
||||
from colossalai.core import global_context as gpc
|
||||
from colossalai.context import ParallelMode
|
||||
|
||||
from .bert_tokenization import FullTokenizer as FullBertTokenizer
|
||||
|
||||
|
||||
def build_tokenizer(vocab_file, tokenizer_type, vocab_extra_ids=0):
|
||||
"""Initialize tokenizer."""
|
||||
if not gpc.is_initialized(ParallelMode.GLOBAL) or gpc.get_global_rank() == 0:
|
||||
print('> building {} tokenizer ...'.format(tokenizer_type),
|
||||
flush=True)
|
||||
|
||||
# Select and instantiate the tokenizer.
|
||||
if tokenizer_type == 'BertWordPieceLowerCase':
|
||||
tokenizer = _BertWordPieceTokenizer(vocab_file=vocab_file,
|
||||
lower_case=True,
|
||||
vocab_extra_ids=vocab_extra_ids)
|
||||
elif tokenizer_type == 'BertWordPieceCase':
|
||||
tokenizer = _BertWordPieceTokenizer(vocab_file=vocab_file,
|
||||
lower_case=False,
|
||||
vocab_extra_ids=vocab_extra_ids)
|
||||
else:
|
||||
raise NotImplementedError('{} tokenizer is not '
|
||||
'implemented.'.format(tokenizer_type))
|
||||
|
||||
# Add vocab size.
|
||||
padded_vocab_size = _vocab_size_with_padding(tokenizer.vocab_size)
|
||||
|
||||
return tokenizer, padded_vocab_size
|
||||
|
||||
|
||||
def _vocab_size_with_padding(orig_vocab_size, make_vocab_size_divisible_by=128):
|
||||
"""Pad vocab size so it is divisible by model parallel size and
|
||||
still having GPU friendly size."""
|
||||
|
||||
after = orig_vocab_size
|
||||
|
||||
if gpc.is_initialized(ParallelMode.TENSOR):
|
||||
multiple = make_vocab_size_divisible_by * gpc.get_world_size(ParallelMode.TENSOR)
|
||||
else:
|
||||
multiple = make_vocab_size_divisible_by
|
||||
while (after % multiple) != 0:
|
||||
after += 1
|
||||
if not gpc.is_initialized(ParallelMode.GLOBAL) or gpc.get_global_rank() == 0:
|
||||
print(' > padded vocab (size: {}) with {} dummy tokens '
|
||||
'(new size: {})'.format(
|
||||
orig_vocab_size, after - orig_vocab_size, after), flush=True)
|
||||
return after
|
||||
|
||||
|
||||
class AbstractTokenizer(ABC):
|
||||
"""Abstract class for tokenizer."""
|
||||
|
||||
def __init__(self, name):
|
||||
self.name = name
|
||||
super().__init__()
|
||||
|
||||
@property
|
||||
@abstractmethod
|
||||
def vocab_size(self):
|
||||
pass
|
||||
|
||||
@property
|
||||
@abstractmethod
|
||||
def vocab(self):
|
||||
"""Dictionary from vocab text token to id token."""
|
||||
pass
|
||||
|
||||
@property
|
||||
@abstractmethod
|
||||
def inv_vocab(self):
|
||||
"""Dictionary from vocab id token to text token."""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def tokenize(self, text):
|
||||
pass
|
||||
|
||||
def detokenize(self, token_ids):
|
||||
raise NotImplementedError('detokenizer is not implemented for {} '
|
||||
'tokenizer'.format(self.name))
|
||||
|
||||
@property
|
||||
def cls(self):
|
||||
raise NotImplementedError('CLS is not provided for {} '
|
||||
'tokenizer'.format(self.name))
|
||||
|
||||
@property
|
||||
def sep(self):
|
||||
raise NotImplementedError('SEP is not provided for {} '
|
||||
'tokenizer'.format(self.name))
|
||||
|
||||
@property
|
||||
def pad(self):
|
||||
raise NotImplementedError('PAD is not provided for {} '
|
||||
'tokenizer'.format(self.name))
|
||||
|
||||
@property
|
||||
def eod(self):
|
||||
raise NotImplementedError('EOD is not provided for {} '
|
||||
'tokenizer'.format(self.name))
|
||||
|
||||
@property
|
||||
def mask(self):
|
||||
raise NotImplementedError('MASK is not provided for {} '
|
||||
'tokenizer'.format(self.name))
|
||||
|
||||
|
||||
class _BertWordPieceTokenizer(AbstractTokenizer):
|
||||
"""Original BERT wordpiece tokenizer."""
|
||||
|
||||
def __init__(self, vocab_file, lower_case=True, vocab_extra_ids=0):
|
||||
if lower_case:
|
||||
name = 'BERT Lower Case'
|
||||
else:
|
||||
name = 'BERT Upper Case'
|
||||
super().__init__(name)
|
||||
self.tokenizer = FullBertTokenizer(vocab_file, do_lower_case=lower_case)
|
||||
self.cls_id = self.tokenizer.vocab['[CLS]']
|
||||
self.sep_id = self.tokenizer.vocab['[SEP]']
|
||||
self.pad_id = self.tokenizer.vocab['[PAD]']
|
||||
self.mask_id = self.tokenizer.vocab['[MASK]']
|
||||
self._additional_special_tokens = []
|
||||
|
||||
# (dsachan) Add BOS and EOS tokens
|
||||
SPECIAL_TOKENS = {'eos_token': '[EOS]',
|
||||
'bos_token': '[BOS]'}
|
||||
self._bos_token = '[BOS]'
|
||||
self.add_token(self._bos_token)
|
||||
self._bos_token_id = self.vocab.get(self._bos_token)
|
||||
|
||||
self._eos_token = '[EOS]'
|
||||
self.add_token(self._eos_token)
|
||||
self._eos_token_id = self.vocab.get(self._eos_token)
|
||||
|
||||
# (dsachan) Add additional special tokens
|
||||
# These can be used as sentinel tokens in T5 model inputs
|
||||
additional_special_tokens = []
|
||||
additional_special_tokens.extend(
|
||||
["<extra_id_{}>".format(i) for i in range(vocab_extra_ids)])
|
||||
self.add_additional_special_tokens(additional_special_tokens)
|
||||
|
||||
def add_token(self, token):
|
||||
if token not in self.vocab:
|
||||
self.inv_vocab[self.vocab_size] = token
|
||||
# self.vocab_size comes from len(vocab)
|
||||
# and it will increase as we add elements
|
||||
self.vocab[token] = self.vocab_size
|
||||
|
||||
def add_additional_special_tokens(self, tokens_list):
|
||||
setattr(self, "additional_special_tokens", tokens_list)
|
||||
for value in tokens_list:
|
||||
self.add_token(value)
|
||||
|
||||
@property
|
||||
def vocab_size(self):
|
||||
return self.tokenizer.vocab_size()
|
||||
|
||||
@property
|
||||
def vocab(self):
|
||||
return self.tokenizer.vocab
|
||||
|
||||
@property
|
||||
def inv_vocab(self):
|
||||
return self.tokenizer.inv_vocab
|
||||
|
||||
def tokenize(self, text):
|
||||
text_tokens = self.tokenizer.tokenize(text)
|
||||
return self.tokenizer.convert_tokens_to_ids(text_tokens)
|
||||
|
||||
def decode(self, ids):
|
||||
tokens = self.tokenizer.convert_ids_to_tokens(ids)
|
||||
return self.tokenizer.convert_tokens_to_string(tokens)
|
||||
|
||||
def decode_token_ids(self, token_ids):
|
||||
tokens = self.tokenizer.convert_ids_to_tokens(token_ids)
|
||||
exclude_list = ['[PAD]', '[CLS]']
|
||||
non_pads = [t for t in tokens if t not in exclude_list]
|
||||
|
||||
result = ""
|
||||
for s in non_pads:
|
||||
if s.startswith("##"):
|
||||
result += s[2:]
|
||||
else:
|
||||
result += " " + s
|
||||
|
||||
return result
|
||||
|
||||
@property
|
||||
def cls(self):
|
||||
return self.cls_id
|
||||
|
||||
@property
|
||||
def sep(self):
|
||||
return self.sep_id
|
||||
|
||||
@property
|
||||
def pad(self):
|
||||
return self.pad_id
|
||||
|
||||
@property
|
||||
def mask(self):
|
||||
return self.mask_id
|
||||
|
||||
@property
|
||||
def bos_token(self):
|
||||
""" Beginning of sentence token id """
|
||||
return self._bos_token
|
||||
|
||||
@property
|
||||
def eos_token(self):
|
||||
""" End of sentence token id """
|
||||
return self._eos_token
|
||||
|
||||
@property
|
||||
def additional_special_tokens(self):
|
||||
""" All the additional special tokens you may want to use (list of strings)."""
|
||||
return self._additional_special_tokens
|
||||
|
||||
@property
|
||||
def bos_token_id(self):
|
||||
""" Id of the beginning of sentence token in the vocabulary."""
|
||||
return self._bos_token_id
|
||||
|
||||
@property
|
||||
def eos_token_id(self):
|
||||
""" Id of the end of sentence token in the vocabulary."""
|
||||
return self._eos_token_id
|
||||
|
||||
@property
|
||||
def additional_special_tokens_ids(self):
|
||||
""" Ids of all the additional special tokens in the vocabulary (list of integers)."""
|
||||
return [self.vocab.get(token) for token in self._additional_special_tokens]
|
||||
|
||||
@additional_special_tokens.setter
|
||||
def additional_special_tokens(self, value):
|
||||
self._additional_special_tokens = value
|
||||
Reference in New Issue
Block a user