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[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
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# coding=utf-8
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# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from .tokenizer import build_tokenizer
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_TOKENIZER = None
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_PADDED_VOCAB_SIZE = -1
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def initialize_tokenizer(vocab_file, tokenizer_type, vocab_extra_ids=0):
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tokenizer, padded_vocab_size = build_tokenizer(vocab_file, tokenizer_type, vocab_extra_ids)
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global _TOKENIZER, _PADDED_VOCAB_SIZE
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_TOKENIZER = tokenizer
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_PADDED_VOCAB_SIZE = padded_vocab_size
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def get_tokenizer():
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global _TOKENIZER
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return _TOKENIZER
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def get_padded_vocab_size():
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global _PADDED_VOCAB_SIZE
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return _PADDED_VOCAB_SIZE
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@@ -0,0 +1,431 @@
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# coding=utf-8
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# Copyright 2018 The Google AI Language Team Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Tokenization classes."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import collections
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import re
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import unicodedata
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import six
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def validate_case_matches_checkpoint(do_lower_case, init_checkpoint):
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"""Checks whether the casing config is consistent with the checkpoint name."""
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# The casing has to be passed in by the user and there is no explicit check
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# as to whether it matches the checkpoint. The casing information probably
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# should have been stored in the bert_config.json file, but it's not, so
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# we have to heuristically detect it to validate.
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if not init_checkpoint:
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return
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m = re.match("^.*?([A-Za-z0-9_-]+)/bert_model.ckpt", init_checkpoint)
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if m is None:
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return
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model_name = m.group(1)
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lower_models = [
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"uncased_L-24_H-1024_A-16", "uncased_L-12_H-768_A-12",
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"multilingual_L-12_H-768_A-12", "chinese_L-12_H-768_A-12"
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]
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cased_models = [
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"cased_L-12_H-768_A-12", "cased_L-24_H-1024_A-16",
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"multi_cased_L-12_H-768_A-12"
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]
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is_bad_config = False
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if model_name in lower_models and not do_lower_case:
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is_bad_config = True
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actual_flag = "False"
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case_name = "lowercased"
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opposite_flag = "True"
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if model_name in cased_models and do_lower_case:
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is_bad_config = True
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actual_flag = "True"
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case_name = "cased"
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opposite_flag = "False"
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if is_bad_config:
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raise ValueError(
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"You passed in `--do_lower_case=%s` with `--init_checkpoint=%s`. "
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"However, `%s` seems to be a %s model, so you "
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"should pass in `--do_lower_case=%s` so that the fine-tuning matches "
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"how the model was pre-training. If this error is wrong, please "
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"just comment out this check." % (actual_flag, init_checkpoint,
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model_name, case_name, opposite_flag))
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def convert_to_unicode(text):
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"""Converts `text` to Unicode (if it's not already), assuming utf-8 input."""
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if six.PY3:
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if isinstance(text, str):
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return text
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elif isinstance(text, bytes):
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return text.decode("utf-8", "ignore")
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else:
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raise ValueError("Unsupported string type: %s" % (type(text)))
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elif six.PY2:
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if isinstance(text, str):
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return text.decode("utf-8", "ignore")
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elif isinstance(text, unicode):
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return text
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else:
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raise ValueError("Unsupported string type: %s" % (type(text)))
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else:
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raise ValueError("Not running on Python2 or Python 3?")
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def printable_text(text):
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"""Returns text encoded in a way suitable for print or `tf.logging`."""
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# These functions want `str` for both Python2 and Python3, but in one case
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# it's a Unicode string and in the other it's a byte string.
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if six.PY3:
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if isinstance(text, str):
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return text
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elif isinstance(text, bytes):
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return text.decode("utf-8", "ignore")
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else:
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raise ValueError("Unsupported string type: %s" % (type(text)))
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elif six.PY2:
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if isinstance(text, str):
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return text
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elif isinstance(text, unicode):
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return text.encode("utf-8")
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else:
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raise ValueError("Unsupported string type: %s" % (type(text)))
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else:
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raise ValueError("Not running on Python2 or Python 3?")
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def load_vocab(vocab_file):
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"""Loads a vocabulary file into a dictionary."""
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vocab = collections.OrderedDict()
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index = 0
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with open(vocab_file, "r") as reader:
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while True:
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token = convert_to_unicode(reader.readline())
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if not token:
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break
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token = token.strip()
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vocab[token] = index
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index += 1
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return vocab
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def convert_by_vocab(vocab, items):
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"""Converts a sequence of [tokens|ids] using the vocab."""
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output = []
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for item in items:
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output.append(vocab[item])
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return output
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def convert_tokens_to_ids(vocab, tokens):
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return convert_by_vocab(vocab, tokens)
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def convert_ids_to_tokens(inv_vocab, ids):
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return convert_by_vocab(inv_vocab, ids)
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def whitespace_tokenize(text):
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"""Runs basic whitespace cleaning and splitting on a piece of text."""
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text = text.strip()
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if not text:
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return []
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tokens = text.split()
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return tokens
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class FullTokenizer(object):
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"""Runs end-to-end tokenization."""
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def __init__(self, vocab_file, do_lower_case=True):
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self.vocab = load_vocab(vocab_file)
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self.inv_vocab = {v: k for k, v in self.vocab.items()}
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self.basic_tokenizer = BasicTokenizer(do_lower_case=do_lower_case)
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self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab)
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def tokenize(self, text):
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split_tokens = []
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for token in self.basic_tokenizer.tokenize(text):
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for sub_token in self.wordpiece_tokenizer.tokenize(token):
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split_tokens.append(sub_token)
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return split_tokens
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def convert_tokens_to_ids(self, tokens):
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return convert_by_vocab(self.vocab, tokens)
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def convert_ids_to_tokens(self, ids):
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return convert_by_vocab(self.inv_vocab, ids)
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@staticmethod
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def convert_tokens_to_string(tokens, clean_up_tokenization_spaces=True):
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""" Converts a sequence of tokens (string) in a single string. """
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def clean_up_tokenization(out_string):
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""" Clean up a list of simple English tokenization artifacts
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like spaces before punctuations and abbreviated forms.
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"""
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out_string = (
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out_string.replace(" .", ".")
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.replace(" ?", "?")
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.replace(" !", "!")
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.replace(" ,", ",")
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.replace(" ' ", "'")
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.replace(" n't", "n't")
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.replace(" 'm", "'m")
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.replace(" 's", "'s")
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.replace(" 've", "'ve")
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.replace(" 're", "'re")
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)
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return out_string
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text = ' '.join(tokens).replace(' ##', '').strip()
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if clean_up_tokenization_spaces:
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clean_text = clean_up_tokenization(text)
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return clean_text
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else:
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return text
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def vocab_size(self):
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return len(self.vocab)
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class BasicTokenizer(object):
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"""Runs basic tokenization (punctuation splitting, lower casing, etc.)."""
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def __init__(self, do_lower_case=True):
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"""Constructs a BasicTokenizer.
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Args:
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do_lower_case: Whether to lower case the input.
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"""
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self.do_lower_case = do_lower_case
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def tokenize(self, text):
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"""Tokenizes a piece of text."""
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text = convert_to_unicode(text)
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text = self._clean_text(text)
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# This was added on November 1st, 2018 for the multilingual and Chinese
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# models. This is also applied to the English models now, but it doesn't
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# matter since the English models were not trained on any Chinese data
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# and generally don't have any Chinese data in them (there are Chinese
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# characters in the vocabulary because Wikipedia does have some Chinese
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# words in the English Wikipedia.).
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text = self._tokenize_chinese_chars(text)
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orig_tokens = whitespace_tokenize(text)
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split_tokens = []
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for token in orig_tokens:
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if self.do_lower_case:
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token = token.lower()
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token = self._run_strip_accents(token)
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split_tokens.extend(self._run_split_on_punc(token))
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output_tokens = whitespace_tokenize(" ".join(split_tokens))
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return output_tokens
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def _run_strip_accents(self, text):
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"""Strips accents from a piece of text."""
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text = unicodedata.normalize("NFD", text)
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output = []
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for char in text:
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cat = unicodedata.category(char)
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if cat == "Mn":
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continue
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output.append(char)
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return "".join(output)
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def _run_split_on_punc(self, text):
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"""Splits punctuation on a piece of text."""
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chars = list(text)
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i = 0
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start_new_word = True
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output = []
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while i < len(chars):
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char = chars[i]
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if _is_punctuation(char):
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output.append([char])
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start_new_word = True
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else:
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if start_new_word:
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output.append([])
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start_new_word = False
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output[-1].append(char)
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i += 1
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return ["".join(x) for x in output]
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def _tokenize_chinese_chars(self, text):
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"""Adds whitespace around any CJK character."""
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output = []
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for char in text:
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cp = ord(char)
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if self._is_chinese_char(cp):
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output.append(" ")
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output.append(char)
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output.append(" ")
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else:
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output.append(char)
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return "".join(output)
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def _is_chinese_char(self, cp):
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"""Checks whether CP is the codepoint of a CJK character."""
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# This defines a "chinese character" as anything in the CJK Unicode block:
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# https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
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#
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# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
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# despite its name. The modern Korean Hangul alphabet is a different block,
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# as is Japanese Hiragana and Katakana. Those alphabets are used to write
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# space-separated words, so they are not treated specially and handled
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# like the all of the other languages.
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if ((cp >= 0x4E00 and cp <= 0x9FFF) or #
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(cp >= 0x3400 and cp <= 0x4DBF) or #
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(cp >= 0x20000 and cp <= 0x2A6DF) or #
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(cp >= 0x2A700 and cp <= 0x2B73F) or #
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(cp >= 0x2B740 and cp <= 0x2B81F) or #
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(cp >= 0x2B820 and cp <= 0x2CEAF) or
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(cp >= 0xF900 and cp <= 0xFAFF) or #
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(cp >= 0x2F800 and cp <= 0x2FA1F)): #
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return True
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return False
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def _clean_text(self, text):
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"""Performs invalid character removal and whitespace cleanup on text."""
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output = []
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for char in text:
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cp = ord(char)
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if cp == 0 or cp == 0xfffd or _is_control(char):
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continue
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if _is_whitespace(char):
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output.append(" ")
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else:
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output.append(char)
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return "".join(output)
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class WordpieceTokenizer(object):
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"""Runs WordPiece tokenization."""
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def __init__(self, vocab, unk_token="[UNK]", max_input_chars_per_word=200):
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self.vocab = vocab
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self.unk_token = unk_token
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self.max_input_chars_per_word = max_input_chars_per_word
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def tokenize(self, text):
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"""Tokenizes a piece of text into its word pieces.
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This uses a greedy longest-match-first algorithm to perform tokenization
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using the given vocabulary.
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For example:
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input = "unaffable"
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output = ["un", "##aff", "##able"]
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Args:
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text: A single token or whitespace separated tokens. This should have
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already been passed through `BasicTokenizer.
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Returns:
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A list of wordpiece tokens.
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"""
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text = convert_to_unicode(text)
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output_tokens = []
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for token in whitespace_tokenize(text):
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chars = list(token)
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if len(chars) > self.max_input_chars_per_word:
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output_tokens.append(self.unk_token)
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continue
|
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is_bad = False
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start = 0
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sub_tokens = []
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while start < len(chars):
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end = len(chars)
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cur_substr = None
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while start < end:
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substr = "".join(chars[start:end])
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if start > 0:
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substr = "##" + substr
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if substr in self.vocab:
|
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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:
|
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output_tokens.extend(sub_tokens)
|
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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