355 lines
16 KiB
Python
355 lines
16 KiB
Python
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# coding=utf-8
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# Copyright 2021 The Facebook AI Research Team Authors and The HuggingFace Inc. team.
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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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import os
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from shutil import copyfile
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from typing import Any, Dict, List, Optional, Tuple
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import sentencepiece as spm
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from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
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from ...utils import logging
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logger = logging.get_logger(__name__)
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SPIECE_UNDERLINE = "▁"
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VOCAB_FILES_NAMES = {"vocab_file": "sentencepiece.bpe.model"}
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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"] # fmt: skip
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class MBart50Tokenizer(PreTrainedTokenizer):
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"""
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Construct a MBart50 tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece).
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This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
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this superclass for more information regarding those methods.
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Args:
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vocab_file (`str`):
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Path to the vocabulary file.
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src_lang (`str`, *optional*):
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A string representing the source language.
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tgt_lang (`str`, *optional*):
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A string representing the target language.
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eos_token (`str`, *optional*, defaults to `"</s>"`):
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The end of sequence token.
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sep_token (`str`, *optional*, defaults to `"</s>"`):
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The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
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sequence classification or for a text and a question for question answering. It is also used as the last
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token of a sequence built with special tokens.
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cls_token (`str`, *optional*, defaults to `"<s>"`):
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The classifier token which is used when doing sequence classification (classification of the whole sequence
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instead of per-token classification). It is the first token of the sequence when built with special tokens.
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unk_token (`str`, *optional*, defaults to `"<unk>"`):
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The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
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token instead.
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pad_token (`str`, *optional*, defaults to `"<pad>"`):
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The token used for padding, for example when batching sequences of different lengths.
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mask_token (`str`, *optional*, defaults to `"<mask>"`):
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The token used for masking values. This is the token used when training this model with masked language
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modeling. This is the token which the model will try to predict.
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sp_model_kwargs (`dict`, *optional*):
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Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for
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SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things,
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to set:
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- `enable_sampling`: Enable subword regularization.
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- `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout.
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- `nbest_size = {0,1}`: No sampling is performed.
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- `nbest_size > 1`: samples from the nbest_size results.
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- `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice)
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using forward-filtering-and-backward-sampling algorithm.
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- `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for
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BPE-dropout.
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Examples:
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```python
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>>> from transformers import MBart50Tokenizer
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>>> tokenizer = MBart50Tokenizer.from_pretrained("facebook/mbart-large-50", src_lang="en_XX", tgt_lang="ro_RO")
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>>> src_text = " UN Chief Says There Is No Military Solution in Syria"
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>>> tgt_text = "Şeful ONU declară că nu există o soluţie militară în Siria"
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>>> model_inputs = tokenizer(src_text, text_target=tgt_text, return_tensors="pt")
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>>> # model(**model_inputs) should work
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```"""
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vocab_files_names = VOCAB_FILES_NAMES
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model_input_names = ["input_ids", "attention_mask"]
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prefix_tokens: List[int] = []
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suffix_tokens: List[int] = []
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def __init__(
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self,
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vocab_file,
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src_lang=None,
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tgt_lang=None,
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eos_token="</s>",
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sep_token="</s>",
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cls_token="<s>",
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unk_token="<unk>",
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pad_token="<pad>",
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mask_token="<mask>",
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sp_model_kwargs: Optional[Dict[str, Any]] = None,
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**kwargs,
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) -> None:
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# Mask token behave like a normal word, i.e. include the space before it
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mask_token = AddedToken(mask_token, lstrip=True, rstrip=False) if isinstance(mask_token, str) else mask_token
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self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
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kwargs["additional_special_tokens"] = kwargs.get("additional_special_tokens", []) or []
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kwargs["additional_special_tokens"] += [
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code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
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]
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self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
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self.sp_model.Load(str(vocab_file))
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self.vocab_file = vocab_file
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# Original fairseq vocab and spm vocab must be "aligned":
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# Vocab | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9
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# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
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# fairseq | '<s>' | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's' | '▁de' | '-'
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# spm | '<unk>' | '<s>' | '</s>' | ',' | '.' | '▁' | 's' | '▁de' | '-' | '▁a'
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# Mimic fairseq token-to-id alignment for the first 4 token
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self.fairseq_tokens_to_ids = {"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}
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# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
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self.fairseq_offset = 1
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self.sp_model_size = len(self.sp_model)
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self.lang_code_to_id = {
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code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(FAIRSEQ_LANGUAGE_CODES)
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}
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self.id_to_lang_code = {v: k for k, v in self.lang_code_to_id.items()}
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self.fairseq_tokens_to_ids["<mask>"] = len(self.sp_model) + len(self.lang_code_to_id) + self.fairseq_offset
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self.fairseq_tokens_to_ids.update(self.lang_code_to_id)
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self.fairseq_ids_to_tokens = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
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super().__init__(
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src_lang=src_lang,
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tgt_lang=tgt_lang,
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eos_token=eos_token,
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unk_token=unk_token,
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sep_token=sep_token,
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cls_token=cls_token,
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pad_token=pad_token,
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mask_token=mask_token,
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sp_model_kwargs=self.sp_model_kwargs,
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**kwargs,
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)
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self._src_lang = src_lang if src_lang is not None else "en_XX"
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self.cur_lang_code_id = self.lang_code_to_id[self._src_lang]
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self.tgt_lang = tgt_lang
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self.set_src_lang_special_tokens(self._src_lang)
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@property
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def vocab_size(self) -> int:
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return len(self.sp_model) + len(self.lang_code_to_id) + self.fairseq_offset + 1 # Plus 1 for the mask token
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@property
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def src_lang(self) -> str:
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return self._src_lang
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@src_lang.setter
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def src_lang(self, new_src_lang: str) -> None:
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self._src_lang = new_src_lang
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self.set_src_lang_special_tokens(self._src_lang)
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def __getstate__(self) -> Dict:
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state = self.__dict__.copy()
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state["sp_model"] = None
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return state
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def __setstate__(self, d: Dict) -> None:
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self.__dict__ = d
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# for backward compatibility
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if not hasattr(self, "sp_model_kwargs"):
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self.sp_model_kwargs = {}
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self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
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self.sp_model.Load(self.vocab_file)
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def get_vocab(self) -> Dict:
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vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
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vocab.update(self.added_tokens_encoder)
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return vocab
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def _tokenize(self, text: str) -> List[str]:
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return self.sp_model.encode(text, out_type=str)
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def _convert_token_to_id(self, token: str) -> int:
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"""Converts a token (str) in an id using the vocab."""
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if token in self.fairseq_tokens_to_ids:
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return self.fairseq_tokens_to_ids[token]
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spm_id = self.sp_model.PieceToId(token)
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# Need to return unknown token if the SP model returned 0
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return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
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def _convert_id_to_token(self, index: int) -> str:
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"""Converts an index (integer) in a token (str) using the vocab."""
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if index in self.fairseq_ids_to_tokens:
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return self.fairseq_ids_to_tokens[index]
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return self.sp_model.IdToPiece(index - self.fairseq_offset)
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# Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.convert_tokens_to_string
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def convert_tokens_to_string(self, tokens):
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"""Converts a sequence of tokens (string) in a single string."""
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current_sub_tokens = []
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out_string = ""
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prev_is_special = False
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for token in tokens:
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# make sure that special tokens are not decoded using sentencepiece model
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if token in self.all_special_tokens:
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if not prev_is_special:
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out_string += " "
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out_string += self.sp_model.decode(current_sub_tokens) + token
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prev_is_special = True
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current_sub_tokens = []
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else:
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current_sub_tokens.append(token)
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prev_is_special = False
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out_string += self.sp_model.decode(current_sub_tokens)
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return out_string.strip()
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def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
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if not os.path.isdir(save_directory):
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logger.error(f"Vocabulary path ({save_directory}) should be a directory")
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return
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out_vocab_file = os.path.join(
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save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
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)
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if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):
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copyfile(self.vocab_file, out_vocab_file)
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elif not os.path.isfile(self.vocab_file):
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with open(out_vocab_file, "wb") as fi:
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content_spiece_model = self.sp_model.serialized_model_proto()
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fi.write(content_spiece_model)
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return (out_vocab_file,)
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def get_special_tokens_mask(
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self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
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) -> List[int]:
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"""
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Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
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special tokens using the tokenizer `prepare_for_model` method.
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Args:
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token_ids_0 (`List[int]`):
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List of IDs.
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token_ids_1 (`List[int]`, *optional*):
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Optional second list of IDs for sequence pairs.
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already_has_special_tokens (`bool`, *optional*, defaults to `False`):
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Whether or not the token list is already formatted with special tokens for the model.
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Returns:
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`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
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"""
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if already_has_special_tokens:
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return super().get_special_tokens_mask(
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token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
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)
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prefix_ones = [1] * len(self.prefix_tokens)
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suffix_ones = [1] * len(self.suffix_tokens)
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if token_ids_1 is None:
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return prefix_ones + ([0] * len(token_ids_0)) + suffix_ones
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return prefix_ones + ([0] * len(token_ids_0)) + ([0] * len(token_ids_1)) + suffix_ones
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def build_inputs_with_special_tokens(
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self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
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) -> List[int]:
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"""
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Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
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adding special tokens. An MBART-50 sequence has the following format, where `X` represents the sequence:
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- `input_ids` (for encoder) `[src_lang_code] X [eos]`
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- `labels`: (for decoder) `[tgt_lang_code] X [eos]`
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BOS is never used. Pairs of sequences are not the expected use case, but they will be handled without a
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separator.
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Args:
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token_ids_0 (`List[int]`):
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List of IDs to which the special tokens will be added.
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token_ids_1 (`List[int]`, *optional*):
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Optional second list of IDs for sequence pairs.
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Returns:
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`List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
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"""
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if token_ids_1 is None:
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return self.prefix_tokens + token_ids_0 + self.suffix_tokens
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# We don't expect to process pairs, but leave the pair logic for API consistency
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return self.prefix_tokens + token_ids_0 + token_ids_1 + self.suffix_tokens
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def _build_translation_inputs(
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self, raw_inputs, return_tensors: str, src_lang: Optional[str], tgt_lang: Optional[str], **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
|
||
|
|
|
||
|
|
def prepare_seq2seq_batch(
|
||
|
|
self,
|
||
|
|
src_texts: List[str],
|
||
|
|
src_lang: str = "en_XX",
|
||
|
|
tgt_texts: Optional[List[str]] = None,
|
||
|
|
tgt_lang: str = "ro_RO",
|
||
|
|
**kwargs,
|
||
|
|
) -> BatchEncoding:
|
||
|
|
self.src_lang = src_lang
|
||
|
|
self.tgt_lang = tgt_lang
|
||
|
|
return super().prepare_seq2seq_batch(src_texts, tgt_texts, **kwargs)
|
||
|
|
|
||
|
|
def _switch_to_input_mode(self):
|
||
|
|
return self.set_src_lang_special_tokens(self.src_lang)
|
||
|
|
|
||
|
|
def _switch_to_target_mode(self):
|
||
|
|
return self.set_tgt_lang_special_tokens(self.tgt_lang)
|
||
|
|
|
||
|
|
def set_src_lang_special_tokens(self, src_lang: str) -> None:
|
||
|
|
"""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]
|
||
|
|
|
||
|
|
def set_tgt_lang_special_tokens(self, tgt_lang: str) -> None:
|
||
|
|
"""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]
|