mirror of
https://github.com/hpcaitech/ColossalAI.git
synced 2025-10-12 18:33:53 +00:00
[example] add GPT
This commit is contained in:
Jiarui Fang
GitHub
305
examples/language/gpt/tools/Megatron/gpt2_tokenization.py
Normal file
305
examples/language/gpt/tools/Megatron/gpt2_tokenization.py
Normal file
@@ -0,0 +1,305 @@
|
||||
# coding=utf-8
|
||||
# Copyright 2018 The Open AI Team Authors and The HuggingFace Inc. team.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
"""Tokenization classes for OpenAI GPT."""
|
||||
|
||||
from __future__ import absolute_import, division, print_function, unicode_literals
|
||||
|
||||
import json
|
||||
import logging
|
||||
import os
|
||||
import sys
|
||||
from io import open
|
||||
|
||||
import regex as re
|
||||
|
||||
try:
|
||||
from functools import lru_cache
|
||||
except ImportError:
|
||||
# Just a dummy decorator to get the checks to run on python2
|
||||
# because honestly I don't want to support a byte-level unicode BPE
|
||||
# tokenizer on python 2 right now.
|
||||
def lru_cache():
|
||||
return lambda func: func
|
||||
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
PRETRAINED_VOCAB_ARCHIVE_MAP = {
|
||||
'gpt2': "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-vocab.json",
|
||||
}
|
||||
PRETRAINED_MERGES_ARCHIVE_MAP = {
|
||||
'gpt2': "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-merges.txt",
|
||||
}
|
||||
PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP = {
|
||||
'gpt2': 1024,
|
||||
}
|
||||
VOCAB_NAME = 'vocab.json'
|
||||
MERGES_NAME = 'merges.txt'
|
||||
SPECIAL_TOKENS_NAME = 'special_tokens.txt'
|
||||
|
||||
|
||||
@lru_cache()
|
||||
def bytes_to_unicode():
|
||||
"""
|
||||
Returns list of utf-8 byte and a corresponding list of unicode strings.
|
||||
The reversible bpe codes work on unicode strings.
|
||||
This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
|
||||
When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
|
||||
This is a significant percentage of your normal, say, 32K bpe vocab.
|
||||
To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
|
||||
And avoids mapping to whitespace/control characters the bpe code barfs on.
|
||||
"""
|
||||
_chr = unichr if sys.version_info[0] == 2 else chr
|
||||
bs = list(range(ord("!"), ord("~") + 1)) + list(range(ord("¡"), ord("¬") + 1)) + \
|
||||
list(range(ord("®"), ord("ÿ") + 1))
|
||||
cs = bs[:]
|
||||
n = 0
|
||||
for b in range(2**8):
|
||||
if b not in bs:
|
||||
bs.append(b)
|
||||
cs.append(2**8 + n)
|
||||
n += 1
|
||||
cs = [_chr(n) for n in cs]
|
||||
return dict(zip(bs, cs))
|
||||
|
||||
|
||||
def get_pairs(word):
|
||||
"""Return set of symbol pairs in a word.
|
||||
|
||||
Word is represented as tuple of symbols (symbols being variable-length strings).
|
||||
"""
|
||||
pairs = set()
|
||||
prev_char = word[0]
|
||||
for char in word[1:]:
|
||||
pairs.add((prev_char, char))
|
||||
prev_char = char
|
||||
return pairs
|
||||
|
||||
|
||||
class GPT2Tokenizer(object):
|
||||
"""
|
||||
GPT-2 BPE tokenizer. Peculiarities:
|
||||
- Byte-level BPE
|
||||
"""
|
||||
|
||||
@classmethod
|
||||
def from_pretrained(cls, pretrained_model_name_or_path, cache_dir=None, *inputs, **kwargs):
|
||||
"""
|
||||
Instantiate a PreTrainedBertModel from a pre-trained model file.
|
||||
Download and cache the pre-trained model file if needed.
|
||||
"""
|
||||
if pretrained_model_name_or_path in PRETRAINED_VOCAB_ARCHIVE_MAP:
|
||||
vocab_file = PRETRAINED_VOCAB_ARCHIVE_MAP[pretrained_model_name_or_path]
|
||||
merges_file = PRETRAINED_MERGES_ARCHIVE_MAP[pretrained_model_name_or_path]
|
||||
special_tokens_file = None
|
||||
else:
|
||||
vocab_file = os.path.join(pretrained_model_name_or_path, VOCAB_NAME)
|
||||
merges_file = os.path.join(pretrained_model_name_or_path, MERGES_NAME)
|
||||
special_tokens_file = os.path.join(pretrained_model_name_or_path, SPECIAL_TOKENS_NAME)
|
||||
if not os.path.exists(special_tokens_file):
|
||||
special_tokens_file = None
|
||||
else:
|
||||
logger.info("loading special tokens file {}".format(special_tokens_file))
|
||||
# redirect to the cache, if necessary
|
||||
try:
|
||||
from cached_path import cached_path
|
||||
resolved_vocab_file = cached_path(vocab_file)
|
||||
resolved_merges_file = cached_path(merges_file)
|
||||
except EnvironmentError:
|
||||
logger.error("Model name '{}' was not found in model name list ({}). "
|
||||
"We assumed '{}' was a path or url but couldn't find files {} and {} "
|
||||
"at this path or url.".format(pretrained_model_name_or_path,
|
||||
', '.join(PRETRAINED_VOCAB_ARCHIVE_MAP.keys()),
|
||||
pretrained_model_name_or_path, vocab_file, merges_file))
|
||||
return None
|
||||
if resolved_vocab_file == vocab_file and resolved_merges_file == merges_file:
|
||||
logger.info("loading vocabulary file {}".format(vocab_file))
|
||||
logger.info("loading merges file {}".format(merges_file))
|
||||
else:
|
||||
logger.info("loading vocabulary file {} from cache at {}".format(vocab_file, resolved_vocab_file))
|
||||
logger.info("loading merges file {} from cache at {}".format(merges_file, resolved_merges_file))
|
||||
if pretrained_model_name_or_path in PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP:
|
||||
# if we're using a pretrained model, ensure the tokenizer wont index sequences longer
|
||||
# than the number of positional embeddings
|
||||
max_len = PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP[pretrained_model_name_or_path]
|
||||
kwargs['max_len'] = min(kwargs.get('max_len', int(1e12)), max_len)
|
||||
# Instantiate tokenizer.
|
||||
if special_tokens_file and 'special_tokens' not in kwargs:
|
||||
special_tokens = open(special_tokens_file, encoding='utf-8').read().split('\n')[:-1]
|
||||
else:
|
||||
special_tokens = kwargs.pop('special_tokens', [])
|
||||
tokenizer = cls(resolved_vocab_file, resolved_merges_file, special_tokens=special_tokens, *inputs, **kwargs)
|
||||
return tokenizer
|
||||
|
||||
def __init__(self, vocab_file, merges_file, errors='replace', special_tokens=None, max_len=None):
|
||||
self.max_len = max_len if max_len is not None else int(1e12)
|
||||
self.encoder = json.load(open(vocab_file))
|
||||
self.decoder = {v: k for k, v in self.encoder.items()}
|
||||
self.errors = errors # how to handle errors in decoding
|
||||
self.byte_encoder = bytes_to_unicode()
|
||||
self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
|
||||
bpe_data = open(merges_file, encoding='utf-8').read().split('\n')[1:-1]
|
||||
bpe_merges = [tuple(merge.split()) for merge in bpe_data]
|
||||
self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges))))
|
||||
self.cache = {}
|
||||
|
||||
# Should haved added re.IGNORECASE so BPE merges can happen for
|
||||
# capitalized versions of contractions
|
||||
self.pat = re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""")
|
||||
|
||||
self.special_tokens = {}
|
||||
self.special_tokens_decoder = {}
|
||||
self.set_special_tokens(special_tokens)
|
||||
|
||||
def __len__(self):
|
||||
return len(self.encoder) + len(self.special_tokens)
|
||||
|
||||
def set_special_tokens(self, special_tokens):
|
||||
""" Add a list of additional tokens to the encoder.
|
||||
The additional tokens are indexed starting from the last index of the
|
||||
current vocabulary in the order of the `special_tokens` list.
|
||||
"""
|
||||
if not special_tokens:
|
||||
self.special_tokens = {}
|
||||
self.special_tokens_decoder = {}
|
||||
return
|
||||
self.special_tokens = dict((tok, len(self.encoder) + i) for i, tok in enumerate(special_tokens))
|
||||
self.special_tokens_decoder = {v: k for k, v in self.special_tokens.items()}
|
||||
logger.info("Special tokens {}".format(self.special_tokens))
|
||||
|
||||
def bpe(self, token):
|
||||
if token in self.cache:
|
||||
return self.cache[token]
|
||||
word = tuple(token)
|
||||
pairs = get_pairs(word)
|
||||
|
||||
if not pairs:
|
||||
return token
|
||||
|
||||
while True:
|
||||
bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float('inf')))
|
||||
if bigram not in self.bpe_ranks:
|
||||
break
|
||||
first, second = bigram
|
||||
new_word = []
|
||||
i = 0
|
||||
while i < len(word):
|
||||
try:
|
||||
j = word.index(first, i)
|
||||
new_word.extend(word[i:j])
|
||||
i = j
|
||||
except BaseException:
|
||||
new_word.extend(word[i:])
|
||||
break
|
||||
|
||||
if word[i] == first and i < len(word) - 1 and word[i + 1] == second:
|
||||
new_word.append(first + second)
|
||||
i += 2
|
||||
else:
|
||||
new_word.append(word[i])
|
||||
i += 1
|
||||
new_word = tuple(new_word)
|
||||
word = new_word
|
||||
if len(word) == 1:
|
||||
break
|
||||
else:
|
||||
pairs = get_pairs(word)
|
||||
word = ' '.join(word)
|
||||
self.cache[token] = word
|
||||
return word
|
||||
|
||||
def tokenize(self, text):
|
||||
""" Tokenize a string. """
|
||||
bpe_tokens = []
|
||||
for token in re.findall(self.pat, text):
|
||||
if sys.version_info[0] == 2:
|
||||
token = ''.join(self.byte_encoder[ord(b)] for b in token)
|
||||
else:
|
||||
token = ''.join(self.byte_encoder[b] for b in token.encode('utf-8'))
|
||||
bpe_tokens.extend(bpe_token for bpe_token in self.bpe(token).split(' '))
|
||||
return bpe_tokens
|
||||
|
||||
def convert_tokens_to_ids(self, tokens):
|
||||
""" Converts a sequence of tokens into ids using the vocab. """
|
||||
ids = []
|
||||
if isinstance(tokens, str) or (sys.version_info[0] == 2 and isinstance(tokens, unicode)):
|
||||
if tokens in self.special_tokens:
|
||||
return self.special_tokens[tokens]
|
||||
else:
|
||||
return self.encoder.get(tokens, 0)
|
||||
for token in tokens:
|
||||
if token in self.special_tokens:
|
||||
ids.append(self.special_tokens[token])
|
||||
else:
|
||||
ids.append(self.encoder.get(token, 0))
|
||||
if len(ids) > self.max_len:
|
||||
logger.warning("Token indices sequence length is longer than the specified maximum "
|
||||
" sequence length for this OpenAI GPT model ({} > {}). Running this"
|
||||
" sequence through the model will result in indexing errors".format(len(ids), self.max_len))
|
||||
return ids
|
||||
|
||||
def convert_ids_to_tokens(self, ids, skip_special_tokens=False):
|
||||
"""Converts a sequence of ids in BPE tokens using the vocab."""
|
||||
tokens = []
|
||||
for i in ids:
|
||||
if i in self.special_tokens_decoder:
|
||||
if not skip_special_tokens:
|
||||
tokens.append(self.special_tokens_decoder[i])
|
||||
else:
|
||||
tokens.append(self.decoder[i])
|
||||
return tokens
|
||||
|
||||
def encode(self, text):
|
||||
return self.convert_tokens_to_ids(self.tokenize(text))
|
||||
|
||||
def decode(self, tokens):
|
||||
text = ''.join([self.decoder[token] for token in tokens])
|
||||
text = bytearray([self.byte_decoder[c] for c in text]).decode('utf-8', errors=self.errors)
|
||||
return text
|
||||
|
||||
def save_vocabulary(self, vocab_path):
|
||||
"""Save the tokenizer vocabulary and merge files to a directory."""
|
||||
if not os.path.isdir(vocab_path):
|
||||
logger.error("Vocabulary path ({}) should be a directory".format(vocab_path))
|
||||
return
|
||||
vocab_file = os.path.join(vocab_path, VOCAB_NAME)
|
||||
merge_file = os.path.join(vocab_path, MERGES_NAME)
|
||||
special_tokens_file = os.path.join(vocab_path, SPECIAL_TOKENS_NAME)
|
||||
|
||||
with open(vocab_file, 'w', encoding='utf-8') as f:
|
||||
f.write(json.dumps(self.encoder, ensure_ascii=False))
|
||||
|
||||
index = 0
|
||||
with open(merge_file, "w", encoding="utf-8") as writer:
|
||||
writer.write(u'#version: 0.2\n')
|
||||
for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda kv: kv[1]):
|
||||
if index != token_index:
|
||||
logger.warning("Saving vocabulary to {}: BPE merge indices are not consecutive."
|
||||
" Please check that the tokenizer is not corrupted!".format(merge_file))
|
||||
index = token_index
|
||||
writer.write(' '.join(bpe_tokens) + u'\n')
|
||||
index += 1
|
||||
|
||||
index = len(self.encoder)
|
||||
with open(special_tokens_file, 'w', encoding='utf-8') as writer:
|
||||
for token, token_index in sorted(self.special_tokens.items(), key=lambda kv: kv[1]):
|
||||
if index != token_index:
|
||||
logger.warning("Saving special tokens vocabulary to {}: BPE indices are not consecutive."
|
||||
" Please check that the tokenizer is not corrupted!".format(special_tokens_file))
|
||||
index = token_index
|
||||
writer.write(token + u'\n')
|
||||
index += 1
|
||||
|
||||
return vocab_file, merge_file, special_tokens_file
|
||||
Reference in New Issue
Block a user