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Added earlystopping mechanism (OpenNMT#1389)
* Added earlystopping mechanism * Fixed earlystopping multi-gpu stoppage
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,194 @@ | ||
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| from enum import Enum | ||
| from onmt.utils.logging import logger | ||
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| class PatienceEnum(Enum): | ||
| IMPROVING = 0 | ||
| DECREASING = 1 | ||
| STOPPED = 2 | ||
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| class Scorer(object): | ||
| def __init__(self, best_score, name): | ||
| self.best_score = best_score | ||
| self.name = name | ||
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| def is_improving(self, stats): | ||
| raise NotImplementedError() | ||
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| def is_decreasing(self, stats): | ||
| raise NotImplementedError() | ||
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| def update(self, stats): | ||
| self.best_score = self._caller(stats) | ||
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| def __call__(self, stats, **kwargs): | ||
| return self._caller(stats) | ||
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| def _caller(self, stats): | ||
| raise NotImplementedError() | ||
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| class PPLScorer(Scorer): | ||
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| def __init__(self): | ||
| super(PPLScorer, self).__init__(float("inf"), "ppl") | ||
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| def is_improving(self, stats): | ||
| return stats.ppl() < self.best_score | ||
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| def is_decreasing(self, stats): | ||
| return stats.ppl() > self.best_score | ||
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| def _caller(self, stats): | ||
| return stats.ppl() | ||
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| class AccuracyScorer(Scorer): | ||
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| def __init__(self): | ||
| super(AccuracyScorer, self).__init__(float("-inf"), "acc") | ||
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| def is_improving(self, stats): | ||
| return stats.accuracy() > self.best_score | ||
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| def is_decreasing(self, stats): | ||
| return stats.accuracy() < self.best_score | ||
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| def _caller(self, stats): | ||
| return stats.accuracy() | ||
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| DEFAULT_SCORERS = [PPLScorer(), AccuracyScorer()] | ||
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| SCORER_BUILDER = { | ||
| "ppl": PPLScorer, | ||
| "accuracy": AccuracyScorer | ||
| } | ||
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| def scorers_from_opts(opt): | ||
| if opt.early_stopping_criteria is None: | ||
| return DEFAULT_SCORERS | ||
| else: | ||
| scorers = [] | ||
| for criterion in set(opt.early_stopping_criteria): | ||
| assert criterion in SCORER_BUILDER.keys(), \ | ||
| "Criterion {} not found".format(criterion) | ||
| scorers.append(SCORER_BUILDER[criterion]()) | ||
| return scorers | ||
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| class EarlyStopping(object): | ||
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| def __init__(self, tolerance, scorers=DEFAULT_SCORERS): | ||
| """ | ||
| Callable class to keep track of early stopping. | ||
| Args: | ||
| tolerance(int): number of validation steps without improving | ||
| scorer(fn): list of scorers to validate performance on dev | ||
| """ | ||
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| self.tolerance = tolerance | ||
| self.stalled_tolerance = self.tolerance | ||
| self.current_tolerance = self.tolerance | ||
| self.early_stopping_scorers = scorers | ||
| self.status = PatienceEnum.IMPROVING | ||
| self.current_step_best = 0 | ||
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| def __call__(self, valid_stats, step): | ||
| """ | ||
| Update the internal state of early stopping mechanism, whether to | ||
| continue training or stop the train procedure. | ||
| Checks whether the scores from all pre-chosen scorers improved. If | ||
| every metric improve, then the status is switched to improving and the | ||
| tolerance is reset. If every metric deteriorate, then the status is | ||
| switched to decreasing and the tolerance is also decreased; if the | ||
| tolerance reaches 0, then the status is changed to stopped. | ||
| Finally, if some improved and others not, then it's considered stalled; | ||
| after tolerance number of stalled, the status is switched to stopped. | ||
| :param valid_stats: Statistics of dev set | ||
| """ | ||
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| if self.status == PatienceEnum.STOPPED: | ||
| # Don't do anything | ||
| return | ||
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| if all([scorer.is_improving(valid_stats) for scorer | ||
| in self.early_stopping_scorers]): | ||
| self._update_increasing(valid_stats, step) | ||
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| elif all([scorer.is_decreasing(valid_stats) for scorer | ||
| in self.early_stopping_scorers]): | ||
| self._update_decreasing() | ||
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| else: | ||
| self._update_stalled() | ||
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| def _update_stalled(self): | ||
| self.stalled_tolerance -= 1 | ||
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| logger.info( | ||
| "Stalled patience: {}/{}".format(self.stalled_tolerance, | ||
| self.tolerance)) | ||
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| if self.stalled_tolerance == 0: | ||
| logger.info( | ||
| "Training finished after stalled validations. Early Stop!" | ||
| ) | ||
| self._log_best_step() | ||
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| self._decreasing_or_stopped_status_update(self.stalled_tolerance) | ||
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| def _update_increasing(self, valid_stats, step): | ||
| self.current_step_best = step | ||
| for scorer in self.early_stopping_scorers: | ||
| logger.info( | ||
| "Model is improving {}: {:g} --> {:g}.".format( | ||
| scorer.name, scorer.best_score, scorer(valid_stats)) | ||
| ) | ||
| # Update best score of each criteria | ||
| scorer.update(valid_stats) | ||
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| # Reset tolerance | ||
| self.current_tolerance = self.tolerance | ||
| self.stalled_tolerance = self.tolerance | ||
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| # Update current status | ||
| self.status = PatienceEnum.IMPROVING | ||
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| def _update_decreasing(self): | ||
| # Decrease tolerance | ||
| self.current_tolerance -= 1 | ||
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| # Log | ||
| logger.info( | ||
| "Decreasing patience: {}/{}".format(self.current_tolerance, | ||
| self.tolerance) | ||
| ) | ||
| # Log | ||
| if self.current_tolerance == 0: | ||
| logger.info("Training finished after not improving. Early Stop!") | ||
| self._log_best_step() | ||
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| self._decreasing_or_stopped_status_update(self.current_tolerance) | ||
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| def _log_best_step(self): | ||
| logger.info("Best model found at step {}".format( | ||
| self.current_step_best)) | ||
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| def _decreasing_or_stopped_status_update(self, tolerance): | ||
| self.status = PatienceEnum.DECREASING \ | ||
| if tolerance > 0 \ | ||
| else PatienceEnum.STOPPED | ||
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| def is_improving(self): | ||
| return self.status == PatienceEnum.IMPROVING | ||
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| def has_stopped(self): | ||
| return self.status == PatienceEnum.STOPPED |