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150 lines
6.2 KiB
150 lines
6.2 KiB
# Copyright 2020 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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from tokenizers import Tokenizer, decoders, pre_tokenizers
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from tokenizers.models import BPE
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from ...tokenization_utils_base import _get_prepend_scheme
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from ...tokenization_utils_tokenizers import TokenizersBackend
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from ...utils import logging
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logger = logging.get_logger(__name__)
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VOCAB_FILES_NAMES = {"vocab_file": "tokenizer.model", "tokenizer_file": "tokenizer.json"}
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B_INST, E_INST = "[INST]", "[/INST]"
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B_SYS, E_SYS = "<<SYS>>\n", "\n<</SYS>>\n\n"
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# fmt: off
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DEFAULT_SYSTEM_PROMPT = """You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your \
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answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\
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that your responses are socially unbiased and positive in nature.
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If a question does not make any sense, or is not factually coherent, explain why instead of answering something not \
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correct. If you don't know the answer to a question, please don't share false information."""
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# fmt: on
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class LlamaTokenizer(TokenizersBackend):
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"""
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Construct a Llama tokenizer. Based on byte-level Byte-Pair-Encoding.
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This uses notably ByteFallback and no normalization.
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```python
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>>> from transformers import LlamaTokenizer
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>>> tokenizer = LlamaTokenizer.from_pretrained("hf-internal-testing/llama-tokenizer")
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>>> tokenizer.encode("Hello this is a test")
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[1, 15043, 445, 338, 263, 1243]
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```
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If you want to change the `bos_token` or the `eos_token`, make sure to specify them when initializing the model, or
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call `tokenizer.update_post_processor()` to make sure that the post-processing is correctly done (otherwise the
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values of the first token and final token of an encoded sequence will not be correct). For more details, checkout
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[post-processors] (https://huggingface.co/docs/tokenizers/api/post-processors) documentation.
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This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should
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refer to this superclass for more information regarding those methods.
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Args:
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vocab (`str`, `dict` or `list`, *optional*):
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Path to the vocabulary file, a dictionary or a list of tokens.
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merges (`str` or `list`, *optional*):
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Path to the merges file or a list of merges.
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clean_up_tokenization_spaces (`bool`, *optional*, defaults to `False`):
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Whether or not to cleanup spaces after decoding, cleanup consists in removing potential artifacts like
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extra spaces.
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unk_token (`str` or `tokenizers.AddedToken`, *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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bos_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"<s>"`):
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The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
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eos_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"</s>"`):
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The end of sequence token.
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add_bos_token (`bool`, *optional*, defaults to `True`):
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Whether or not to add an `bos_token` at the start of sequences.
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add_eos_token (`bool`, *optional*, defaults to `False`):
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Whether or not to add an `eos_token` at the end of sequences.
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use_default_system_prompt (`bool`, *optional*, defaults to `False`):
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Whether or not the default system prompt for Llama should be used
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add_prefix_space (`bool`, *optional*):
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Whether or not the tokenizer should automatically add a prefix space
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"""
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vocab_files_names = VOCAB_FILES_NAMES
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padding_side = "left"
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model_input_names = ["input_ids", "attention_mask"]
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model = BPE
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def __init__(
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self,
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vocab: str | dict | list | None = None,
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merges: str | list | None = None,
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clean_up_tokenization_spaces=False,
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unk_token="<unk>",
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bos_token="<s>",
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eos_token="</s>",
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use_default_system_prompt=False,
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legacy=False,
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add_prefix_space=None,
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**kwargs,
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):
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self.add_prefix_space = add_prefix_space if add_prefix_space is not None else True
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self.legacy = legacy
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self._vocab = vocab
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if vocab is None:
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self._vocab = {
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str(unk_token): 0,
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str(bos_token): 1,
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str(eos_token): 2,
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}
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self._merges = merges or []
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self._tokenizer = Tokenizer(
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BPE(vocab=self._vocab, merges=self._merges, fuse_unk=True, byte_fallback=True, dropout=None)
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)
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self._tokenizer.normalizer = None
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self._tokenizer.pre_tokenizer = pre_tokenizers.Metaspace(
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replacement="▁", prepend_scheme=_get_prepend_scheme(self.add_prefix_space, self), split=False
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)
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sequence = [
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decoders.Replace("▁", " "),
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decoders.ByteFallback(),
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decoders.Fuse(),
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]
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if self.add_prefix_space:
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sequence += [decoders.Strip(content=" ", left=1)]
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self._tokenizer.decoder = decoders.Sequence(sequence)
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self.use_default_system_prompt = use_default_system_prompt
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super().__init__(
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clean_up_tokenization_spaces=clean_up_tokenization_spaces,
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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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use_default_system_prompt=use_default_system_prompt,
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add_prefix_space=add_prefix_space,
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**kwargs,
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)
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__all__ = ["LlamaTokenizer", "LlamaTokenizerFast"]
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# Backward alias
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LlamaTokenizerFast = LlamaTokenizer
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