hpo.py

# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.

"""Wrappers of HPO tuners as NAS strategy."""

import logging
import time
from typing import Optional

from nni.nas import Sampler, submit_models, query_available_resources, is_stopped_exec, budget_exhausted
from .base import BaseStrategy

_logger = logging.getLogger(__name__)


class TPESampler(Sampler):
    def __init__(self, optimize_mode='minimize'):
        # Move import here to eliminate some warning messages about dill.
        from nni.algorithms.hpo.hyperopt_tuner import HyperoptTuner

        self.tpe_tuner = HyperoptTuner('tpe', optimize_mode)
        self.cur_sample: Optional[dict] = None
        self.index: Optional[int] = None
        self.total_parameters = {}

    def update_sample_space(self, sample_space):
        search_space = {}
        for i, each in enumerate(sample_space):
            search_space[str(i)] = {'_type': 'choice', '_value': each}
        self.tpe_tuner.update_search_space(search_space)

    def generate_samples(self, model_id):
        self.cur_sample = self.tpe_tuner.generate_parameters(model_id)
        self.total_parameters[model_id] = self.cur_sample
        self.index = 0

    def receive_result(self, model_id, result):
        self.tpe_tuner.receive_trial_result(model_id, self.total_parameters[model_id], result)

    def choice(self, candidates, mutator, model, index):
        assert isinstance(self.index, int) and isinstance(self.cur_sample, dict)
        chosen = self.cur_sample[str(self.index)]
        self.index += 1
        return chosen


class TPE(BaseStrategy):
    """
    The Tree-structured Parzen Estimator (TPE) is a sequential model-based optimization (SMBO) approach.

    Find the details in
    `Algorithms for Hyper-Parameter Optimization <https://papers.nips.cc/paper/2011/file/86e8f7ab32cfd12577bc2619bc635690-Paper.pdf>`__.

    SMBO methods sequentially construct models to approximate the performance of hyperparameters based on historical measurements,
    and then subsequently choose new hyperparameters to test based on this model.
    """

    def __init__(self):
        self.tpe_sampler = TPESampler()
        self.model_id = 0
        self.running_models = {}

    def run(self, base_model, applied_mutators):
        sample_space = []
        new_model = base_model
        for mutator in applied_mutators:
            recorded_candidates, new_model = mutator.dry_run(new_model)
            sample_space.extend(recorded_candidates)
        self.tpe_sampler.update_sample_space(sample_space)

        _logger.info('TPE strategy has been started.')
        while not budget_exhausted():
            avail_resource = query_available_resources()
            if avail_resource > 0:
                model = base_model
                _logger.debug('New model created. Applied mutators: %s', str(applied_mutators))
                self.tpe_sampler.generate_samples(self.model_id)
                for mutator in applied_mutators:
                    mutator.bind_sampler(self.tpe_sampler)
                    model = mutator.apply(model)
                # run models
                submit_models(model)
                self.running_models[self.model_id] = model
                self.model_id += 1
            else:
                time.sleep(2)

            _logger.debug('num of running models: %d', len(self.running_models))
            to_be_deleted = []
            for _id, _model in self.running_models.items():
                if is_stopped_exec(_model):
                    if _model.metric is not None:
                        self.tpe_sampler.receive_result(_id, _model.metric)
                        _logger.debug('tpe receive results: %d, %s', _id, _model.metric)
                    to_be_deleted.append(_id)
            for _id in to_be_deleted:
                del self.running_models[_id]


# alias for backward compatibility
TPEStrategy = TPE