import inspect
import logging
import pickle
from typing import Dict, Optional, Type, Union, List, Sequence
from ray.tune.result import DEFAULT_METRIC
from ray.tune.search.sample import (
Categorical,
Domain,
Float,
Integer,
LogUniform,
Quantized,
)
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
try:
import nevergrad as ng
from nevergrad.optimization import Optimizer
from nevergrad.optimization.base import ConfiguredOptimizer
Parameter = ng.p.Parameter
except ImportError:
ng = None
Optimizer = None
ConfiguredOptimizer = None
Parameter = None
logger = logging.getLogger(__name__)
[docs]class NevergradSearch(Searcher):
"""Uses Nevergrad to optimize hyperparameters.
Nevergrad is an open source tool from Facebook for derivative free
optimization. More info can be found at:
https://github.com/facebookresearch/nevergrad.
You will need to install Nevergrad via the following command:
.. code-block:: bash
$ pip install nevergrad
Parameters:
optimizer: Optimizer class provided from Nevergrad.
See here for available optimizers:
https://facebookresearch.github.io/nevergrad/optimizers_ref.html#optimizers
This can also be an instance of a `ConfiguredOptimizer`. See the
section on configured optimizers in the above link.
optimizer_kwargs: Kwargs passed in when instantiating the `optimizer`
space: Nevergrad parametrization
to be passed to optimizer on instantiation, or list of parameter
names if you passed an optimizer object.
metric: The training result objective value attribute. If None
but a mode was passed, the anonymous metric `_metric` will be used
per default.
mode: One of {min, max}. Determines whether objective is
minimizing or maximizing the metric attribute.
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.
Tune automatically converts search spaces to Nevergrad's format:
.. code-block:: python
import nevergrad as ng
config = {
"width": tune.uniform(0, 20),
"height": tune.uniform(-100, 100),
"activation": tune.choice(["relu", "tanh"])
}
current_best_params = [{
"width": 10,
"height": 0,
"activation": relu",
}]
ng_search = NevergradSearch(
optimizer=ng.optimizers.OnePlusOne,
metric="mean_loss",
mode="min",
points_to_evaluate=current_best_params)
run(my_trainable, config=config, search_alg=ng_search)
If you would like to pass the search space manually, the code would
look like this:
.. code-block:: python
import nevergrad as ng
space = ng.p.Dict(
width=ng.p.Scalar(lower=0, upper=20),
height=ng.p.Scalar(lower=-100, upper=100),
activation=ng.p.Choice(choices=["relu", "tanh"])
)
ng_search = NevergradSearch(
optimizer=ng.optimizers.OnePlusOne,
space=space,
metric="mean_loss",
mode="min")
run(my_trainable, search_alg=ng_search)
"""
def __init__(
self,
optimizer: Optional[
Union[Optimizer, Type[Optimizer], ConfiguredOptimizer]
] = None,
optimizer_kwargs: Optional[Dict] = None,
space: Optional[Union[Dict, Parameter]] = None,
metric: Optional[str] = None,
mode: Optional[str] = None,
points_to_evaluate: Optional[List[Dict]] = None,
):
assert (
ng is not None
), """Nevergrad must be installed!
You can install Nevergrad with the command:
`pip install nevergrad`."""
if mode:
assert mode in ["min", "max"], "`mode` must be 'min' or 'max'."
super(NevergradSearch, self).__init__(metric=metric, mode=mode)
self._space = None
self._opt_factory = None
self._nevergrad_opt = None
self._optimizer_kwargs = optimizer_kwargs or {}
if points_to_evaluate is None:
self._points_to_evaluate = None
elif not isinstance(points_to_evaluate, Sequence):
raise ValueError(
"Invalid object type passed for `points_to_evaluate`: "
f"{type(points_to_evaluate)}. "
"Please pass a list of points (dictionaries) instead."
)
else:
self._points_to_evaluate = list(points_to_evaluate)
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))
)
space = self.convert_search_space(space)
if isinstance(optimizer, Optimizer):
if space is not None and not isinstance(space, list):
raise ValueError(
"If you pass a configured optimizer to Nevergrad, either "
"pass a list of parameter names or None as the `space` "
"parameter."
)
if self._optimizer_kwargs:
raise ValueError(
"If you pass in optimizer kwargs, either pass "
"an `Optimizer` subclass or an instance of "
"`ConfiguredOptimizer`."
)
self._parameters = space
self._nevergrad_opt = optimizer
elif (
inspect.isclass(optimizer) and issubclass(optimizer, Optimizer)
) or isinstance(optimizer, ConfiguredOptimizer):
self._opt_factory = optimizer
self._parameters = None
self._space = space
else:
raise ValueError(
"The `optimizer` argument passed to NevergradSearch must be "
"either an `Optimizer` or a `ConfiguredOptimizer`."
)
self._live_trial_mapping = {}
if self._nevergrad_opt or self._space:
self._setup_nevergrad()
def _setup_nevergrad(self):
if self._opt_factory:
self._nevergrad_opt = self._opt_factory(
self._space, **self._optimizer_kwargs
)
# nevergrad.tell internally minimizes, so "max" => -1
if self._mode == "max":
self._metric_op = -1.0
elif self._mode == "min":
self._metric_op = 1.0
if self._metric is None and self._mode:
# If only a mode was passed, use anonymous metric
self._metric = DEFAULT_METRIC
if hasattr(self._nevergrad_opt, "instrumentation"): # added in v0.2.0
if self._nevergrad_opt.instrumentation.kwargs:
if self._nevergrad_opt.instrumentation.args:
raise ValueError("Instrumented optimizers should use kwargs only")
if self._parameters is not None:
raise ValueError(
"Instrumented optimizers should provide "
"None as parameter_names"
)
else:
if self._parameters is None:
raise ValueError(
"Non-instrumented optimizers should have "
"a list of parameter_names"
)
if len(self._nevergrad_opt.instrumentation.args) != 1:
raise ValueError("Instrumented optimizers should use kwargs only")
if self._parameters is not None and self._nevergrad_opt.dimension != len(
self._parameters
):
raise ValueError(
"len(parameters_names) must match optimizer "
"dimension for non-instrumented optimizers"
)
if self._points_to_evaluate:
# Nevergrad is LIFO, so we add the points to evaluate in reverse
# order.
for i in range(len(self._points_to_evaluate) - 1, -1, -1):
self._nevergrad_opt.suggest(self._points_to_evaluate[i])
def set_search_properties(
self, metric: Optional[str], mode: Optional[str], config: Dict, **spec
) -> bool:
if self._nevergrad_opt or 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_nevergrad()
return True
def suggest(self, trial_id: str) -> Optional[Dict]:
if not self._nevergrad_opt:
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
)
)
suggested_config = self._nevergrad_opt.ask()
self._live_trial_mapping[trial_id] = suggested_config
# in v0.2.0+, output of ask() is a Candidate,
# with fields args and kwargs
if not suggested_config.kwargs:
if self._parameters:
return unflatten_dict(
dict(zip(self._parameters, suggested_config.args[0]))
)
return unflatten_dict(suggested_config.value)
else:
return unflatten_dict(suggested_config.kwargs)
[docs] def on_trial_complete(
self, trial_id: str, result: Optional[Dict] = None, error: bool = False
):
"""Notification for the completion of trial.
The result is internally negated when interacting with Nevergrad
so that Nevergrad Optimizers can "maximize" this value,
as it minimizes on default.
"""
if result:
self._process_result(trial_id, result)
self._live_trial_mapping.pop(trial_id)
def _process_result(self, trial_id: str, result: Dict):
ng_trial_info = self._live_trial_mapping[trial_id]
self._nevergrad_opt.tell(ng_trial_info, self._metric_op * result[self._metric])
def save(self, checkpoint_path: str):
save_object = self.__dict__
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)
self.__dict__.update(save_object)
@staticmethod
def convert_search_space(spec: Dict) -> Parameter:
resolved_vars, domain_vars, grid_vars = parse_spec_vars(spec)
if grid_vars:
raise ValueError(
"Grid search parameters cannot be automatically converted "
"to a Nevergrad 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) -> Parameter:
sampler = domain.get_sampler()
if isinstance(sampler, Quantized):
logger.warning(
"Nevergrad does not support quantization. Dropped quantization."
)
sampler = sampler.get_sampler()
if isinstance(domain, Float):
if isinstance(sampler, LogUniform):
return ng.p.Log(
lower=domain.lower, upper=domain.upper, exponent=sampler.base
)
return ng.p.Scalar(lower=domain.lower, upper=domain.upper)
elif isinstance(domain, Integer):
if isinstance(sampler, LogUniform):
return ng.p.Log(
lower=domain.lower,
upper=domain.upper - 1, # Upper bound exclusive
exponent=sampler.base,
).set_integer_casting()
return ng.p.Scalar(
lower=domain.lower,
upper=domain.upper - 1, # Upper bound exclusive
).set_integer_casting()
elif isinstance(domain, Categorical):
return ng.p.Choice(choices=domain.categories)
raise ValueError(
"Nevergrad 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
space = {"/".join(path): resolve_value(domain) for path, domain in domain_vars}
return ng.p.Dict(**space)