import time
import logging
import pickle
import functools
import warnings
from packaging import version
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
from ray.air.constants import TRAINING_ITERATION
from ray.tune.result import DEFAULT_METRIC
from ray.tune.search.sample import (
Categorical,
Domain,
Float,
Integer,
LogUniform,
Quantized,
Uniform,
)
from ray.tune.search import (
UNRESOLVED_SEARCH_SPACE,
UNDEFINED_METRIC_MODE,
UNDEFINED_SEARCH_SPACE,
Searcher,
)
from ray.tune.search.variant_generator import parse_spec_vars
from ray.tune.utils.util import flatten_dict, unflatten_dict, validate_warmstart
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
logger = logging.getLogger(__name__)
# print a warning if define by run function takes longer than this to execute
DEFINE_BY_RUN_WARN_THRESHOLD_S = 1 # 1 is arbitrary
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
[docs]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.
Multi-objective optimization is supported.
Args:
space: 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 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.Tuner()``.
metric: The training result objective value attribute. If
None but a mode was passed, the anonymous metric ``_metric``
will be used per default. Can be a list of metrics for
multi-objective optimization.
mode: One of {min, max}. Determines whether objective is
minimizing or maximizing the metric attribute. Can be a list of
modes for multi-objective optimization (corresponding to
``metric``).
points_to_evaluate: 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 sampler used to
draw hyperparameter configurations. Defaults to ``MOTPESampler``
for multi-objective optimization with Optuna<2.9.0, and
``TPESampler`` in every other case.
See https://optuna.readthedocs.io/en/stable/reference/samplers/index.html
for available Optuna samplers.
.. warning::
Please note that with Optuna 2.10.0 and earlier
default ``MOTPESampler``/``TPESampler`` suffer
from performance issues when dealing with a large number of
completed trials (approx. >100). This will manifest as
a delay when suggesting new configurations.
This is an Optuna issue and may be fixed in a future
Optuna release.
seed: 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: 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.
.. warning::
When using ``evaluated_rewards``, the search space ``space``
must be provided as a :class:`dict` with parameter names as
keys and ``optuna.distributions`` instances as values. The
define-by-run search space definition is not yet supported with
this functionality.
Tune automatically converts search spaces to Optuna's format:
.. code-block:: python
from ray.tune.search.optuna import OptunaSearch
config = {
"a": tune.uniform(6, 8)
"b": tune.loguniform(1e-4, 1e-2)
}
optuna_search = OptunaSearch(
metric="loss",
mode="min")
tuner = tune.Tuner(
trainable,
tune_config=tune.TuneConfig(
search_alg=optuna_search,
),
param_space=config,
)
tuner.fit()
If you would like to pass the search space manually, the code would
look like this:
.. code-block:: python
from ray.tune.search.optuna import OptunaSearch
import optuna
space = {
"a": optuna.distributions.FloatDistribution(6, 8),
"b": optuna.distributions.FloatDistribution(1e-4, 1e-2, log=True),
}
optuna_search = OptunaSearch(
space,
metric="loss",
mode="min")
tuner = tune.Tuner(
trainable,
tune_config=tune.TuneConfig(
search_alg=optuna_search,
),
)
tuner.fit()
# 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")
tuner = tune.Tuner(
trainable,
tune_config=tune.TuneConfig(
search_alg=optuna_search,
),
)
tuner.fit()
Multi-objective optimization is supported:
.. code-block:: python
from ray.tune.search.optuna import OptunaSearch
import optuna
space = {
"a": optuna.distributions.FloatDistribution(6, 8),
"b": optuna.distributions.FloatDistribution(1e-4, 1e-2, log=True),
}
# Note you have to specify metric and mode here instead of
# in tune.TuneConfig
optuna_search = OptunaSearch(
space,
metric=["loss1", "loss2"],
mode=["min", "max"])
# Do not specify metric and mode here!
tuner = tune.Tuner(
trainable,
tune_config=tune.TuneConfig(
search_alg=optuna_search,
),
)
tuner.fit()
You can pass configs that will be evaluated first using
``points_to_evaluate``:
.. code-block:: python
from ray.tune.search.optuna import OptunaSearch
import optuna
space = {
"a": optuna.distributions.FloatDistribution(6, 8),
"b": optuna.distributions.FloatDistribution(1e-4, 1e-2, log=True),
}
optuna_search = OptunaSearch(
space,
points_to_evaluate=[{"a": 6.5, "b": 5e-4}, {"a": 7.5, "b": 1e-3}]
metric="loss",
mode="min")
tuner = tune.Tuner(
trainable,
tune_config=tune.TuneConfig(
search_alg=optuna_search,
),
)
tuner.fit()
Avoid re-running evaluated trials by passing the rewards together with
`points_to_evaluate`:
.. code-block:: python
from ray.tune.search.optuna import OptunaSearch
import optuna
space = {
"a": optuna.distributions.FloatDistribution(6, 8),
"b": optuna.distributions.FloatDistribution(1e-4, 1e-2, log=True),
}
optuna_search = OptunaSearch(
space,
points_to_evaluate=[{"a": 6.5, "b": 5e-4}, {"a": 7.5, "b": 1e-3}]
evaluated_rewards=[0.89, 0.42]
metric="loss",
mode="min")
tuner = tune.Tuner(
trainable,
tune_config=tune.TuneConfig(
search_alg=optuna_search,
),
)
tuner.fit()
.. versionadded:: 0.8.8
"""
def __init__(
self,
space: Optional[
Union[
Dict[str, "OptunaDistribution"],
List[Tuple],
Callable[["OptunaTrial"], Optional[Dict[str, Any]]],
]
] = None,
metric: Optional[Union[str, List[str]]] = None,
mode: Optional[Union[str, List[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)
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."
)
elif sampler:
assert isinstance(sampler, BaseSampler), (
"You can only pass an instance of "
"`optuna.samplers.BaseSampler` "
"as a sampler to `OptunaSearcher`."
)
self._sampler = sampler
self._seed = seed
self._completed_trials = set()
self._ot_trials = {}
self._ot_study = None
if self._space:
self._setup_study(mode)
def _setup_study(self, mode: Union[str, list]):
if self._metric is None and self._mode:
if isinstance(self._mode, list):
raise ValueError(
"If ``mode`` is a list (multi-objective optimization "
"case), ``metric`` must be defined."
)
# If only a mode was passed, use anonymous metric
self._metric = DEFAULT_METRIC
pruner = ot.pruners.NopPruner()
storage = ot.storages.InMemoryStorage()
if self._sampler:
sampler = self._sampler
elif isinstance(mode, list) and version.parse(ot.__version__) < version.parse(
"2.9.0"
):
# MOTPESampler deprecated in Optuna>=2.9.0
sampler = ot.samplers.MOTPESampler(seed=self._seed)
else:
sampler = ot.samplers.TPESampler(seed=self._seed)
if isinstance(mode, list):
study_direction_args = dict(
directions=["minimize" if m == "min" else "maximize" for m in mode],
)
else:
study_direction_args = dict(
direction="minimize" if mode == "min" else "maximize",
)
self._ot_study = ot.study.create_study(
storage=storage,
sampler=sampler,
pruner=pruner,
study_name=self._study_name,
load_if_exists=True,
**study_direction_args,
)
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, **spec
) -> 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(self._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.Tuner()`."
)
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 callable(self._space):
# Define-by-run case
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):
if isinstance(self.metric, list):
# Optuna doesn't support incremental results
# for multi-objective optimization
return
if trial_id in self._completed_trials:
logger.warning(
f"Received additional result for trial {trial_id}, but "
f"it already finished. Result: {result}"
)
return
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
):
if trial_id in self._completed_trials:
logger.warning(
f"Received additional completion for trial {trial_id}, but "
f"it already finished. Result: {result}"
)
return
ot_trial = self._ot_trials[trial_id]
if result:
if isinstance(self.metric, list):
val = [result.get(metric, None) for metric in self.metric]
else:
val = result.get(self.metric, None)
else:
val = 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 Exception as exc:
logger.warning(exc) # E.g. if NaN was reported
self._completed_trials.add(trial_id)
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
)
)
if callable(self._space):
raise TypeError(
"Define-by-run function passed in `space` argument is not "
"yet supported when using `evaluated_rewards`. Please provide "
"an `OptunaDistribution` dict or pass a Ray Tune "
"search space to `tune.Tuner()`."
)
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.__dict__.copy()
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 isinstance(save_object, dict):
self.__dict__.update(save_object)
else:
# Backwards compatibility
(
self._sampler,
self._ot_trials,
self._ot_study,
self._points_to_evaluate,
self._evaluated_rewards,
) = 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.FloatDistribution(
domain.lower, domain.upper, log=True
)
elif isinstance(sampler, Uniform):
if quantize:
return ot.distributions.FloatDistribution(
domain.lower, domain.upper, step=quantize
)
return ot.distributions.FloatDistribution(
domain.lower, domain.upper
)
elif isinstance(domain, Integer):
if isinstance(sampler, LogUniform):
return ot.distributions.IntDistribution(
domain.lower, domain.upper - 1, step=quantize or 1, log=True
)
elif isinstance(sampler, Uniform):
# Upper bound should be inclusive for quantization and
# exclusive otherwise
return ot.distributions.IntDistribution(
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