553 lines
24 KiB
Python
553 lines
24 KiB
Python
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#!/usr/bin/env python
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# coding=utf-8
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# Copyright 2020 The HuggingFace Team All rights reserved.
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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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"""
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Fine-tuning the library models for masked language modeling (BERT, ALBERT, RoBERTa...) on a text file or a dataset.
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Here is the full list of checkpoints on the hub that can be fine-tuned by this script:
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https://huggingface.co/models?filter=masked-lm
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"""
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# You can also adapt this script on your own masked language modeling task. Pointers for this are left as comments.
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import logging
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import math
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import os
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import sys
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from dataclasses import dataclass, field
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from typing import Optional
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import datasets
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from datasets import load_dataset
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import transformers
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from transformers import (
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CONFIG_MAPPING,
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MODEL_FOR_MASKED_LM_MAPPING,
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AutoConfig,
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AutoModelForMaskedLM,
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AutoTokenizer,
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DataCollatorForLanguageModeling,
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HfArgumentParser,
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Trainer,
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TrainingArguments,
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set_seed,
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)
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from transformers.trainer_utils import get_last_checkpoint
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from transformers.utils import check_min_version
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from transformers.utils.versions import require_version
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# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
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check_min_version("4.12.0.dev0")
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require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/language-modeling/requirements.txt")
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logger = logging.getLogger(__name__)
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MODEL_CONFIG_CLASSES = list(MODEL_FOR_MASKED_LM_MAPPING.keys())
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MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
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@dataclass
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class ModelArguments:
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"""
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Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch.
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"""
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model_name_or_path: Optional[str] = field(
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default=None,
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metadata={
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"help": "The model checkpoint for weights initialization."
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"Don't set if you want to train a model from scratch."
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},
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)
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model_type: Optional[str] = field(
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default=None,
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metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(MODEL_TYPES)},
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)
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config_overrides: Optional[str] = field(
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default=None,
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metadata={
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"help": "Override some existing default config settings when a model is trained from scratch. Example: "
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"n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"
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},
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)
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config_name: Optional[str] = field(
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default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
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)
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tokenizer_name: Optional[str] = field(
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default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
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)
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cache_dir: Optional[str] = field(
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default=None,
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metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"},
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)
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use_fast_tokenizer: bool = field(
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default=True,
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metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
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)
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model_revision: str = field(
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default="main",
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metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
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)
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use_auth_token: bool = field(
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default=False,
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metadata={
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"help": "Will use the token generated when running `transformers-cli login` (necessary to use this script "
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"with private models)."
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},
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)
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def __post_init__(self):
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if self.config_overrides is not None and (self.config_name is not None or self.model_name_or_path is not None):
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raise ValueError(
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"--config_overrides can't be used in combination with --config_name or --model_name_or_path"
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)
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@dataclass
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class DataTrainingArguments:
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"""
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Arguments pertaining to what data we are going to input our model for training and eval.
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"""
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dataset_name: Optional[str] = field(
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default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
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)
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dataset_config_name: Optional[str] = field(
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default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
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)
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train_file: Optional[str] = field(default=None, metadata={"help": "The input training data file (a text file)."})
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validation_file: Optional[str] = field(
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default=None,
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metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."},
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)
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overwrite_cache: bool = field(
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default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
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)
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validation_split_percentage: Optional[int] = field(
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default=5,
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metadata={
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"help": "The percentage of the train set used as validation set in case there's no validation split"
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},
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)
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max_seq_length: Optional[int] = field(
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default=None,
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metadata={
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"help": "The maximum total input sequence length after tokenization. Sequences longer "
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"than this will be truncated."
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},
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)
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preprocessing_num_workers: Optional[int] = field(
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default=None,
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metadata={"help": "The number of processes to use for the preprocessing."},
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)
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mlm_probability: float = field(
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default=0.15, metadata={"help": "Ratio of tokens to mask for masked language modeling loss"}
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)
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line_by_line: bool = field(
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default=False,
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metadata={"help": "Whether distinct lines of text in the dataset are to be handled as distinct sequences."},
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)
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pad_to_max_length: bool = field(
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default=False,
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metadata={
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"help": "Whether to pad all samples to `max_seq_length`. "
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"If False, will pad the samples dynamically when batching to the maximum length in the batch."
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},
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)
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max_train_samples: Optional[int] = field(
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default=None,
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metadata={
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"help": "For debugging purposes or quicker training, truncate the number of training examples to this "
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"value if set."
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},
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)
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max_eval_samples: Optional[int] = field(
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default=None,
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metadata={
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"help": "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
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"value if set."
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},
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)
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def __post_init__(self):
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if self.dataset_name is None and self.train_file is None and self.validation_file is None:
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raise ValueError("Need either a dataset name or a training/validation file.")
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else:
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if self.train_file is not None:
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extension = self.train_file.split(".")[-1]
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assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
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if self.validation_file is not None:
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extension = self.validation_file.split(".")[-1]
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assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."
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def main():
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# See all possible arguments in src/transformers/training_args.py
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# or by passing the --help flag to this script.
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# We now keep distinct sets of args, for a cleaner separation of concerns.
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parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
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if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
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# If we pass only one argument to the script and it's the path to a json file,
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# let's parse it to get our arguments.
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model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
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else:
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model_args, data_args, training_args = parser.parse_args_into_dataclasses()
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# Setup logging
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logging.basicConfig(
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format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
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datefmt="%m/%d/%Y %H:%M:%S",
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handlers=[logging.StreamHandler(sys.stdout)],
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)
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log_level = training_args.get_process_log_level()
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logger.setLevel(log_level)
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datasets.utils.logging.set_verbosity(log_level)
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transformers.utils.logging.set_verbosity(log_level)
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transformers.utils.logging.enable_default_handler()
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transformers.utils.logging.enable_explicit_format()
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# Log on each process the small summary:
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logger.warning(
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f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
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+ f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
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)
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# Set the verbosity to info of the Transformers logger (on main process only):
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logger.info(f"Training/evaluation parameters {training_args}")
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# Detecting last checkpoint.
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last_checkpoint = None
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if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
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last_checkpoint = get_last_checkpoint(training_args.output_dir)
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if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
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raise ValueError(
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f"Output directory ({training_args.output_dir}) already exists and is not empty. "
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"Use --overwrite_output_dir to overcome."
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)
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elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
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logger.info(
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f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
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"the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
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)
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# Set seed before initializing model.
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set_seed(training_args.seed)
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# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
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# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
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# (the dataset will be downloaded automatically from the datasets Hub
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#
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# For CSV/JSON files, this script will use the column called 'text' or the first column. You can easily tweak this
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# behavior (see below)
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#
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# In distributed training, the load_dataset function guarantee that only one local process can concurrently
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# download the dataset.
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if data_args.dataset_name is not None:
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# Downloading and loading a dataset from the hub.
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raw_datasets = load_dataset(
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data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir
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)
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if "validation" not in raw_datasets.keys():
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raw_datasets["validation"] = load_dataset(
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data_args.dataset_name,
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data_args.dataset_config_name,
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split=f"train[:{data_args.validation_split_percentage}%]",
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cache_dir=model_args.cache_dir,
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)
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raw_datasets["train"] = load_dataset(
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data_args.dataset_name,
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data_args.dataset_config_name,
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split=f"train[{data_args.validation_split_percentage}%:]",
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cache_dir=model_args.cache_dir,
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)
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else:
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data_files = {}
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if data_args.train_file is not None:
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data_files["train"] = data_args.train_file
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extension = data_args.train_file.split(".")[-1]
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if data_args.validation_file is not None:
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data_files["validation"] = data_args.validation_file
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extension = data_args.validation_file.split(".")[-1]
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if extension == "txt":
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extension = "text"
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raw_datasets = load_dataset(extension, data_files=data_files, cache_dir=model_args.cache_dir, sep='\t')
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# If no validation data is there, validation_split_percentage will be used to divide the dataset.
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if "validation" not in raw_datasets.keys():
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raw_datasets["validation"] = load_dataset(
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extension,
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data_files=data_files,
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split=f"train[:{data_args.validation_split_percentage}%]",
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cache_dir=model_args.cache_dir,
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)
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raw_datasets["train"] = load_dataset(
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extension,
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data_files=data_files,
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split=f"train[{data_args.validation_split_percentage}%:]",
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cache_dir=model_args.cache_dir,
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)
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# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
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# https://huggingface.co/docs/datasets/loading_datasets.html.
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# Load pretrained model and tokenizer
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#
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# Distributed training:
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# The .from_pretrained methods guarantee that only one local process can concurrently
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# download model & vocab.
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config_kwargs = {
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"cache_dir": model_args.cache_dir,
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"revision": model_args.model_revision,
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"use_auth_token": True if model_args.use_auth_token else None,
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}
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if model_args.config_name:
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config = AutoConfig.from_pretrained(model_args.config_name, **config_kwargs)
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elif model_args.model_name_or_path:
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config = AutoConfig.from_pretrained(model_args.model_name_or_path, **config_kwargs)
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else:
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config = CONFIG_MAPPING[model_args.model_type]()
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logger.warning("You are instantiating a new config instance from scratch.")
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if model_args.config_overrides is not None:
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logger.info(f"Overriding config: {model_args.config_overrides}")
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config.update_from_string(model_args.config_overrides)
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tokenizer_kwargs = {
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"cache_dir": model_args.cache_dir,
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"use_fast": model_args.use_fast_tokenizer,
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"revision": model_args.model_revision,
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"use_auth_token": True if model_args.use_auth_token else None,
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}
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if model_args.tokenizer_name:
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tokenizer = AutoTokenizer.from_pretrained(model_args.tokenizer_name, **tokenizer_kwargs)
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elif model_args.model_name_or_path:
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tokenizer = AutoTokenizer.from_pretrained(model_args.model_name_or_path, **tokenizer_kwargs)
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else:
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raise ValueError(
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|
"You are instantiating a new tokenizer from scratch. This is not supported by this script."
|
||
|
|
"You can do it from another script, save it, and load it from here, using --tokenizer_name."
|
||
|
|
)
|
||
|
|
|
||
|
|
if model_args.model_name_or_path:
|
||
|
|
model = AutoModelForMaskedLM.from_pretrained(
|
||
|
|
model_args.model_name_or_path,
|
||
|
|
from_tf=bool(".ckpt" in model_args.model_name_or_path),
|
||
|
|
config=config,
|
||
|
|
cache_dir=model_args.cache_dir,
|
||
|
|
revision=model_args.model_revision,
|
||
|
|
use_auth_token=True if model_args.use_auth_token else None,
|
||
|
|
)
|
||
|
|
else:
|
||
|
|
logger.info("Training new model from scratch")
|
||
|
|
model = AutoModelForMaskedLM.from_config(config)
|
||
|
|
|
||
|
|
model.resize_token_embeddings(len(tokenizer))
|
||
|
|
|
||
|
|
# Preprocessing the datasets.
|
||
|
|
# First we tokenize all the texts.
|
||
|
|
if training_args.do_train:
|
||
|
|
column_names = raw_datasets["train"].column_names
|
||
|
|
else:
|
||
|
|
column_names = raw_datasets["validation"].column_names
|
||
|
|
text_column_name = "text" if "text" in column_names else column_names[0]
|
||
|
|
|
||
|
|
if data_args.max_seq_length is None:
|
||
|
|
max_seq_length = tokenizer.model_max_length
|
||
|
|
if max_seq_length > 1024:
|
||
|
|
logger.warning(
|
||
|
|
f"The tokenizer picked seems to have a very large `model_max_length` ({tokenizer.model_max_length}). "
|
||
|
|
"Picking 1024 instead. You can change that default value by passing --max_seq_length xxx."
|
||
|
|
)
|
||
|
|
max_seq_length = 1024
|
||
|
|
else:
|
||
|
|
if data_args.max_seq_length > tokenizer.model_max_length:
|
||
|
|
logger.warning(
|
||
|
|
f"The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the"
|
||
|
|
f"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}."
|
||
|
|
)
|
||
|
|
max_seq_length = min(data_args.max_seq_length, tokenizer.model_max_length)
|
||
|
|
|
||
|
|
if data_args.line_by_line:
|
||
|
|
# When using line_by_line, we just tokenize each nonempty line.
|
||
|
|
padding = "max_length" if data_args.pad_to_max_length else False
|
||
|
|
|
||
|
|
def tokenize_function(examples):
|
||
|
|
# Remove empty lines
|
||
|
|
examples[text_column_name] = [
|
||
|
|
line for line in examples[text_column_name] if len(line) > 0 and not line.isspace()
|
||
|
|
]
|
||
|
|
return tokenizer(
|
||
|
|
examples[text_column_name],
|
||
|
|
padding=padding,
|
||
|
|
truncation=True,
|
||
|
|
max_length=max_seq_length,
|
||
|
|
# We use this option because DataCollatorForLanguageModeling (see below) is more efficient when it
|
||
|
|
# receives the `special_tokens_mask`.
|
||
|
|
return_special_tokens_mask=True,
|
||
|
|
)
|
||
|
|
|
||
|
|
with training_args.main_process_first(desc="dataset map tokenization"):
|
||
|
|
tokenized_datasets = raw_datasets.map(
|
||
|
|
tokenize_function,
|
||
|
|
batched=True,
|
||
|
|
num_proc=data_args.preprocessing_num_workers,
|
||
|
|
remove_columns=[text_column_name],
|
||
|
|
load_from_cache_file=not data_args.overwrite_cache,
|
||
|
|
desc="Running tokenizer on dataset line_by_line",
|
||
|
|
)
|
||
|
|
else:
|
||
|
|
# Otherwise, we tokenize every text, then concatenate them together before splitting them in smaller parts.
|
||
|
|
# We use `return_special_tokens_mask=True` because DataCollatorForLanguageModeling (see below) is more
|
||
|
|
# efficient when it receives the `special_tokens_mask`.
|
||
|
|
def tokenize_function(examples):
|
||
|
|
return tokenizer(examples[text_column_name], return_special_tokens_mask=True)
|
||
|
|
|
||
|
|
with training_args.main_process_first(desc="dataset map tokenization"):
|
||
|
|
tokenized_datasets = raw_datasets.map(
|
||
|
|
tokenize_function,
|
||
|
|
batched=True,
|
||
|
|
num_proc=data_args.preprocessing_num_workers,
|
||
|
|
remove_columns=column_names,
|
||
|
|
load_from_cache_file=not data_args.overwrite_cache,
|
||
|
|
desc="Running tokenizer on every text in dataset",
|
||
|
|
)
|
||
|
|
|
||
|
|
# Main data processing function that will concatenate all texts from our dataset and generate chunks of
|
||
|
|
# max_seq_length.
|
||
|
|
def group_texts(examples):
|
||
|
|
# Concatenate all texts.
|
||
|
|
concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}
|
||
|
|
total_length = len(concatenated_examples[list(examples.keys())[0]])
|
||
|
|
# We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
|
||
|
|
# customize this part to your needs.
|
||
|
|
if total_length >= max_seq_length:
|
||
|
|
total_length = (total_length // max_seq_length) * max_seq_length
|
||
|
|
# Split by chunks of max_len.
|
||
|
|
result = {
|
||
|
|
k: [t[i : i + max_seq_length] for i in range(0, total_length, max_seq_length)]
|
||
|
|
for k, t in concatenated_examples.items()
|
||
|
|
}
|
||
|
|
return result
|
||
|
|
|
||
|
|
# Note that with `batched=True`, this map processes 1,000 texts together, so group_texts throws away a
|
||
|
|
# remainder for each of those groups of 1,000 texts. You can adjust that batch_size here but a higher value
|
||
|
|
# might be slower to preprocess.
|
||
|
|
#
|
||
|
|
# To speed up this part, we use multiprocessing. See the documentation of the map method for more information:
|
||
|
|
# https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.map
|
||
|
|
|
||
|
|
with training_args.main_process_first(desc="grouping texts together"):
|
||
|
|
tokenized_datasets = tokenized_datasets.map(
|
||
|
|
group_texts,
|
||
|
|
batched=True,
|
||
|
|
num_proc=data_args.preprocessing_num_workers,
|
||
|
|
load_from_cache_file=not data_args.overwrite_cache,
|
||
|
|
desc=f"Grouping texts in chunks of {max_seq_length}",
|
||
|
|
)
|
||
|
|
|
||
|
|
if training_args.do_train:
|
||
|
|
if "train" not in tokenized_datasets:
|
||
|
|
raise ValueError("--do_train requires a train dataset")
|
||
|
|
train_dataset = tokenized_datasets["train"]
|
||
|
|
if data_args.max_train_samples is not None:
|
||
|
|
train_dataset = train_dataset.select(range(data_args.max_train_samples))
|
||
|
|
|
||
|
|
if training_args.do_eval:
|
||
|
|
if "validation" not in tokenized_datasets:
|
||
|
|
raise ValueError("--do_eval requires a validation dataset")
|
||
|
|
eval_dataset = tokenized_datasets["validation"]
|
||
|
|
if data_args.max_eval_samples is not None:
|
||
|
|
eval_dataset = eval_dataset.select(range(data_args.max_eval_samples))
|
||
|
|
|
||
|
|
# Data collator
|
||
|
|
# This one will take care of randomly masking the tokens.
|
||
|
|
pad_to_multiple_of_8 = data_args.line_by_line and training_args.fp16 and not data_args.pad_to_max_length
|
||
|
|
data_collator = DataCollatorForLanguageModeling(
|
||
|
|
tokenizer=tokenizer,
|
||
|
|
mlm_probability=data_args.mlm_probability,
|
||
|
|
pad_to_multiple_of=8 if pad_to_multiple_of_8 else None,
|
||
|
|
)
|
||
|
|
|
||
|
|
# Initialize our Trainer
|
||
|
|
trainer = Trainer(
|
||
|
|
model=model,
|
||
|
|
args=training_args,
|
||
|
|
train_dataset=train_dataset if training_args.do_train else None,
|
||
|
|
eval_dataset=eval_dataset if training_args.do_eval else None,
|
||
|
|
tokenizer=tokenizer,
|
||
|
|
data_collator=data_collator,
|
||
|
|
)
|
||
|
|
|
||
|
|
# Training
|
||
|
|
if training_args.do_train:
|
||
|
|
checkpoint = None
|
||
|
|
if training_args.resume_from_checkpoint is not None:
|
||
|
|
checkpoint = training_args.resume_from_checkpoint
|
||
|
|
elif last_checkpoint is not None:
|
||
|
|
checkpoint = last_checkpoint
|
||
|
|
train_result = trainer.train(resume_from_checkpoint=checkpoint)
|
||
|
|
trainer.save_model() # Saves the tokenizer too for easy upload
|
||
|
|
metrics = train_result.metrics
|
||
|
|
|
||
|
|
max_train_samples = (
|
||
|
|
data_args.max_train_samples if data_args.max_train_samples is not None else len(train_dataset)
|
||
|
|
)
|
||
|
|
metrics["train_samples"] = min(max_train_samples, len(train_dataset))
|
||
|
|
|
||
|
|
trainer.log_metrics("train", metrics)
|
||
|
|
trainer.save_metrics("train", metrics)
|
||
|
|
trainer.save_state()
|
||
|
|
|
||
|
|
# Evaluation
|
||
|
|
if training_args.do_eval:
|
||
|
|
logger.info("*** Evaluate ***")
|
||
|
|
|
||
|
|
metrics = trainer.evaluate()
|
||
|
|
|
||
|
|
max_eval_samples = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(eval_dataset)
|
||
|
|
metrics["eval_samples"] = min(max_eval_samples, len(eval_dataset))
|
||
|
|
try:
|
||
|
|
perplexity = math.exp(metrics["eval_loss"])
|
||
|
|
except OverflowError:
|
||
|
|
perplexity = float("inf")
|
||
|
|
metrics["perplexity"] = perplexity
|
||
|
|
|
||
|
|
trainer.log_metrics("eval", metrics)
|
||
|
|
trainer.save_metrics("eval", metrics)
|
||
|
|
|
||
|
|
kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "fill-mask"}
|
||
|
|
if data_args.dataset_name is not None:
|
||
|
|
kwargs["dataset_tags"] = data_args.dataset_name
|
||
|
|
if data_args.dataset_config_name is not None:
|
||
|
|
kwargs["dataset_args"] = data_args.dataset_config_name
|
||
|
|
kwargs["dataset"] = f"{data_args.dataset_name} {data_args.dataset_config_name}"
|
||
|
|
else:
|
||
|
|
kwargs["dataset"] = data_args.dataset_name
|
||
|
|
|
||
|
|
if training_args.push_to_hub:
|
||
|
|
trainer.push_to_hub(**kwargs)
|
||
|
|
else:
|
||
|
|
trainer.create_model_card(**kwargs)
|
||
|
|
|
||
|
|
|
||
|
|
def _mp_fn(index):
|
||
|
|
# For xla_spawn (TPUs)
|
||
|
|
main()
|
||
|
|
|
||
|
|
|
||
|
|
if __name__ == "__main__":
|
||
|
|
main()
|