Source code for orion.algo.asha.asha

Asynchronous Successive Halving Algorithm
from __future__ import annotations

import copy
import logging
from collections import defaultdict
from typing import Sequence

import numpy as np

from orion.algo.hyperband import (
from import Space
from orion.core.utils.flatten import flatten
from orion.core.worker.trial import Trial

logger = logging.getLogger(__name__)

Bad fidelity level {fidelity}. Should be in {budgets}.
Params: {params}

ASHA can only be used if there is one fidelity dimension.
For more information on the configuration and usage of ASHA, see

Cannot build budgets below max_resources;
(max: {}) - (min: {}) > (num_rungs: {})

[docs]def compute_budgets( min_resources: float, max_resources: float, reduction_factor: int, num_rungs: int, num_brackets: int, ): """Compute the budgets used for ASHA""" # Tracks state for new trial add if num_brackets > num_rungs: logger.warning( "The input num_brackets %i is larger than the number of rungs %i, " "set num_brackets as %i", num_brackets, num_rungs, num_rungs, ) num_brackets = num_rungs budgets = np.logspace( np.log(min_resources) / np.log(reduction_factor), np.log(max_resources) / np.log(reduction_factor), num_rungs, base=reduction_factor, ) budgets = (budgets + 0.5).astype(int) for i in range(num_rungs - 1): if budgets[i] >= budgets[i + 1]: budgets[i + 1] = budgets[i] + 1 if budgets[-1] > max_resources: raise ValueError(BUDGET_ERROR.format(min_resources, max_resources, num_rungs)) resources = [budgets[bracket_index:] for bracket_index in range(num_brackets)] budgets_lists: list[list[BudgetTuple]] = [] budgets_tab: dict[int, list[BudgetTuple]] = defaultdict(list) for bracket_ressources in resources: bracket_budgets: list[BudgetTuple] = [] for i, min_ressources in enumerate(bracket_ressources[::-1]): budget = BudgetTuple(reduction_factor**i, min_ressources) bracket_budgets.append(budget) budgets_tab[len(bracket_ressources) - i - 1].append(budget) budgets_lists.append(bracket_budgets[::-1]) display_budgets(budgets_tab, max_resources, reduction_factor) return list(budgets_lists)
[docs]class ASHA(Hyperband): """Asynchronous Successive Halving Algorithm `A simple and robust hyperparameter tuning algorithm with solid theoretical underpinnings that exploits parallelism and aggressive early-stopping.` For more information on the algorithm, see original paper at Li, Liam, et al. "Massively parallel hyperparameter tuning." arXiv preprint arXiv:1810.05934 (2018) Parameters ---------- space: `` Optimisation space with priors for each dimension. seed: None, int or sequence of int Seed for the random number generator used to sample new trials. Default: ``None`` num_rungs: int, optional Number of rungs for the largest bracket. If not defined, it will be equal to ``(base + 1)`` of the fidelity dimension. In the original paper, ``num_rungs == log(fidelity.high/fidelity.low) / log(fidelity.base) + 1``. Default: ``log(fidelity.high/fidelity.low) / log(fidelity.base) + 1`` num_brackets: int Using a grace period that is too small may bias ASHA too strongly towards fast converging trials that do not lead to best results at convergence (stagglers). To overcome this, you can increase the number of brackets, which increases the amount of resource required for optimisation but decreases the bias towards stragglers. Default: 1 repetitions: int Number of execution of ASHA. Default is np.inf which means to run ASHA until no new trials can be suggested. """ def __init__( self, space: Space, seed: int | Sequence[int] | None = None, num_rungs: int | None = None, num_brackets: int = 1, repetitions: int | float = np.inf, ): super().__init__(space, seed=seed, repetitions=repetitions) self.num_rungs = num_rungs self.num_brackets = num_brackets self._param_names += ["num_rungs", "num_brackets"] if self.reduction_factor < 2: raise AttributeError("Reduction factor for ASHA needs to be at least 2.") if num_rungs is None: num_rungs = int( np.log(self.max_resources / self.min_resources) / np.log(self.reduction_factor) + 1 ) self.num_rungs = num_rungs self.budgets = compute_budgets( self.min_resources, self.max_resources, self.reduction_factor, self.num_rungs, self.num_brackets, ) self.brackets = self.create_brackets() self.seed_rng(seed) # pylint: disable=missing-function-docstring def compute_bracket_idx(self, num: int) -> np.ndarray: # pylint: disable=invalid-name def assign_resources( n: int, remainings: np.ndarray, totals: np.ndarray ) -> np.ndarray: if n == 0 or remainings.sum() == 0: return remainings ratios = remainings / totals fractions = np.nan_to_num(ratios / ratios.sum(), nan=0) index = np.argmax(fractions) remainings[index] -= 1 n -= 1 if n > 0: return assign_resources(n, remainings, totals) return remainings assert self.brackets is not None remainings = np.asarray( [bracket.remainings for bracket in self.brackets], dtype=np.int64 ) totals = np.asarray( [bracket.rungs[0]["n_trials"] for bracket in self.brackets], dtype=np.int64 ) remainings_after = copy.deepcopy(remainings) assign_resources(num, remainings_after, totals) allocations = remainings - remainings_after return allocations def sample(self, num: int) -> list[Trial]: samples = [] bracket_nums = self.compute_bracket_idx(num) for idx, bracket_num in enumerate(bracket_nums): assert self.brackets is not None bracket = self.brackets[idx] bracket_samples = self.sample_from_bracket(bracket, bracket_num) self.register_samples(bracket, bracket_samples) samples += bracket_samples return samples
[docs] def suggest(self, num: int) -> list[Trial]: return super().suggest(num)
def create_bracket(self, budgets: list[BudgetTuple], iteration: int) -> ASHABracket: return ASHABracket(self, budgets, iteration)
[docs]class ASHABracket(HyperbandBracket[ASHA]): """Bracket of rungs for the algorithm ASHA. Parameters ---------- asha: `ASHA` algorithm The asha algorithm object which this bracket will be part of. budgets: list of tuple Each tuple gives the (n_trials, resource_budget) for the respective rung. repetition_id: int The id of hyperband execution this bracket belongs to """ def __init__( self, owner: ASHA, budgets: list[BudgetTuple], repetition_id: int, ): super().__init__(owner=owner, budgets=budgets, repetition_id=repetition_id)
[docs] def get_candidates(self, rung_id: int) -> list[Trial]: """Get a candidate for promotion Raises ------ TypeError If get_candidates is called before the entire rung is completed. """ rung_results = self.rungs[rung_id]["results"] next_rung = self.rungs[rung_id + 1]["results"] rung = list( sorted( ( (objective, trial) for objective, trial in rung_results.values() if objective is not None ), key=lambda item: item[0], ) ) k = len(rung) // self.owner.reduction_factor k = min(k, len(rung)) candidates: list[Trial] = [] for i in range(k): trial = rung[i][1] _id = self.owner.get_id(trial, ignore_fidelity=True) if _id not in next_rung: candidates.append(trial) return candidates
@property def is_filled(self) -> bool: """ASHA's first rung can always sample new trials""" return False
[docs] def is_ready(self, rung_id: int | None = None) -> bool: """ASHA's always ready for promotions""" return True
[docs] def promote(self, num: int) -> list[Trial]: """Promote the first candidate that is found and return it The rungs are iterated over in reversed order, so that high rungs are prioritised for promotions. When a candidate is promoted, the loop is broken and the method returns the promoted trial. .. note :: All trials are part of the rungs, for any state. Only completed trials are eligible for promotion, i.e., only completed trials can be part of top-k. Lookup for promotion in rung l + 1 contains trials of any status. """ if num < 1 or self.is_done: return [] candidates = [] for rung_id in range(len(self.rungs) - 2, -1, -1): for candidate in self.get_candidates(rung_id): # pylint: disable=logging-format-interpolation logger.debug( f"Promoting {candidate} from rung {rung_id} with fidelity " f"{flatten(candidate.params)[self.owner.fidelity_index]} to " f"rung {rung_id + 1} with fidelity {self.rungs[rung_id + 1]['resources']}" ) candidate = candidate.branch( status="new", params={ self.owner.fidelity_index: self.rungs[rung_id + 1]["resources"] }, ) if not self.owner.has_suggested(candidate): candidates.append(candidate) if len(candidates) >= num: return candidates return candidates