673 lines
24 KiB
Python
673 lines
24 KiB
Python
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#!/usr/bin/env python
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# coding=utf-8
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# Copyright 2021 The HuggingFace Inc. 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...)
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# on a text file or a dataset without using HuggingFace Trainer.
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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 mlm task. Pointers for this are left as comments.
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import argparse
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import logging
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import math
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import os
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import random
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import datasets
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import numpy as np
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import tokenizers
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import torch
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import transformers
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from accelerate import Accelerator
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from datasets import load_dataset
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from torch.utils.data.dataloader import DataLoader
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from tqdm.auto import tqdm
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from transformers import (
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CONFIG_MAPPING,
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MODEL_MAPPING,
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AdamW,
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AutoConfig,
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AutoModelForMaskedLM,
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AutoTokenizer,
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BatchEncoding,
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BertConfig,
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BertForPreTraining,
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DataCollatorForLanguageModeling,
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SchedulerType,
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get_scheduler,
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set_seed,
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)
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logger = logging.getLogger(__name__)
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MODEL_CONFIG_CLASSES = list(MODEL_MAPPING.keys())
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MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
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def parse_args():
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parser = argparse.ArgumentParser(
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description="Finetune a transformers model on a Masked Language Modeling task"
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)
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# parser.add_argument(
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# "--dataset_name",
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# type=str,
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# default=None,
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# help="The name of the dataset to use (via the datasets library).",
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# )
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# parser.add_argument(
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# "--dataset_config_name",
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# type=str,
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# default=None,
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# help="The configuration name of the dataset to use (via the datasets library).",
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# )
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# parser.add_argument(
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# "--train_file",
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# type=str,
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# default=None,
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# help="A csv or a json file containing the training data.",
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# )
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# parser.add_argument(
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# "--validation_file",
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# type=str,
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# default=None,
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# help="A csv or a json file containing the validation data.",
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# )
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# parser.add_argument(
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# "--validation_split_percentage",
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# default=5,
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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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# parser.add_argument(
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# "--pad_to_max_length",
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# action="store_true",
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# help="If passed, pad all samples to `max_length`. Otherwise, dynamic padding is used.",
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# )
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# parser.add_argument(
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# "--model_name_or_path",
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# type=str,
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# help="Path to pretrained model or model identifier from huggingface.co/models.",
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# required=True,
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# )
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# parser.add_argument(
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# "--config_name",
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# type=str,
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# default=None,
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# help="Pretrained config name or path if not the same as model_name",
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# )
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# parser.add_argument(
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# "--tokenizer_name",
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# type=str,
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# default=None,
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# help="Pretrained tokenizer name or path if not the same as model_name",
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# )
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# parser.add_argument(
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# "--use_slow_tokenizer",
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# action="store_true",
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# help="If passed, will use a slow tokenizer (not backed by the 🤗 Tokenizers library).",
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# )
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parser.add_argument(
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"--per_device_train_batch_size",
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type=int,
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default=8,
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help="Batch size (per device) for the training dataloader.",
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)
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parser.add_argument(
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"--per_device_eval_batch_size",
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type=int,
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default=8,
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help="Batch size (per device) for the evaluation dataloader.",
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)
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parser.add_argument(
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"--learning_rate",
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type=float,
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default=5e-5,
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help="Initial learning rate (after the potential warmup period) to use.",
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)
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parser.add_argument(
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"--weight_decay", type=float, default=0.0, help="Weight decay to use."
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)
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parser.add_argument(
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"--num_train_epochs",
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type=int,
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default=40,
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help="Total number of training epochs to perform.",
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)
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# parser.add_argument(
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# "--max_train_steps",
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# type=int,
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# default=None,
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# help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
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# )
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# parser.add_argument(
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# "--gradient_accumulation_steps",
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# type=int,
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# default=1,
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# help="Number of updates steps to accumulate before performing a backward/update pass.",
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# )
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parser.add_argument(
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"--lr_scheduler_type",
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type=SchedulerType,
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default="linear",
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help="The scheduler type to use.",
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choices=[
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"linear",
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"cosine",
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"cosine_with_restarts",
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"polynomial",
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"constant",
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"constant_with_warmup",
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],
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)
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parser.add_argument(
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"--num_warmup_steps",
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type=int,
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default=0,
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help="Number of steps for the warmup in the lr scheduler.",
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)
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parser.add_argument(
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"--output_dir", type=str, default=None, help="Where to store the final model."
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)
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parser.add_argument(
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"--seed", type=int, default=None, help="A seed for reproducible training."
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)
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# parser.add_argument(
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# "--model_type",
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# type=str,
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# default=None,
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# help="Model type to use if training from scratch.",
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# choices=MODEL_TYPES,
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# )
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# parser.add_argument(
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# "--max_seq_length",
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# type=int,
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# default=None,
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# help="The maximum total input sequence length after tokenization. Sequences longer than this will be truncated.",
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# )
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# parser.add_argument(
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# "--line_by_line",
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# type=bool,
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# default=False,
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# help="Whether distinct lines of text in the dataset are to be handled as distinct sequences.",
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# )
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parser.add_argument(
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"--preprocessing_num_workers",
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type=int,
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default=None,
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help="The number of processes to use for the preprocessing.",
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)
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# parser.add_argument(
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# "--overwrite_cache",
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# type=bool,
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# default=False,
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# help="Overwrite the cached training and evaluation sets",
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# )
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parser.add_argument(
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"--mlm_probability",
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type=float,
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default=0.15,
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help="Ratio of tokens to mask for masked language modeling loss",
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)
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args = parser.parse_args()
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# Sanity checks
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# if (
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# args.dataset_name is None
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# and args.train_file is None
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# and args.validation_file is None
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# ):
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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 args.train_file is not None:
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# extension = args.train_file.split(".")[-1]
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# assert extension in [
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# "csv",
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# "json",
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# "txt",
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# ], "`train_file` should be a csv, json or txt file."
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# if args.validation_file is not None:
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# extension = args.validation_file.split(".")[-1]
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# assert extension in [
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# "csv",
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# "json",
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# "txt",
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# ], "`validation_file` should be a csv, json or txt file."
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if args.output_dir is not None:
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os.makedirs(args.output_dir, exist_ok=True)
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return args
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def main():
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args = parse_args()
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# Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
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accelerator = Accelerator()
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# Make one log on every process with the configuration for debugging.
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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,
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)
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logger.info(accelerator.state)
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# Setup logging, we only want one process per machine to log things on the screen.
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# accelerator.is_local_main_process is only True for one process per machine.
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logger.setLevel(
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logging.INFO if accelerator.is_local_main_process else logging.ERROR
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)
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if accelerator.is_local_main_process:
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datasets.utils.logging.set_verbosity_warning()
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transformers.utils.logging.set_verbosity_info()
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else:
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datasets.utils.logging.set_verbosity_error()
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transformers.utils.logging.set_verbosity_error()
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# If passed along, set the training seed now.
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if args.seed is not None:
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set_seed(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 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(args.dataset_name, args.dataset_config_name)
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# if "validation" not in raw_datasets.keys():
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# raw_datasets["validation"] = load_dataset(
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# args.dataset_name,
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# args.dataset_config_name,
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# split=f"train[:{args.validation_split_percentage}%]",
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# )
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# raw_datasets["train"] = load_dataset(
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# args.dataset_name,
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# args.dataset_config_name,
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# split=f"train[{args.validation_split_percentage}%:]",
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# )
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# else:
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# data_files = {}
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# if args.train_file is not None:
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# data_files["train"] = args.train_file
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# if args.validation_file is not None:
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# data_files["validation"] = args.validation_file
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# extension = args.train_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)
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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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# we use dataset in json format,
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# `raw_dataset` has two features: `text`:"sentA\tsentB" and `is_next`:0 or 1
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# we take control of the load of dataset by oursevles
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# there will be several json file for training
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# raw_datasets = load_dataset("json", data_files=args.train_file, field="data")
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raw_datasets = load_dataset(
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"json",
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data_files="/home/ming/malware/inst2vec_bert/data/test_lm/inst.json",
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field="data",
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)
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# we use the tokenizer trained on the positive dataset before
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# tokenizer = tokenizers.Tokenizer.from_file(args.tokenizer_file)
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tokenizer = tokenizers.Tokenizer.from_file(
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"/home/ming/malware/inst2vec_bert/bert/tokenizer-inst.json"
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)
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tokenizer.enable_padding(
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pad_id=tokenizer.token_to_id("[PAD]"), pad_token="[PAD]", length=64
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)
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# we use a much smaller BERT, config is:
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config = BertConfig(
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vocab_size=tokenizer.get_vocab_size(),
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hidden_size=64,
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num_hidden_layers=8,
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num_attention_heads=8,
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intermediate_size=256,
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max_position_embeddings=64,
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)
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# initalize the new BERT for pre-training
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model = BertForPreTraining(config)
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# all_special_tokens = ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]"]
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# all_special_ids = [tokenizer.token_to_id(token) for token in all_special_tokens]
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# # Load pretrained model and tokenizer
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# #
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# # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
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# # download model & vocab.
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# if args.config_name:
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# config = AutoConfig.from_pretrained(args.config_name)
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# elif args.model_name_or_path:
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# config = AutoConfig.from_pretrained(args.model_name_or_path)
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# else:
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# config = CONFIG_MAPPING[args.model_type]()
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# logger.warning("You are instantiating a new config instance from scratch.")
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|
|
# if args.tokenizer_name:
|
||
|
|
# tokenizer = AutoTokenizer.from_pretrained(
|
||
|
|
# args.tokenizer_name, use_fast=not args.use_slow_tokenizer
|
||
|
|
# )
|
||
|
|
# elif args.model_name_or_path:
|
||
|
|
# tokenizer = AutoTokenizer.from_pretrained(
|
||
|
|
# args.model_name_or_path, use_fast=not args.use_slow_tokenizer
|
||
|
|
# )
|
||
|
|
# else:
|
||
|
|
# raise ValueError(
|
||
|
|
# "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 args.model_name_or_path:
|
||
|
|
# model = AutoModelForMaskedLM.from_pretrained(
|
||
|
|
# args.model_name_or_path,
|
||
|
|
# from_tf=bool(".ckpt" in args.model_name_or_path),
|
||
|
|
# config=config,
|
||
|
|
# )
|
||
|
|
# 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.
|
||
|
|
column_names = raw_datasets["train"].column_names
|
||
|
|
text_column_name = "text" if "text" in column_names else column_names[0]
|
||
|
|
|
||
|
|
# if 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 args.max_seq_length > tokenizer.model_max_length:
|
||
|
|
# logger.warning(
|
||
|
|
# f"The max_seq_length passed ({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(args.max_seq_length, tokenizer.model_max_length)
|
||
|
|
|
||
|
|
# if args.line_by_line:
|
||
|
|
# # When using line_by_line, we just tokenize each nonempty line.
|
||
|
|
# padding = "max_length" if args.pad_to_max_length else False
|
||
|
|
|
||
|
|
# def tokenize_function(examples):
|
||
|
|
# # Remove empty lines
|
||
|
|
# examples["text"] = [
|
||
|
|
# line
|
||
|
|
# for line in examples["text"]
|
||
|
|
# if len(line) > 0 and not line.isspace()
|
||
|
|
# ]
|
||
|
|
# return tokenizer(
|
||
|
|
# examples["text"],
|
||
|
|
# 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,
|
||
|
|
# )
|
||
|
|
|
||
|
|
# tokenized_datasets = raw_datasets.map(
|
||
|
|
# tokenize_function,
|
||
|
|
# batched=True,
|
||
|
|
# num_proc=args.preprocessing_num_workers,
|
||
|
|
# remove_columns=[text_column_name],
|
||
|
|
# load_from_cache_file=not args.overwrite_cache,
|
||
|
|
# )
|
||
|
|
# 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
|
||
|
|
# )
|
||
|
|
|
||
|
|
# tokenized_datasets = raw_datasets.map(
|
||
|
|
# tokenize_function,
|
||
|
|
# batched=True,
|
||
|
|
# num_proc=args.preprocessing_num_workers,
|
||
|
|
# remove_columns=column_names,
|
||
|
|
# load_from_cache_file=not args.overwrite_cache,
|
||
|
|
# )
|
||
|
|
|
||
|
|
# # 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.
|
||
|
|
# 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
|
||
|
|
|
||
|
|
# tokenized_datasets = tokenized_datasets.map(
|
||
|
|
# group_texts,
|
||
|
|
# batched=True,
|
||
|
|
# num_proc=args.preprocessing_num_workers,
|
||
|
|
# load_from_cache_file=not args.overwrite_cache,
|
||
|
|
# )
|
||
|
|
|
||
|
|
def tokenize_function(examples):
|
||
|
|
text = [tuple(sent) for sent in examples["text"]]
|
||
|
|
encoded_inputs = {}
|
||
|
|
results = tokenizer.encode_batch(text)
|
||
|
|
# input_ids, type_ids, special_token_masks = [], [], []
|
||
|
|
# for i, result in enumerate(results):
|
||
|
|
# input_ids.append(result.ids)
|
||
|
|
# type_ids.append(result.type_ids)
|
||
|
|
# special_token_masks.append(result.special_tokens_mask)
|
||
|
|
encoded_inputs["input_ids"] = [result.ids for result in results]
|
||
|
|
encoded_inputs["token_type_ids"] = [result.type_ids for result in results]
|
||
|
|
# special_tokens_mask = [1 if token in all_special_ids else 0 for token in ids]
|
||
|
|
encoded_inputs["special_tokens_mask"] = [
|
||
|
|
result.special_tokens_mask for result in results
|
||
|
|
]
|
||
|
|
# 0: is ; 1 : is not
|
||
|
|
encoded_inputs["next_sentence_label "] = [
|
||
|
|
1 - label for label in examples["is_next"]
|
||
|
|
]
|
||
|
|
batch_outputs = BatchEncoding(
|
||
|
|
encoded_inputs, tensor_type="np", prepend_batch_axis=False,
|
||
|
|
)
|
||
|
|
return batch_outputs
|
||
|
|
|
||
|
|
tokenized_datasets = raw_datasets.map(
|
||
|
|
tokenize_function,
|
||
|
|
batched=True,
|
||
|
|
num_proc=args.preprocessing_num_workers,
|
||
|
|
remove_columns=column_names,
|
||
|
|
# load_from_cache_file=not args.overwrite_cache,
|
||
|
|
)
|
||
|
|
|
||
|
|
train_dataset = tokenized_datasets["train"]
|
||
|
|
eval_dataset = tokenized_datasets["validation"]
|
||
|
|
|
||
|
|
# Log a few random samples from the training set:
|
||
|
|
for index in random.sample(range(len(train_dataset)), 3):
|
||
|
|
logger.info(f"Sample {index} of the training set: {train_dataset[index]}.")
|
||
|
|
|
||
|
|
# Data collator
|
||
|
|
# This one will take care of randomly masking the tokens.
|
||
|
|
data_collator = DataCollatorForLanguageModeling(
|
||
|
|
tokenizer=tokenizer, mlm_probability=args.mlm_probability
|
||
|
|
)
|
||
|
|
|
||
|
|
# DataLoaders creation:
|
||
|
|
train_dataloader = DataLoader(
|
||
|
|
train_dataset,
|
||
|
|
shuffle=True,
|
||
|
|
collate_fn=data_collator,
|
||
|
|
batch_size=args.per_device_train_batch_size,
|
||
|
|
)
|
||
|
|
eval_dataloader = DataLoader(
|
||
|
|
eval_dataset,
|
||
|
|
collate_fn=data_collator,
|
||
|
|
batch_size=args.per_device_eval_batch_size,
|
||
|
|
)
|
||
|
|
|
||
|
|
# Optimizer
|
||
|
|
# Split weights in two groups, one with weight decay and the other not.
|
||
|
|
no_decay = ["bias", "LayerNorm.weight"]
|
||
|
|
optimizer_grouped_parameters = [
|
||
|
|
{
|
||
|
|
"params": [
|
||
|
|
p
|
||
|
|
for n, p in model.named_parameters()
|
||
|
|
if not any(nd in n for nd in no_decay)
|
||
|
|
],
|
||
|
|
"weight_decay": args.weight_decay,
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"params": [
|
||
|
|
p
|
||
|
|
for n, p in model.named_parameters()
|
||
|
|
if any(nd in n for nd in no_decay)
|
||
|
|
],
|
||
|
|
"weight_decay": 0.0,
|
||
|
|
},
|
||
|
|
]
|
||
|
|
optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate)
|
||
|
|
|
||
|
|
# Prepare everything with our `accelerator`.
|
||
|
|
model, optimizer, train_dataloader, eval_dataloader = accelerator.prepare(
|
||
|
|
model, optimizer, train_dataloader, eval_dataloader
|
||
|
|
)
|
||
|
|
|
||
|
|
# Note -> the training dataloader needs to be prepared before we grab his length below (cause its length will be
|
||
|
|
# shorter in multiprocess)
|
||
|
|
|
||
|
|
# Scheduler and math around the number of training steps.
|
||
|
|
num_update_steps_per_epoch = math.ceil(
|
||
|
|
len(train_dataloader) / args.gradient_accumulation_steps
|
||
|
|
)
|
||
|
|
if args.max_train_steps is None:
|
||
|
|
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
|
||
|
|
else:
|
||
|
|
args.num_train_epochs = math.ceil(
|
||
|
|
args.max_train_steps / num_update_steps_per_epoch
|
||
|
|
)
|
||
|
|
|
||
|
|
lr_scheduler = get_scheduler(
|
||
|
|
name=args.lr_scheduler_type,
|
||
|
|
optimizer=optimizer,
|
||
|
|
num_warmup_steps=args.num_warmup_steps,
|
||
|
|
num_training_steps=args.max_train_steps,
|
||
|
|
)
|
||
|
|
|
||
|
|
# Train!
|
||
|
|
total_batch_size = (
|
||
|
|
args.per_device_train_batch_size
|
||
|
|
* accelerator.num_processes
|
||
|
|
* args.gradient_accumulation_steps
|
||
|
|
)
|
||
|
|
|
||
|
|
logger.info("***** Running training *****")
|
||
|
|
logger.info(f" Num examples = {len(train_dataset)}")
|
||
|
|
logger.info(f" Num Epochs = {args.num_train_epochs}")
|
||
|
|
logger.info(
|
||
|
|
f" Instantaneous batch size per device = {args.per_device_train_batch_size}"
|
||
|
|
)
|
||
|
|
logger.info(
|
||
|
|
f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}"
|
||
|
|
)
|
||
|
|
logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}")
|
||
|
|
logger.info(f" Total optimization steps = {args.max_train_steps}")
|
||
|
|
# Only show the progress bar once on each machine.
|
||
|
|
progress_bar = tqdm(
|
||
|
|
range(args.max_train_steps), disable=not accelerator.is_local_main_process
|
||
|
|
)
|
||
|
|
completed_steps = 0
|
||
|
|
|
||
|
|
for epoch in range(args.num_train_epochs):
|
||
|
|
model.train()
|
||
|
|
for step, batch in enumerate(train_dataloader):
|
||
|
|
outputs = model(**batch)
|
||
|
|
loss = outputs.loss
|
||
|
|
loss = loss / args.gradient_accumulation_steps
|
||
|
|
accelerator.backward(loss)
|
||
|
|
if (
|
||
|
|
step % args.gradient_accumulation_steps == 0
|
||
|
|
or step == len(train_dataloader) - 1
|
||
|
|
):
|
||
|
|
optimizer.step()
|
||
|
|
lr_scheduler.step()
|
||
|
|
optimizer.zero_grad()
|
||
|
|
progress_bar.update(1)
|
||
|
|
completed_steps += 1
|
||
|
|
|
||
|
|
if completed_steps >= args.max_train_steps:
|
||
|
|
break
|
||
|
|
|
||
|
|
model.eval()
|
||
|
|
losses = []
|
||
|
|
for step, batch in enumerate(eval_dataloader):
|
||
|
|
with torch.no_grad():
|
||
|
|
outputs = model(**batch)
|
||
|
|
|
||
|
|
loss = outputs.loss
|
||
|
|
losses.append(
|
||
|
|
accelerator.gather(loss.repeat(args.per_device_eval_batch_size))
|
||
|
|
)
|
||
|
|
|
||
|
|
losses = torch.cat(losses)
|
||
|
|
losses = losses[: len(eval_dataset)]
|
||
|
|
try:
|
||
|
|
perplexity = math.exp(torch.mean(losses))
|
||
|
|
except OverflowError:
|
||
|
|
perplexity = float("inf")
|
||
|
|
|
||
|
|
logger.info(f"epoch {epoch}: perplexity: {perplexity}")
|
||
|
|
|
||
|
|
if args.output_dir is not None:
|
||
|
|
accelerator.wait_for_everyone()
|
||
|
|
unwrapped_model = accelerator.unwrap_model(model)
|
||
|
|
unwrapped_model.save_pretrained(args.output_dir, save_function=accelerator.save)
|
||
|
|
|
||
|
|
|
||
|
|
if __name__ == "__main__":
|
||
|
|
main()
|