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| # 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. | |
| """ | |
| A subclass of `Trainer` specific to Question-Answering tasks | |
| """ | |
| import math | |
| import time | |
| from transformers import Trainer, is_torch_tpu_available | |
| from transformers.trainer_utils import PredictionOutput, speed_metrics | |
| if is_torch_tpu_available(check_device=False): | |
| import torch_xla.core.xla_model as xm | |
| import torch_xla.debug.metrics as met | |
| 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, metric_key_prefix: str = "eval"): | |
| 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 | |
| # Temporarily disable metric computation, we will do it in the loop here. | |
| compute_metrics = self.compute_metrics | |
| self.compute_metrics = None | |
| eval_loop = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop | |
| start_time = time.time() | |
| try: | |
| output = eval_loop( | |
| eval_dataloader, | |
| description="Evaluation", | |
| # No point gathering the predictions if there are no metrics, otherwise we defer to | |
| # self.args.prediction_loss_only | |
| prediction_loss_only=True if compute_metrics is None else None, | |
| ignore_keys=ignore_keys, | |
| metric_key_prefix=metric_key_prefix, | |
| ) | |
| finally: | |
| self.compute_metrics = compute_metrics | |
| total_batch_size = self.args.eval_batch_size * self.args.world_size | |
| if f"{metric_key_prefix}_jit_compilation_time" in output.metrics: | |
| start_time += output.metrics[f"{metric_key_prefix}_jit_compilation_time"] | |
| output.metrics.update( | |
| speed_metrics( | |
| metric_key_prefix, | |
| start_time, | |
| num_samples=output.num_samples, | |
| num_steps=math.ceil(output.num_samples / total_batch_size), | |
| ) | |
| ) | |
| if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save: | |
| # Only the main node write the results by default | |
| eval_preds = self.post_process_function(eval_examples, eval_dataset, output.predictions) | |
| metrics = self.compute_metrics(eval_preds) | |
| # Prefix all keys with metric_key_prefix + '_' | |
| for key in list(metrics.keys()): | |
| if not key.startswith(f"{metric_key_prefix}_"): | |
| metrics[f"{metric_key_prefix}_{key}"] = metrics.pop(key) | |
| metrics.update(output.metrics) | |
| else: | |
| metrics = output.metrics | |
| if self.args.should_log: | |
| # Only the main node log the results by default | |
| self.log(metrics) | |
| if self.args.tpu_metrics_debug or self.args.debug: | |
| # tpu-comment: Logging debug metrics for PyTorch/XLA (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, predict_dataset, predict_examples, ignore_keys=None, metric_key_prefix: str = "test"): | |
| predict_dataloader = self.get_test_dataloader(predict_dataset) | |
| # Temporarily disable metric computation, we will do it in the loop here. | |
| compute_metrics = self.compute_metrics | |
| self.compute_metrics = None | |
| eval_loop = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop | |
| start_time = time.time() | |
| try: | |
| output = eval_loop( | |
| predict_dataloader, | |
| description="Prediction", | |
| # No point gathering the predictions if there are no metrics, otherwise we defer to | |
| # self.args.prediction_loss_only | |
| prediction_loss_only=True if compute_metrics is None else None, | |
| ignore_keys=ignore_keys, | |
| metric_key_prefix=metric_key_prefix, | |
| ) | |
| finally: | |
| self.compute_metrics = compute_metrics | |
| total_batch_size = self.args.eval_batch_size * self.args.world_size | |
| if f"{metric_key_prefix}_jit_compilation_time" in output.metrics: | |
| start_time += output.metrics[f"{metric_key_prefix}_jit_compilation_time"] | |
| output.metrics.update( | |
| speed_metrics( | |
| metric_key_prefix, | |
| start_time, | |
| num_samples=output.num_samples, | |
| num_steps=math.ceil(output.num_samples / total_batch_size), | |
| ) | |
| ) | |
| if self.post_process_function is None or self.compute_metrics is None: | |
| return output | |
| predictions = self.post_process_function(predict_examples, predict_dataset, output.predictions, "predict") | |
| metrics = self.compute_metrics(predictions) | |
| # Prefix all keys with metric_key_prefix + '_' | |
| for key in list(metrics.keys()): | |
| if not key.startswith(f"{metric_key_prefix}_"): | |
| metrics[f"{metric_key_prefix}_{key}"] = metrics.pop(key) | |
| metrics.update(output.metrics) | |
| return PredictionOutput(predictions=predictions.predictions, label_ids=predictions.label_ids, metrics=metrics) | |