# Copyright 2020 The Ray Authors. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # http://www.apache.org/licenses/LICENSE-2.0 # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # This source file is adapted here because ray does not fully support Windows. # Copyright (c) Microsoft Corporation. import time import functools import warnings import copy import logging from typing import Any, Dict, Optional, Union, List, Tuple, Callable import pickle from .variant_generator import parse_spec_vars from ..tune.sample import ( Categorical, Domain, Float, Integer, LogUniform, Quantized, Uniform, ) from ..tune.trial import flatten_dict, unflatten_dict logger = logging.getLogger(__name__) UNRESOLVED_SEARCH_SPACE = str( "You passed a `{par}` parameter to {cls} that contained unresolved search " "space definitions. {cls} should however be instantiated with fully " "configured search spaces only. To use Ray Tune's automatic search space " "conversion, pass the space definition as part of the `config` argument " "to `tune.run()` instead." ) UNDEFINED_SEARCH_SPACE = str( "Trying to sample a configuration from {cls}, but no search " "space has been defined. Either pass the `{space}` argument when " "instantiating the search algorithm, or pass a `config` to " "`tune.run()`." ) UNDEFINED_METRIC_MODE = str( "Trying to sample a configuration from {cls}, but the `metric` " "({metric}) or `mode` ({mode}) parameters have not been set. " "Either pass these arguments when instantiating the search algorithm, " "or pass them to `tune.run()`." ) class Searcher: """Abstract class for wrapping suggesting algorithms. Custom algorithms can extend this class easily by overriding the `suggest` method provide generated parameters for the trials. Any subclass that implements ``__init__`` must also call the constructor of this class: ``super(Subclass, self).__init__(...)``. To track suggestions and their corresponding evaluations, the method `suggest` will be passed a trial_id, which will be used in subsequent notifications. Not all implementations support multi objectives. Args: metric (str or list): The training result objective value attribute. If list then list of training result objective value attributes mode (str or list): If string One of {min, max}. If list then list of max and min, determines whether objective is minimizing or maximizing the metric attribute. Must match type of metric. ```python class ExampleSearch(Searcher): def __init__(self, metric="mean_loss", mode="min", **kwargs): super(ExampleSearch, self).__init__( metric=metric, mode=mode, **kwargs) self.optimizer = Optimizer() self.configurations = {} def suggest(self, trial_id): configuration = self.optimizer.query() self.configurations[trial_id] = configuration def on_trial_complete(self, trial_id, result, **kwargs): configuration = self.configurations[trial_id] if result and self.metric in result: self.optimizer.update(configuration, result[self.metric]) tune.run(trainable_function, search_alg=ExampleSearch()) ``` """ FINISHED = "FINISHED" CKPT_FILE_TMPL = "searcher-state-{}.pkl" def __init__( self, metric: Optional[str] = None, mode: Optional[str] = None, max_concurrent: Optional[int] = None, use_early_stopped_trials: Optional[bool] = None, ): self._metric = metric self._mode = mode if not mode or not metric: # Early return to avoid assertions return assert isinstance( metric, type(mode) ), "metric and mode must be of the same type" if isinstance(mode, str): assert mode in ["min", "max"], "if `mode` is a str must be 'min' or 'max'!" elif isinstance(mode, list): assert len(mode) == len(metric), "Metric and mode must be the same length" assert all( mod in ["min", "max", "obs"] for mod in mode ), "All of mode must be 'min' or 'max' or 'obs'!" else: raise ValueError("Mode most either be a list or string") def set_search_properties( self, metric: Optional[str], mode: Optional[str], config: Dict ) -> bool: """Pass search properties to searcher. This method acts as an alternative to instantiating search algorithms with their own specific search spaces. Instead they can accept a Tune config through this method. A searcher should return ``True`` if setting the config was successful, or ``False`` if it was unsuccessful, e.g. when the search space has already been set. Args: metric (str): Metric to optimize mode (str): One of ["min", "max"]. Direction to optimize. config (dict): Tune config dict. """ return False def on_trial_result(self, trial_id: str, result: Dict): """Optional notification for result during training. Note that by default, the result dict may include NaNs or may not include the optimization metric. It is up to the subclass implementation to preprocess the result to avoid breaking the optimization process. Args: trial_id (str): A unique string ID for the trial. result (dict): Dictionary of metrics for current training progress. Note that the result dict may include NaNs or may not include the optimization metric. It is up to the subclass implementation to preprocess the result to avoid breaking the optimization process. """ pass @property def metric(self) -> str: """The training result objective value attribute.""" return self._metric @property def mode(self) -> str: """Specifies if minimizing or maximizing the metric.""" return self._mode class ConcurrencyLimiter(Searcher): """A wrapper algorithm for limiting the number of concurrent trials. Args: searcher (Searcher): Searcher object that the ConcurrencyLimiter will manage. max_concurrent (int): Maximum concurrent samples from the underlying searcher. batch (bool): Whether to wait for all concurrent samples to finish before updating the underlying searcher. Example: ```python from ray.tune.suggest import ConcurrencyLimiter search_alg = HyperOptSearch(metric="accuracy") search_alg = ConcurrencyLimiter(search_alg, max_concurrent=2) tune.run(trainable, search_alg=search_alg) ``` """ def __init__(self, searcher: Searcher, max_concurrent: int, batch: bool = False): assert type(max_concurrent) is int and max_concurrent > 0 self.searcher = searcher self.max_concurrent = max_concurrent self.batch = batch self.live_trials = set() self.cached_results = {} super(ConcurrencyLimiter, self).__init__( metric=self.searcher.metric, mode=self.searcher.mode ) def suggest(self, trial_id: str) -> Optional[Dict]: assert ( trial_id not in self.live_trials ), f"Trial ID {trial_id} must be unique: already found in set." if len(self.live_trials) >= self.max_concurrent: logger.debug( f"Not providing a suggestion for {trial_id} due to " "concurrency limit: %s/%s.", len(self.live_trials), self.max_concurrent, ) return suggestion = self.searcher.suggest(trial_id) if suggestion not in (None, Searcher.FINISHED): self.live_trials.add(trial_id) return suggestion def on_trial_complete( self, trial_id: str, result: Optional[Dict] = None, error: bool = False ): if trial_id not in self.live_trials: return elif self.batch: self.cached_results[trial_id] = (result, error) if len(self.cached_results) == self.max_concurrent: # Update the underlying searcher once the # full batch is completed. for trial_id, (result, error) in self.cached_results.items(): self.searcher.on_trial_complete( trial_id, result=result, error=error ) self.live_trials.remove(trial_id) self.cached_results = {} else: return else: self.searcher.on_trial_complete(trial_id, result=result, error=error) self.live_trials.remove(trial_id) def get_state(self) -> Dict: state = self.__dict__.copy() del state["searcher"] return copy.deepcopy(state) def set_state(self, state: Dict): self.__dict__.update(state) def save(self, checkpoint_path: str): self.searcher.save(checkpoint_path) def restore(self, checkpoint_path: str): self.searcher.restore(checkpoint_path) def on_pause(self, trial_id: str): self.searcher.on_pause(trial_id) def on_unpause(self, trial_id: str): self.searcher.on_unpause(trial_id) def set_search_properties( self, metric: Optional[str], mode: Optional[str], config: Dict ) -> bool: return self.searcher.set_search_properties(metric, mode, config) try: import optuna as ot from optuna.distributions import BaseDistribution as OptunaDistribution from optuna.samplers import BaseSampler from optuna.trial import TrialState as OptunaTrialState from optuna.trial import Trial as OptunaTrial except ImportError: ot = None OptunaDistribution = None BaseSampler = None OptunaTrialState = None OptunaTrial = None # (Optional) Default (anonymous) metric when using tune.report(x) DEFAULT_METRIC = "_metric" # (Auto-filled) The index of this training iteration. TRAINING_ITERATION = "training_iteration" # print a warning if define by run function takes longer than this to execute DEFINE_BY_RUN_WARN_THRESHOLD_S = 1 # 1 is arbitrary def validate_warmstart( parameter_names: List[str], points_to_evaluate: List[Union[List, Dict]], evaluated_rewards: List, validate_point_name_lengths: bool = True, ): """Generic validation of a Searcher's warm start functionality. Raises exceptions in case of type and length mismatches between parameters. If ``validate_point_name_lengths`` is False, the equality of lengths between ``points_to_evaluate`` and ``parameter_names`` will not be validated. """ if points_to_evaluate: if not isinstance(points_to_evaluate, list): raise TypeError( "points_to_evaluate expected to be a list, got {}.".format( type(points_to_evaluate) ) ) for point in points_to_evaluate: if not isinstance(point, (dict, list)): raise TypeError( f"points_to_evaluate expected to include list or dict, " f"got {point}." ) if validate_point_name_lengths and (not len(point) == len(parameter_names)): raise ValueError( "Dim of point {}".format(point) + " and parameter_names {}".format(parameter_names) + " do not match." ) if points_to_evaluate and evaluated_rewards: if not isinstance(evaluated_rewards, list): raise TypeError( "evaluated_rewards expected to be a list, got {}.".format( type(evaluated_rewards) ) ) if not len(evaluated_rewards) == len(points_to_evaluate): raise ValueError( "Dim of evaluated_rewards {}".format(evaluated_rewards) + " and points_to_evaluate {}".format(points_to_evaluate) + " do not match." ) class _OptunaTrialSuggestCaptor: """Utility to capture returned values from Optuna's suggest_ methods. This will wrap around the ``optuna.Trial` object and decorate all `suggest_` callables with a function capturing the returned value, which will be saved in the ``captured_values`` dict. """ def __init__(self, ot_trial: OptunaTrial) -> None: self.ot_trial = ot_trial self.captured_values: Dict[str, Any] = {} def _get_wrapper(self, func: Callable) -> Callable: @functools.wraps(func) def wrapper(*args, **kwargs): # name is always the first arg for suggest_ methods name = kwargs.get("name", args[0]) ret = func(*args, **kwargs) self.captured_values[name] = ret return ret return wrapper def __getattr__(self, item_name: str) -> Any: item = getattr(self.ot_trial, item_name) if item_name.startswith("suggest_") and callable(item): return self._get_wrapper(item) return item class OptunaSearch(Searcher): """A wrapper around Optuna to provide trial suggestions. [Optuna](https://optuna.org/) is a hyperparameter optimization library. In contrast to other libraries, it employs define-by-run style hyperparameter definitions. This Searcher is a thin wrapper around Optuna's search algorithms. You can pass any Optuna sampler, which will be used to generate hyperparameter suggestions. Args: space (dict|Callable): Hyperparameter search space definition for Optuna's sampler. This can be either a class `dict` with parameter names as keys and ``optuna.distributions`` as values, or a Callable - in which case, it should be a define-by-run function using ``optuna.trial`` to obtain the hyperparameter values. The function should return either a class `dict` of constant values with names as keys, or None. For more information, see [tutorial](https://optuna.readthedocs.io/en/stable/tutorial/10_key_features/002_configurations.html). Warning - No actual computation should take place in the define-by-run function. Instead, put the training logic inside the function or class trainable passed to tune.run. metric (str): The training result objective value attribute. If None but a mode was passed, the anonymous metric `_metric` will be used per default. mode (str): One of {min, max}. Determines whether objective is minimizing or maximizing the metric attribute. points_to_evaluate (list): Initial parameter suggestions to be run first. This is for when you already have some good parameters you want to run first to help the algorithm make better suggestions for future parameters. Needs to be a list of dicts containing the configurations. sampler (optuna.samplers.BaseSampler): Optuna sampler used to draw hyperparameter configurations. Defaults to ``TPESampler``. seed (int): Seed to initialize sampler with. This parameter is only used when ``sampler=None``. In all other cases, the sampler you pass should be initialized with the seed already. evaluated_rewards (list): If you have previously evaluated the parameters passed in as points_to_evaluate you can avoid re-running those trials by passing in the reward attributes as a list so the optimiser can be told the results without needing to re-compute the trial. Must be the same length as points_to_evaluate. Tune automatically converts search spaces to Optuna's format: ````python from ray.tune.suggest.optuna import OptunaSearch config = { "a": tune.uniform(6, 8), "b": tune.loguniform(1e-4, 1e-2)} optuna_search = OptunaSearch(metric="loss", mode="min") tune.run(trainable, config=config, search_alg=optuna_search) ```` If you would like to pass the search space manually, the code would look like this: ```python from ray.tune.suggest.optuna import OptunaSearch import optuna config = { "a": optuna.distributions.UniformDistribution(6, 8), "b": optuna.distributions.LogUniformDistribution(1e-4, 1e-2)} optuna_search = OptunaSearch(space,metric="loss",mode="min") tune.run(trainable, search_alg=optuna_search) # Equivalent Optuna define-by-run function approach: def define_search_space(trial: optuna.Trial): trial.suggest_float("a", 6, 8) trial.suggest_float("b", 1e-4, 1e-2, log=True) # training logic goes into trainable, this is just # for search space definition optuna_search = OptunaSearch( define_search_space, metric="loss", mode="min") tune.run(trainable, search_alg=optuna_search) .. versionadded:: 0.8.8 ``` """ def __init__( self, space: Optional[ Union[ Dict[str, "OptunaDistribution"], List[Tuple], Callable[["OptunaTrial"], Optional[Dict[str, Any]]], ] ] = None, metric: Optional[str] = None, mode: Optional[str] = None, points_to_evaluate: Optional[List[Dict]] = None, sampler: Optional["BaseSampler"] = None, seed: Optional[int] = None, evaluated_rewards: Optional[List] = None, ): assert ot is not None, "Optuna must be installed! Run `pip install optuna`." super(OptunaSearch, self).__init__( metric=metric, mode=mode, max_concurrent=None, use_early_stopped_trials=None ) if isinstance(space, dict) and space: resolved_vars, domain_vars, grid_vars = parse_spec_vars(space) if domain_vars or grid_vars: logger.warning( UNRESOLVED_SEARCH_SPACE.format(par="space", cls=type(self).__name__) ) space = self.convert_search_space(space) else: # Flatten to support nested dicts space = flatten_dict(space, "/") self._space = space self._points_to_evaluate = points_to_evaluate or [] self._evaluated_rewards = evaluated_rewards self._study_name = "optuna" # Fixed study name for in-memory storage if sampler and seed: logger.warning( "You passed an initialized sampler to `OptunaSearch`. The " "`seed` parameter has to be passed to the sampler directly " "and will be ignored." ) self._sampler = sampler or ot.samplers.TPESampler(seed=seed) assert isinstance(self._sampler, BaseSampler), ( "You can only pass an instance of `optuna.samplers.BaseSampler` " "as a sampler to `OptunaSearcher`." ) self._ot_trials = {} self._ot_study = None if self._space: self._setup_study(mode) def _setup_study(self, mode: str): if self._metric is None and self._mode: # If only a mode was passed, use anonymous metric self._metric = DEFAULT_METRIC pruner = ot.pruners.NopPruner() storage = ot.storages.InMemoryStorage() self._ot_study = ot.study.create_study( storage=storage, sampler=self._sampler, pruner=pruner, study_name=self._study_name, direction="minimize" if mode == "min" else "maximize", load_if_exists=True, ) if self._points_to_evaluate: validate_warmstart( self._space, self._points_to_evaluate, self._evaluated_rewards, validate_point_name_lengths=not callable(self._space), ) if self._evaluated_rewards: for point, reward in zip( self._points_to_evaluate, self._evaluated_rewards ): self.add_evaluated_point(point, reward) else: for point in self._points_to_evaluate: self._ot_study.enqueue_trial(point) def set_search_properties( self, metric: Optional[str], mode: Optional[str], config: Dict ) -> bool: if self._space: return False space = self.convert_search_space(config) self._space = space if metric: self._metric = metric if mode: self._mode = mode self._setup_study(mode) return True def _suggest_from_define_by_run_func( self, func: Callable[["OptunaTrial"], Optional[Dict[str, Any]]], ot_trial: "OptunaTrial", ) -> Dict: captor = _OptunaTrialSuggestCaptor(ot_trial) time_start = time.time() ret = func(captor) time_taken = time.time() - time_start if time_taken > DEFINE_BY_RUN_WARN_THRESHOLD_S: warnings.warn( "Define-by-run function passed in the `space` argument " f"took {time_taken} seconds to " "run. Ensure that actual computation, training takes " "place inside Tune's train functions or Trainables " "passed to `tune.run`." ) if ret is not None: if not isinstance(ret, dict): raise TypeError( "The return value of the define-by-run function " "passed in the `space` argument should be " "either None or a `dict` with `str` keys. " f"Got {type(ret)}." ) if not all(isinstance(k, str) for k in ret.keys()): raise TypeError( "At least one of the keys in the dict returned by the " "define-by-run function passed in the `space` argument " "was not a `str`." ) return {**captor.captured_values, **ret} if ret else captor.captured_values def suggest(self, trial_id: str) -> Optional[Dict]: if not self._space: raise RuntimeError( UNDEFINED_SEARCH_SPACE.format( cls=self.__class__.__name__, space="space" ) ) if not self._metric or not self._mode: raise RuntimeError( UNDEFINED_METRIC_MODE.format( cls=self.__class__.__name__, metric=self._metric, mode=self._mode ) ) if isinstance(self._space, list): # Keep for backwards compatibility # Deprecate: 1.5 if trial_id not in self._ot_trials: self._ot_trials[trial_id] = self._ot_study.ask() ot_trial = self._ot_trials[trial_id] # getattr will fetch the trial.suggest_ function on Optuna trials params = { args[0] if len(args) > 0 else kwargs["name"]: getattr(ot_trial, fn)(*args, **kwargs) for (fn, args, kwargs) in self._space } elif callable(self._space): if trial_id not in self._ot_trials: self._ot_trials[trial_id] = self._ot_study.ask() ot_trial = self._ot_trials[trial_id] params = self._suggest_from_define_by_run_func(self._space, ot_trial) else: # Use Optuna ask interface (since version 2.6.0) if trial_id not in self._ot_trials: self._ot_trials[trial_id] = self._ot_study.ask( fixed_distributions=self._space ) ot_trial = self._ot_trials[trial_id] params = ot_trial.params return unflatten_dict(params) def on_trial_result(self, trial_id: str, result: Dict): metric = result[self.metric] step = result[TRAINING_ITERATION] ot_trial = self._ot_trials[trial_id] ot_trial.report(metric, step) def on_trial_complete( self, trial_id: str, result: Optional[Dict] = None, error: bool = False ): ot_trial = self._ot_trials[trial_id] val = result.get(self.metric, None) if result else None ot_trial_state = OptunaTrialState.COMPLETE if val is None: if error: ot_trial_state = OptunaTrialState.FAIL else: ot_trial_state = OptunaTrialState.PRUNED try: self._ot_study.tell(ot_trial, val, state=ot_trial_state) except ValueError as exc: logger.warning(exc) # E.g. if NaN was reported def add_evaluated_point( self, parameters: Dict, value: float, error: bool = False, pruned: bool = False, intermediate_values: Optional[List[float]] = None, ): if not self._space: raise RuntimeError( UNDEFINED_SEARCH_SPACE.format( cls=self.__class__.__name__, space="space" ) ) if not self._metric or not self._mode: raise RuntimeError( UNDEFINED_METRIC_MODE.format( cls=self.__class__.__name__, metric=self._metric, mode=self._mode ) ) ot_trial_state = OptunaTrialState.COMPLETE if error: ot_trial_state = OptunaTrialState.FAIL elif pruned: ot_trial_state = OptunaTrialState.PRUNED if intermediate_values: intermediate_values_dict = { i: value for i, value in enumerate(intermediate_values) } else: intermediate_values_dict = None trial = ot.trial.create_trial( state=ot_trial_state, value=value, params=parameters, distributions=self._space, intermediate_values=intermediate_values_dict, ) self._ot_study.add_trial(trial) def save(self, checkpoint_path: str): save_object = ( self._sampler, self._ot_trials, self._ot_study, self._points_to_evaluate, self._evaluated_rewards, ) with open(checkpoint_path, "wb") as outputFile: pickle.dump(save_object, outputFile) def restore(self, checkpoint_path: str): with open(checkpoint_path, "rb") as inputFile: save_object = pickle.load(inputFile) if len(save_object) == 5: ( self._sampler, self._ot_trials, self._ot_study, self._points_to_evaluate, self._evaluated_rewards, ) = save_object else: # Backwards compatibility ( self._sampler, self._ot_trials, self._ot_study, self._points_to_evaluate, ) = save_object @staticmethod def convert_search_space(spec: Dict) -> Dict[str, Any]: resolved_vars, domain_vars, grid_vars = parse_spec_vars(spec) if not domain_vars and not grid_vars: return {} if grid_vars: raise ValueError( "Grid search parameters cannot be automatically converted " "to an Optuna search space." ) # Flatten and resolve again after checking for grid search. spec = flatten_dict(spec, prevent_delimiter=True) resolved_vars, domain_vars, grid_vars = parse_spec_vars(spec) def resolve_value(domain: Domain) -> ot.distributions.BaseDistribution: quantize = None sampler = domain.get_sampler() if isinstance(sampler, Quantized): quantize = sampler.q sampler = sampler.sampler if isinstance(sampler, LogUniform): logger.warning( "Optuna does not handle quantization in loguniform " "sampling. The parameter will be passed but it will " "probably be ignored." ) if isinstance(domain, Float): if isinstance(sampler, LogUniform): if quantize: logger.warning( "Optuna does not support both quantization and " "sampling from LogUniform. Dropped quantization." ) return ot.distributions.LogUniformDistribution( domain.lower, domain.upper ) elif isinstance(sampler, Uniform): if quantize: return ot.distributions.DiscreteUniformDistribution( domain.lower, domain.upper, quantize ) return ot.distributions.UniformDistribution( domain.lower, domain.upper ) elif isinstance(domain, Integer): if isinstance(sampler, LogUniform): return ot.distributions.IntLogUniformDistribution( domain.lower, domain.upper - 1, step=quantize or 1 ) elif isinstance(sampler, Uniform): # Upper bound should be inclusive for quantization and # exclusive otherwise return ot.distributions.IntUniformDistribution( domain.lower, domain.upper - int(bool(not quantize)), step=quantize or 1, ) elif isinstance(domain, Categorical): if isinstance(sampler, Uniform): return ot.distributions.CategoricalDistribution(domain.categories) raise ValueError( "Optuna search does not support parameters of type " "`{}` with samplers of type `{}`".format( type(domain).__name__, type(domain.sampler).__name__ ) ) # Parameter name is e.g. "a/b/c" for nested dicts values = {"/".join(path): resolve_value(domain) for path, domain in domain_vars} return values