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chore: import upstream snapshot with attribution
2026-07-13 13:37:14 +08:00

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# 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.
# 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.
import os
import numpy as np
import paddle
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,
}
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)
@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
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
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
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))
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
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
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)]
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]
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 beginning 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, optional): Whether or not the input text
(`source`, `target` and `title`) has been pretokenized.
Defaults to False.
continuous_position(bool, optional): 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 dictionary
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 dimension 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 dimension 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 dimension 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 dimensions for batch_size and num_heads
encoded_inputs["attention_mask"] = paddle.to_tensor(encoded_inputs["attention_mask"]).unsqueeze((0, 1))
return encoded_inputs
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