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https://github.com/huggingface/transformers.git
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4f24058c58
* Update postporcessing accordingly to use SQuAD metric. * Update assets accordingly based on SQuAD metrics. * Fix function naming error.
121 lines
5.2 KiB
Python
121 lines
5.2 KiB
Python
# coding=utf-8
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# Copyright 2021 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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"""
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A subclass of `Trainer` specific to Question-Answering tasks
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"""
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from typing import Dict, List, Optional
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from torch.utils.data import Dataset
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from transformers import Seq2SeqTrainer, is_torch_tpu_available
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from transformers.trainer_utils import PredictionOutput
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if is_torch_tpu_available():
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import torch_xla.core.xla_model as xm
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import torch_xla.debug.metrics as met
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class QuestionAnsweringSeq2SeqTrainer(Seq2SeqTrainer):
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def __init__(self, *args, eval_examples=None, post_process_function=None, **kwargs):
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super().__init__(*args, **kwargs)
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self.eval_examples = eval_examples
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self.post_process_function = post_process_function
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# def evaluate(self, eval_dataset=None, eval_examples=None, ignore_keys=None, metric_key_prefix: str = "eval"):
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def evaluate(
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self,
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eval_dataset: Optional[Dataset] = None,
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eval_examples=None,
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ignore_keys: Optional[List[str]] = None,
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metric_key_prefix: str = "eval",
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max_length: Optional[int] = None,
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num_beams: Optional[int] = None,
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) -> Dict[str, float]:
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self._max_length = max_length if max_length is not None else self.args.generation_max_length
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self._num_beams = num_beams if num_beams is not None else self.args.generation_num_beams
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eval_dataset = self.eval_dataset if eval_dataset is None else eval_dataset
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eval_dataloader = self.get_eval_dataloader(eval_dataset)
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eval_examples = self.eval_examples if eval_examples is None else eval_examples
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# Temporarily disable metric computation, we will do it in the loop here.
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compute_metrics = self.compute_metrics
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self.compute_metrics = None
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eval_loop = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
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try:
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output = eval_loop(
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eval_dataloader,
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description="Evaluation",
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# No point gathering the predictions if there are no metrics, otherwise we defer to
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# self.args.prediction_loss_only
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prediction_loss_only=True if compute_metrics is None else None,
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ignore_keys=ignore_keys,
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)
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finally:
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self.compute_metrics = compute_metrics
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if self.post_process_function is not None and self.compute_metrics is not None:
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eval_preds = self.post_process_function(eval_examples, eval_dataset, output)
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metrics = self.compute_metrics(eval_preds)
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# Prefix all keys with metric_key_prefix + '_'
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for key in list(metrics.keys()):
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if not key.startswith(f"{metric_key_prefix}_"):
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metrics[f"{metric_key_prefix}_{key}"] = metrics.pop(key)
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self.log(metrics)
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else:
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metrics = {}
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if self.args.tpu_metrics_debug or self.args.debug:
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# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
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xm.master_print(met.metrics_report())
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self.control = self.callback_handler.on_evaluate(self.args, self.state, self.control, metrics)
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return metrics
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def predict(self, predict_dataset, predict_examples, ignore_keys=None, metric_key_prefix: str = "test"):
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predict_dataloader = self.get_test_dataloader(predict_dataset)
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# Temporarily disable metric computation, we will do it in the loop here.
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compute_metrics = self.compute_metrics
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self.compute_metrics = None
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eval_loop = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
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try:
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output = eval_loop(
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predict_dataloader,
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description="Prediction",
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# No point gathering the predictions if there are no metrics, otherwise we defer to
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# self.args.prediction_loss_only
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prediction_loss_only=True if compute_metrics is None else None,
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ignore_keys=ignore_keys,
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)
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finally:
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self.compute_metrics = compute_metrics
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if self.post_process_function is None or self.compute_metrics is None:
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return output
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predictions = self.post_process_function(predict_examples, predict_dataset, output.predictions, "predict")
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metrics = self.compute_metrics(predictions)
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# Prefix all keys with metric_key_prefix + '_'
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for key in list(metrics.keys()):
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if not key.startswith(f"{metric_key_prefix}_"):
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metrics[f"{metric_key_prefix}_{key}"] = metrics.pop(key)
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return PredictionOutput(predictions=predictions.predictions, label_ids=predictions.label_ids, metrics=metrics)
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