1037 lines
46 KiB
Python
Executable File
1037 lines
46 KiB
Python
Executable File
#!/usr/bin/env python
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# coding=utf-8
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# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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Fine-tuning a 🤗 Transformers model for question answering using 🤗 Accelerate.
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"""
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# You can also adapt this script on your own question answering 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 pathlib import Path
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import datasets
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import evaluate
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import numpy as np
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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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from utils_qa import postprocess_qa_predictions
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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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AutoModelForQuestionAnswering,
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AutoTokenizer,
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DataCollatorWithPadding,
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EvalPrediction,
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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 check_min_version, send_example_telemetry
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from transformers.utils.versions import require_version
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# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
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check_min_version("4.42.0.dev0")
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require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/question-answering/requirements.txt")
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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 save_prefixed_metrics(results, output_dir, file_name: str = "all_results.json", metric_key_prefix: str = "eval"):
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"""
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Save results while prefixing metric names.
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Args:
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results: (:obj:`dict`):
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A dictionary of results.
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output_dir: (:obj:`str`):
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An output directory.
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file_name: (:obj:`str`, `optional`, defaults to :obj:`all_results.json`):
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An output file name.
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metric_key_prefix: (:obj:`str`, `optional`, defaults to :obj:`eval`):
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A metric name prefix.
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"""
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# Prefix all keys with metric_key_prefix + '_'
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for key in list(results.keys()):
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if not key.startswith(f"{metric_key_prefix}_"):
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results[f"{metric_key_prefix}_{key}"] = results.pop(key)
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with open(os.path.join(output_dir, file_name), "w") as f:
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json.dump(results, f, indent=4)
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def parse_args():
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parser = argparse.ArgumentParser(description="Finetune a transformers model on a Question Answering 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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"--preprocessing_num_workers", type=int, default=1, help="A csv or a json file containing the training data."
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)
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parser.add_argument("--do_predict", action="store_true", help="To do prediction on the question answering model")
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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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"--test_file", type=str, default=None, help="A csv or a json file containing the Prediction 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=384,
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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_seq_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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"--doc_stride",
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type=int,
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default=128,
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help="When splitting up a long document into chunks how much stride to take between chunks.",
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)
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parser.add_argument(
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"--n_best_size",
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type=int,
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default=20,
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help="The total number of n-best predictions to generate when looking for an answer.",
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)
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parser.add_argument(
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"--null_score_diff_threshold",
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type=float,
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default=0.0,
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help=(
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"The threshold used to select the null answer: if the best answer has a score that is less than "
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"the score of the null answer minus this threshold, the null answer is selected for this example. "
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"Only useful when `version_2_with_negative=True`."
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),
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)
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parser.add_argument(
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"--version_2_with_negative",
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action="store_true",
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help="If true, some of the examples do not have an answer.",
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)
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parser.add_argument(
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"--max_answer_length",
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type=int,
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default=30,
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help=(
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"The maximum length of an answer that can be generated. This is needed because the start "
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"and end predictions are not conditioned on one another."
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),
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)
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parser.add_argument(
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"--max_train_samples",
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type=int,
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default=None,
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help=(
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"For debugging purposes or quicker training, truncate the number of training examples to this "
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"value if set."
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),
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)
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parser.add_argument(
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"--max_eval_samples",
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type=int,
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default=None,
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help=(
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"For debugging purposes or quicker training, truncate the number of evaluation examples to this "
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"value if set."
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),
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)
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parser.add_argument(
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"--overwrite_cache", action="store_true", help="Overwrite the cached training and evaluation sets"
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)
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parser.add_argument(
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"--max_predict_samples",
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type=int,
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default=None,
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help="For debugging purposes or quicker training, truncate the number of prediction examples to this",
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)
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parser.add_argument(
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"--model_type",
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type=str,
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default=None,
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help="Model type to use if training from scratch.",
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choices=MODEL_TYPES,
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)
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parser.add_argument("--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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# Sanity checks
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if (
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args.dataset_name is None
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and args.train_file is None
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and args.validation_file is None
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and args.test_file is None
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):
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raise ValueError("Need either a dataset name or a training/validation/test 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.test_file is not None:
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extension = args.test_file.split(".")[-1]
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assert extension in ["csv", "json"], "`test_file` should be a csv or a json file."
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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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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_qa_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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if args.test_file is not None:
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data_files["test"] = args.test_file
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extension = args.test_file.split(".")[-1]
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raw_datasets = load_dataset(extension, data_files=data_files, field="data")
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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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# Load pretrained model and tokenizer
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#
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# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
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# download model & vocab.
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if args.config_name:
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config = AutoConfig.from_pretrained(args.config_name, 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=True, 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=True, 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 = AutoModelForQuestionAnswering.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 = AutoModelForQuestionAnswering.from_config(config, trust_remote_code=args.trust_remote_code)
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# Preprocessing the datasets.
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# Preprocessing is slightly different for training and evaluation.
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column_names = raw_datasets["train"].column_names
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question_column_name = "question" if "question" in column_names else column_names[0]
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context_column_name = "context" if "context" in column_names else column_names[1]
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answer_column_name = "answers" if "answers" in column_names else column_names[2]
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# Padding side determines if we do (question|context) or (context|question).
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pad_on_right = tokenizer.padding_side == "right"
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if args.max_seq_length > tokenizer.model_max_length:
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logger.warning(
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f"The max_seq_length passed ({args.max_seq_length}) is larger than the maximum length for the "
|
|
f"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}."
|
|
)
|
|
|
|
max_seq_length = min(args.max_seq_length, tokenizer.model_max_length)
|
|
|
|
# Training preprocessing
|
|
def prepare_train_features(examples):
|
|
# Some of the questions have lots of whitespace on the left, which is not useful and will make the
|
|
# truncation of the context fail (the tokenized question will take a lots of space). So we remove that
|
|
# left whitespace
|
|
examples[question_column_name] = [q.lstrip() for q in examples[question_column_name]]
|
|
|
|
# Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
|
|
# in one example possible giving several features when a context is long, each of those features having a
|
|
# context that overlaps a bit the context of the previous feature.
|
|
tokenized_examples = tokenizer(
|
|
examples[question_column_name if pad_on_right else context_column_name],
|
|
examples[context_column_name if pad_on_right else question_column_name],
|
|
truncation="only_second" if pad_on_right else "only_first",
|
|
max_length=max_seq_length,
|
|
stride=args.doc_stride,
|
|
return_overflowing_tokens=True,
|
|
return_offsets_mapping=True,
|
|
padding="max_length" if args.pad_to_max_length else False,
|
|
)
|
|
|
|
# Since one example might give us several features if it has a long context, we need a map from a feature to
|
|
# its corresponding example. This key gives us just that.
|
|
sample_mapping = tokenized_examples.pop("overflow_to_sample_mapping")
|
|
# The offset mappings will give us a map from token to character position in the original context. This will
|
|
# help us compute the start_positions and end_positions.
|
|
offset_mapping = tokenized_examples.pop("offset_mapping")
|
|
|
|
# Let's label those examples!
|
|
tokenized_examples["start_positions"] = []
|
|
tokenized_examples["end_positions"] = []
|
|
|
|
for i, offsets in enumerate(offset_mapping):
|
|
# We will label impossible answers with the index of the CLS token.
|
|
input_ids = tokenized_examples["input_ids"][i]
|
|
if tokenizer.cls_token_id in input_ids:
|
|
cls_index = input_ids.index(tokenizer.cls_token_id)
|
|
elif tokenizer.bos_token_id in input_ids:
|
|
cls_index = input_ids.index(tokenizer.bos_token_id)
|
|
else:
|
|
cls_index = 0
|
|
|
|
# Grab the sequence corresponding to that example (to know what is the context and what is the question).
|
|
sequence_ids = tokenized_examples.sequence_ids(i)
|
|
|
|
# One example can give several spans, this is the index of the example containing this span of text.
|
|
sample_index = sample_mapping[i]
|
|
answers = examples[answer_column_name][sample_index]
|
|
# If no answers are given, set the cls_index as answer.
|
|
if len(answers["answer_start"]) == 0:
|
|
tokenized_examples["start_positions"].append(cls_index)
|
|
tokenized_examples["end_positions"].append(cls_index)
|
|
else:
|
|
# Start/end character index of the answer in the text.
|
|
start_char = answers["answer_start"][0]
|
|
end_char = start_char + len(answers["text"][0])
|
|
|
|
# Start token index of the current span in the text.
|
|
token_start_index = 0
|
|
while sequence_ids[token_start_index] != (1 if pad_on_right else 0):
|
|
token_start_index += 1
|
|
|
|
# End token index of the current span in the text.
|
|
token_end_index = len(input_ids) - 1
|
|
while sequence_ids[token_end_index] != (1 if pad_on_right else 0):
|
|
token_end_index -= 1
|
|
|
|
# Detect if the answer is out of the span (in which case this feature is labeled with the CLS index).
|
|
if not (offsets[token_start_index][0] <= start_char and offsets[token_end_index][1] >= end_char):
|
|
tokenized_examples["start_positions"].append(cls_index)
|
|
tokenized_examples["end_positions"].append(cls_index)
|
|
else:
|
|
# Otherwise move the token_start_index and token_end_index to the two ends of the answer.
|
|
# Note: we could go after the last offset if the answer is the last word (edge case).
|
|
while token_start_index < len(offsets) and offsets[token_start_index][0] <= start_char:
|
|
token_start_index += 1
|
|
tokenized_examples["start_positions"].append(token_start_index - 1)
|
|
while offsets[token_end_index][1] >= end_char:
|
|
token_end_index -= 1
|
|
tokenized_examples["end_positions"].append(token_end_index + 1)
|
|
|
|
return tokenized_examples
|
|
|
|
if "train" not in raw_datasets:
|
|
raise ValueError("--do_train requires a train dataset")
|
|
train_dataset = raw_datasets["train"]
|
|
if args.max_train_samples is not None:
|
|
# We will select sample from whole data if argument is specified
|
|
train_dataset = train_dataset.select(range(args.max_train_samples))
|
|
|
|
# Create train feature from dataset
|
|
with accelerator.main_process_first():
|
|
train_dataset = train_dataset.map(
|
|
prepare_train_features,
|
|
batched=True,
|
|
num_proc=args.preprocessing_num_workers,
|
|
remove_columns=column_names,
|
|
load_from_cache_file=not args.overwrite_cache,
|
|
desc="Running tokenizer on train dataset",
|
|
)
|
|
if args.max_train_samples is not None:
|
|
# Number of samples might increase during Feature Creation, We select only specified max samples
|
|
train_dataset = train_dataset.select(range(args.max_train_samples))
|
|
|
|
# Validation preprocessing
|
|
def prepare_validation_features(examples):
|
|
# Some of the questions have lots of whitespace on the left, which is not useful and will make the
|
|
# truncation of the context fail (the tokenized question will take a lots of space). So we remove that
|
|
# left whitespace
|
|
examples[question_column_name] = [q.lstrip() for q in examples[question_column_name]]
|
|
|
|
# Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
|
|
# in one example possible giving several features when a context is long, each of those features having a
|
|
# context that overlaps a bit the context of the previous feature.
|
|
tokenized_examples = tokenizer(
|
|
examples[question_column_name if pad_on_right else context_column_name],
|
|
examples[context_column_name if pad_on_right else question_column_name],
|
|
truncation="only_second" if pad_on_right else "only_first",
|
|
max_length=max_seq_length,
|
|
stride=args.doc_stride,
|
|
return_overflowing_tokens=True,
|
|
return_offsets_mapping=True,
|
|
padding="max_length" if args.pad_to_max_length else False,
|
|
)
|
|
|
|
# Since one example might give us several features if it has a long context, we need a map from a feature to
|
|
# its corresponding example. This key gives us just that.
|
|
sample_mapping = tokenized_examples.pop("overflow_to_sample_mapping")
|
|
|
|
# For evaluation, we will need to convert our predictions to substrings of the context, so we keep the
|
|
# corresponding example_id and we will store the offset mappings.
|
|
tokenized_examples["example_id"] = []
|
|
|
|
for i in range(len(tokenized_examples["input_ids"])):
|
|
# Grab the sequence corresponding to that example (to know what is the context and what is the question).
|
|
sequence_ids = tokenized_examples.sequence_ids(i)
|
|
context_index = 1 if pad_on_right else 0
|
|
|
|
# One example can give several spans, this is the index of the example containing this span of text.
|
|
sample_index = sample_mapping[i]
|
|
tokenized_examples["example_id"].append(examples["id"][sample_index])
|
|
|
|
# Set to None the offset_mapping that are not part of the context so it's easy to determine if a token
|
|
# position is part of the context or not.
|
|
tokenized_examples["offset_mapping"][i] = [
|
|
(o if sequence_ids[k] == context_index else None)
|
|
for k, o in enumerate(tokenized_examples["offset_mapping"][i])
|
|
]
|
|
|
|
return tokenized_examples
|
|
|
|
if "validation" not in raw_datasets:
|
|
raise ValueError("--do_eval requires a validation dataset")
|
|
eval_examples = raw_datasets["validation"]
|
|
if args.max_eval_samples is not None:
|
|
# We will select sample from whole data
|
|
eval_examples = eval_examples.select(range(args.max_eval_samples))
|
|
# Validation Feature Creation
|
|
with accelerator.main_process_first():
|
|
eval_dataset = eval_examples.map(
|
|
prepare_validation_features,
|
|
batched=True,
|
|
num_proc=args.preprocessing_num_workers,
|
|
remove_columns=column_names,
|
|
load_from_cache_file=not args.overwrite_cache,
|
|
desc="Running tokenizer on validation dataset",
|
|
)
|
|
|
|
if args.max_eval_samples is not None:
|
|
# During Feature creation dataset samples might increase, we will select required samples again
|
|
eval_dataset = eval_dataset.select(range(args.max_eval_samples))
|
|
|
|
if args.do_predict:
|
|
if "test" not in raw_datasets:
|
|
raise ValueError("--do_predict requires a test dataset")
|
|
predict_examples = raw_datasets["test"]
|
|
if args.max_predict_samples is not None:
|
|
# We will select sample from whole data
|
|
predict_examples = predict_examples.select(range(args.max_predict_samples))
|
|
# Predict Feature Creation
|
|
with accelerator.main_process_first():
|
|
predict_dataset = predict_examples.map(
|
|
prepare_validation_features,
|
|
batched=True,
|
|
num_proc=args.preprocessing_num_workers,
|
|
remove_columns=column_names,
|
|
load_from_cache_file=not args.overwrite_cache,
|
|
desc="Running tokenizer on prediction dataset",
|
|
)
|
|
if args.max_predict_samples is not None:
|
|
# During Feature creation dataset samples might increase, we will select required samples again
|
|
predict_dataset = predict_dataset.select(range(args.max_predict_samples))
|
|
|
|
# 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:
|
|
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 = DataCollatorWithPadding(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_dataset_for_model = eval_dataset.remove_columns(["example_id", "offset_mapping"])
|
|
eval_dataloader = DataLoader(
|
|
eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
|
|
)
|
|
|
|
if args.do_predict:
|
|
predict_dataset_for_model = predict_dataset.remove_columns(["example_id", "offset_mapping"])
|
|
predict_dataloader = DataLoader(
|
|
predict_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
|
|
)
|
|
|
|
# Post-processing:
|
|
def post_processing_function(examples, features, predictions, stage="eval"):
|
|
# Post-processing: we match the start logits and end logits to answers in the original context.
|
|
predictions = postprocess_qa_predictions(
|
|
examples=examples,
|
|
features=features,
|
|
predictions=predictions,
|
|
version_2_with_negative=args.version_2_with_negative,
|
|
n_best_size=args.n_best_size,
|
|
max_answer_length=args.max_answer_length,
|
|
null_score_diff_threshold=args.null_score_diff_threshold,
|
|
output_dir=args.output_dir,
|
|
prefix=stage,
|
|
)
|
|
# Format the result to the format the metric expects.
|
|
if args.version_2_with_negative:
|
|
formatted_predictions = [
|
|
{"id": k, "prediction_text": v, "no_answer_probability": 0.0} for k, v in predictions.items()
|
|
]
|
|
else:
|
|
formatted_predictions = [{"id": k, "prediction_text": v} for k, v in predictions.items()]
|
|
|
|
references = [{"id": ex["id"], "answers": ex[answer_column_name]} for ex in examples]
|
|
return EvalPrediction(predictions=formatted_predictions, label_ids=references)
|
|
|
|
metric = evaluate.load("squad_v2" if args.version_2_with_negative else "squad")
|
|
|
|
# Create and fill numpy array of size len_of_validation_data * max_length_of_output_tensor
|
|
def create_and_fill_np_array(start_or_end_logits, dataset, max_len):
|
|
"""
|
|
Create and fill numpy array of size len_of_validation_data * max_length_of_output_tensor
|
|
|
|
Args:
|
|
start_or_end_logits(:obj:`tensor`):
|
|
This is the output predictions of the model. We can only enter either start or end logits.
|
|
eval_dataset: Evaluation dataset
|
|
max_len(:obj:`int`):
|
|
The maximum length of the output tensor. ( See the model.eval() part for more details )
|
|
"""
|
|
|
|
step = 0
|
|
# create a numpy array and fill it with -100.
|
|
logits_concat = np.full((len(dataset), max_len), -100, dtype=np.float64)
|
|
# Now since we have create an array now we will populate it with the outputs gathered using accelerator.gather_for_metrics
|
|
for i, output_logit in enumerate(start_or_end_logits): # populate columns
|
|
# We have to fill it such that we have to take the whole tensor and replace it on the newly created array
|
|
# And after every iteration we have to change the step
|
|
|
|
batch_size = output_logit.shape[0]
|
|
cols = output_logit.shape[1]
|
|
|
|
if step + batch_size < len(dataset):
|
|
logits_concat[step : step + batch_size, :cols] = output_logit
|
|
else:
|
|
logits_concat[step:, :cols] = output_logit[: len(dataset) - step]
|
|
|
|
step += batch_size
|
|
|
|
return logits_concat
|
|
|
|
# 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)
|
|
|
|
# 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("qa_no_trainer", experiment_config)
|
|
|
|
# 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
|
|
|
|
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.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,
|
|
)
|
|
|
|
# Evaluation
|
|
logger.info("***** Running Evaluation *****")
|
|
logger.info(f" Num examples = {len(eval_dataset)}")
|
|
logger.info(f" Batch size = {args.per_device_eval_batch_size}")
|
|
|
|
all_start_logits = []
|
|
all_end_logits = []
|
|
|
|
model.eval()
|
|
|
|
for step, batch in enumerate(eval_dataloader):
|
|
with torch.no_grad():
|
|
outputs = model(**batch)
|
|
start_logits = outputs.start_logits
|
|
end_logits = outputs.end_logits
|
|
|
|
if not args.pad_to_max_length: # necessary to pad predictions and labels for being gathered
|
|
start_logits = accelerator.pad_across_processes(start_logits, dim=1, pad_index=-100)
|
|
end_logits = accelerator.pad_across_processes(end_logits, dim=1, pad_index=-100)
|
|
|
|
all_start_logits.append(accelerator.gather_for_metrics(start_logits).cpu().numpy())
|
|
all_end_logits.append(accelerator.gather_for_metrics(end_logits).cpu().numpy())
|
|
|
|
max_len = max([x.shape[1] for x in all_start_logits]) # Get the max_length of the tensor
|
|
|
|
# concatenate the numpy array
|
|
start_logits_concat = create_and_fill_np_array(all_start_logits, eval_dataset, max_len)
|
|
end_logits_concat = create_and_fill_np_array(all_end_logits, eval_dataset, max_len)
|
|
|
|
# delete the list of numpy arrays
|
|
del all_start_logits
|
|
del all_end_logits
|
|
|
|
outputs_numpy = (start_logits_concat, end_logits_concat)
|
|
prediction = post_processing_function(eval_examples, eval_dataset, outputs_numpy)
|
|
eval_metric = metric.compute(predictions=prediction.predictions, references=prediction.label_ids)
|
|
logger.info(f"Evaluation metrics: {eval_metric}")
|
|
|
|
# Prediction
|
|
if args.do_predict:
|
|
logger.info("***** Running Prediction *****")
|
|
logger.info(f" Num examples = {len(predict_dataset)}")
|
|
logger.info(f" Batch size = {args.per_device_eval_batch_size}")
|
|
|
|
all_start_logits = []
|
|
all_end_logits = []
|
|
|
|
model.eval()
|
|
|
|
for step, batch in enumerate(predict_dataloader):
|
|
with torch.no_grad():
|
|
outputs = model(**batch)
|
|
start_logits = outputs.start_logits
|
|
end_logits = outputs.end_logits
|
|
|
|
if not args.pad_to_max_length: # necessary to pad predictions and labels for being gathered
|
|
start_logits = accelerator.pad_across_processes(start_logits, dim=1, pad_index=-100)
|
|
end_logits = accelerator.pad_across_processes(end_logits, dim=1, pad_index=-100)
|
|
|
|
all_start_logits.append(accelerator.gather_for_metrics(start_logits).cpu().numpy())
|
|
all_end_logits.append(accelerator.gather_for_metrics(end_logits).cpu().numpy())
|
|
|
|
max_len = max([x.shape[1] for x in all_start_logits]) # Get the max_length of the tensor
|
|
# concatenate the numpy array
|
|
start_logits_concat = create_and_fill_np_array(all_start_logits, predict_dataset, max_len)
|
|
end_logits_concat = create_and_fill_np_array(all_end_logits, predict_dataset, max_len)
|
|
|
|
# delete the list of numpy arrays
|
|
del all_start_logits
|
|
del all_end_logits
|
|
|
|
outputs_numpy = (start_logits_concat, end_logits_concat)
|
|
prediction = post_processing_function(predict_examples, predict_dataset, outputs_numpy)
|
|
predict_metric = metric.compute(predictions=prediction.predictions, references=prediction.label_ids)
|
|
logger.info(f"Predict metrics: {predict_metric}")
|
|
|
|
if args.with_tracking:
|
|
log = {
|
|
"squad_v2" if args.version_2_with_negative else "squad": eval_metric,
|
|
"train_loss": total_loss.item() / len(train_dataloader),
|
|
"epoch": epoch,
|
|
"step": completed_steps,
|
|
}
|
|
if args.do_predict:
|
|
log["squad_v2_predict" if args.version_2_with_negative else "squad_predict"] = predict_metric
|
|
|
|
accelerator.log(log, step=completed_steps)
|
|
|
|
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,
|
|
)
|
|
logger.info(json.dumps(eval_metric, indent=4))
|
|
save_prefixed_metrics(eval_metric, args.output_dir)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|