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Commit 9485aa1d authored by qianyj's avatar qianyj
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Update code to v2.8.0

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official/nlp/bert/tf2_encoder_checkpoint_converter.py 0 → 100644
View file @ 9485aa1d
# Copyright 2021 The TensorFlow Authors. 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.
"""A converter from a V1 BERT encoder checkpoint to a V2 encoder checkpoint.
The conversion will yield an object-oriented checkpoint that can be used
to restore a BertEncoder or BertPretrainerV2 object (see the `converted_model`
FLAG below).
"""
import os
from absl import app
from absl import flags
import tensorflow as tf
from official.modeling import tf_utils
from official.nlp.bert import configs
from official.nlp.bert import tf1_checkpoint_converter_lib
from official.nlp.modeling import models
from official.nlp.modeling import networks
FLAGS = flags.FLAGS
flags.DEFINE_string("bert_config_file", None,
"Bert configuration file to define core bert layers.")
flags.DEFINE_string(
"checkpoint_to_convert", None,
"Initial checkpoint from a pretrained BERT model core (that is, only the "
"BertModel, with no task heads.)")
flags.DEFINE_string("converted_checkpoint_path", None,
"Name for the created object-based V2 checkpoint.")
flags.DEFINE_string("checkpoint_model_name", "encoder",
"The name of the model when saving the checkpoint, i.e., "
"the checkpoint will be saved using: "
"tf.train.Checkpoint(FLAGS.checkpoint_model_name=model).")
flags.DEFINE_enum(
"converted_model", "encoder", ["encoder", "pretrainer"],
"Whether to convert the checkpoint to a `BertEncoder` model or a "
"`BertPretrainerV2` model (with mlm but without classification heads).")
def _create_bert_model(cfg):
"""Creates a BERT keras core model from BERT configuration.
Args:
cfg: A `BertConfig` to create the core model.
Returns:
A BertEncoder network.
"""
bert_encoder = networks.BertEncoder(
vocab_size=cfg.vocab_size,
hidden_size=cfg.hidden_size,
num_layers=cfg.num_hidden_layers,
num_attention_heads=cfg.num_attention_heads,
intermediate_size=cfg.intermediate_size,
activation=tf_utils.get_activation(cfg.hidden_act),
dropout_rate=cfg.hidden_dropout_prob,
attention_dropout_rate=cfg.attention_probs_dropout_prob,
max_sequence_length=cfg.max_position_embeddings,
type_vocab_size=cfg.type_vocab_size,
initializer=tf.keras.initializers.TruncatedNormal(
stddev=cfg.initializer_range),
embedding_width=cfg.embedding_size)
return bert_encoder
def _create_bert_pretrainer_model(cfg):
"""Creates a BERT keras core model from BERT configuration.
Args:
cfg: A `BertConfig` to create the core model.
Returns:
A BertPretrainerV2 model.
"""
bert_encoder = _create_bert_model(cfg)
pretrainer = models.BertPretrainerV2(
encoder_network=bert_encoder,
mlm_activation=tf_utils.get_activation(cfg.hidden_act),
mlm_initializer=tf.keras.initializers.TruncatedNormal(
stddev=cfg.initializer_range))
# Makes sure the pretrainer variables are created.
_ = pretrainer(pretrainer.inputs)
return pretrainer
def convert_checkpoint(bert_config,
output_path,
v1_checkpoint,
checkpoint_model_name="model",
converted_model="encoder"):
"""Converts a V1 checkpoint into an OO V2 checkpoint."""
output_dir, _ = os.path.split(output_path)
tf.io.gfile.makedirs(output_dir)
# Create a temporary V1 name-converted checkpoint in the output directory.
temporary_checkpoint_dir = os.path.join(output_dir, "temp_v1")
temporary_checkpoint = os.path.join(temporary_checkpoint_dir, "ckpt")
tf1_checkpoint_converter_lib.convert(
checkpoint_from_path=v1_checkpoint,
checkpoint_to_path=temporary_checkpoint,
num_heads=bert_config.num_attention_heads,
name_replacements=tf1_checkpoint_converter_lib.BERT_V2_NAME_REPLACEMENTS,
permutations=tf1_checkpoint_converter_lib.BERT_V2_PERMUTATIONS,
exclude_patterns=["adam", "Adam"])
if converted_model == "encoder":
model = _create_bert_model(bert_config)
elif converted_model == "pretrainer":
model = _create_bert_pretrainer_model(bert_config)
else:
raise ValueError("Unsupported converted_model: %s" % converted_model)
# Create a V2 checkpoint from the temporary checkpoint.
tf1_checkpoint_converter_lib.create_v2_checkpoint(model, temporary_checkpoint,
output_path,
checkpoint_model_name)
# Clean up the temporary checkpoint, if it exists.
try:
tf.io.gfile.rmtree(temporary_checkpoint_dir)
except tf.errors.OpError:
# If it doesn't exist, we don't need to clean it up; continue.
pass
def main(argv):
if len(argv) > 1:
raise app.UsageError("Too many command-line arguments.")
output_path = FLAGS.converted_checkpoint_path
v1_checkpoint = FLAGS.checkpoint_to_convert
checkpoint_model_name = FLAGS.checkpoint_model_name
converted_model = FLAGS.converted_model
bert_config = configs.BertConfig.from_json_file(FLAGS.bert_config_file)
convert_checkpoint(
bert_config=bert_config,
output_path=output_path,
v1_checkpoint=v1_checkpoint,
checkpoint_model_name=checkpoint_model_name,
converted_model=converted_model)
if __name__ == "__main__":
app.run(main)
official/nlp/bert/tokenization.py 0 → 100644
View file @ 9485aa1d
# Copyright 2021 The TensorFlow Authors. 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.
# coding=utf-8
"""Tokenization classes implementation.
The file is forked from:
https://github.com/google-research/bert/blob/master/tokenization.py.
"""
import collections
import re
import unicodedata
import six
import tensorflow as tf
import sentencepiece as spm
SPIECE_UNDERLINE = "▁"
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 tf.io.gfile.GFile(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 tokenziation."""
def __init__(self, vocab_file, do_lower_case=True, split_on_punc=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, split_on_punc=split_on_punc)
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)
class BasicTokenizer(object):
"""Runs basic tokenization (punctuation splitting, lower casing, etc.)."""
def __init__(self, do_lower_case=True, split_on_punc=True):
"""Constructs a BasicTokenizer.
Args:
do_lower_case: Whether to lower case the input.
split_on_punc: Whether to apply split on punctuations. By default BERT
starts a new token for punctuations. This makes detokenization difficult
for tasks like seq2seq decoding.
"""
self.do_lower_case = do_lower_case
self.split_on_punc = split_on_punc
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)
if self.split_on_punc:
split_tokens.extend(self._run_split_on_punc(token))
else:
split_tokens.append(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 tokenziation."""
def __init__(self, vocab, unk_token="[UNK]", max_input_chars_per_word=400):
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
def preprocess_text(inputs, remove_space=True, lower=False):
"""Preprocesses data by removing extra space and normalize data.
This method is used together with sentence piece tokenizer and is forked from:
https://github.com/google-research/google-research/blob/e1f6fa00/albert/tokenization.py
Args:
inputs: The input text.
remove_space: Whether to remove the extra space.
lower: Whether to lowercase the text.
Returns:
The preprocessed text.
"""
outputs = inputs
if remove_space:
outputs = " ".join(inputs.strip().split())
if six.PY2 and isinstance(outputs, str):
try:
outputs = six.ensure_text(outputs, "utf-8")
except UnicodeDecodeError:
outputs = six.ensure_text(outputs, "latin-1")
outputs = unicodedata.normalize("NFKD", outputs)
outputs = "".join([c for c in outputs if not unicodedata.combining(c)])
if lower:
outputs = outputs.lower()
return outputs
def encode_pieces(sp_model, text, sample=False):
"""Segements text into pieces.
This method is used together with sentence piece tokenizer and is forked from:
https://github.com/google-research/google-research/blob/e1f6fa00/albert/tokenization.py
Args:
sp_model: A spm.SentencePieceProcessor object.
text: The input text to be segemented.
sample: Whether to randomly sample a segmentation output or return a
deterministic one.
Returns:
A list of token pieces.
"""
if six.PY2 and isinstance(text, six.text_type):
text = six.ensure_binary(text, "utf-8")
if not sample:
pieces = sp_model.EncodeAsPieces(text)
else:
pieces = sp_model.SampleEncodeAsPieces(text, 64, 0.1)
new_pieces = []
for piece in pieces:
piece = printable_text(piece)
if len(piece) > 1 and piece[-1] == "," and piece[-2].isdigit():
cur_pieces = sp_model.EncodeAsPieces(piece[:-1].replace(
SPIECE_UNDERLINE, ""))
if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
if len(cur_pieces[0]) == 1:
cur_pieces = cur_pieces[1:]
else:
cur_pieces[0] = cur_pieces[0][1:]
cur_pieces.append(piece[-1])
new_pieces.extend(cur_pieces)
else:
new_pieces.append(piece)
return new_pieces
def encode_ids(sp_model, text, sample=False):
"""Segments text and return token ids.
This method is used together with sentence piece tokenizer and is forked from:
https://github.com/google-research/google-research/blob/e1f6fa00/albert/tokenization.py
Args:
sp_model: A spm.SentencePieceProcessor object.
text: The input text to be segemented.
sample: Whether to randomly sample a segmentation output or return a
deterministic one.
Returns:
A list of token ids.
"""
pieces = encode_pieces(sp_model, text, sample=sample)
ids = [sp_model.PieceToId(piece) for piece in pieces]
return ids
class FullSentencePieceTokenizer(object):
"""Runs end-to-end sentence piece tokenization.
The interface of this class is intended to keep the same as above
`FullTokenizer` class for easier usage.
"""
def __init__(self, sp_model_file):
"""Inits FullSentencePieceTokenizer.
Args:
sp_model_file: The path to the sentence piece model file.
"""
self.sp_model = spm.SentencePieceProcessor()
self.sp_model.Load(sp_model_file)
self.vocab = {
self.sp_model.IdToPiece(i): i
for i in six.moves.range(self.sp_model.GetPieceSize())
}
def tokenize(self, text):
"""Tokenizes text into pieces."""
return encode_pieces(self.sp_model, text)
def convert_tokens_to_ids(self, tokens):
"""Converts a list of tokens to a list of ids."""
return [self.sp_model.PieceToId(printable_text(token)) for token in tokens]
def convert_ids_to_tokens(self, ids):
"""Converts a list of ids ot a list of tokens."""
return [self.sp_model.IdToPiece(id_) for id_ in ids]
official/nlp/bert/tokenization_test.py 0 → 100644
View file @ 9485aa1d
# Copyright 2021 The TensorFlow Authors. 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.
import os
import tempfile
import six
import tensorflow as tf
from official.nlp.bert import tokenization
class TokenizationTest(tf.test.TestCase):
"""Tokenization test.
The implementation is forked from
https://github.com/google-research/bert/blob/master/tokenization_test.py."
"""
def test_full_tokenizer(self):
vocab_tokens = [
"[UNK]", "[CLS]", "[SEP]", "want", "##want", "##ed", "wa", "un", "runn",
"##ing", ","
]
with tempfile.NamedTemporaryFile(delete=False) as vocab_writer:
if six.PY2:
vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))
else:
vocab_writer.write("".join([x + "\n" for x in vocab_tokens
]).encode("utf-8"))
vocab_file = vocab_writer.name
tokenizer = tokenization.FullTokenizer(vocab_file)
os.unlink(vocab_file)
tokens = tokenizer.tokenize(u"UNwant\u00E9d,running")
self.assertAllEqual(tokens, ["un", "##want", "##ed", ",", "runn", "##ing"])
self.assertAllEqual(
tokenizer.convert_tokens_to_ids(tokens), [7, 4, 5, 10, 8, 9])
def test_chinese(self):
tokenizer = tokenization.BasicTokenizer()
self.assertAllEqual(
tokenizer.tokenize(u"ah\u535A\u63A8zz"),
[u"ah", u"\u535A", u"\u63A8", u"zz"])
def test_basic_tokenizer_lower(self):
tokenizer = tokenization.BasicTokenizer(do_lower_case=True)
self.assertAllEqual(
tokenizer.tokenize(u" \tHeLLo!how \n Are yoU? "),
["hello", "!", "how", "are", "you", "?"])
self.assertAllEqual(tokenizer.tokenize(u"H\u00E9llo"), ["hello"])
def test_basic_tokenizer_no_lower(self):
tokenizer = tokenization.BasicTokenizer(do_lower_case=False)
self.assertAllEqual(
tokenizer.tokenize(u" \tHeLLo!how \n Are yoU? "),
["HeLLo", "!", "how", "Are", "yoU", "?"])
def test_basic_tokenizer_no_split_on_punc(self):
tokenizer = tokenization.BasicTokenizer(
do_lower_case=True, split_on_punc=False)
self.assertAllEqual(
tokenizer.tokenize(u" \tHeLLo!how \n Are yoU? "),
["hello!how", "are", "you?"])
def test_wordpiece_tokenizer(self):
vocab_tokens = [
"[UNK]", "[CLS]", "[SEP]", "want", "##want", "##ed", "wa", "un", "runn",
"##ing", "##!", "!"
]
vocab = {}
for (i, token) in enumerate(vocab_tokens):
vocab[token] = i
tokenizer = tokenization.WordpieceTokenizer(vocab=vocab)
self.assertAllEqual(tokenizer.tokenize(""), [])
self.assertAllEqual(
tokenizer.tokenize("unwanted running"),
["un", "##want", "##ed", "runn", "##ing"])
self.assertAllEqual(
tokenizer.tokenize("unwanted running !"),
["un", "##want", "##ed", "runn", "##ing", "!"])
self.assertAllEqual(
tokenizer.tokenize("unwanted running!"),
["un", "##want", "##ed", "runn", "##ing", "##!"])
self.assertAllEqual(
tokenizer.tokenize("unwantedX running"), ["[UNK]", "runn", "##ing"])
def test_convert_tokens_to_ids(self):
vocab_tokens = [
"[UNK]", "[CLS]", "[SEP]", "want", "##want", "##ed", "wa", "un", "runn",
"##ing"
]
vocab = {}
for (i, token) in enumerate(vocab_tokens):
vocab[token] = i
self.assertAllEqual(
tokenization.convert_tokens_to_ids(
vocab, ["un", "##want", "##ed", "runn", "##ing"]), [7, 4, 5, 8, 9])
def test_is_whitespace(self):
self.assertTrue(tokenization._is_whitespace(u" "))
self.assertTrue(tokenization._is_whitespace(u"\t"))
self.assertTrue(tokenization._is_whitespace(u"\r"))
self.assertTrue(tokenization._is_whitespace(u"\n"))
self.assertTrue(tokenization._is_whitespace(u"\u00A0"))
self.assertFalse(tokenization._is_whitespace(u"A"))
self.assertFalse(tokenization._is_whitespace(u"-"))
def test_is_control(self):
self.assertTrue(tokenization._is_control(u"\u0005"))
self.assertFalse(tokenization._is_control(u"A"))
self.assertFalse(tokenization._is_control(u" "))
self.assertFalse(tokenization._is_control(u"\t"))
self.assertFalse(tokenization._is_control(u"\r"))
self.assertFalse(tokenization._is_control(u"\U0001F4A9"))
def test_is_punctuation(self):
self.assertTrue(tokenization._is_punctuation(u"-"))
self.assertTrue(tokenization._is_punctuation(u"$"))
self.assertTrue(tokenization._is_punctuation(u"`"))
self.assertTrue(tokenization._is_punctuation(u"."))
self.assertFalse(tokenization._is_punctuation(u"A"))
self.assertFalse(tokenization._is_punctuation(u" "))
if __name__ == "__main__":
tf.test.main()
official/nlp/configs/__init__.py 0 → 100644
View file @ 9485aa1d
# Copyright 2021 The TensorFlow Authors. 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.
official/nlp/configs/bert.py 0 → 100644
View file @ 9485aa1d
# Copyright 2021 The TensorFlow Authors. 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.
"""Multi-head BERT encoder network with classification heads.
Includes configurations and instantiation methods.
"""
from typing import List, Optional, Text
import dataclasses
from official.modeling.hyperparams import base_config
from official.nlp.configs import encoders
@dataclasses.dataclass
class ClsHeadConfig(base_config.Config):
inner_dim: int = 0
num_classes: int = 2
activation: Optional[Text] = "tanh"
dropout_rate: float = 0.0
cls_token_idx: int = 0
name: Optional[Text] = None
@dataclasses.dataclass
class PretrainerConfig(base_config.Config):
"""Pretrainer configuration."""
encoder: encoders.EncoderConfig = encoders.EncoderConfig()
cls_heads: List[ClsHeadConfig] = dataclasses.field(default_factory=list)
mlm_activation: str = "gelu"
mlm_initializer_range: float = 0.02
official/nlp/configs/electra.py 0 → 100644
View file @ 9485aa1d
# Copyright 2021 The TensorFlow Authors. 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.
"""ELECTRA model configurations and instantiation methods."""
from typing import List
import dataclasses
from official.modeling.hyperparams import base_config
from official.nlp.configs import bert
from official.nlp.configs import encoders
@dataclasses.dataclass
class ElectraPretrainerConfig(base_config.Config):
"""ELECTRA pretrainer configuration."""
num_masked_tokens: int = 76
sequence_length: int = 512
num_classes: int = 2
discriminator_loss_weight: float = 50.0
tie_embeddings: bool = True
disallow_correct: bool = False
generator_encoder: encoders.EncoderConfig = encoders.EncoderConfig()
discriminator_encoder: encoders.EncoderConfig = encoders.EncoderConfig()
cls_heads: List[bert.ClsHeadConfig] = dataclasses.field(default_factory=list)
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