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079bf3cb26
* [misc] update pre-commit * [misc] run pre-commit * [misc] remove useless configuration files * [misc] ignore cuda for clang-format
242 lines
7.7 KiB
Python
242 lines
7.7 KiB
Python
# 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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"""Megatron tokenizers."""
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from abc import ABC, abstractmethod
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from colossalai.legacy.context import ParallelMode
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from colossalai.legacy.core import global_context as gpc
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from .bert_tokenization import FullTokenizer as FullBertTokenizer
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def build_tokenizer(vocab_file, tokenizer_type, vocab_extra_ids=0):
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"""Initialize tokenizer."""
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if not gpc.is_initialized(ParallelMode.GLOBAL) or gpc.get_global_rank() == 0:
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print("> building {} tokenizer ...".format(tokenizer_type), flush=True)
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# Select and instantiate the tokenizer.
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if tokenizer_type == "BertWordPieceLowerCase":
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tokenizer = _BertWordPieceTokenizer(vocab_file=vocab_file, lower_case=True, vocab_extra_ids=vocab_extra_ids)
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elif tokenizer_type == "BertWordPieceCase":
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tokenizer = _BertWordPieceTokenizer(vocab_file=vocab_file, lower_case=False, vocab_extra_ids=vocab_extra_ids)
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else:
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raise NotImplementedError("{} tokenizer is not " "implemented.".format(tokenizer_type))
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# Add vocab size.
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padded_vocab_size = _vocab_size_with_padding(tokenizer.vocab_size)
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return tokenizer, padded_vocab_size
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def _vocab_size_with_padding(orig_vocab_size, make_vocab_size_divisible_by=128):
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"""Pad vocab size so it is divisible by model parallel size and
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still having GPU friendly size."""
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after = orig_vocab_size
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if gpc.is_initialized(ParallelMode.TENSOR):
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multiple = make_vocab_size_divisible_by * gpc.get_world_size(ParallelMode.TENSOR)
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else:
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multiple = make_vocab_size_divisible_by
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while (after % multiple) != 0:
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after += 1
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if not gpc.is_initialized(ParallelMode.GLOBAL) or gpc.get_global_rank() == 0:
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print(
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" > padded vocab (size: {}) with {} dummy tokens "
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"(new size: {})".format(orig_vocab_size, after - orig_vocab_size, after),
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flush=True,
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)
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return after
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class AbstractTokenizer(ABC):
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"""Abstract class for tokenizer."""
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def __init__(self, name):
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self.name = name
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super().__init__()
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@property
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@abstractmethod
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def vocab_size(self):
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pass
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@property
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@abstractmethod
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def vocab(self):
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"""Dictionary from vocab text token to id token."""
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@property
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@abstractmethod
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def inv_vocab(self):
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"""Dictionary from vocab id token to text token."""
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@abstractmethod
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def tokenize(self, text):
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pass
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def detokenize(self, token_ids):
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raise NotImplementedError("detokenizer is not implemented for {} " "tokenizer".format(self.name))
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@property
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def cls(self):
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raise NotImplementedError("CLS is not provided for {} " "tokenizer".format(self.name))
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@property
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def sep(self):
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raise NotImplementedError("SEP is not provided for {} " "tokenizer".format(self.name))
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@property
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def pad(self):
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raise NotImplementedError("PAD is not provided for {} " "tokenizer".format(self.name))
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@property
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def eod(self):
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raise NotImplementedError("EOD is not provided for {} " "tokenizer".format(self.name))
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@property
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def mask(self):
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raise NotImplementedError("MASK is not provided for {} " "tokenizer".format(self.name))
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class _BertWordPieceTokenizer(AbstractTokenizer):
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"""Original BERT wordpiece tokenizer."""
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def __init__(self, vocab_file, lower_case=True, vocab_extra_ids=0):
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if lower_case:
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name = "BERT Lower Case"
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else:
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name = "BERT Upper Case"
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super().__init__(name)
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self.tokenizer = FullBertTokenizer(vocab_file, do_lower_case=lower_case)
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self.cls_id = self.tokenizer.vocab["[CLS]"]
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self.sep_id = self.tokenizer.vocab["[SEP]"]
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self.pad_id = self.tokenizer.vocab["[PAD]"]
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self.mask_id = self.tokenizer.vocab["[MASK]"]
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self._additional_special_tokens = []
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# (dsachan) Add BOS and EOS tokens
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SPECIAL_TOKENS = {"eos_token": "[EOS]", "bos_token": "[BOS]"}
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self._bos_token = "[BOS]"
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self.add_token(self._bos_token)
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self._bos_token_id = self.vocab.get(self._bos_token)
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self._eos_token = "[EOS]"
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self.add_token(self._eos_token)
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self._eos_token_id = self.vocab.get(self._eos_token)
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# (dsachan) Add additional special tokens
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# These can be used as sentinel tokens in T5 model inputs
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additional_special_tokens = []
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additional_special_tokens.extend(["<extra_id_{}>".format(i) for i in range(vocab_extra_ids)])
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self.add_additional_special_tokens(additional_special_tokens)
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def add_token(self, token):
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if token not in self.vocab:
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self.inv_vocab[self.vocab_size] = token
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# self.vocab_size comes from len(vocab)
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# and it will increase as we add elements
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self.vocab[token] = self.vocab_size
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def add_additional_special_tokens(self, tokens_list):
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setattr(self, "additional_special_tokens", tokens_list)
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for value in tokens_list:
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self.add_token(value)
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@property
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def vocab_size(self):
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return self.tokenizer.vocab_size()
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@property
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def vocab(self):
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return self.tokenizer.vocab
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@property
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def inv_vocab(self):
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return self.tokenizer.inv_vocab
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def tokenize(self, text):
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text_tokens = self.tokenizer.tokenize(text)
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return self.tokenizer.convert_tokens_to_ids(text_tokens)
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def decode(self, ids):
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tokens = self.tokenizer.convert_ids_to_tokens(ids)
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return self.tokenizer.convert_tokens_to_string(tokens)
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def decode_token_ids(self, token_ids):
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tokens = self.tokenizer.convert_ids_to_tokens(token_ids)
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exclude_list = ["[PAD]", "[CLS]"]
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non_pads = [t for t in tokens if t not in exclude_list]
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result = ""
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for s in non_pads:
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if s.startswith("##"):
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result += s[2:]
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else:
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result += " " + s
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return result
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@property
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def cls(self):
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return self.cls_id
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@property
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def sep(self):
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return self.sep_id
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@property
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def pad(self):
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return self.pad_id
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@property
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def mask(self):
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return self.mask_id
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@property
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def bos_token(self):
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"""Beginning of sentence token id"""
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return self._bos_token
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@property
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def eos_token(self):
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"""End of sentence token id"""
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return self._eos_token
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@property
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def additional_special_tokens(self):
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"""All the additional special tokens you may want to use (list of strings)."""
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return self._additional_special_tokens
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@property
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def bos_token_id(self):
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"""Id of the beginning of sentence token in the vocabulary."""
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return self._bos_token_id
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@property
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def eos_token_id(self):
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"""Id of the end of sentence token in the vocabulary."""
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return self._eos_token_id
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@property
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def additional_special_tokens_ids(self):
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"""Ids of all the additional special tokens in the vocabulary (list of integers)."""
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return [self.vocab.get(token) for token in self._additional_special_tokens]
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@additional_special_tokens.setter
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def additional_special_tokens(self, value):
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self._additional_special_tokens = value
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