paddlenlp.transformers.unimo.tokenizer 源代码

# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
# Copyright 2018 The Google AI Language 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.
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#     http://www.apache.org/licenses/LICENSE-2.0
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import os
import pickle
import six
import shutil
import paddle
from paddle.utils import try_import
from paddlenlp.utils.env import MODEL_HOME
import numpy as np
from ...data.vocab import Vocab

from .. import BasicTokenizer, PretrainedTokenizer, WordpieceTokenizer

__all__ = ['UNIMOTokenizer']

PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
    "unimo-text-1.0": 513,
    "unimo-text-1.0-lcsts-new": 513,
    "unimo-text-1.0-large": 512
}


[文档]class UNIMOTokenizer(PretrainedTokenizer): r""" Constructs an UNIMO tokenizer. It uses a basic tokenizer to do punctuation splitting, lower casing and so on, and follows a WordPiece tokenizer to tokenize as subwords. This tokenizer inherits from :class:`~paddlenlp.transformers.tokenizer_utils.PretrainedTokenizer` which contains most of the main methods. For more information regarding those methods, please refer to this superclass. Args: vocab_file (str): The vocabulary file path (ends with '.txt') required to instantiate a `WordpieceTokenizer`. do_lower_case (str, optional): Whether or not to lowercase the input when tokenizing. Defaults to`True`. unk_token (str): A special token representing the *unknown (out-of-vocabulary)* token. An unknown token is set to be `unk_token` inorder to be converted to an ID. Defaults to "[UNK]". sep_token (str): A special token separating two different sentences in the same input. Defaults to "[SEP]". pad_token (str): A special token used to make arrays of tokens the same size for batching purposes. Defaults to "[PAD]". cls_token (str): A special token used for sequence classification. It is the last token of the sequence when built with special tokens. Defaults to "[CLS]". mask_token (str): A special token representing a masked token. This is the token used in the masked language modeling task which the model tries to predict the original unmasked ones. Defaults to "[MASK]". Examples: .. code-block:: from paddlenlp.transformers import UNIMOTokenizer tokenizer = UNIMOTokenizer.from_pretrained('unimo-text-1.0') encoded_inputs = tokenizer('He was a puppeteer') # encoded_inputs #{ # 'input_ids': [1, 4444, 4385, 1545, 6712, 10062, 9568, 9756, 9500, 2], # 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0] #} """ resource_files_names = {"vocab_file": "vocab.txt"} # for save_pretrained pretrained_resource_files_map = { "vocab_file": { "unimo-text-1.0": "https://bj.bcebos.com/paddlenlp/models/transformers/unimo/unimo-text-1.0-vocab.txt", "unimo-text-1.0-lcsts-new": "https://bj.bcebos.com/paddlenlp/models/transformers/unimo/unimo-text-1.0-vocab.txt", "unimo-text-1.0-large": "https://bj.bcebos.com/paddlenlp/models/transformers/unimo/unimo-text-1.0-large-vocab.txt", "unimo-text-1.0-summary": "https://bj.bcebos.com/paddlenlp/models/transformers/unimo/unimo-text-1.0-vocab.txt", "unimo-text-1.0-dureader_qg": "https://bj.bcebos.com/paddlenlp/models/transformers/unimo/unimo-text-1.0-vocab.txt", "unimo-text-1.0-question-generation": "https://bj.bcebos.com/paddlenlp/models/transformers/unimo/unimo-text-1.0-vocab.txt", "unimo-text-1.0-question-generation-full_domain": "https://bj.bcebos.com/paddlenlp/models/transformers/unimo/unimo-text-1.0-vocab.txt", "unimo-text-1.0-question-generation-dureader_qg": "https://bj.bcebos.com/paddlenlp/models/transformers/unimo/unimo-text-1.0-vocab.txt", } } pretrained_init_configuration = { "unimo-text-1.0": { "do_lower_case": True }, "unimo-text-1.0-lcsts-new": { "do_lower_case": True }, "unimo-text-1.0-large": { "do_lower_case": True }, "unimo-text-1.0-summary": { "do_lower_case": True }, "unimo-text-1.0-dureader_qg": { "do_lower_case": True }, "unimo-text-1.0-question-generation": { "do_lower_case": True }, "unimo-text-1.0-question-generation-full_domain": { "do_lower_case": True }, "unimo-text-1.0-question-generation-dureader_qg": { "do_lower_case": True }, } max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES def __init__(self, vocab_file, do_lower_case=True, unk_token="[UNK]", sep_token="[SEP]", pad_token="[PAD]", cls_token="[CLS]", mask_token="[MASK]", **kwargs): if not os.path.isfile(vocab_file): raise ValueError( "Can't find a vocabulary file at path '{}'. To load the " "vocabulary from a pretrained model please use " "`tokenizer = UNIMOTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`" .format(vocab_file)) self.vocab = self.load_vocabulary(vocab_file, unk_token=unk_token) self.basic_tokenizer = BasicTokenizer(do_lower_case=do_lower_case) self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab, unk_token=unk_token) @property def vocab_size(self): """ Return the size of vocabulary. Returns: int: The size of vocabulary. """ return len(self.vocab)
[文档] @staticmethod def load_vocabulary(filepath, unk_token=None, pad_token=None, bos_token=None, eos_token=None, **kwargs): token_to_idx = {} with open(filepath, 'r', encoding='utf-8') as f: for line in f: token, index = line.rstrip('\n').split('\t') token_to_idx[token] = int(index) vocab = Vocab.from_dict(token_to_idx, unk_token=unk_token, pad_token=pad_token, bos_token=bos_token, eos_token=eos_token, **kwargs) return vocab
[文档] def get_vocab(self): vocab = { self.convert_ids_to_tokens(i): i for i in range(self.vocab_size) } return vocab
def _tokenize(self, text): r""" End-to-end tokenization for UNIMO models. Args: text (str): The text to be tokenized. Returns: List[str]: A list of string representing converted tokens. """ 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
[文档] def convert_tokens_to_string(self, tokens): r""" Converts a sequence of tokens (list of string) in a single string. Since the usage of WordPiece introducing `##` to concat subwords, also remove `##` when converting. Args: tokens (list): A list of string representing tokens to be converted. Returns: str: Converted string from tokens. Examples: .. code-block:: from paddlenlp.transformers import UNIMOTokenizer tokenizer = UNIMOTokenizer.from_pretrained('unimo-text-1.0') tokens = tokenizer.tokenize('He was a puppeteer') strings = tokenizer.convert_tokens_to_string(tokens) ''' he was a puppeteer ''' """ out_string = " ".join(tokens).replace(" ##", "").strip() return out_string
[文档] def num_special_tokens_to_add(self, pair=False): r""" Returns the number of added tokens when encoding a sequence with special tokens. Args: pair(bool): Whether the input is a sequence pair or a single sequence. Defaults to `False` and the input is a single sequence. Returns: int: Number of tokens added to sequences. """ token_ids_0 = [] token_ids_1 = [] return len( self.build_inputs_with_special_tokens( token_ids_0, token_ids_1 if pair else None))
[文档] def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None): r""" Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. A UNIMO sequence has the following format: - single sequence: ``[CLS] X [SEP]`` - pair of sequences: ``[CLS] A [SEP] B [SEP]`` Args: token_ids_0 (List[int]): List of IDs to which the special tokens will be added. token_ids_1 (List[int], optional): Optional second list of IDs for sequence pairs. Defaults to `None`. Returns: List[int]: List of input_id with the appropriate special tokens. """ if token_ids_1 is None: return [self.cls_token_id] + token_ids_0 + [self.sep_token_id] _cls = [self.cls_token_id] _sep = [self.sep_token_id] return _cls + token_ids_0 + _sep + token_ids_1 + _sep
[文档] def merge_subword(self, tokens): r""" Converts the subwords in a sequence of tokens (list of string) to whole words, also remove `##` when converting. Args: tokens (List[str]): A list of string representing tokens to be converted. Returns: List[str]: Converted sequence of whole words. """ ret = [] for token in tokens: if token.startswith("##"): real_token = token[2:] if len(ret): ret[-1] += real_token else: ret.append(real_token) else: ret.append(token) return ret
[文档] def build_offset_mapping_with_special_tokens(self, offset_mapping_0, offset_mapping_1=None): r""" Build offset map from a pair of offset map by concatenating and adding offsets of special tokens. A UNIMO offset_mapping has the following format: :: - single sequence: ``(0,0) X (0,0)`` - pair of sequences: `(0,0) A (0,0) B (0,0)`` Args: offset_mapping_ids_0 (List[tuple]): List of char offsets to which the special tokens will be added. offset_mapping_ids_1 (List[tuple], optional): Optional second list of char offsets for offset mapping pairs. Defaults to `None`. Returns: List[tuple]: List of char offsets with the appropriate offsets of special tokens. """ if offset_mapping_1 is None: return [(0, 0)] + offset_mapping_0 + [(0, 0)] return [(0, 0)] + offset_mapping_0 + [(0, 0) ] + offset_mapping_1 + [(0, 0)]
[文档] def create_token_type_ids_from_sequences(self, token_ids_0, token_ids_1=None): r""" Create a mask from the two sequences passed to be used in a sequence-pair classification task. A UNIMO sequence pair mask has the following format: :: 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 | first sequence | second sequence | If `token_ids_1` is `None`, this method only returns the first portion of the mask (0s). Args: token_ids_0 (List[int]): List of IDs. token_ids_1 (List[int], optional): Optional second list of IDs for sequence pairs. Defaults to `None`. Returns: List[int]: List of token_type_id according to the given sequence(s). """ _sep = [self.sep_token_id] _cls = [self.cls_token_id] if token_ids_1 is None: return len(_cls + token_ids_0 + _sep) * [0] return len(_cls + token_ids_0 + _sep) * [0] + len(token_ids_1 + _sep) * [1]
[文档] def gen_encode(self, source, title=None, target=None, max_seq_len=512, max_title_len=128, max_target_len=128, return_position_ids=True, return_token_type_ids=True, return_attention_mask=True, return_length=False, add_start_token_for_decoding=False, pad_to_max_seq_len=False, return_tensors=False, is_split_into_words=False, continuous_position=False): """ Main method for encoding the source for generation. It will return a dictionary containing the encoded sequence and other relative informations which meets the input format requirements of the UNIMO-text model. Args: source (str): The source text of generation. It should be a string. target (str, optional): The target text of generation. It should be set when training the model and should be None when running inference. Defaults to None. title (str, optional): The additional information of some of the generation tasks such as summary. Defaults to None. max_seq_len (int, optional): The maximum encoded sequence length. Defaults to 512. max_target_len (int, optional): The maximum encoded sequence length of the input `target`. Defaults to 128. max_title_len (int, optional): The maximum encoded sequence length of the input `title`. Defaults to 128. return_position_ids (bool, optional): Whether to return the position_ids. Defaults to True. return_token_type_ids (bool, optional): Whether to return the token_type_ids. Defaults to True. return_attention_mask (bool, optional): Whether to return the attention_mask. Defaults to True. return_length (bool, optional): Whether to return the length of the encoded sequence. Defaults to False. add_start_token_for_decoding (bool, optional): Whether to add the special token "[CLS]" at the end of sequence as the begining of the target when running inference to force the model to start generating target sequence. Defaults to False. pad_to_max_seq_len (bool, optional): Whether to pad the returned sequences to the `max_seq_len`. Note that, in this method, returned sequences will be padded on the left. Defaults to False. return_tensors (bool, optional): Whether to convert the returned sequences to Tensor. Defaults to False. is_split_into_words(bool, optinal): Whether or not the input text (`source`, `target` and `title`) has been pretokenized. Defaults to False. continuous_position(bool, optinal): Whether the position ids is continuous between source ids and target ids. Defaults to False. Returns: dict: A dictionary containing the encoded sequence and other relative informations. With the corresponding fields: - input_ids (list[int]|Tensor): A list of indices of input tokens to be feed to UNIMO-text model. If `return_tensors` is True, it is a Tensor with shape [1, sequence_length] and data type 'int64'. - token_type_ids (list[int]|Tensor, optional): A list of segment token indices to indicate whether the token belongs to the dialogue target. If `return_tensors` is True, it is a Tensor with shape [1, sequence_length] and data type 'int64'. Being returned when `return_token_type_ids` is set to True. - position_ids (list[int]|Tensor, optional): A list of The position indices. If `return_tensors` is True, it is a Tensor with shape [1, sequence_length] and data type 'int64'. Being returned when `return_position_ids` is set to True. - attention_mask (numpy.ndarray|Tensor, optional): A numpy.ndarray to prevents attention to some unwanted positions, with shape [sequence_length, sequence_length] and data type 'float32'. If `return_tensors` is True, it is a Tensor with shape [1, 1, sequence_length, sequence_length] and data type 'float32'. Being returned when `return_attention_mask` is set to True. - seq_len (int, optional): The actual length of the `input_ids`, excluding the pad token. Being returned when `return_length` is set to True. Example: .. code-block:: from paddlenlp.transformers import UNIMOTokenizer tokenizer = UNIMOTokenizer.from_pretrained('unimo-text-1.0') inputs = tokenizer.gen_encode('He was a puppeteer') #{'input_ids': [1, 4444, 4385, 1545, 6712, 10062, 9568, 9756, 9500, 2], #'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], #'position_ids': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], #'attention_mask': array([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], #[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], #[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], #[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], #[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], #[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], #[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], #[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], #[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], #[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]], dtype=float32)} """ # Input type checking for clearer error assert isinstance(source, str), ( "The input `source` must be with type `str` (single context). " " But received: {}".format(source)) assert target is None or isinstance(target, str), ( "The input `target` must of be with type `str`. But received: {}". format(target)) assert title is None or isinstance(title, str), ( "The input `title` must of be with type `str`. But received: {}". format(title)) assert max_seq_len > max_title_len + max_target_len, ( "`max_seq_len` must be greater than the sum of `max_target_len` " "and `max_title_len`. But received `max_seq_len` is {}, " "`max_target_len` is {}, `max_title_len` is {}.".format( max_seq_len, max_title_len, max_target_len)) assert target is None or not add_start_token_for_decoding, ( "`add_start_token_for_decoding` only works when `target` is " "`None`. But received `add_start_token_for_decoding`: `{}`, " "`target`: {}.".format(add_start_token_for_decoding, target)) title_ids = [] if title is not None: tokens = self._tokenize(title) title_ids = self.convert_tokens_to_ids(tokens) if len(title_ids) > max_title_len - 1: title_ids = title_ids[:max_title_len - 1] title_ids += [self.sep_token_id] target_ids = [] if target is not None: tokens = self._tokenize(target) target_ids = [self.cls_token_id ] + self.convert_tokens_to_ids(tokens) if len(target_ids) > max_target_len - 1: target_ids = target_ids[:max_target_len - 1] target_ids += [self.mask_token_id] elif add_start_token_for_decoding: target_ids = [self.cls_token_id] title_ids = [self.cls_token_id] + title_ids max_source_len = max_seq_len - len(title_ids) - len(target_ids) source_ids = [] tokens = self._tokenize(source) source_ids = self.convert_tokens_to_ids(tokens) if len(source_ids) > max_source_len - 1: source_ids = source_ids[:max_source_len - 1] source_ids += [self.sep_token_id] source_ids = title_ids + source_ids # Build output dictionnary encoded_inputs = {} encoded_inputs["input_ids"] = source_ids + target_ids # Check lengths sequence_length = len(encoded_inputs["input_ids"]) assert sequence_length <= max_seq_len # Considering that the logits at the last time step in the API of # generative task are taken to generate the next token. In order to # avoid the last time step being a pad, so take padding on the left. pad_length = max_seq_len - sequence_length if pad_to_max_seq_len else 0 if pad_length > 0: encoded_inputs["input_ids"] = [ self.pad_token_id ] * pad_length + encoded_inputs["input_ids"] if return_tensors: # Add dimention for batch_size encoded_inputs["input_ids"] = paddle.to_tensor( encoded_inputs["input_ids"]).unsqueeze(0) if return_token_type_ids: encoded_inputs["token_type_ids"] = [0] * len( source_ids) + [1] * len(target_ids) if pad_length > 0: encoded_inputs["token_type_ids"] = [ self.pad_token_id ] * pad_length + encoded_inputs["token_type_ids"] if return_tensors: # Add dimention for batch_size encoded_inputs["token_type_ids"] = paddle.to_tensor( encoded_inputs["token_type_ids"]).unsqueeze(0) if return_length: encoded_inputs["seq_len"] = sequence_length if return_position_ids: if continuous_position: encoded_inputs["position_ids"] = list(range(sequence_length)) else: encoded_inputs["position_ids"] = list(range( len(source_ids))) + list(range(len(target_ids))) if pad_length > 0: encoded_inputs["position_ids"] = [ self.pad_token_id ] * pad_length + encoded_inputs["position_ids"] if return_tensors: # Add dimention for batch_size encoded_inputs["position_ids"] = paddle.to_tensor( encoded_inputs["position_ids"]).unsqueeze(0) if return_attention_mask: attention_mask = np.ones( (sequence_length, sequence_length), dtype='float32') * -1e4 start = len(source_ids) end = sequence_length attention_mask[:end, :start] = 0.0 # Generate the lower triangular matrix using the slice of matrix tmp = np.triu( np.ones([end - start, end - start], dtype='float32') * -1e4, 1) attention_mask[start:end, start:end] = tmp encoded_inputs["attention_mask"] = attention_mask if pad_length > 0: new_mask = np.ones( (max_seq_len, max_seq_len), dtype='float32') * -1e4 new_mask[-sequence_length:, -sequence_length:] = attention_mask encoded_inputs["attention_mask"] = new_mask if return_tensors: # Add dimentions for batch_size and num_heads encoded_inputs["attention_mask"] = paddle.to_tensor( encoded_inputs["attention_mask"]).unsqueeze((0, 1)) return encoded_inputs