trainer_qa.py

# coding=utf-8
# Copyright 2020 The HuggingFace Team All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Question-Answering task와 관련된 'Trainer'의 subclass 코드 입니다.
"""

from transformers import Trainer, is_datasets_available, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput


if is_datasets_available():
    import datasets

if is_torch_tpu_available():
    import torch_xla.core.xla_model as xm
    import torch_xla.debug.metrics as met

# Huggingface의 Trainer를 상속받아 QuestionAnswering을 위한 Trainer를 생성합니다.
class QuestionAnsweringTrainer(Trainer):
    def __init__(self, *args, eval_examples=None, post_process_function=None, **kwargs):
        super().__init__(*args, **kwargs)
        self.eval_examples = eval_examples
        self.post_process_function = post_process_function

    def evaluate(self, eval_dataset=None, eval_examples=None, ignore_keys=None):
        eval_dataset = self.eval_dataset if eval_dataset is None else eval_dataset
        eval_dataloader = self.get_eval_dataloader(eval_dataset)
        eval_examples = self.eval_examples if eval_examples is None else eval_examples

        # 일시적으로 metric computation를 불가능하게 한 상태이며, 해당 코드에서는 loop 내에서 metric 계산을 수행합니다.
        compute_metrics = self.compute_metrics
        self.compute_metrics = None
        try:
            output = self.prediction_loop(
                eval_dataloader,
                description="Evaluation",
                # metric이 없으면 예측값을 모으는 이유가 없으므로 아래의 코드를 따르게 됩니다.
                # self.args.prediction_loss_only
                prediction_loss_only=True if compute_metrics is None else None,
                ignore_keys=ignore_keys,
            )
        finally:
            self.compute_metrics = compute_metrics

        if isinstance(eval_dataset, datasets.Dataset):
            eval_dataset.set_format(
                type=eval_dataset.format["type"],
                columns=list(eval_dataset.features.keys()),
            )

        if self.post_process_function is not None and self.compute_metrics is not None:
            eval_preds = self.post_process_function(
                eval_examples, eval_dataset, output.predictions, self.args
            )
            metrics = self.compute_metrics(eval_preds)

            self.log(metrics)
        else:
            metrics = {}

        if self.args.tpu_metrics_debug or self.args.debug:
            # tpu-comment: PyTorch/XLA에 대한 Logging debug metrics (compile, execute times, ops, etc.)
            xm.master_print(met.metrics_report())

        self.control = self.callback_handler.on_evaluate(
            self.args, self.state, self.control, metrics
        )
        return metrics

    def predict(self, test_dataset, test_examples, ignore_keys=None):
        test_dataloader = self.get_test_dataloader(test_dataset)

        # 일시적으로 metric computation를 불가능하게 한 상태이며, 해당 코드에서는 loop 내에서 metric 계산을 수행합니다.
        # evaluate 함수와 동일하게 구성되어있습니다
        compute_metrics = self.compute_metrics
        self.compute_metrics = None
        try:
            output = self.prediction_loop(
                test_dataloader,
                description="Evaluation",
                # metric이 없으면 예측값을 모으는 이유가 없으므로 아래의 코드를 따르게 됩니다.
                # self.args.prediction_loss_only
                prediction_loss_only=True if compute_metrics is None else None,
                ignore_keys=ignore_keys,
            )
        finally:
            self.compute_metrics = compute_metrics

        if self.post_process_function is None or self.compute_metrics is None:
            return output

        if isinstance(test_dataset, datasets.Dataset):
            test_dataset.set_format(
                type=test_dataset.format["type"],
                columns=list(test_dataset.features.keys()),
            )

        predictions = self.post_process_function(
            test_examples, test_dataset, output.predictions, self.args
        )
        return predictions