377 lines
15 KiB
Python
377 lines
15 KiB
Python
# 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 permutation language modeling.
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"""
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# You can also adapt this script on your own permutation 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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from datasets import load_dataset
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import transformers
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from transformers import (
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AutoConfig,
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AutoTokenizer,
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DataCollatorForPermutationLanguageModeling,
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HfArgumentParser,
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Trainer,
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TrainingArguments,
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XLNetConfig,
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XLNetLMHeadModel,
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set_seed,
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)
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from transformers.trainer_utils import is_main_process
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logger = logging.getLogger(__name__)
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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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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, metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}
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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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@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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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. Default to the max input length of the model."
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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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plm_probability: float = field(
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default=1 / 6,
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metadata={
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"help": "Ratio of length of a span of masked tokens to surrounding context length for "
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"permutation language modeling."
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},
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)
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max_span_length: int = field(
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default=5, metadata={"help": "Maximum length of a span of masked tokens for permutation language modeling."}
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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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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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if (
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os.path.exists(training_args.output_dir)
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and os.listdir(training_args.output_dir)
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and training_args.do_train
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and not training_args.overwrite_output_dir
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):
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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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# 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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level=logging.INFO if is_main_process(training_args.local_rank) else logging.WARN,
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)
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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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if is_main_process(training_args.local_rank):
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transformers.utils.logging.set_verbosity_info()
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logger.info("Training/evaluation parameters %s", training_args)
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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 if no column called
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# 'text' is found. You can easily tweak this 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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datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name)
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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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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.train_file.split(".")[-1]
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if extension == "txt":
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extension = "text"
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datasets = load_dataset(extension, data_files=data_files)
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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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if model_args.config_name:
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config = AutoConfig.from_pretrained(model_args.config_name, cache_dir=model_args.cache_dir)
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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, cache_dir=model_args.cache_dir)
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else:
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config = XLNetConfig()
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logger.warning("You are instantiating a new config instance from scratch.")
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if model_args.tokenizer_name:
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tokenizer = AutoTokenizer.from_pretrained(
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model_args.tokenizer_name, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer
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)
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elif model_args.model_name_or_path:
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tokenizer = AutoTokenizer.from_pretrained(
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model_args.model_name_or_path, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer
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)
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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."
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"You can do it from another script, save it, and load it from here, using --tokenizer_name."
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)
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if model_args.model_name_or_path:
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model = XLNetLMHeadModel.from_pretrained(
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model_args.model_name_or_path,
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from_tf=bool(".ckpt" in model_args.model_name_or_path),
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config=config,
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cache_dir=model_args.cache_dir,
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)
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else:
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logger.info("Training new model from scratch")
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model = XLNetLMHeadModel.from_config(config)
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model.resize_token_embeddings(len(tokenizer))
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# Preprocessing the datasets.
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# First we tokenize all the texts.
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if training_args.do_train:
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column_names = datasets["train"].column_names
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else:
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column_names = datasets["validation"].column_names
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text_column_name = "text" if "text" in column_names else column_names[0]
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if data_args.line_by_line:
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# When using line_by_line, we just tokenize each nonempty line.
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padding = "max_length" if data_args.pad_to_max_length else False
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def tokenize_function(examples):
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# Remove empty lines
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examples["text"] = [line for line in examples["text"] if len(line) > 0 and not line.isspace()]
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return tokenizer(examples["text"], padding=padding, truncation=True, max_length=data_args.max_seq_length)
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tokenized_datasets = datasets.map(
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tokenize_function,
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batched=True,
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num_proc=data_args.preprocessing_num_workers,
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remove_columns=[text_column_name],
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load_from_cache_file=not data_args.overwrite_cache,
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)
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else:
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# Otherwise, we tokenize every text, then concatenate them together before splitting them in smaller parts.
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def tokenize_function(examples):
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return tokenizer(examples[text_column_name])
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tokenized_datasets = datasets.map(
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tokenize_function,
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batched=True,
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num_proc=data_args.preprocessing_num_workers,
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remove_columns=[text_column_name],
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load_from_cache_file=not data_args.overwrite_cache,
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)
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if data_args.max_seq_length is None:
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max_seq_length = tokenizer.model_max_length
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else:
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if data_args.max_seq_length > tokenizer.model_max_length:
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logger.warn(
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f"The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the"
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f"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}."
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)
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max_seq_length = min(data_args.max_seq_length, tokenizer.model_max_length)
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# Main data processing function that will concatenate all texts from our dataset and generate chunks of
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# max_seq_length.
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def group_texts(examples):
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# Concatenate all texts.
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concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}
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total_length = len(concatenated_examples[list(examples.keys())[0]])
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# We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
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# customize this part to your needs.
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total_length = (total_length // max_seq_length) * max_seq_length
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# Split by chunks of max_len.
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result = {
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k: [t[i : i + max_seq_length] for i in range(0, total_length, max_seq_length)]
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for k, t in concatenated_examples.items()
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}
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return result
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# Note that with `batched=True`, this map processes 1,000 texts together, so group_texts throws away a
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# remainder for each of those groups of 1,000 texts. You can adjust that batch_size here but a higher value
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# might be slower to preprocess.
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#
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# To speed up this part, we use multiprocessing. See the documentation of the map method for more information:
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# https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.map
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tokenized_datasets = tokenized_datasets.map(
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group_texts,
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batched=True,
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num_proc=data_args.preprocessing_num_workers,
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load_from_cache_file=not data_args.overwrite_cache,
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)
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# Data collator
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data_collator = DataCollatorForPermutationLanguageModeling(
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tokenizer=tokenizer,
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plm_probability=data_args.plm_probability,
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max_span_length=data_args.max_span_length,
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)
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# Initialize our Trainer
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trainer = Trainer(
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model=model,
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args=training_args,
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train_dataset=tokenized_datasets["train"] if training_args.do_train else None,
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eval_dataset=tokenized_datasets["validation"] if training_args.do_eval else None,
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tokenizer=tokenizer,
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data_collator=data_collator,
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)
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# Training
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if training_args.do_train:
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trainer.train(
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model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path) else None
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)
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trainer.save_model() # Saves the tokenizer too for easy upload
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# Evaluation
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results = {}
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if training_args.do_eval:
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logger.info("*** Evaluate ***")
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eval_output = trainer.evaluate()
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perplexity = math.exp(eval_output["eval_loss"])
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results["perplexity"] = perplexity
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output_eval_file = os.path.join(training_args.output_dir, "eval_results_plm.txt")
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if trainer.is_world_process_zero():
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with open(output_eval_file, "w") as writer:
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logger.info("***** Eval results *****")
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for key, value in results.items():
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logger.info(f" {key} = {value}")
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writer.write(f"{key} = {value}\n")
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return results
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def _mp_fn(index):
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# For xla_spawn (TPUs)
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main()
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if __name__ == "__main__":
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main()
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