Add `examples/multiple-choice/run_swag_no_trainer.py` (#10934)
* Initial commit * Another bunch of updates * make style quliaty + delete debug arg from bash script * Use compue_metrics func * Do a few fixes * Add copyright * Fix typos
This commit is contained in:
Daniel Stancl
GitHub
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Коммит
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@ -14,7 +14,7 @@ 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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## Multiple Choice
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# Multiple Choice
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Based on the script [`run_swag.py`]().
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@ -41,6 +41,73 @@ eval_acc = 0.8338998300509847
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eval_loss = 0.44457291918821606
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```
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## PyTorch version, no Trainer
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Based on the script [run_ner_no_trainer.py](https://github.com/huggingface/transformers/blob/master/examples/multiple-choice/run_swag_no_trainer.py).
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Like `run_swag.py`, this script allows you to fine-tune any of the models on the [hub](https://huggingface.co/models) (as long as its architecture as a `ForMultipleChoice` version in the library) on
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the SWAG dataset or your own data in a csv or a JSON file. The main difference is that this
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script exposes the bare training loop, to allow you to quickly experiment and add any customization you would like.
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It offers less options than the script with `Trainer` (but you can easily change the options for the optimizer
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or the dataloaders directly in the script) but still run in a distributed setup, on TPU and supports mixed precision by
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the mean of the [🤗 `Accelerate`](https://github.com/huggingface/accelerate) library. You can use the script normally
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after installing it:
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```bash
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pip install accelerate
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```
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then
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```bash
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export DATASET_NAME=swag
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python run_swag_no_trainer.py \
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--model_name_or_path bert-base-cased \
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--dataset_name $DATASET_NAME \
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--max_seq_length 128 \
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--per_device_train_batch_size 32 \
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--learning_rate 2e-5 \
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--num_train_epochs 3 \
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--output_dir /tmp/$DATASET_NAME/
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```
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You can then use your usual launchers to run in it in a distributed environment, but the easiest way is to run
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```bash
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accelerate config
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```
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and reply to the questions asked. Then
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```bash
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accelerate test
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```
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that will check everything is ready for training. Finally, you can launch training with
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```bash
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export DATASET_NAME=swag
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accelerate launch run_swag_no_trainer.py \
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--model_name_or_path bert-base-cased \
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--dataset_name $DATASET_NAME \
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--max_seq_length 128 \
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--per_device_train_batch_size 32 \
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--learning_rate 2e-5 \
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--num_train_epochs 3 \
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--output_dir /tmp/$DATASET_NAME/
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```
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This command is the same and will work for:
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- a CPU-only setup
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- a setup with one GPU
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- a distributed training with several GPUs (single or multi node)
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- a training on TPUs
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Note that this library is in alpha release so your feedback is more than welcome if you encounter any problem using it.
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## Tensorflow
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@ -0,0 +1,19 @@
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# Copyright 2020 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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accelerate launch run_swag_no_trainer.py \
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--model_name_or_path bert-base-uncased \
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--dataset_name swag \
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--output_dir /tmp/test-swag-no-trainer \
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--pad_to_max_length
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@ -0,0 +1,488 @@
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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 multiple choice relying on the accelerate library without using a Trainer.
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"""
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# You can also adapt this script on your own multiple choice task. Pointers for this are left as comments.
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import argparse
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import logging
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import math
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import os
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import random
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from dataclasses import dataclass
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from typing import Optional, Union
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import datasets
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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 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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AutoModelForMultipleChoice,
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AutoTokenizer,
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PreTrainedTokenizerBase,
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SchedulerType,
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default_data_collator,
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get_scheduler,
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set_seed,
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)
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from transformers.file_utils import PaddingStrategy
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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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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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"--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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"--pad_to_max_length",
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action="store_true",
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help="If passed, pad all samples to `max_length`. Otherwise, dynamic padding is used.",
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)
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parser.add_argument(
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"--model_name_or_path",
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type=str,
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help="Path to pretrained model or model identifier from huggingface.co/models.",
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required=True,
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)
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parser.add_argument(
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"--config_name",
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type=str,
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default=None,
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help="Pretrained config name or path if not the same as model_name",
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)
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parser.add_argument(
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"--tokenizer_name",
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type=str,
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default=None,
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help="Pretrained tokenizer name or path if not the same as model_name",
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)
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parser.add_argument(
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"--use_slow_tokenizer",
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action="store_true",
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help="If passed, will use a slow tokenizer (not backed by the 🤗 Tokenizers library).",
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)
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parser.add_argument(
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"--per_device_train_batch_size",
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type=int,
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default=8,
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help="Batch size (per device) for the training dataloader.",
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)
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parser.add_argument(
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"--per_device_eval_batch_size",
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type=int,
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default=8,
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help="Batch size (per device) for the evaluation dataloader.",
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)
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parser.add_argument(
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"--learning_rate",
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type=float,
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default=5e-5,
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help="Initial learning rate (after the potential warmup period) to use.",
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)
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parser.add_argument("--weight_decay", type=float, default=0.0, help="Weight decay to use.")
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parser.add_argument("--num_train_epochs", type=int, default=3, help="Total number of training epochs to perform.")
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parser.add_argument(
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"--max_train_steps",
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type=int,
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default=None,
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help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
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)
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parser.add_argument(
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"--gradient_accumulation_steps",
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type=int,
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default=1,
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help="Number of updates steps to accumulate before performing a backward/update pass.",
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)
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parser.add_argument(
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"--lr_scheduler_type",
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type=SchedulerType,
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default="linear",
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help="The scheduler type to use.",
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choices=["linear", "cosine", "cosine_with_restarts", "polynomial", "constant", "constant_with_warmup"],
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)
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parser.add_argument(
|
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"--num_warmup_steps", type=int, default=0, help="Number of steps for the warmup in the lr scheduler."
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)
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parser.add_argument("--output_dir", type=str, default=None, help="Where to store the final model.")
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parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
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parser.add_argument(
|
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"--model_type",
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type=str,
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default=None,
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help="Model type to use if training from scratch.",
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choices=MODEL_TYPES,
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)
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parser.add_argument(
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"--debug",
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action="store_true",
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help="Activate debug mode and run training only with a subset of data.",
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)
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args = parser.parse_args()
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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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@dataclass
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class DataCollatorForMultipleChoice:
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"""
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Data collator that will dynamically pad the inputs for multiple choice received.
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Args:
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tokenizer (:class:`~transformers.PreTrainedTokenizer` or :class:`~transformers.PreTrainedTokenizerFast`):
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The tokenizer used for encoding the data.
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padding (:obj:`bool`, :obj:`str` or :class:`~transformers.file_utils.PaddingStrategy`, `optional`, defaults to :obj:`True`):
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Select a strategy to pad the returned sequences (according to the model's padding side and padding index)
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among:
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* :obj:`True` or :obj:`'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
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sequence if provided).
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* :obj:`'max_length'`: Pad to a maximum length specified with the argument :obj:`max_length` or to the
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maximum acceptable input length for the model if that argument is not provided.
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* :obj:`False` or :obj:`'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of
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different lengths).
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max_length (:obj:`int`, `optional`):
|
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Maximum length of the returned list and optionally padding length (see above).
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pad_to_multiple_of (:obj:`int`, `optional`):
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If set will pad the sequence to a multiple of the provided value.
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This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >=
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7.5 (Volta).
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"""
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tokenizer: PreTrainedTokenizerBase
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padding: Union[bool, str, PaddingStrategy] = True
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max_length: Optional[int] = None
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pad_to_multiple_of: Optional[int] = None
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def __call__(self, features):
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label_name = "label" if "label" in features[0].keys() else "labels"
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labels = [feature.pop(label_name) for feature in features]
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batch_size = len(features)
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num_choices = len(features[0]["input_ids"])
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flattened_features = [
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[{k: v[i] for k, v in feature.items()} for i in range(num_choices)] for feature in features
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]
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flattened_features = sum(flattened_features, [])
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batch = self.tokenizer.pad(
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flattened_features,
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padding=self.padding,
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max_length=self.max_length,
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pad_to_multiple_of=self.pad_to_multiple_of,
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return_tensors="pt",
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)
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# Un-flatten
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batch = {k: v.view(batch_size, num_choices, -1) for k, v in batch.items()}
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# Add back labels
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batch["labels"] = torch.tensor(labels, dtype=torch.int64)
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return batch
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def main():
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args = parse_args()
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|
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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()
|
||||
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)
|
||||
# Trim a number of training examples
|
||||
if args.debug:
|
||||
for split in raw_datasets.keys():
|
||||
raw_datasets[split] = raw_datasets[split].select(range(100))
|
||||
# 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.
|
||||
|
||||
if raw_datasets["train"] is not None:
|
||||
column_names = raw_datasets["train"].column_names
|
||||
else:
|
||||
column_names = raw_datasets["validation"].column_names
|
||||
|
||||
# When using your own dataset or a different dataset from swag, you will probably need to change this.
|
||||
ending_names = [f"ending{i}" for i in range(4)]
|
||||
context_name = "sent1"
|
||||
question_header_name = "sent2"
|
||||
label_column_name = "label" if "label" in column_names else "labels"
|
||||
|
||||
# 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 = AutoModelForMultipleChoice.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 = AutoModelForMultipleChoice.from_config(config)
|
||||
|
||||
model.resize_token_embeddings(len(tokenizer))
|
||||
|
||||
# Preprocessing the datasets.
|
||||
# First we tokenize all the texts.
|
||||
padding = "max_length" if args.pad_to_max_length else False
|
||||
|
||||
def preprocess_function(examples):
|
||||
first_sentences = [[context] * 4 for context in examples[context_name]]
|
||||
question_headers = examples[question_header_name]
|
||||
second_sentences = [
|
||||
[f"{header} {examples[end][i]}" for end in ending_names] for i, header in enumerate(question_headers)
|
||||
]
|
||||
labels = examples[label_column_name]
|
||||
|
||||
# Flatten out
|
||||
first_sentences = sum(first_sentences, [])
|
||||
second_sentences = sum(second_sentences, [])
|
||||
|
||||
# Tokenize
|
||||
tokenized_examples = tokenizer(
|
||||
first_sentences,
|
||||
second_sentences,
|
||||
max_length=args.max_length,
|
||||
padding=padding,
|
||||
truncation=True,
|
||||
)
|
||||
# Un-flatten
|
||||
tokenized_inputs = {k: [v[i : i + 4] for i in range(0, len(v), 4)] for k, v in tokenized_examples.items()}
|
||||
tokenized_inputs["labels"] = labels
|
||||
return tokenized_inputs
|
||||
|
||||
processed_datasets = raw_datasets.map(
|
||||
preprocess_function, batched=True, remove_columns=raw_datasets["train"].column_names
|
||||
)
|
||||
|
||||
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:
|
||||
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 = DataCollatorForMultipleChoice(
|
||||
tokenizer, pad_to_multiple_of=(8 if accelerator.use_fp16 else None)
|
||||
)
|
||||
|
||||
train_dataloader = DataLoader(
|
||||
train_dataset, shuffle=True, collate_fn=data_collator, batch_size=args.per_device_train_batch_size
|
||||
)
|
||||
eval_dataloader = DataLoader(eval_dataset, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size)
|
||||
|
||||
# Optimizer
|
||||
# Split weights in two groups, one with weight decay and the other not.
|
||||
no_decay = ["bias", "LayerNorm.weight"]
|
||||
optimizer_grouped_parameters = [
|
||||
{
|
||||
"params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],
|
||||
"weight_decay": args.weight_decay,
|
||||
},
|
||||
{
|
||||
"params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)],
|
||||
"weight_decay": 0.0,
|
||||
},
|
||||
]
|
||||
optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate)
|
||||
|
||||
# Use the device given by the `accelerator` object.
|
||||
device = accelerator.device
|
||||
model.to(device)
|
||||
|
||||
# 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,
|
||||
)
|
||||
|
||||
# Metrics
|
||||
metric = load_metric("accuracy")
|
||||
|
||||
# Train!
|
||||
total_batch_size = args.per_device_train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
|
||||
|
||||
logger.info("***** Running training *****")
|
||||
logger.info(f" Num examples = {len(train_dataset)}")
|
||||
logger.info(f" Num Epochs = {args.num_train_epochs}")
|
||||
logger.info(f" Instantaneous batch size per device = {args.per_device_train_batch_size}")
|
||||
logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
|
||||
logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}")
|
||||
logger.info(f" Total optimization steps = {args.max_train_steps}")
|
||||
# Only show the progress bar once on each machine.
|
||||
progress_bar = tqdm(range(args.max_train_steps), disable=not accelerator.is_local_main_process)
|
||||
completed_steps = 0
|
||||
|
||||
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()
|
||||
for step, batch in enumerate(eval_dataloader):
|
||||
with torch.no_grad():
|
||||
outputs = model(**batch)
|
||||
predictions = outputs.logits.argmax(dim=-1)
|
||||
metric.add_batch(
|
||||
predictions=accelerator.gather(predictions),
|
||||
references=accelerator.gather(batch["labels"]),
|
||||
)
|
||||
|
||||
eval_metric = metric.compute()
|
||||
accelerator.print(f"epoch {epoch}: {eval_metric}")
|
||||
|
||||
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()
|
||||
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