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Added translation example script (#11196)
* initial changes * modified evaluation * updated evaluation * updated evaluation on text translation example script * added translation example script * Formatted translation example script * Reformatted translation example * Fixed evaluation bug and added support for other tokenisers * Fixed evaluation bug and added support for other tokenisers * Added translation example script * Formatted summarization example script * Removed typos from summarization example script
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examples/seq2seq/run_summarization_no_trainer.py
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568
examples/seq2seq/run_summarization_no_trainer.py
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#!/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 summarization.
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"""
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# You can also adapt this script on your own summarization 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 nltk
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import numpy as np
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import torch
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from datasets import load_dataset, load_metric
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from torch.utils.data.dataloader import DataLoader
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from tqdm.auto import tqdm
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import transformers
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from accelerate import Accelerator
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from filelock import FileLock
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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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AutoModelForSeq2SeqLM,
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AutoTokenizer,
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DataCollatorForSeq2Seq,
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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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from transformers.file_utils import is_offline_mode
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logger = logging.getLogger(__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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try:
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nltk.data.find("tokenizers/punkt")
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except (LookupError, OSError):
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if is_offline_mode():
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raise LookupError(
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"Offline mode: run this script without TRANSFORMERS_OFFLINE first to download nltk data files"
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)
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with FileLock(".lock") as lock:
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nltk.download("punkt", quiet=True)
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summarization_name_mapping = {
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"amazon_reviews_multi": ("review_body", "review_title"),
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"big_patent": ("description", "abstract"),
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"cnn_dailymail": ("article", "highlights"),
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"orange_sum": ("text", "summary"),
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"pn_summary": ("article", "summary"),
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"psc": ("extract_text", "summary_text"),
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"samsum": ("dialogue", "summary"),
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"thaisum": ("body", "summary"),
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"xglue": ("news_body", "news_title"),
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"xsum": ("document", "summary"),
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"wiki_summary": ("article", "highlights"),
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}
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def parse_args():
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parser = argparse.ArgumentParser(description="Finetune a transformers model on a text classification 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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"--ignore_pad_token_for_loss",
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type=bool,
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default=True,
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help="Whether to ignore the tokens corresponding to " "padded labels in the loss computation or not.",
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)
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parser.add_argument(
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"--max_source_length",
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type=int,
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default=1024,
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help="The maximum total input sequence length after "
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"tokenization.Sequences longer than this will be truncated, sequences shorter will be padded.",
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)
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parser.add_argument(
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"--source_prefix",
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type=str,
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default=None,
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help="A prefix to add before every source text " "(useful for T5 models).",
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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", type=bool, default=None, help="Overwrite the cached training and evaluation sets"
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)
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parser.add_argument(
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"--max_target_length",
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type=int,
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default=128,
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help="The maximum total sequence length for target text after "
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"tokenization. Sequences longer than this will be truncated, sequences shorter will be padded."
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"during ``evaluate`` and ``predict``.",
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)
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parser.add_argument(
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"--val_max_target_length",
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type=int,
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default=None,
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help="The maximum total sequence length for validation "
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"target text after tokenization.Sequences longer than this will be truncated, sequences shorter will be "
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"padded. Will default to `max_target_length`.This argument is also used to override the ``max_length`` "
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"param of ``model.generate``, which is used during ``evaluate`` and ``predict``.",
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)
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parser.add_argument(
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"--max_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_lengh` is passed."
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),
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)
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parser.add_argument(
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"--num_beams",
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type=int,
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default=None,
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help="Number of beams to use for evaluation. This argument will be "
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"passed to ``model.generate``, which is used during ``evaluate`` and ``predict``.",
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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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"--summary_column",
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type=str,
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default=None,
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help="The name of the column in the datasets containing the summaries (for summarization).",
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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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args = parser.parse_args()
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# Sanity checks
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if args.dataset_name is None and args.train_file is None and args.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 args.train_file is not None:
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extension = args.train_file.split(".")[-1]
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assert extension in ["csv", "json"], "`train_file` should be a csv or a json 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 ["csv", "json"], "`validation_file` should be a csv or a json 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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if args.source_prefix is None and args.model_name_or_path in [
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"t5-small",
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"t5-base",
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"t5-large",
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"t5-3b",
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"t5-11b",
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]:
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logger.warning(
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"You're running a t5 model but didn't provide a source prefix, which is the expected, e.g. with "
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"`--source_prefix 'summarize: ' `"
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)
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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(logging.INFO if accelerator.is_local_main_process else logging.ERROR)
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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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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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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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# 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)
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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:
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tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_name, use_fast=not args.use_slow_tokenizer)
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elif args.model_name_or_path:
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tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path, use_fast=not args.use_slow_tokenizer)
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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 = AutoModelForSeq2SeqLM.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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)
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else:
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logger.info("Training new model from scratch")
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model = AutoModelForSeq2SeqLM.from_config(config)
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model.resize_token_embeddings(len(tokenizer))
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if model.config.decoder_start_token_id is None:
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raise ValueError("Make sure that `config.decoder_start_token_id` is correctly defined")
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prefix = args.source_prefix if args.source_prefix is not None else ""
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# Preprocessing the datasets.
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# First we tokenize all the texts.
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column_names = raw_datasets["train"].column_names
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# Get the column names for input/target.
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dataset_columns = summarization_name_mapping.get(args.dataset_name, None)
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text_column_name = dataset_columns[0] if dataset_columns is not None else column_names[0]
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padding = "max_length" if args.pad_to_max_length else False
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if args.summary_column is None:
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summary_column = dataset_columns[1] if dataset_columns is not None else column_names[1]
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else:
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summary_column = args.summary_column
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if summary_column not in column_names:
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raise ValueError(
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f"--summary_column' value '{args.summary_column}' needs to be one of: {', '.join(column_names)}"
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)
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# Temporarily set max_target_length for training.
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max_target_length = args.max_target_length
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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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inputs = examples[text_column_name]
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targets = examples[summary_column]
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inputs = [prefix + inp for inp in inputs]
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model_inputs = tokenizer(inputs, max_length=args.max_source_length, padding=padding, truncation=True)
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# Setup the tokenizer for targets
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with tokenizer.as_target_tokenizer():
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labels = tokenizer(targets, max_length=max_target_length, padding=padding, truncation=True)
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# If we are padding here, replace all tokenizer.pad_token_id in the labels by -100 when we want to ignore
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# padding in the loss.
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if padding == "max_length" and args.ignore_pad_token_for_loss:
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labels["input_ids"] = [
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[(l if l != tokenizer.pad_token_id else -100) for l in label] for label in labels["input_ids"]
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]
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model_inputs["labels"] = labels["input_ids"]
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return model_inputs
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processed_datasets = raw_datasets.map(
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preprocess_function, batched=True, remove_columns=column_names, load_from_cache_file=not args.overwrite_cache
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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)), 1):
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logger.info(f"Sample {index} of the training set: {train_dataset[index]}.")
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label_pad_token_id = -100 if args.ignore_pad_token_for_loss else tokenizer.pad_token_id
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data_collator = DataCollatorForSeq2Seq(
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tokenizer,
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model=model,
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label_pad_token_id=label_pad_token_id,
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pad_to_multiple_of=8 if accelerator.use_fp16 else None,
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)
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def postprocess_text(preds, labels):
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preds = [pred.strip() for pred in preds]
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labels = [label.strip() for label in labels]
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# rougeLSum expects newline after each sentence
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preds = ["\n".join(nltk.sent_tokenize(pred)) for pred in preds]
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labels = ["\n".join(nltk.sent_tokenize(label)) for label in labels]
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return preds, labels
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|
||||
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,
|
||||
)
|
||||
|
||||
# Metric
|
||||
metric = load_metric("rouge")
|
||||
|
||||
# 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()
|
||||
if args.val_max_target_length is None:
|
||||
args.val_max_target_length = args.max_target_length
|
||||
|
||||
gen_kwargs = {
|
||||
"max_length": args.val_max_target_length if args is not None else config.max_length,
|
||||
"num_beams": args.num_beams,
|
||||
}
|
||||
for step, batch in enumerate(eval_dataloader):
|
||||
with torch.no_grad():
|
||||
generated_tokens = accelerator.unwrap_model(model).generate(
|
||||
batch["input_ids"],
|
||||
attention_mask=batch["attention_mask"],
|
||||
**gen_kwargs,
|
||||
)
|
||||
|
||||
generated_tokens = accelerator.pad_across_processes(
|
||||
generated_tokens, dim=1, pad_index=tokenizer.pad_token_id
|
||||
)
|
||||
labels = batch["labels"]
|
||||
if not args.pad_to_max_length:
|
||||
# If we did not pad to max length, we need to pad the labels too
|
||||
labels = accelerator.pad_across_processes(batch["labels"], dim=1, pad_index=tokenizer.pad_token_id)
|
||||
|
||||
generated_tokens = accelerator.gather(generated_tokens).cpu().numpy()
|
||||
labels = accelerator.gather(labels).cpu().numpy()
|
||||
|
||||
if args.ignore_pad_token_for_loss:
|
||||
# Replace -100 in the labels as we can't decode them.
|
||||
labels = np.where(labels != -100, labels, tokenizer.pad_token_id)
|
||||
if isinstance(generated_tokens, tuple):
|
||||
generated_tokens = generated_tokens[0]
|
||||
decoded_preds = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
|
||||
decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)
|
||||
|
||||
decoded_preds, decoded_labels = postprocess_text(decoded_preds, decoded_labels)
|
||||
|
||||
metric.add_batch(predictions=decoded_preds, references=decoded_labels)
|
||||
result = metric.compute(use_stemmer=True)
|
||||
# Extract a few results from ROUGE
|
||||
result = {key: value.mid.fmeasure * 100 for key, value in result.items()}
|
||||
|
||||
result = {k: round(v, 4) for k, v in result.items()}
|
||||
|
||||
logger.info(result)
|
||||
|
||||
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()
|
||||
560
examples/seq2seq/run_translation_no_trainer.py
Normal file
560
examples/seq2seq/run_translation_no_trainer.py
Normal file
@ -0,0 +1,560 @@
|
||||
#!/usr/bin/env python
|
||||
# coding=utf-8
|
||||
# Copyright The HuggingFace Team and The HuggingFace Inc. team. All rights reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
"""
|
||||
Fine-tuning a 🤗 Transformers model on text translation.
|
||||
"""
|
||||
# You can also adapt this script on your own text translation task. Pointers for this are left as comments.
|
||||
|
||||
import argparse
|
||||
import logging
|
||||
import math
|
||||
import os
|
||||
import random
|
||||
|
||||
import datasets
|
||||
import numpy as np
|
||||
import torch
|
||||
from datasets import load_dataset, load_metric
|
||||
from torch.utils.data.dataloader import DataLoader
|
||||
from tqdm.auto import tqdm
|
||||
|
||||
import transformers
|
||||
from accelerate import Accelerator
|
||||
from transformers import (
|
||||
CONFIG_MAPPING,
|
||||
MODEL_MAPPING,
|
||||
AdamW,
|
||||
AutoConfig,
|
||||
AutoModelForSeq2SeqLM,
|
||||
AutoTokenizer,
|
||||
DataCollatorForSeq2Seq,
|
||||
MBartTokenizer,
|
||||
MBartTokenizerFast,
|
||||
SchedulerType,
|
||||
default_data_collator,
|
||||
get_scheduler,
|
||||
set_seed,
|
||||
)
|
||||
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
# You should update this to your particular problem to have better documentation of `model_type`
|
||||
MODEL_CONFIG_CLASSES = list(MODEL_MAPPING.keys())
|
||||
MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
|
||||
|
||||
|
||||
# Parsing input arguments
|
||||
def parse_args():
|
||||
|
||||
parser = argparse.ArgumentParser(description="Finetune a transformers model on a text classification task")
|
||||
parser.add_argument(
|
||||
"--dataset_name",
|
||||
type=str,
|
||||
default=None,
|
||||
help="The name of the dataset to use (via the datasets library).",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--predict_with_generate",
|
||||
type=bool,
|
||||
default=True,
|
||||
help="",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--dataset_config_name",
|
||||
type=str,
|
||||
default=None,
|
||||
help="The configuration name of the dataset to use (via the datasets library).",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--train_file", type=str, default=None, help="A csv or a json file containing the training data."
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--num_beams",
|
||||
type=int,
|
||||
default=None,
|
||||
help="Number of beams to use for evaluation. This argument will be "
|
||||
"passed to ``model.generate``, which is used during ``evaluate`` and ``predict``.",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--max_source_length",
|
||||
type=int,
|
||||
default=1024,
|
||||
help="The maximum total input sequence length after "
|
||||
"tokenization.Sequences longer than this will be truncated, sequences shorter will be padded.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--max_target_length",
|
||||
type=int,
|
||||
default=128,
|
||||
help="The maximum total sequence length for target text after "
|
||||
"tokenization. Sequences longer than this will be truncated, sequences shorter will be padded."
|
||||
"during ``evaluate`` and ``predict``.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--val_max_target_length",
|
||||
type=int,
|
||||
default=None,
|
||||
help="The maximum total sequence length for validation "
|
||||
"target text after tokenization.Sequences longer than this will be truncated, sequences shorter will be "
|
||||
"padded. Will default to `max_target_length`.This argument is also used to override the ``max_length`` "
|
||||
"param of ``model.generate``, which is used during ``evaluate`` and ``predict``.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--pad_to_max_length",
|
||||
type=bool,
|
||||
default=False,
|
||||
help="Whether to pad all samples to model maximum sentence "
|
||||
"length. If False, will pad the samples dynamically when batching to the maximum length in the batch. More"
|
||||
"efficient on GPU but very bad for TPU.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--validation_file", type=str, default=None, help="A csv or a json file containing the validation data."
|
||||
)
|
||||
parser.add_argument(
|
||||
"--ignore_pad_token_for_loss",
|
||||
type=bool,
|
||||
default=True,
|
||||
help="Whether to ignore the tokens corresponding to " "padded labels in the loss computation or not.",
|
||||
)
|
||||
parser.add_argument("--source_lang", type=str, default=None, help="Source language id for translation.")
|
||||
parser.add_argument("--target_lang", type=str, default=None, help="Target language id for translation.")
|
||||
parser.add_argument(
|
||||
"--source_prefix",
|
||||
type=str,
|
||||
default=None,
|
||||
help="A prefix to add before every source text " "(useful for T5 models).",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--preprocessing_num_workers",
|
||||
type=int,
|
||||
default=None,
|
||||
help="The number of processes to use for the preprocessing.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--overwrite_cache", type=bool, default=None, help="Overwrite the cached training and evaluation sets"
|
||||
)
|
||||
parser.add_argument(
|
||||
"--max_length",
|
||||
type=int,
|
||||
default=128,
|
||||
help=(
|
||||
"The maximum total input sequence length after tokenization. Sequences longer than this will be truncated,"
|
||||
" sequences shorter will be padded if `--pad_to_max_lengh` is passed."
|
||||
),
|
||||
)
|
||||
parser.add_argument(
|
||||
"--model_name_or_path",
|
||||
type=str,
|
||||
help="Path to pretrained model or model identifier from huggingface.co/models.",
|
||||
required=True,
|
||||
)
|
||||
parser.add_argument(
|
||||
"--config_name",
|
||||
type=str,
|
||||
default=None,
|
||||
help="Pretrained config name or path if not the same as model_name",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--tokenizer_name",
|
||||
type=str,
|
||||
default=None,
|
||||
help="Pretrained tokenizer name or path if not the same as model_name",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--use_slow_tokenizer",
|
||||
action="store_true",
|
||||
help="If passed, will use a slow tokenizer (not backed by the 🤗 Tokenizers library).",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--per_device_train_batch_size",
|
||||
type=int,
|
||||
default=8,
|
||||
help="Batch size (per device) for the training dataloader.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--per_device_eval_batch_size",
|
||||
type=int,
|
||||
default=8,
|
||||
help="Batch size (per device) for the evaluation dataloader.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--learning_rate",
|
||||
type=float,
|
||||
default=5e-5,
|
||||
help="Initial learning rate (after the potential warmup period) to use.",
|
||||
)
|
||||
parser.add_argument("--weight_decay", type=float, default=0.0, help="Weight decay to use.")
|
||||
parser.add_argument("--num_train_epochs", type=int, default=3, help="Total number of training epochs to perform.")
|
||||
parser.add_argument(
|
||||
"--max_train_steps",
|
||||
type=int,
|
||||
default=None,
|
||||
help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--gradient_accumulation_steps",
|
||||
type=int,
|
||||
default=1,
|
||||
help="Number of updates steps to accumulate before performing a backward/update pass.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--lr_scheduler_type",
|
||||
type=SchedulerType,
|
||||
default="linear",
|
||||
help="The scheduler type to use.",
|
||||
choices=["linear", "cosine", "cosine_with_restarts", "polynomial", "constant", "constant_with_warmup"],
|
||||
)
|
||||
parser.add_argument(
|
||||
"--num_warmup_steps", type=int, default=0, help="Number of steps for the warmup in the lr scheduler."
|
||||
)
|
||||
parser.add_argument("--output_dir", type=str, default=None, help="Where to store the final model.")
|
||||
parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
|
||||
parser.add_argument(
|
||||
"--model_type",
|
||||
type=str,
|
||||
default=None,
|
||||
help="Model type to use if training from scratch.",
|
||||
choices=MODEL_TYPES,
|
||||
)
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
# Sanity checks
|
||||
|
||||
if args.dataset_name is None and args.train_file is None and args.validation_file is None:
|
||||
raise ValueError("Need either a task name or a training/validation file.")
|
||||
|
||||
if args.train_file is not None:
|
||||
extension = args.train_file.split(".")[-1]
|
||||
assert extension in ["csv", "json"], "`train_file` should be a csv or a json file."
|
||||
if args.validation_file is not None:
|
||||
extension = args.validation_file.split(".")[-1]
|
||||
assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file."
|
||||
|
||||
if args.output_dir is not None:
|
||||
os.makedirs(args.output_dir, exist_ok=True)
|
||||
return args
|
||||
|
||||
|
||||
def main():
|
||||
# Parse the arguments
|
||||
args = parse_args()
|
||||
|
||||
# Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
|
||||
accelerator = Accelerator()
|
||||
|
||||
# Make one log on every process with the configuration for debugging.
|
||||
logging.basicConfig(
|
||||
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
|
||||
datefmt="%m/%d/%Y %H:%M:%S",
|
||||
level=logging.INFO,
|
||||
)
|
||||
logger.info(accelerator.state)
|
||||
|
||||
# Setup logging, we only want one process per machine to log things on the screen.
|
||||
# accelerator.is_local_main_process is only True for one process per machine.
|
||||
logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR)
|
||||
if accelerator.is_local_main_process:
|
||||
datasets.utils.logging.set_verbosity_warning()
|
||||
transformers.utils.logging.set_verbosity_info()
|
||||
else:
|
||||
datasets.utils.logging.set_verbosity_error()
|
||||
transformers.utils.logging.set_verbosity_error()
|
||||
|
||||
# If passed along, set the training seed now.
|
||||
if args.seed is not None:
|
||||
set_seed(args.seed)
|
||||
|
||||
# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
|
||||
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
|
||||
# (the dataset will be downloaded automatically from the datasets Hub).
|
||||
#
|
||||
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
|
||||
# 'text' is found. You can easily tweak this behavior (see below).
|
||||
#
|
||||
# In distributed training, the load_dataset function guarantee that only one local process can concurrently
|
||||
# download the dataset.
|
||||
if args.dataset_name is not None:
|
||||
# Downloading and loading a dataset from the hub.
|
||||
raw_datasets = load_dataset(args.dataset_name, args.dataset_config_name)
|
||||
else:
|
||||
data_files = {}
|
||||
if args.train_file is not None:
|
||||
data_files["train"] = args.train_file
|
||||
if args.validation_file is not None:
|
||||
data_files["validation"] = args.validation_file
|
||||
extension = args.train_file.split(".")[-1]
|
||||
raw_datasets = load_dataset(extension, data_files=data_files)
|
||||
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
|
||||
# https://huggingface.co/docs/datasets/loading_datasets.html.
|
||||
|
||||
# Load pretrained model and tokenizer
|
||||
#
|
||||
# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
|
||||
# download model & vocab.
|
||||
if args.config_name:
|
||||
config = AutoConfig.from_pretrained(args.model_name_or_path)
|
||||
elif args.model_name_or_path:
|
||||
config = AutoConfig.from_pretrained(args.model_name_or_path)
|
||||
else:
|
||||
config = CONFIG_MAPPING[args.model_type]()
|
||||
logger.warning("You are instantiating a new config instance from scratch.")
|
||||
|
||||
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 = AutoModelForSeq2SeqLM.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 = AutoModelForSeq2SeqLM.from_config(config)
|
||||
|
||||
model.resize_token_embeddings(len(tokenizer))
|
||||
|
||||
# Set decoder_start_token_id
|
||||
if model.config.decoder_start_token_id is None and isinstance(tokenizer, (MBartTokenizer, MBartTokenizerFast)):
|
||||
assert (
|
||||
args.target_lang is not None and args.source_lang is not None
|
||||
), "mBart requires --target_lang and --source_lang"
|
||||
if isinstance(tokenizer, MBartTokenizer):
|
||||
model.config.decoder_start_token_id = tokenizer.lang_code_to_id[args.target_lang]
|
||||
else:
|
||||
model.config.decoder_start_token_id = tokenizer.convert_tokens_to_ids(args.target_lang)
|
||||
|
||||
if model.config.decoder_start_token_id is None:
|
||||
raise ValueError("Make sure that `config.decoder_start_token_id` is correctly defined")
|
||||
|
||||
prefix = args.source_prefix if args.source_prefix is not None else ""
|
||||
|
||||
# Preprocessing the datasets.
|
||||
# First we tokenize all the texts.
|
||||
column_names = raw_datasets["train"].column_names
|
||||
|
||||
# For translation we set the codes of our source and target languages (only useful for mBART, the others will
|
||||
# ignore those attributes).
|
||||
if isinstance(tokenizer, (MBartTokenizer, MBartTokenizerFast)):
|
||||
if args.source_lang is not None:
|
||||
tokenizer.src_lang = args.source_lang
|
||||
if args.target_lang is not None:
|
||||
tokenizer.tgt_lang = args.target_lang
|
||||
|
||||
# Get the language codes for input/target.
|
||||
source_lang = args.source_lang.split("_")[0]
|
||||
target_lang = args.target_lang.split("_")[0]
|
||||
|
||||
padding = "max_length" if args.pad_to_max_length else False
|
||||
|
||||
# Temporarily set max_target_length for training.
|
||||
max_target_length = args.max_target_length
|
||||
padding = "max_length" if args.pad_to_max_length else False
|
||||
|
||||
def preprocess_function(examples):
|
||||
inputs = [ex[source_lang] for ex in examples["translation"]]
|
||||
targets = [ex[target_lang] for ex in examples["translation"]]
|
||||
inputs = [prefix + inp for inp in inputs]
|
||||
model_inputs = tokenizer(inputs, max_length=args.max_source_length, padding=padding, truncation=True)
|
||||
|
||||
# Setup the tokenizer for targets
|
||||
with tokenizer.as_target_tokenizer():
|
||||
labels = tokenizer(targets, max_length=max_target_length, padding=padding, truncation=True)
|
||||
|
||||
# If we are padding here, replace all tokenizer.pad_token_id in the labels by -100 when we want to ignore
|
||||
# padding in the loss.
|
||||
if padding == "max_length" and args.ignore_pad_token_for_loss:
|
||||
labels["input_ids"] = [
|
||||
[(l if l != tokenizer.pad_token_id else -100) for l in label] for label in labels["input_ids"]
|
||||
]
|
||||
|
||||
model_inputs["labels"] = labels["input_ids"]
|
||||
return model_inputs
|
||||
|
||||
processed_datasets = raw_datasets.map(
|
||||
preprocess_function,
|
||||
batched=True,
|
||||
num_proc=args.preprocessing_num_workers,
|
||||
remove_columns=column_names,
|
||||
load_from_cache_file=not args.overwrite_cache,
|
||||
)
|
||||
|
||||
train_dataset = processed_datasets["train"]
|
||||
eval_dataset = processed_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]}.")
|
||||
|
||||
# DataLoaders creation:
|
||||
label_pad_token_id = -100 if args.ignore_pad_token_for_loss else tokenizer.pad_token_id
|
||||
if args.pad_to_max_length:
|
||||
# If padding was already done ot max length, we use the default data collator that will just convert everything
|
||||
# to tensors.
|
||||
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 = DataCollatorForSeq2Seq(
|
||||
tokenizer,
|
||||
model=model,
|
||||
label_pad_token_id=label_pad_token_id,
|
||||
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 = 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,
|
||||
)
|
||||
|
||||
metric = load_metric("sacrebleu")
|
||||
|
||||
def postprocess_text(preds, labels):
|
||||
preds = [pred.strip() for pred in preds]
|
||||
labels = [[label.strip()] for label in labels]
|
||||
|
||||
return preds, labels
|
||||
|
||||
# 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()
|
||||
|
||||
if args.val_max_target_length is None:
|
||||
args.val_max_target_length = args.max_target_length
|
||||
|
||||
gen_kwargs = {
|
||||
"max_length": args.val_max_target_length if args is not None else config.max_length,
|
||||
"num_beams": args.num_beams,
|
||||
}
|
||||
for step, batch in enumerate(eval_dataloader):
|
||||
with torch.no_grad():
|
||||
generated_tokens = accelerator.unwrap_model(model).generate(
|
||||
batch["input_ids"],
|
||||
attention_mask=batch["attention_mask"],
|
||||
**gen_kwargs,
|
||||
)
|
||||
|
||||
generated_tokens = accelerator.pad_across_processes(
|
||||
generated_tokens, dim=1, pad_index=tokenizer.pad_token_id
|
||||
)
|
||||
labels = batch["labels"]
|
||||
if not args.pad_to_max_length:
|
||||
# If we did not pad to max length, we need to pad the labels too
|
||||
labels = accelerator.pad_across_processes(batch["labels"], dim=1, pad_index=tokenizer.pad_token_id)
|
||||
|
||||
generated_tokens = accelerator.gather(generated_tokens).cpu().numpy()
|
||||
labels = accelerator.gather(labels).cpu().numpy()
|
||||
|
||||
if args.ignore_pad_token_for_loss:
|
||||
# Replace -100 in the labels as we can't decode them.
|
||||
labels = np.where(labels != -100, labels, tokenizer.pad_token_id)
|
||||
|
||||
decoded_preds = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
|
||||
decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)
|
||||
|
||||
decoded_preds, decoded_labels = postprocess_text(decoded_preds, decoded_labels)
|
||||
|
||||
metric.add_batch(predictions=decoded_preds, references=decoded_labels)
|
||||
eval_metric = metric.compute()
|
||||
logger.info({"bleu": eval_metric["score"]})
|
||||
|
||||
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()
|
||||
Loading…
Reference in New Issue
Block a user