121 lines
4.5 KiB
Python
121 lines
4.5 KiB
Python
# coding=utf-8
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# Copyright 2020, The RAG 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 RAG."""
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import os
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import warnings
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from typing import List, Optional
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from ...tokenization_utils_base import BatchEncoding
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from ...utils import logging
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from .configuration_rag import RagConfig
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logger = logging.get_logger(__name__)
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class RagTokenizer:
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def __init__(self, question_encoder, generator):
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self.question_encoder = question_encoder
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self.generator = generator
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self.current_tokenizer = self.question_encoder
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def save_pretrained(self, save_directory):
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if os.path.isfile(save_directory):
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raise ValueError(f"Provided path ({save_directory}) should be a directory, not a file")
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os.makedirs(save_directory, exist_ok=True)
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question_encoder_path = os.path.join(save_directory, "question_encoder_tokenizer")
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generator_path = os.path.join(save_directory, "generator_tokenizer")
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self.question_encoder.save_pretrained(question_encoder_path)
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self.generator.save_pretrained(generator_path)
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@classmethod
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def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
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# dynamically import AutoTokenizer
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from ..auto.tokenization_auto import AutoTokenizer
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config = kwargs.pop("config", None)
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if config is None:
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config = RagConfig.from_pretrained(pretrained_model_name_or_path)
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question_encoder = AutoTokenizer.from_pretrained(
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pretrained_model_name_or_path, config=config.question_encoder, subfolder="question_encoder_tokenizer"
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)
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generator = AutoTokenizer.from_pretrained(
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pretrained_model_name_or_path, config=config.generator, subfolder="generator_tokenizer"
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)
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return cls(question_encoder=question_encoder, generator=generator)
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def __call__(self, *args, **kwargs):
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return self.current_tokenizer(*args, **kwargs)
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def batch_decode(self, *args, **kwargs):
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return self.generator.batch_decode(*args, **kwargs)
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def decode(self, *args, **kwargs):
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return self.generator.decode(*args, **kwargs)
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def _switch_to_input_mode(self):
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self.current_tokenizer = self.question_encoder
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def _switch_to_target_mode(self):
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self.current_tokenizer = self.generator
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def prepare_seq2seq_batch(
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self,
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src_texts: List[str],
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tgt_texts: Optional[List[str]] = None,
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max_length: Optional[int] = None,
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max_target_length: Optional[int] = None,
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padding: str = "longest",
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return_tensors: str = None,
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truncation: bool = True,
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**kwargs,
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) -> BatchEncoding:
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warnings.warn(
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"`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the "
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"regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` "
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"context manager to prepare your targets. See the documentation of your specific tokenizer for more "
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"details",
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FutureWarning,
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)
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if max_length is None:
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max_length = self.current_tokenizer.model_max_length
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model_inputs = self(
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src_texts,
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add_special_tokens=True,
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return_tensors=return_tensors,
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max_length=max_length,
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padding=padding,
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truncation=truncation,
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**kwargs,
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)
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if tgt_texts is None:
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return model_inputs
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# Process tgt_texts
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if max_target_length is None:
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max_target_length = self.current_tokenizer.model_max_length
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labels = self(
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text_target=tgt_texts,
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add_special_tokens=True,
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return_tensors=return_tensors,
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padding=padding,
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max_length=max_target_length,
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truncation=truncation,
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**kwargs,
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)
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model_inputs["labels"] = labels["input_ids"]
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return model_inputs
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