# Copyright (c) 2021 PaddlePaddle Authors. 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
# limitations under the License.
import os
from shutil import copyfile
import sentencepiece as spm
from .. import AddedToken, PretrainedTokenizer
__all__ = ["MBartTokenizer", "MBart50Tokenizer"]
MBART_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
"mbart-large-cc25": 1024,
"mbart-large-en-ro": 1024,
}
MBART50_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
"mbart-large-50-one-to-many-mmt": 1024,
"mbart-large-50-many-to-one-mmt": 1024,
"mbart-large-50-many-to-many-mmt": 1024,
}
[docs]class MBartTokenizer(PretrainedTokenizer):
resource_files_names = {
"vocab_file": "sentencepiece.bpe.model",
}
pretrained_resource_files_map = {
"vocab_file": {
"mbart-large-en-ro": "https://bj.bcebos.com/paddlenlp/models/transformers/mbart/mbart-large-en-ro.sentencepiece.bpe.model",
"mbart-large-cc25": "https://bj.bcebos.com/paddlenlp/models/transformers/mbart/mbart-large-cc25.sentencepiece.bpe.model",
}
}
pretrained_init_configuration = {"mbart-large-cc25": {}, "mbart-large-en-ro": {}}
max_model_input_sizes = MBART_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
model_input_names = ["input_ids"]
FAIRSEQ_LANGUAGE_CODES = [
"ar_AR",
"cs_CZ",
"de_DE",
"en_XX",
"es_XX",
"et_EE",
"fi_FI",
"fr_XX",
"gu_IN",
"hi_IN",
"it_IT",
"ja_XX",
"kk_KZ",
"ko_KR",
"lt_LT",
"lv_LV",
"my_MM",
"ne_NP",
"nl_XX",
"ro_RO",
"ru_RU",
"si_LK",
"tr_TR",
"vi_VN",
"zh_CN",
]
def __init__(
self,
vocab_file,
src_lang=None,
tgt_lang=None,
bos_token="<s>",
eos_token="</s>",
sep_token="</s>",
cls_token="<s>",
unk_token="<unk>",
pad_token="<pad>",
mask_token="<mask>",
sp_model_kwargs=None,
additional_special_tokens=None,
**kwargs
):
self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
mask_token = AddedToken(mask_token, lstrip=True, rstrip=False) if isinstance(mask_token, str) else mask_token
self._build_special_tokens_map_extended(mask_token=mask_token)
self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
self.vocab_file = vocab_file
self.sp_model.Load(str(vocab_file))
self.fairseq_offset = 1
self.fairseq_tokens_to_ids = {"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}
self.sp_model_size = len(self.sp_model)
self.lang_code_to_id = {
code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(self.FAIRSEQ_LANGUAGE_CODES)
}
self.fairseq_tokens_to_ids["<mask>"] = len(self.sp_model) + len(self.lang_code_to_id) + self.fairseq_offset
self.fairseq_tokens_to_ids.update(self.lang_code_to_id)
self.fairseq_ids_to_tokens = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
self.src_lang = src_lang if src_lang is not None else "en_XX"
self.tgt_lang = tgt_lang
# Get `special_tokens_map` after `_wrap_init()`
self.eos_token_id = self.fairseq_tokens_to_ids[eos_token]
self.unk_token_id = self.fairseq_tokens_to_ids[unk_token]
self.set_src_lang_special_tokens(self.src_lang)
self._additional_special_tokens = list(self.lang_code_to_id.keys())
if additional_special_tokens is not None:
# Only add those special tokens if they are not already there.
self._additional_special_tokens.extend(
[t for t in additional_special_tokens if t not in self._additional_special_tokens]
)
def __call__(
self,
text,
text_pair=None,
max_length=None,
stride=0,
is_split_into_words=False,
padding=None,
truncation="longest_first",
return_position_ids=False,
return_token_type_ids=False,
return_attention_mask=True,
return_length=False,
return_overflowing_tokens=False,
return_special_tokens_mask=False,
**kwargs
):
if "pad_to_max_seq_len" in kwargs and padding is None:
pad_to_max_seq_len = kwargs.pop("pad_to_max_seq_len")
padding = "max_length" if pad_to_max_seq_len else False
elif padding is None:
padding = False
if "max_seq_len" in kwargs and max_length is None:
max_length = kwargs["max_seq_len"]
if "truncation_strategy" in kwargs and kwargs["truncation_strategy"] != "longest_first":
truncation = kwargs["truncation_strategy"]
return super(MBartTokenizer, self).__call__(
text=text,
text_pair=text_pair,
max_length=max_length,
stride=stride,
is_split_into_words=is_split_into_words,
padding=padding,
truncation=truncation,
return_position_ids=return_position_ids,
return_token_type_ids=return_token_type_ids,
return_attention_mask=return_attention_mask,
return_length=return_length,
return_overflowing_tokens=return_overflowing_tokens,
return_special_tokens_mask=return_special_tokens_mask,
**kwargs,
)
def __getstate__(self):
state = self.__dict__.copy()
state["sp_model"] = None
state["sp_model_proto"] = self.sp_model.serialized_model_proto()
return state
def __setstate__(self, d):
self.__dict__ = d
# for backward compatibility
if not hasattr(self, "sp_model_kwargs"):
self.sp_model_kwargs = {}
self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
self.sp_model.LoadFromSerializedProto(self.sp_model_proto)
[docs] def save_resources(self, save_directory):
for name, file_name in self.resource_files_names.items():
save_path = os.path.join(save_directory, file_name)
if os.path.abspath(self.vocab_file) != os.path.abspath(save_path) and os.path.isfile(self.vocab_file):
copyfile(self.vocab_file, save_path)
elif not os.path.isfile(self.vocab_file):
with open(save_path, "wb") as fi:
content_spiece_model = self.sp_model.serialized_model_proto()
fi.write(content_spiece_model)
@property
def vocab_size(self):
"""
Returns the size of vocabulary.
Returns:
int: The sum of size of vocabulary and the size of speical tokens.
"""
return len(self.sp_model) + len(self.lang_code_to_id) + self.fairseq_offset + 1
[docs] def get_vocab(self):
vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
vocab.update(self.added_tokens_encoder)
return vocab
def _tokenize(self, text):
return self.sp_model.encode(text, out_type=str)
def _convert_token_to_id(self, token):
"""
Converts a token (str) in an id using the vocab.
"""
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
spm_id = self.sp_model.PieceToId(token)
return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
def _convert_id_to_token(self, index):
"""
Converts an index (integer) in a token (str) using the vocab.
"""
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(index - self.fairseq_offset)
[docs] def convert_tokens_to_string(self, tokens):
"""
Converts a sequence of tokens (strings for sub-words) in a single string.
"""
out_string = "".join(tokens).replace("▁", " ").strip()
return out_string
[docs] def convert_ids_to_string(self, ids):
"""
Converts a sequence of tokens (strings for sub-words) in a single string.
"""
tokens = self.convert_ids_to_tokens(ids)
out_string = "".join(tokens).replace("▁", " ").strip()
return out_string
[docs] def get_special_tokens_mask(self, token_ids_0, token_ids_1=None, already_has_special_tokens=False):
"""
Retrieve sequence ids from a token list that has no special tokens added.
"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
)
prefix_ones = [1] * len(self.prefix_tokens)
suffix_ones = [1] * len(self.suffix_tokens)
if token_ids_1 is None:
return prefix_ones + ([0] * len(token_ids_0)) + suffix_ones
return prefix_ones + ([0] * len(token_ids_0)) + ([0] * len(token_ids_1)) + suffix_ones
[docs] def build_offset_mapping_with_special_tokens(self, offset_mapping_0, offset_mapping_1=None):
"""
Build offset map from a pair of offset map by concatenating and adding offsets of special tokens.
Should be overridden in a subclass if the model has a special way of building those.
Args:
offset_mapping_0 (List[tuple]):
List of char offsets to which the special tokens will be added.
offset_mapping_1 (List[tuple], optional):
Optional second list of char offsets for offset mapping pairs.
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 + offset_mapping_1 + [(0, 0)]
[docs] def set_src_lang_special_tokens(self, src_lang):
"""Reset the special tokens to the source lang setting. No prefix and suffix=[eos, src_lang_code]."""
self.cur_lang_code_id = self.lang_code_to_id[src_lang]
self.prefix_tokens = []
self.suffix_tokens = [self.eos_token_id, self.cur_lang_code_id]
[docs] def set_tgt_lang_special_tokens(self, tgt_lang):
"""Reset the special tokens to the target language setting. No prefix and suffix=[eos, tgt_lang_code]."""
self.cur_lang_code_id = self.lang_code_to_id[tgt_lang]
self.prefix_tokens = []
self.suffix_tokens = [self.eos_token_id, self.cur_lang_code_id]
[docs]class MBart50Tokenizer(PretrainedTokenizer):
resource_files_names = {
"vocab_file": "sentencepiece.bpe.model",
}
pretrained_resource_files_map = {
"vocab_file": {
"mbart-large-50-one-to-many-mmt": "https://bj.bcebos.com/paddlenlp/models/transformers/mbart50/mbart-large-50-one-to-many-mmt.sentencepiece.bpe.model",
"mbart-large-50-many-to-one-mmt": "https://bj.bcebos.com/paddlenlp/models/transformers/mbart50/mbart-large-50-many-to-one-mmt.sentencepiece.bpe.model",
"mbart-large-50-many-to-many-mmt": "https://bj.bcebos.com/paddlenlp/models/transformers/mbart50/mbart-large-50-many-to-many-mmt.sentencepiece.bpe.model",
}
}
pretrained_init_configuration = {
"mbart-large-50-one-to-many-mmt": {},
"mbart-large-50-many-to-one-mmt": {},
"mbart-large-50-many-to-many-mmt": {},
}
max_model_input_sizes = MBART50_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
model_input_names = ["input_ids"]
FAIRSEQ_LANGUAGE_CODES = [
"ar_AR",
"cs_CZ",
"de_DE",
"en_XX",
"es_XX",
"et_EE",
"fi_FI",
"fr_XX",
"gu_IN",
"hi_IN",
"it_IT",
"ja_XX",
"kk_KZ",
"ko_KR",
"lt_LT",
"lv_LV",
"my_MM",
"ne_NP",
"nl_XX",
"ro_RO",
"ru_RU",
"si_LK",
"tr_TR",
"vi_VN",
"zh_CN",
"af_ZA",
"az_AZ",
"bn_IN",
"fa_IR",
"he_IL",
"hr_HR",
"id_ID",
"ka_GE",
"km_KH",
"mk_MK",
"ml_IN",
"mn_MN",
"mr_IN",
"pl_PL",
"ps_AF",
"pt_XX",
"sv_SE",
"sw_KE",
"ta_IN",
"te_IN",
"th_TH",
"tl_XX",
"uk_UA",
"ur_PK",
"xh_ZA",
"gl_ES",
"sl_SI",
]
def __init__(
self,
vocab_file,
src_lang=None,
tgt_lang=None,
bos_token="<s>",
eos_token="</s>",
sep_token="</s>",
cls_token="<s>",
unk_token="<unk>",
pad_token="<pad>",
mask_token="<mask>",
sp_model_kwargs=None,
additional_special_tokens=None,
**kwargs
):
self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
mask_token = AddedToken(mask_token, lstrip=True, rstrip=False) if isinstance(mask_token, str) else mask_token
self._build_special_tokens_map_extended(mask_token=mask_token)
self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
self.vocab_file = vocab_file
self.sp_model.Load(str(vocab_file))
self.fairseq_offset = 1
self.fairseq_tokens_to_ids = {"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}
self.sp_model_size = len(self.sp_model)
self.lang_code_to_id = {
code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(self.FAIRSEQ_LANGUAGE_CODES)
}
self.fairseq_tokens_to_ids["<mask>"] = len(self.sp_model) + len(self.lang_code_to_id) + self.fairseq_offset
self.fairseq_tokens_to_ids.update(self.lang_code_to_id)
self.fairseq_ids_to_tokens = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
self.src_lang = src_lang if src_lang is not None else "en_XX"
self.tgt_lang = tgt_lang
# Get `special_tokens_map` after `_wrap_init()`
self.eos_token_id = self.fairseq_tokens_to_ids[eos_token]
self.unk_token_id = self.fairseq_tokens_to_ids[unk_token]
self.set_src_lang_special_tokens(self.src_lang)
self._additional_special_tokens = list(self.lang_code_to_id.keys())
if additional_special_tokens is not None:
# Only add those special tokens if they are not already there.
self._additional_special_tokens.extend(
[t for t in additional_special_tokens if t not in self._additional_special_tokens]
)
def __call__(
self,
text,
text_pair=None,
max_length=None,
stride=0,
is_split_into_words=False,
padding=None,
truncation="longest_first",
return_position_ids=False,
return_token_type_ids=False,
return_attention_mask=True,
return_length=False,
return_overflowing_tokens=False,
return_special_tokens_mask=False,
**kwargs
):
if "pad_to_max_seq_len" in kwargs and padding is None:
pad_to_max_seq_len = kwargs.pop("pad_to_max_seq_len")
padding = "max_length" if pad_to_max_seq_len else False
elif padding is None:
padding = False
if "max_seq_len" in kwargs and max_length is None:
max_length = kwargs["max_seq_len"]
if "truncation_strategy" in kwargs and kwargs["truncation_strategy"] != "longest_first":
truncation = kwargs["truncation_strategy"]
return super(MBart50Tokenizer, self).__call__(
text=text,
text_pair=text_pair,
max_length=max_length,
stride=stride,
is_split_into_words=is_split_into_words,
padding=padding,
truncation=truncation,
return_position_ids=return_position_ids,
return_token_type_ids=return_token_type_ids,
return_attention_mask=return_attention_mask,
return_length=return_length,
return_overflowing_tokens=return_overflowing_tokens,
return_special_tokens_mask=return_special_tokens_mask,
**kwargs,
)
def __getstate__(self):
state = self.__dict__.copy()
state["sp_model"] = None
state["sp_model_proto"] = self.sp_model.serialized_model_proto()
return state
def __setstate__(self, d):
self.__dict__ = d
# for backward compatibility
if not hasattr(self, "sp_model_kwargs"):
self.sp_model_kwargs = {}
self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
self.sp_model.LoadFromSerializedProto(self.sp_model_proto)
[docs] def save_resources(self, save_directory):
for name, file_name in self.resource_files_names.items():
save_path = os.path.join(save_directory, file_name)
if os.path.abspath(self.vocab_file) != os.path.abspath(save_path) and os.path.isfile(self.vocab_file):
copyfile(self.vocab_file, save_path)
elif not os.path.isfile(self.vocab_file):
with open(save_path, "wb") as fi:
content_spiece_model = self.sp_model.serialized_model_proto()
fi.write(content_spiece_model)
[docs] def get_vocab(self):
vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
vocab.update(self.added_tokens_encoder)
return vocab
def _tokenize(self, text):
return self.sp_model.encode(text, out_type=str)
@property
def vocab_size(self):
"""
Returns the size of vocabulary.
Returns:
int: The sum of size of vocabulary and the size of speical tokens.
"""
return len(self.sp_model) + len(self.lang_code_to_id) + self.fairseq_offset + 1
def _convert_token_to_id(self, token):
"""
Converts a token (str) in an id using the vocab.
"""
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
spm_id = self.sp_model.PieceToId(token)
return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
def _convert_id_to_token(self, index):
"""
Converts an index (integer) in a token (str) using the vocab.
"""
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(index - self.fairseq_offset)
[docs] def convert_tokens_to_string(self, tokens):
"""
Converts a sequence of tokens (strings for sub-words) in a single string.
"""
out_string = "".join(tokens).replace("▁", " ").strip()
return out_string
[docs] def convert_ids_to_string(self, ids):
"""
Converts a sequence of tokens (strings for sub-words) in a single string.
"""
tokens = self.convert_ids_to_tokens(ids)
out_string = "".join(tokens).replace("▁", " ").strip()
return out_string
[docs] def get_special_tokens_mask(self, token_ids_0, token_ids_1=None, already_has_special_tokens=False):
"""
Retrieve sequence ids from a token list that has no special tokens added.
"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
)
prefix_ones = [1] * len(self.prefix_tokens)
suffix_ones = [1] * len(self.suffix_tokens)
if token_ids_1 is None:
return prefix_ones + ([0] * len(token_ids_0)) + suffix_ones
return prefix_ones + ([0] * len(token_ids_0)) + ([0] * len(token_ids_1)) + suffix_ones
[docs] def build_offset_mapping_with_special_tokens(self, offset_mapping_0, offset_mapping_1=None):
"""
Build offset map from a pair of offset map by concatenating and adding offsets of special tokens.
Should be overridden in a subclass if the model has a special way of building those.
Args:
offset_mapping_0 (List[tuple]):
List of char offsets to which the special tokens will be added.
offset_mapping_1 (List[tuple], optional):
Optional second list of char offsets for offset mapping pairs.
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 + offset_mapping_1 + [(0, 0)]
[docs] def set_src_lang_special_tokens(self, src_lang):
"""Reset the special tokens to the source lang setting. prefix=[src_lang_code] and suffix=[eos]."""
self.cur_lang_code_id = self.lang_code_to_id[src_lang]
self.prefix_tokens = [self.cur_lang_code_id]
self.suffix_tokens = [self.eos_token_id]
[docs] def set_tgt_lang_special_tokens(self, tgt_lang):
"""Reset the special tokens to the target language setting. prefix=[tgt_lang_code] and suffix=[eos]."""
self.cur_lang_code_id = self.lang_code_to_id[tgt_lang]
self.prefix_tokens = [self.cur_lang_code_id]
self.suffix_tokens = [self.eos_token_id]
def _build_translation_inputs(self, raw_inputs, return_tensors, src_lang, tgt_lang, **extra_kwargs):
"""Used by translation pipeline, to prepare inputs for the generate function"""
if src_lang is None or tgt_lang is None:
raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model")
self.src_lang = src_lang
inputs = self(raw_inputs, add_special_tokens=True, return_tensors=return_tensors, **extra_kwargs)
tgt_lang_id = self.convert_tokens_to_ids(tgt_lang)
inputs["forced_bos_token_id"] = tgt_lang_id
return inputs
