run_cls.py

# encoding=utf-8
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import os
import collections
from functools import partial

import paddle
from paddlenlp.trainer import PdArgumentParser, TrainingArguments
from paddlenlp.trainer import get_last_checkpoint
from paddlenlp.transformers import AutoTokenizer, AutoModelForSequenceClassification
from paddlenlp.utils.log import logger
from paddle.metric import Accuracy
from datasets import load_dataset, load_metric
import datasets
from data_collator import DataCollator

from finetune_args import DataArguments, ModelArguments
from utils import PreProcessor, PostProcessor, get_label_ld
from layout_trainer import LayoutTrainer


def main():
    parser = PdArgumentParser(
        (ModelArguments, DataArguments, TrainingArguments))
    model_args, data_args, training_args = parser.parse_args_into_dataclasses()
    training_args.print_config(model_args, "Model")
    training_args.print_config(data_args, "Data")

    paddle.set_device(training_args.device)

    # Detecting last checkpoint.
    last_checkpoint = None
    if os.path.isdir(
            training_args.output_dir
    ) and training_args.do_train and not training_args.overwrite_output_dir:
        last_checkpoint = get_last_checkpoint(training_args.output_dir)
        if last_checkpoint is None and len(os.listdir(
                training_args.output_dir)) > 0:
            raise ValueError(
                f"Output directory ({training_args.output_dir}) already exists and is not empty. "
                "Use --overwrite_output_dir to overcome.")
        elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
            logger.info(
                f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
                "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
            )

    train_ds, dev_ds, test_ds = load_dataset(
        data_args.dataset_name, split=["train", "validation", "test"])

    if training_args.do_train:
        column_names = train_ds.column_names
    elif training_args.do_eval:
        column_names = dev_ds.column_names
    elif training_args.do_predict:
        column_names = test_ds.column_names
    else:
        logger.info(
            "There is nothing to do. Please pass `do_train`, `do_eval` and/or `do_predict`."
        )
        raise NotImplementedError

    label_list, label_to_id = get_label_ld(train_ds["qas"], scheme="cls")
    num_labels = len(label_list)

    # Load Model and Tokenizer
    tokenizer = AutoTokenizer.from_pretrained(model_args.model_name_or_path)
    model = AutoModelForSequenceClassification.from_pretrained(
        model_args.model_name_or_path, num_classes=num_labels)
    model.config['has_visual_segment_embedding'] = False

    preprocessor = PreProcessor()
    postprocessor = PostProcessor()
    training_args.label_names = ["labels"]
    max_seq_length = min(data_args.max_seq_length, tokenizer.model_max_length)

    preprocess_func = partial(
        preprocessor.preprocess_cls,
        tokenizer=tokenizer,
        max_seq_length=max_seq_length,
        doc_stride=data_args.doc_stride,
        label_dict=label_to_id,
        max_size=data_args.target_size,
        target_size=data_args.target_size,
        use_segment_box=data_args.use_segment_box,
        preprocessing_num_workers=data_args.preprocessing_num_workers)
    preprocess_func_for_valid = preprocess_func

    postprocess_func = partial(postprocessor.postprocess_cls,
                               label_list=label_list,
                               tokenizer=tokenizer)

    # Dataset pre-process
    if training_args.do_train:
        if data_args.train_nshard > 1:
            logger.info(
                f"spliting train dataset into {data_args.train_nshard} shard")
            train_shards = []
            for idx in range(data_args.train_nshard):
                train_shards.append(
                    train_ds.shard(
                        num_shards=data_args.train_nshard, index=idx).map(
                            preprocess_func,
                            batched=True,
                            remove_columns=column_names,
                            num_proc=data_args.preprocessing_num_workers,
                            load_from_cache_file=not data_args.overwrite_cache,
                        ))
            train_dataset = datasets.concatenate_datasets(train_shards)
        else:
            train_dataset = train_ds.map(
                preprocess_func,
                batched=True,
                remove_columns=column_names,
                num_proc=data_args.preprocessing_num_workers,
                load_from_cache_file=not data_args.overwrite_cache,
            )
    if training_args.do_eval:
        eval_dataset = dev_ds.map(
            preprocess_func_for_valid,
            batched=True,
            remove_columns=column_names,
            num_proc=data_args.preprocessing_num_workers,
            load_from_cache_file=not data_args.overwrite_cache,
        )
    if training_args.do_predict:
        test_dataset = test_ds.map(
            preprocess_func_for_valid,
            batched=True,
            remove_columns=column_names,
            num_proc=data_args.preprocessing_num_workers,
            load_from_cache_file=not data_args.overwrite_cache,
        )

    # Data collator
    data_collator = DataCollator(tokenizer,
                                 padding="max_length",
                                 label_pad_token_id=-100,
                                 max_length=max_seq_length,
                                 return_tensors="pd")

    def compute_metrics(eval_preds):
        preds = paddle.to_tensor(eval_preds.predictions)
        labels = paddle.to_tensor(eval_preds.label_ids)

        metric = Accuracy()
        metric.reset()
        correct = preds == labels
        correct = paddle.cast(paddle.unsqueeze(correct, axis=-1),
                              dtype='float32')

        metric.update(correct)
        accu = metric.accumulate()
        metric.reset()
        return {"acc": accu}

    trainer = LayoutTrainer(
        model=model,
        args=training_args,
        data_collator=data_collator,
        train_dataset=train_dataset if training_args.do_train else None,
        eval_dataset=eval_dataset if training_args.do_eval else None,
        eval_examples=dev_ds,
        tokenizer=tokenizer,
        compute_metrics=compute_metrics,
        post_process_function=postprocess_func)

    checkpoint = None
    if training_args.resume_from_checkpoint is not None:
        checkpoint = training_args.resume_from_checkpoint
    elif last_checkpoint is not None:
        checkpoint = last_checkpoint

    # Training
    if training_args.do_train:
        train_result = trainer.train(resume_from_checkpoint=checkpoint)
        metrics = train_result.metrics
        trainer.save_model()

        max_train_samples = (data_args.max_train_samples
                             if data_args.max_train_samples is not None else
                             len(train_dataset))
        metrics["train_samples"] = min(max_train_samples, len(train_dataset))

        trainer.log_metrics("train", metrics)
        trainer.save_metrics("train", metrics)
        trainer.save_state()

    # Evaluate and tests model
    if training_args.do_eval:
        eval_metrics = trainer.evaluate()

        max_val_samples = (data_args.max_val_samples
                           if data_args.max_val_samples is not None else
                           len(eval_dataset))
        metrics["eval_samples"] = min(max_val_samples, len(eval_dataset))

        trainer.log_metrics("eval", eval_metrics)
        trainer.save_metrics("eval", metrics)

    if training_args.do_predict:
        postprocessor.examples_cache = collections.defaultdict(list)
        postprocessor.features_cache = collections.defaultdict(list)
        metrics = trainer.predict(test_dataset, test_ds)
        trainer.log_metrics("test", metrics)
        trainer.save_metrics("test", metrics)


if __name__ == "__main__":
    main()