703 lines
29 KiB
Python
Executable File
703 lines
29 KiB
Python
Executable File
#!/usr/bin/env python
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# coding=utf-8
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# Copyright The HuggingFace Team and 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 a 🤗 Transformers model on multiple choice relying on the accelerate library without using a Trainer.
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"""
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# You can also adapt this script on your own multiple choice task. Pointers for this are left as comments.
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import argparse
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import json
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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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from dataclasses import dataclass
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from itertools import chain
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from pathlib import Path
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from typing import Optional, Union
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import datasets
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import evaluate
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import torch
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from accelerate import Accelerator
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from accelerate.logging import get_logger
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from accelerate.utils import set_seed
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from datasets import load_dataset
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from huggingface_hub import HfApi
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from torch.utils.data import DataLoader
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from tqdm.auto import tqdm
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import transformers
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from transformers import (
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CONFIG_MAPPING,
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MODEL_MAPPING,
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AutoConfig,
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AutoModelForMultipleChoice,
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AutoTokenizer,
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PreTrainedTokenizerBase,
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SchedulerType,
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default_data_collator,
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get_scheduler,
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)
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from transformers.utils import PaddingStrategy, check_min_version, send_example_telemetry
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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("4.42.0.dev0")
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logger = get_logger(__name__)
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# You should update this to your particular problem to have better documentation of `model_type`
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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(description="Finetune a transformers model on a multiple choice task")
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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", type=str, default=None, 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", type=str, default=None, 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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"--max_seq_length",
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type=int,
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default=128,
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help=(
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"The maximum total input sequence length after tokenization. Sequences longer than this will be truncated,"
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" sequences shorter will be padded if `--pad_to_max_length` is passed."
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),
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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=False,
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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("--weight_decay", type=float, default=0.0, help="Weight decay to use.")
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parser.add_argument("--num_train_epochs", type=int, default=3, help="Total number of training epochs to perform.")
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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=["linear", "cosine", "cosine_with_restarts", "polynomial", "constant", "constant_with_warmup"],
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)
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parser.add_argument(
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"--num_warmup_steps", type=int, default=0, help="Number of steps for the warmup in the lr scheduler."
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)
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parser.add_argument("--output_dir", type=str, default=None, help="Where to store the final model.")
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parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
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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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"--debug",
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action="store_true",
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help="Activate debug mode and run training only with a subset of data.",
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)
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parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
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parser.add_argument(
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"--hub_model_id", type=str, help="The name of the repository to keep in sync with the local `output_dir`."
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)
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parser.add_argument("--hub_token", type=str, help="The token to use to push to the Model Hub.")
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parser.add_argument(
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"--trust_remote_code",
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type=bool,
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default=False,
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help=(
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"Whether or not to allow for custom models defined on the Hub in their own modeling files. This option "
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"should only be set to `True` for repositories you trust and in which you have read the code, as it will "
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"execute code present on the Hub on your local machine."
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),
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)
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parser.add_argument(
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"--checkpointing_steps",
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type=str,
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default=None,
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help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch.",
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)
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parser.add_argument(
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"--resume_from_checkpoint",
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type=str,
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default=None,
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help="If the training should continue from a checkpoint folder.",
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)
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parser.add_argument(
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"--with_tracking",
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action="store_true",
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help="Whether to enable experiment trackers for logging.",
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)
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parser.add_argument(
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"--report_to",
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type=str,
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default="all",
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help=(
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'The integration to report the results and logs to. Supported platforms are `"tensorboard"`,'
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' `"wandb"`, `"comet_ml"` and `"clearml"`. Use `"all"` (default) to report to all integrations. '
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"Only applicable when `--with_tracking` is passed."
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),
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)
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args = parser.parse_args()
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if args.push_to_hub:
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assert args.output_dir is not None, "Need an `output_dir` to create a repo when `--push_to_hub` is passed."
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return args
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@dataclass
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class DataCollatorForMultipleChoice:
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"""
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Data collator that will dynamically pad the inputs for multiple choice received.
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Args:
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tokenizer ([`PreTrainedTokenizer`] or [`PreTrainedTokenizerFast`]):
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The tokenizer used for encoding the data.
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padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `True`):
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Select a strategy to pad the returned sequences (according to the model's padding side and padding index)
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among:
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- `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence
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if provided).
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- `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
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acceptable input length for the model if that argument is not provided.
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- `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
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lengths).
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max_length (`int`, *optional*):
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Maximum length of the returned list and optionally padding length (see above).
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pad_to_multiple_of (`int`, *optional*):
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If set will pad the sequence to a multiple of the provided value.
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This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >=
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7.5 (Volta).
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"""
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tokenizer: PreTrainedTokenizerBase
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padding: Union[bool, str, PaddingStrategy] = True
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max_length: Optional[int] = None
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pad_to_multiple_of: Optional[int] = None
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def __call__(self, features):
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label_name = "label" if "label" in features[0].keys() else "labels"
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labels = [feature.pop(label_name) for feature in features]
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batch_size = len(features)
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num_choices = len(features[0]["input_ids"])
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flattened_features = [
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[{k: v[i] for k, v in feature.items()} for i in range(num_choices)] for feature in features
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]
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flattened_features = list(chain(*flattened_features))
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batch = self.tokenizer.pad(
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flattened_features,
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padding=self.padding,
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max_length=self.max_length,
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pad_to_multiple_of=self.pad_to_multiple_of,
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return_tensors="pt",
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)
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# Un-flatten
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batch = {k: v.view(batch_size, num_choices, -1) for k, v in batch.items()}
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# Add back labels
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batch["labels"] = torch.tensor(labels, dtype=torch.int64)
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return batch
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def main():
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args = parse_args()
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# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
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# information sent is the one passed as arguments along with your Python/PyTorch versions.
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send_example_telemetry("run_swag_no_trainer", 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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# If we're using tracking, we also need to initialize it here and it will by default pick up all supported trackers
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# in the environment
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accelerator_log_kwargs = {}
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if args.with_tracking:
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accelerator_log_kwargs["log_with"] = args.report_to
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accelerator_log_kwargs["project_dir"] = args.output_dir
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accelerator = Accelerator(gradient_accumulation_steps=args.gradient_accumulation_steps, **accelerator_log_kwargs)
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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, main_process_only=False)
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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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# Handle the repository creation
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if accelerator.is_main_process:
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if args.push_to_hub:
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# Retrieve of infer repo_name
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repo_name = args.hub_model_id
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if repo_name is None:
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repo_name = Path(args.output_dir).absolute().name
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# Create repo and retrieve repo_id
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api = HfApi()
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repo_id = api.create_repo(repo_name, exist_ok=True, token=args.hub_token).repo_id
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with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
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if "step_*" not in gitignore:
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gitignore.write("step_*\n")
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if "epoch_*" not in gitignore:
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gitignore.write("epoch_*\n")
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elif args.output_dir is not None:
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os.makedirs(args.output_dir, exist_ok=True)
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accelerator.wait_for_everyone()
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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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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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extension = args.train_file.split(".")[-1]
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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.validation_file.split(".")[-1]
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raw_datasets = load_dataset(extension, data_files=data_files)
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# Trim a number of training examples
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if args.debug:
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for split in raw_datasets.keys():
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raw_datasets[split] = raw_datasets[split].select(range(100))
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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.
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if raw_datasets["train"] is not None:
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column_names = raw_datasets["train"].column_names
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else:
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column_names = raw_datasets["validation"].column_names
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# When using your own dataset or a different dataset from swag, you will probably need to change this.
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ending_names = [f"ending{i}" for i in range(4)]
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context_name = "sent1"
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question_header_name = "sent2"
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label_column_name = "label" if "label" in column_names else "labels"
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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.model_name_or_path, trust_remote_code=args.trust_remote_code)
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elif args.model_name_or_path:
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config = AutoConfig.from_pretrained(args.model_name_or_path, trust_remote_code=args.trust_remote_code)
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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:
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tokenizer = AutoTokenizer.from_pretrained(
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args.tokenizer_name, use_fast=not args.use_slow_tokenizer, trust_remote_code=args.trust_remote_code
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)
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elif args.model_name_or_path:
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tokenizer = AutoTokenizer.from_pretrained(
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args.model_name_or_path, use_fast=not args.use_slow_tokenizer, trust_remote_code=args.trust_remote_code
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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 args.model_name_or_path:
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model = AutoModelForMultipleChoice.from_pretrained(
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args.model_name_or_path,
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from_tf=bool(".ckpt" in args.model_name_or_path),
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config=config,
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trust_remote_code=args.trust_remote_code,
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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 = AutoModelForMultipleChoice.from_config(config, trust_remote_code=args.trust_remote_code)
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# We resize the embeddings only when necessary to avoid index errors. If you are creating a model from scratch
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# on a small vocab and want a smaller embedding size, remove this test.
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embedding_size = model.get_input_embeddings().weight.shape[0]
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if len(tokenizer) > embedding_size:
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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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padding = "max_length" if args.pad_to_max_length else False
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def preprocess_function(examples):
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first_sentences = [[context] * 4 for context in examples[context_name]]
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question_headers = examples[question_header_name]
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second_sentences = [
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[f"{header} {examples[end][i]}" for end in ending_names] for i, header in enumerate(question_headers)
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]
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labels = examples[label_column_name]
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# Flatten out
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first_sentences = list(chain(*first_sentences))
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second_sentences = list(chain(*second_sentences))
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# Tokenize
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tokenized_examples = tokenizer(
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first_sentences,
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second_sentences,
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max_length=args.max_seq_length,
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padding=padding,
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truncation=True,
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)
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# Un-flatten
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tokenized_inputs = {k: [v[i : i + 4] for i in range(0, len(v), 4)] for k, v in tokenized_examples.items()}
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tokenized_inputs["labels"] = labels
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return tokenized_inputs
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with accelerator.main_process_first():
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processed_datasets = raw_datasets.map(
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preprocess_function, batched=True, remove_columns=raw_datasets["train"].column_names
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)
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train_dataset = processed_datasets["train"]
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eval_dataset = processed_datasets["validation"]
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# Log a few random samples from the training set:
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for index in random.sample(range(len(train_dataset)), 3):
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logger.info(f"Sample {index} of the training set: {train_dataset[index]}.")
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# DataLoaders creation:
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if args.pad_to_max_length:
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# If padding was already done ot max length, we use the default data collator that will just convert everything
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# to tensors.
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data_collator = default_data_collator
|
|
else:
|
|
# Otherwise, `DataCollatorWithPadding` will apply dynamic padding for us (by padding to the maximum length of
|
|
# the samples passed). When using mixed precision, we add `pad_to_multiple_of=8` to pad all tensors to multiple
|
|
# of 8s, which will enable the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta).
|
|
data_collator = DataCollatorForMultipleChoice(
|
|
tokenizer, pad_to_multiple_of=(8 if accelerator.use_fp16 else None)
|
|
)
|
|
|
|
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 = torch.optim.AdamW(optimizer_grouped_parameters, lr=args.learning_rate)
|
|
|
|
# Use the device given by the `accelerator` object.
|
|
device = accelerator.device
|
|
model.to(device)
|
|
|
|
# Scheduler and math around the number of training steps.
|
|
overrode_max_train_steps = False
|
|
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
|
|
overrode_max_train_steps = True
|
|
|
|
lr_scheduler = get_scheduler(
|
|
name=args.lr_scheduler_type,
|
|
optimizer=optimizer,
|
|
num_warmup_steps=args.num_warmup_steps * accelerator.num_processes,
|
|
num_training_steps=args.max_train_steps
|
|
if overrode_max_train_steps
|
|
else args.max_train_steps * accelerator.num_processes,
|
|
)
|
|
|
|
# Prepare everything with our `accelerator`.
|
|
model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = accelerator.prepare(
|
|
model, optimizer, train_dataloader, eval_dataloader, lr_scheduler
|
|
)
|
|
|
|
# We need to recalculate our total training steps as the size of the training dataloader may have changed.
|
|
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
|
|
if overrode_max_train_steps:
|
|
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
|
|
# Afterwards we recalculate our number of training epochs
|
|
args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
|
|
|
|
# Figure out how many steps we should save the Accelerator states
|
|
checkpointing_steps = args.checkpointing_steps
|
|
if checkpointing_steps is not None and checkpointing_steps.isdigit():
|
|
checkpointing_steps = int(checkpointing_steps)
|
|
|
|
# We need to initialize the trackers we use, and also store our configuration.
|
|
# The trackers initializes automatically on the main process.
|
|
if args.with_tracking:
|
|
experiment_config = vars(args)
|
|
# TensorBoard cannot log Enums, need the raw value
|
|
experiment_config["lr_scheduler_type"] = experiment_config["lr_scheduler_type"].value
|
|
accelerator.init_trackers("swag_no_trainer", experiment_config)
|
|
|
|
# Metrics
|
|
metric = evaluate.load("accuracy")
|
|
|
|
# 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
|
|
starting_epoch = 0
|
|
|
|
# Potentially load in the weights and states from a previous save
|
|
if args.resume_from_checkpoint:
|
|
if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
|
|
checkpoint_path = args.resume_from_checkpoint
|
|
path = os.path.basename(args.resume_from_checkpoint)
|
|
else:
|
|
# Get the most recent checkpoint
|
|
dirs = [f.name for f in os.scandir(os.getcwd()) if f.is_dir()]
|
|
dirs.sort(key=os.path.getctime)
|
|
path = dirs[-1] # Sorts folders by date modified, most recent checkpoint is the last
|
|
checkpoint_path = path
|
|
path = os.path.basename(checkpoint_path)
|
|
|
|
accelerator.print(f"Resumed from checkpoint: {checkpoint_path}")
|
|
accelerator.load_state(checkpoint_path)
|
|
# Extract `epoch_{i}` or `step_{i}`
|
|
training_difference = os.path.splitext(path)[0]
|
|
|
|
if "epoch" in training_difference:
|
|
starting_epoch = int(training_difference.replace("epoch_", "")) + 1
|
|
resume_step = None
|
|
completed_steps = starting_epoch * num_update_steps_per_epoch
|
|
else:
|
|
# need to multiply `gradient_accumulation_steps` to reflect real steps
|
|
resume_step = int(training_difference.replace("step_", "")) * args.gradient_accumulation_steps
|
|
starting_epoch = resume_step // len(train_dataloader)
|
|
completed_steps = resume_step // args.gradient_accumulation_steps
|
|
resume_step -= starting_epoch * len(train_dataloader)
|
|
|
|
# update the progress_bar if load from checkpoint
|
|
progress_bar.update(completed_steps)
|
|
|
|
for epoch in range(starting_epoch, args.num_train_epochs):
|
|
model.train()
|
|
if args.with_tracking:
|
|
total_loss = 0
|
|
if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
|
|
# We skip the first `n` batches in the dataloader when resuming from a checkpoint
|
|
active_dataloader = accelerator.skip_first_batches(train_dataloader, resume_step)
|
|
else:
|
|
active_dataloader = train_dataloader
|
|
for step, batch in enumerate(active_dataloader):
|
|
with accelerator.accumulate(model):
|
|
outputs = model(**batch)
|
|
loss = outputs.loss
|
|
# We keep track of the loss at each epoch
|
|
if args.with_tracking:
|
|
total_loss += loss.detach().float()
|
|
accelerator.backward(loss)
|
|
optimizer.step()
|
|
lr_scheduler.step()
|
|
optimizer.zero_grad()
|
|
|
|
# Checks if the accelerator has performed an optimization step behind the scenes
|
|
if accelerator.sync_gradients:
|
|
progress_bar.update(1)
|
|
completed_steps += 1
|
|
|
|
if isinstance(checkpointing_steps, int):
|
|
if completed_steps % checkpointing_steps == 0:
|
|
output_dir = f"step_{completed_steps}"
|
|
if args.output_dir is not None:
|
|
output_dir = os.path.join(args.output_dir, output_dir)
|
|
accelerator.save_state(output_dir)
|
|
|
|
if completed_steps >= args.max_train_steps:
|
|
break
|
|
|
|
model.eval()
|
|
for step, batch in enumerate(eval_dataloader):
|
|
with torch.no_grad():
|
|
outputs = model(**batch)
|
|
predictions = outputs.logits.argmax(dim=-1)
|
|
predictions, references = accelerator.gather_for_metrics((predictions, batch["labels"]))
|
|
metric.add_batch(
|
|
predictions=predictions,
|
|
references=references,
|
|
)
|
|
|
|
eval_metric = metric.compute()
|
|
accelerator.print(f"epoch {epoch}: {eval_metric}")
|
|
|
|
if args.with_tracking:
|
|
accelerator.log(
|
|
{
|
|
"accuracy": eval_metric,
|
|
"train_loss": total_loss.item() / len(train_dataloader),
|
|
"epoch": epoch,
|
|
"step": completed_steps,
|
|
},
|
|
step=completed_steps,
|
|
)
|
|
|
|
if args.push_to_hub and epoch < args.num_train_epochs - 1:
|
|
accelerator.wait_for_everyone()
|
|
unwrapped_model = accelerator.unwrap_model(model)
|
|
unwrapped_model.save_pretrained(
|
|
args.output_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save
|
|
)
|
|
if accelerator.is_main_process:
|
|
tokenizer.save_pretrained(args.output_dir)
|
|
api.upload_folder(
|
|
commit_message=f"Training in progress epoch {epoch}",
|
|
folder_path=args.output_dir,
|
|
repo_id=repo_id,
|
|
repo_type="model",
|
|
token=args.hub_token,
|
|
)
|
|
|
|
if args.checkpointing_steps == "epoch":
|
|
output_dir = f"epoch_{epoch}"
|
|
if args.output_dir is not None:
|
|
output_dir = os.path.join(args.output_dir, output_dir)
|
|
accelerator.save_state(output_dir)
|
|
|
|
if args.with_tracking:
|
|
accelerator.end_training()
|
|
|
|
if args.output_dir is not None:
|
|
accelerator.wait_for_everyone()
|
|
unwrapped_model = accelerator.unwrap_model(model)
|
|
unwrapped_model.save_pretrained(
|
|
args.output_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save
|
|
)
|
|
if accelerator.is_main_process:
|
|
tokenizer.save_pretrained(args.output_dir)
|
|
if args.push_to_hub:
|
|
api.upload_folder(
|
|
commit_message="End of training",
|
|
folder_path=args.output_dir,
|
|
repo_id=repo_id,
|
|
repo_type="model",
|
|
token=args.hub_token,
|
|
)
|
|
all_results = {f"eval_{k}": v for k, v in eval_metric.items()}
|
|
with open(os.path.join(args.output_dir, "all_results.json"), "w") as f:
|
|
json.dump(all_results, f)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|