Source code for nni.nas.strategy.evolution

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

import collections
import dataclasses
import logging
import random
import time

from nni.nas.execution import query_available_resources, submit_models
from nni.nas.execution.common import ModelStatus
from .base import BaseStrategy
from .utils import dry_run_for_search_space, get_targeted_model, filter_model

_logger = logging.getLogger(__name__)

class Individual:
    A class that represents an individual.
    Holds two attributes, where ``x`` is the model and ``y`` is the metric (e.g., accuracy).
    x: dict
    y: float

[docs]class RegularizedEvolution(BaseStrategy): """ Algorithm for regularized evolution (i.e. aging evolution). Follows "Algorithm 1" in Real et al. "Regularized Evolution for Image Classifier Architecture Search". Parameters ---------- optimize_mode : str Can be one of "maximize" and "minimize". Default: maximize. population_size : int The number of individuals to keep in the population. Default: 100. cycles : int The number of cycles (trials) the algorithm should run for. Default: 20000. sample_size : int The number of individuals that should participate in each tournament. Default: 25. mutation_prob : float Probability that mutation happens in each dim. Default: 0.05 on_failure : str Can be one of "ignore" and "worst". If "ignore", simply give up the model and find a new one. If "worst", mark the model as -inf (if maximize, inf if minimize), so that the algorithm "learns" to avoid such model. Default: ignore. model_filter: Callable[[Model], bool] Feed the model and return a bool. This will filter the models in search space and select which to submit. """ def __init__(self, optimize_mode='maximize', population_size=100, sample_size=25, cycles=20000, mutation_prob=0.05, on_failure='ignore', model_filter=None): assert optimize_mode in ['maximize', 'minimize'] assert on_failure in ['ignore', 'worst'] assert sample_size < population_size self.optimize_mode = optimize_mode self.population_size = population_size self.sample_size = sample_size self.cycles = cycles self.mutation_prob = mutation_prob self.on_failure = on_failure self._worst = float('-inf') if self.optimize_mode == 'maximize' else float('inf') self._success_count = 0 self._population = collections.deque() self._running_models = [] self._polling_interval = 2. self.filter = model_filter def random(self, search_space): return {k: random.choice(v) for k, v in search_space.items()} def mutate(self, parent, search_space): child = {} for k, v in parent.items(): if random.uniform(0, 1) < self.mutation_prob: # NOTE: we do not exclude the original choice here for simplicity, # which is slightly different from the original paper. child[k] = random.choice(search_space[k]) else: child[k] = v return child def best_parent(self): samples = [p for p in self._population] # copy population random.shuffle(samples) samples = list(samples)[:self.sample_size] if self.optimize_mode == 'maximize': parent = max(samples, key=lambda sample: sample.y) else: parent = min(samples, key=lambda sample: sample.y) return parent.x def run(self, base_model, applied_mutators): search_space = dry_run_for_search_space(base_model, applied_mutators) # Run the first population regardless concurrency'Initializing the first population.') while len(self._population) + len(self._running_models) <= self.population_size: # try to submit new models while len(self._population) + len(self._running_models) < self.population_size: config = self.random(search_space) self._submit_config(config, base_model, applied_mutators) # collect results self._move_succeeded_models_to_population() self._remove_failed_models_from_running_list() time.sleep(self._polling_interval) if len(self._population) >= self.population_size: break # Resource-aware mutation of models'Running mutations.') while self._success_count + len(self._running_models) <= self.cycles: # try to submit new models while query_available_resources() > 0 and self._success_count + len(self._running_models) < self.cycles: config = self.mutate(self.best_parent(), search_space) self._submit_config(config, base_model, applied_mutators) # collect results self._move_succeeded_models_to_population() self._remove_failed_models_from_running_list() time.sleep(self._polling_interval) if self._success_count >= self.cycles: break def _submit_config(self, config, base_model, mutators): _logger.debug('Model submitted to running queue: %s', config) model = get_targeted_model(base_model, mutators, config) if not filter_model(self.filter, model): if self.on_failure == "worst": model.status = ModelStatus.Failed self._running_models.append((config, model)) else: submit_models(model) self._running_models.append((config, model)) return model def _move_succeeded_models_to_population(self): completed_indices = [] for i, (config, model) in enumerate(self._running_models): metric = None if self.on_failure == 'worst' and model.status == ModelStatus.Failed: metric = self._worst elif model.status == ModelStatus.Trained: metric = model.metric if metric is not None: individual = Individual(config, metric) _logger.debug('Individual created: %s', str(individual)) self._population.append(individual) if len(self._population) > self.population_size: self._population.popleft() completed_indices.append(i) for i in completed_indices[::-1]: # delete from end to start so that the index number will not be affected. self._success_count += 1 self._running_models.pop(i) def _remove_failed_models_from_running_list(self): # This is only done when on_failure policy is set to "ignore". # Otherwise, failed models will be treated as inf when processed. if self.on_failure == 'ignore': number_of_failed_models = len([g for g in self._running_models if g[1].status == ModelStatus.Failed]) self._running_models = [g for g in self._running_models if g[1].status != ModelStatus.Failed] if number_of_failed_models > 0:'%d failed models are ignored. Will retry.', number_of_failed_models)