ColossalAI/examples/tutorial/sequence_parallel/data/tokenizer/tokenizer.py

# 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
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"""Megatron tokenizers."""

from abc import ABC, abstractmethod

from colossalai.legacy.context import ParallelMode
from colossalai.legacy.core import global_context as gpc

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."""

    @property
    @abstractmethod
    def inv_vocab(self):
        """Dictionary from vocab id token to text token."""

    @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