418 lines
16 KiB
Python
418 lines
16 KiB
Python
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# coding=utf-8
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# Copyright 2022 The Salesforce authors, The Open AI 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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"""Tokenization classes for CodeGen"""
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import json
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import os
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from functools import lru_cache
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from typing import TYPE_CHECKING, List, Optional, Tuple, Union
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import numpy as np
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import regex as re
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from ...utils import is_tf_available, is_torch_available, logging, to_py_obj
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if TYPE_CHECKING:
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if is_torch_available():
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import torch
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if is_tf_available():
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import tensorflow as tf
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from ...tokenization_utils import AddedToken, PreTrainedTokenizer
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logger = logging.get_logger(__name__)
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VOCAB_FILES_NAMES = {
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"vocab_file": "vocab.json",
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"merges_file": "merges.txt",
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}
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@lru_cache()
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def bytes_to_unicode():
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"""
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Returns list of utf-8 byte and a mapping to unicode strings. We specifically avoids mapping to whitespace/control
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characters the bpe code barfs on.
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The reversible bpe codes work on unicode strings. This means you need a large # of unicode characters in your vocab
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if you want to avoid UNKs. When you're at something like a 10B token dataset you end up needing around 5K for
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decent coverage. This is a significant percentage of your normal, say, 32K bpe vocab. To avoid that, we want lookup
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tables between utf-8 bytes and unicode strings.
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"""
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bs = (
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list(range(ord("!"), ord("~") + 1)) + list(range(ord("¡"), ord("¬") + 1)) + list(range(ord("®"), ord("ÿ") + 1))
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)
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cs = bs[:]
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n = 0
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for b in range(2**8):
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if b not in bs:
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bs.append(b)
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cs.append(2**8 + n)
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n += 1
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cs = [chr(n) for n in cs]
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return dict(zip(bs, cs))
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def get_pairs(word):
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"""
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Return set of symbol pairs in a word.
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Word is represented as tuple of symbols (symbols being variable-length strings).
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"""
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pairs = set()
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prev_char = word[0]
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for char in word[1:]:
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pairs.add((prev_char, char))
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prev_char = char
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return pairs
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class CodeGenTokenizer(PreTrainedTokenizer):
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"""
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Construct a CodeGen tokenizer. Based on byte-level Byte-Pair-Encoding.
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This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
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be encoded differently whether it is at the beginning of the sentence (without space) or not:
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```python
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>>> from transformers import CodeGenTokenizer
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>>> tokenizer = CodeGenTokenizer.from_pretrained("Salesforce/codegen-350M-mono")
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>>> tokenizer("Hello world")["input_ids"]
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[15496, 995]
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>>> tokenizer(" Hello world")["input_ids"]
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[18435, 995]
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```
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You can get around that behavior by passing `add_prefix_space=True` when instantiating this tokenizer or when you
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call it on some text, but since the model was not pretrained this way, it might yield a decrease in performance.
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<Tip>
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When used with `is_split_into_words=True`, this tokenizer will add a space before each word (even the first one).
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</Tip>
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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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merges_file (`str`):
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Path to the merges file.
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errors (`str`, *optional*, defaults to `"replace"`):
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Paradigm to follow when decoding bytes to UTF-8. See
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[bytes.decode](https://docs.python.org/3/library/stdtypes.html#bytes.decode) for more information.
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unk_token (`str`, *optional*, defaults to `"<|endoftext|>"`):
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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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bos_token (`str`, *optional*, defaults to `"<|endoftext|>"`):
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The beginning of sequence token.
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eos_token (`str`, *optional*, defaults to `"<|endoftext|>"`):
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The end of sequence token.
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pad_token (`str`, *optional*):
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The token used for padding, for example when batching sequences of different lengths.
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add_prefix_space (`bool`, *optional*, defaults to `False`):
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Whether or not to add an initial space to the input. This allows to treat the leading word just as any
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other word. (CodeGen tokenizer detect beginning of words by the preceding space).
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add_bos_token (`bool`, *optional*, defaults to `False`):
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Whether to add a beginning of sequence token at the start of sequences.
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return_token_type_ids (`bool`, *optional*, defaults to `False`):
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Whether to return token type IDs.
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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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def __init__(
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self,
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vocab_file,
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merges_file,
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errors="replace",
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unk_token="<|endoftext|>",
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bos_token="<|endoftext|>",
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eos_token="<|endoftext|>",
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pad_token=None,
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add_prefix_space=False,
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add_bos_token=False,
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return_token_type_ids=False,
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**kwargs,
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):
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bos_token = AddedToken(bos_token, special=True) if isinstance(bos_token, str) else bos_token
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eos_token = AddedToken(eos_token, special=True) if isinstance(eos_token, str) else eos_token
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unk_token = AddedToken(unk_token, special=True) if isinstance(unk_token, str) else unk_token
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pad_token = AddedToken(pad_token, special=True) if isinstance(pad_token, str) else pad_token
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self.add_bos_token = add_bos_token
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self.return_token_type_ids = return_token_type_ids
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if self.return_token_type_ids:
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self.model_input_names.append("token_type_ids")
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with open(vocab_file, encoding="utf-8") as vocab_handle:
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self.encoder = json.load(vocab_handle)
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self.decoder = {v: k for k, v in self.encoder.items()}
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self.errors = errors # how to handle errors in decoding
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self.byte_encoder = bytes_to_unicode()
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self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
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with open(merges_file, encoding="utf-8") as merges_handle:
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bpe_merges = merges_handle.read().split("\n")[1:-1]
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bpe_merges = [tuple(merge.split()) for merge in bpe_merges]
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self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges))))
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self.cache = {}
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self.add_prefix_space = add_prefix_space
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# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
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self.pat = re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""")
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super().__init__(
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errors=errors,
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unk_token=unk_token,
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bos_token=bos_token,
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eos_token=eos_token,
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pad_token=pad_token,
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add_prefix_space=add_prefix_space,
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add_bos_token=add_bos_token,
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return_token_type_ids=return_token_type_ids,
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**kwargs,
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)
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@property
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def vocab_size(self):
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return len(self.encoder)
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def get_vocab(self):
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return dict(self.encoder, **self.added_tokens_encoder)
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def bpe(self, token):
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if token in self.cache:
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return self.cache[token]
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word = tuple(token)
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pairs = get_pairs(word)
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if not pairs:
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return token
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while True:
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bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf")))
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if bigram not in self.bpe_ranks:
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break
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first, second = bigram
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new_word = []
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i = 0
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while i < len(word):
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try:
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j = word.index(first, i)
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except ValueError:
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new_word.extend(word[i:])
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break
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else:
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new_word.extend(word[i:j])
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i = j
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if word[i] == first and i < len(word) - 1 and word[i + 1] == second:
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new_word.append(first + second)
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i += 2
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else:
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new_word.append(word[i])
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i += 1
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new_word = tuple(new_word)
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word = new_word
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if len(word) == 1:
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break
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else:
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pairs = get_pairs(word)
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word = " ".join(word)
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self.cache[token] = word
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return word
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def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
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if self.add_bos_token:
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bos_token_ids = [self.bos_token_id]
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else:
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bos_token_ids = []
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output = bos_token_ids + token_ids_0
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if token_ids_1 is None:
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return output
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return output + bos_token_ids + token_ids_1
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def _tokenize(self, text):
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"""Tokenize a string."""
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bpe_tokens = []
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for token in re.findall(self.pat, text):
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token = "".join(
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self.byte_encoder[b] for b in token.encode("utf-8")
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) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
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bpe_tokens.extend(bpe_token for bpe_token in self.bpe(token).split(" "))
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return bpe_tokens
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def _convert_token_to_id(self, token):
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"""Converts a token (str) in an id using the vocab."""
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return self.encoder.get(token, self.encoder.get(self.unk_token))
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def _convert_id_to_token(self, index):
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"""Converts an index (integer) in a token (str) using the vocab."""
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return self.decoder.get(index)
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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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text = "".join(tokens)
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text = bytearray([self.byte_decoder[c] for c in text]).decode("utf-8", errors=self.errors)
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return text
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def create_token_type_ids_from_sequences(
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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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Create a mask from the two sequences passed to be used in a sequence-pair classification task. A sequence
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pair mask has the following format:
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```
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0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
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| first sequence | second sequence |
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```
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If `token_ids_1` is `None`, this method only returns the first portion of the mask (0s).
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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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Returns:
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`List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).
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"""
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sep = [self.sep_token_id] if self.sep_token_id is not None else []
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cls = [self.cls_token_id] if self.sep_token_id is not None else []
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if token_ids_1 is None:
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return len(cls + token_ids_0 + sep) * [0]
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return len(cls + token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1]
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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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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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merge_file = os.path.join(
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save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]
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)
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with open(vocab_file, "w", encoding="utf-8") as f:
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f.write(json.dumps(self.encoder, indent=2, sort_keys=True, ensure_ascii=False) + "\n")
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index = 0
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with open(merge_file, "w", encoding="utf-8") as writer:
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writer.write("#version: 0.2\n")
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for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda kv: kv[1]):
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if index != token_index:
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logger.warning(
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f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
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" Please check that the tokenizer is not corrupted!"
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)
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index = token_index
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writer.write(" ".join(bpe_tokens) + "\n")
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index += 1
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return vocab_file, merge_file
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def prepare_for_tokenization(self, text, is_split_into_words=False, **kwargs):
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add_prefix_space = kwargs.pop("add_prefix_space", self.add_prefix_space)
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if is_split_into_words or add_prefix_space:
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text = " " + text
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return (text, kwargs)
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def decode(
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self,
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token_ids: Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"],
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skip_special_tokens: bool = False,
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clean_up_tokenization_spaces: bool = None,
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truncate_before_pattern: Optional[List[str]] = None,
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|
**kwargs,
|
||
|
|
) -> str:
|
||
|
|
"""
|
||
|
|
Converts a sequence of ids in a string, using the tokenizer and vocabulary with options to remove special
|
||
|
|
tokens and clean up tokenization spaces.
|
||
|
|
|
||
|
|
Similar to doing `self.convert_tokens_to_string(self.convert_ids_to_tokens(token_ids))`.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
token_ids (`Union[int, List[int], np.ndarray, torch.Tensor, tf.Tensor]`):
|
||
|
|
List of tokenized input ids. Can be obtained using the `__call__` method.
|
||
|
|
skip_special_tokens (`bool`, *optional*, defaults to `False`):
|
||
|
|
Whether or not to remove special tokens in the decoding.
|
||
|
|
clean_up_tokenization_spaces (`bool`, *optional*):
|
||
|
|
Whether or not to clean up the tokenization spaces. If `None`, will default to
|
||
|
|
`self.clean_up_tokenization_spaces` (available in the `tokenizer_config`).
|
||
|
|
truncate_before_pattern (`List[str]`, *optional*, defaults to `None`):
|
||
|
|
A list of regular expression strings that will be used to truncate the returned string. This can be
|
||
|
|
used to remove extra pieces of code (e.g. truncate if observing a comment symbol "#" at the beginning
|
||
|
|
of a new line). An example pattern could be `["^#", re.escape("<|endoftext|>"), "^'''", "\n\n\n"]`.
|
||
|
|
kwargs (additional keyword arguments, *optional*):
|
||
|
|
Will be passed to the underlying model specific decode method.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
`str`: The decoded sentence.
|
||
|
|
"""
|
||
|
|
|
||
|
|
token_ids = to_py_obj(token_ids)
|
||
|
|
|
||
|
|
decoded_text = super()._decode(
|
||
|
|
token_ids=token_ids,
|
||
|
|
skip_special_tokens=skip_special_tokens,
|
||
|
|
clean_up_tokenization_spaces=clean_up_tokenization_spaces,
|
||
|
|
**kwargs,
|
||
|
|
)
|
||
|
|
|
||
|
|
if truncate_before_pattern is not None and len(truncate_before_pattern) > 0:
|
||
|
|
decoded_text = self.truncate(decoded_text, truncate_before_pattern)
|
||
|
|
|
||
|
|
return decoded_text
|
||
|
|
|
||
|
|
def truncate(self, completion, truncate_before_pattern):
|
||
|
|
def find_re(string, pattern, start_pos):
|
||
|
|
m = pattern.search(string, start_pos)
|
||
|
|
return m.start() if m else -1
|
||
|
|
|
||
|
|
terminals = [re.compile(pattern, re.MULTILINE) for pattern in truncate_before_pattern]
|
||
|
|
|
||
|
|
prints = list(re.finditer("^print", completion, re.MULTILINE))
|
||
|
|
|
||
|
|
if len(prints) > 1:
|
||
|
|
completion = completion[: prints[1].start()]
|
||
|
|
|
||
|
|
defs = list(re.finditer("^def", completion, re.MULTILINE))
|
||
|
|
|
||
|
|
if len(defs) > 1:
|
||
|
|
completion = completion[: defs[1].start()]
|
||
|
|
|
||
|
|
start_pos = 0
|
||
|
|
|
||
|
|
terminals_pos = [
|
||
|
|
pos for pos in [find_re(completion, terminal, start_pos) for terminal in terminals] if pos != -1
|
||
|
|
]
|
||
|
|
|
||
|
|
if len(terminals_pos) > 0:
|
||
|
|
return completion[: min(terminals_pos)]
|
||
|
|
else:
|
||
|
|
return completion
|