#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# pyre-strict
import functools
from copy import deepcopy
from functools import partial
from typing import Any, Callable, Optional
import torch
from ax.core.search_space import SearchSpaceDigest
from ax.models.torch.botorch_modular.acquisition import Acquisition
from ax.models.torch.botorch_modular.optimizer_argparse import optimizer_argparse
from ax.models.torch.botorch_modular.surrogate import Surrogate
from ax.models.torch_base import TorchOptConfig
from ax.utils.common.constants import Keys
from ax.utils.common.typeutils import not_none
from botorch.acquisition.acquisition import AcquisitionFunction
from botorch.acquisition.multi_objective.monte_carlo import (
qExpectedHypervolumeImprovement,
)
from botorch.acquisition.penalized import L0Approximation
from botorch.models.deterministic import GenericDeterministicModel
from botorch.models.model import ModelList
from botorch.optim import (
Homotopy,
HomotopyParameter,
LogLinearHomotopySchedule,
optimize_acqf_homotopy,
)
from botorch.utils.datasets import SupervisedDataset
from botorch.utils.multi_objective.pareto import is_non_dominated
from torch import Tensor
from torch.quasirandom import SobolEngine
CLAMP_TOL = 0.01
[docs]class SEBOAcquisition(Acquisition):
"""
Implement the acquisition function of Sparsity Exploring Bayesian
Optimization (SEBO).
The SEBO is a hyperparameter-free method to simultaneously maximize a target
objective and sparsity. When L0 norm is used, SEBO uses a novel differentiable
relaxation based on homotopy continuation to efficiently optimize for sparsity.
"""
def __init__(
self,
surrogates: dict[str, Surrogate],
search_space_digest: SearchSpaceDigest,
torch_opt_config: TorchOptConfig,
botorch_acqf_class: type[AcquisitionFunction],
options: Optional[dict[str, Any]] = None,
) -> None:
if len(surrogates) > 1:
raise ValueError("SEBO does not support support multiple surrogates.")
surrogate = surrogates[Keys.ONLY_SURROGATE]
tkwargs: dict[str, Any] = {"dtype": surrogate.dtype, "device": surrogate.device}
options = {} if options is None else options
self.penalty_name: str = options.pop("penalty", "L0_norm")
self.target_point: Tensor = options.pop("target_point", None)
if self.target_point is None:
raise ValueError("please provide target point.")
self.target_point.to(**tkwargs)
self.sparsity_threshold: int = options.pop(
"sparsity_threshold", surrogate.Xs[0].shape[-1]
)
# construct determinsitic model for penalty term
# pyre-fixme[4]: Attribute must be annotated.
self.deterministic_model = self._construct_penalty()
surrogate_f = deepcopy(surrogate)
# update the training data in new surrogate
not_none(surrogate_f._training_data).append(
SupervisedDataset(
surrogate_f.Xs[0],
self.deterministic_model(surrogate_f.Xs[0]),
# append Yvar as zero for penalty term
Yvar=torch.zeros(surrogate_f.Xs[0].shape[0], 1, **tkwargs),
feature_names=surrogate_f.training_data[0].feature_names,
outcome_names=[self.penalty_name],
)
)
# update the model in new surrogate
surrogate_f._model = ModelList(surrogate.model, self.deterministic_model)
self.det_metric_indx = -1
# update objective weights and thresholds in the torch config
torch_opt_config_sebo = self._transform_torch_config(
torch_opt_config, **tkwargs
)
# instantiate botorch_acqf_class
if not issubclass(botorch_acqf_class, qExpectedHypervolumeImprovement):
raise ValueError("botorch_acqf_class must be qEHVI to use SEBO")
super().__init__(
surrogates={"sebo": surrogate_f},
search_space_digest=search_space_digest,
torch_opt_config=torch_opt_config_sebo,
botorch_acqf_class=botorch_acqf_class,
options=options,
)
if not isinstance(self.acqf, qExpectedHypervolumeImprovement):
raise ValueError("botorch_acqf_class must be qEHVI to use SEBO")
# update objective threshold for deterministic model (penalty term)
self.acqf.ref_point[-1] = self.sparsity_threshold * -1
# pyre-ignore
self._objective_thresholds[-1] = self.sparsity_threshold
Y_pareto = torch.cat(
[d.Y for d in self.surrogates["sebo"].training_data],
dim=-1,
)
ind_pareto = is_non_dominated(Y_pareto * self._full_objective_weights)
# pyre-ignore
self.X_pareto = self.surrogates["sebo"].Xs[0][ind_pareto].clone()
def _construct_penalty(self) -> GenericDeterministicModel:
"""Construct a penalty term as deterministic model to be included in
SEBO acqusition function. Currently only L0 and L1 penalty are supported.
"""
if self.penalty_name == "L0_norm":
L0 = L0Approximation(target_point=self.target_point)
return GenericDeterministicModel(f=L0)
elif self.penalty_name == "L1_norm":
L1 = functools.partial(
L1_norm_func,
init_point=self.target_point,
)
return GenericDeterministicModel(f=L1)
else:
raise NotImplementedError(
f"{self.penalty_name} is not currently implemented."
)
def _transform_torch_config(
self,
torch_opt_config: TorchOptConfig,
**tkwargs: Any,
) -> TorchOptConfig:
"""Transform torch config to include penalty term (deterministic model) as
an additional outcomes in BoTorch model.
"""
# update objective weights by appending the weight (-1) of penalty term
# at the end
ow_sebo = torch.cat(
[torch_opt_config.objective_weights, torch.tensor([-1], **tkwargs)]
)
if torch_opt_config.outcome_constraints is not None:
# update the shape of A matrix in outcome_constraints
oc_sebo = (
torch.cat(
[
torch_opt_config.outcome_constraints[0],
torch.zeros(
# pyre-ignore
torch_opt_config.outcome_constraints[0].shape[0],
1,
**tkwargs,
),
],
dim=1,
),
torch_opt_config.outcome_constraints[1],
)
else:
oc_sebo = None
if torch_opt_config.objective_thresholds is not None:
# append the sparsity threshold at the end if objective_thresholds
# is not None
ot_sebo = torch.cat(
[
torch_opt_config.objective_thresholds,
torch.tensor([self.sparsity_threshold], **tkwargs),
]
)
else:
ot_sebo = None
# update pending observations (if not none) by appending an obs for
# the new penalty outcome
pending_observations = torch_opt_config.pending_observations
if torch_opt_config.pending_observations is not None:
pending_observations = torch_opt_config.pending_observations + [
torch_opt_config.pending_observations[0]
]
return TorchOptConfig(
objective_weights=ow_sebo,
outcome_constraints=oc_sebo,
objective_thresholds=ot_sebo,
linear_constraints=torch_opt_config.linear_constraints,
fixed_features=torch_opt_config.fixed_features,
pending_observations=pending_observations,
model_gen_options=torch_opt_config.model_gen_options,
rounding_func=torch_opt_config.rounding_func,
opt_config_metrics=torch_opt_config.opt_config_metrics,
is_moo=torch_opt_config.is_moo,
)
[docs] def optimize(
self,
n: int,
search_space_digest: SearchSpaceDigest,
inequality_constraints: Optional[list[tuple[Tensor, Tensor, float]]] = None,
fixed_features: Optional[dict[int, float]] = None,
rounding_func: Optional[Callable[[Tensor], Tensor]] = None,
optimizer_options: Optional[dict[str, Any]] = None,
) -> tuple[Tensor, Tensor, Tensor]:
"""Generate a set of candidates via multi-start optimization. Obtains
candidates and their associated acquisition function values.
Args:
n: The number of candidates to generate.
search_space_digest: A ``SearchSpaceDigest`` object containing search space
properties, e.g. ``bounds`` for optimization.
inequality_constraints: A list of tuples (indices, coefficients, rhs),
with each tuple encoding an inequality constraint of the form
``sum_i (X[indices[i]] * coefficients[i]) >= rhs``.
fixed_features: A map `{feature_index: value}` for features that
should be fixed to a particular value during generation.
rounding_func: A function that post-processes an optimization
result appropriately (i.e., according to `round-trip`
transformations).
optimizer_options: Options for the optimizer function, e.g. ``sequential``
or ``raw_samples``.
Returns:
A three-element tuple containing an `n x d`-dim tensor of generated
candidates, a tensor with the associated acquisition values, and a tensor
with the weight for each candidate.
"""
if self.penalty_name == "L0_norm":
if inequality_constraints is not None:
raise NotImplementedError(
"Homotopy does not support optimization with inequality "
+ "constraints. Use L1 penalty norm instead."
)
candidates, expected_acquisition_value, weights = (
self._optimize_with_homotopy(
n=n,
search_space_digest=search_space_digest,
fixed_features=fixed_features,
rounding_func=rounding_func,
optimizer_options=optimizer_options,
)
)
else:
# if L1 norm use standard moo-opt
candidates, expected_acquisition_value, weights = super().optimize(
n=n,
search_space_digest=search_space_digest,
inequality_constraints=inequality_constraints,
fixed_features=fixed_features,
rounding_func=rounding_func,
optimizer_options=optimizer_options,
)
# similar, make sure if applies to sparse dimensions only
candidates = clamp_candidates(
X=candidates,
target_point=self.target_point,
clamp_tol=CLAMP_TOL,
device=self.device,
dtype=self.dtype,
)
return candidates, expected_acquisition_value, weights
def _optimize_with_homotopy(
self,
n: int,
search_space_digest: SearchSpaceDigest,
fixed_features: Optional[dict[int, float]] = None,
rounding_func: Optional[Callable[[Tensor], Tensor]] = None,
optimizer_options: Optional[dict[str, Any]] = None,
) -> tuple[Tensor, Tensor, Tensor]:
"""Optimize SEBO ACQF with L0 norm using homotopy."""
# extend to fixed a no homotopy_schedule schedule
_tensorize = partial(torch.tensor, dtype=self.dtype, device=self.device)
ssd = search_space_digest
bounds = _tensorize(ssd.bounds).t()
homotopy_schedule = LogLinearHomotopySchedule(start=0.1, end=1e-3, num_steps=30)
# Prepare arguments for optimizer
optimizer_options_with_defaults = optimizer_argparse(
self.acqf,
bounds=bounds,
q=n,
optimizer_options=optimizer_options,
optimizer="optimize_acqf_homotopy",
)
def callback() -> None:
if (
self.acqf.cache_pending
): # If true, pending points are concatenated with X_baseline
if self.acqf._max_iep != 0:
raise ValueError(
"The maximum number of pending points (max_iep) must be 0"
)
X_baseline = self.acqf._X_baseline_and_pending.clone()
self.acqf.__init__( # pyre-ignore
X_baseline=X_baseline,
model=self.surrogates["sebo"].model,
ref_point=self.acqf.ref_point,
objective=self.acqf.objective,
)
else: # We can directly get the pending points here
X_pending = self.acqf.X_pending
self.acqf.__init__( # pyre-ignore
X_baseline=self.X_observed,
model=self.surrogates["sebo"].model,
ref_point=self.acqf.ref_point,
objective=self.acqf.objective,
)
self.acqf.set_X_pending(X_pending)
homotopy = Homotopy(
homotopy_parameters=[
HomotopyParameter(
parameter=self.deterministic_model._f.a,
schedule=homotopy_schedule,
)
],
callbacks=[callback],
)
# need to know sparse dimensions
batch_initial_conditions = get_batch_initial_conditions(
acq_function=self.acqf,
raw_samples=optimizer_options_with_defaults["raw_samples"],
X_pareto=self.X_pareto,
target_point=self.target_point,
num_restarts=optimizer_options_with_defaults["num_restarts"],
**{"device": self.device, "dtype": self.dtype},
)
candidates, expected_acquisition_value = optimize_acqf_homotopy(
q=n,
acq_function=self.acqf,
bounds=bounds,
homotopy=homotopy,
num_restarts=optimizer_options_with_defaults["num_restarts"],
raw_samples=optimizer_options_with_defaults["raw_samples"],
post_processing_func=rounding_func,
fixed_features=fixed_features,
batch_initial_conditions=batch_initial_conditions,
)
return (
candidates,
expected_acquisition_value,
torch.ones(n, dtype=candidates.dtype),
)
[docs]def L1_norm_func(X: Tensor, init_point: Tensor) -> Tensor:
r"""L1_norm takes in a a `batch_shape x n x d`-dim input tensor `X`
to a `batch_shape x n x 1`-dimensional L1 norm tensor. To be used
for constructing a GenericDeterministicModel.
"""
return torch.linalg.norm((X - init_point), ord=1, dim=-1, keepdim=True)
[docs]def clamp_candidates(
X: Tensor, target_point: Tensor, clamp_tol: float, **tkwargs: Any
) -> Tensor:
"""Clamp generated candidates within the given ranges to the target point."""
clamp_mask = (X - target_point).abs() < clamp_tol
clamp_mask = clamp_mask
X[clamp_mask] = (
target_point.clone().repeat(*X.shape[:-1], 1).to(**tkwargs)[clamp_mask]
)
return X
[docs]def get_batch_initial_conditions(
acq_function: AcquisitionFunction,
raw_samples: int,
X_pareto: Tensor,
target_point: Tensor,
num_restarts: int = 20,
**tkwargs: Any,
) -> Tensor:
"""Generate starting points for the SEBO acquisition function optimization."""
dim = X_pareto.shape[-1] # dimension
# (1) Global Sobol points
X_cand1 = SobolEngine(dimension=dim, scramble=True).draw(raw_samples).to(**tkwargs)
X_cand1 = X_cand1[
acq_function(X_cand1.unsqueeze(1)).topk(num_restarts // 5).indices
]
# (2) Global Sobol points with a Bernoulli mask
X_cand2 = SobolEngine(dimension=dim, scramble=True).draw(raw_samples).to(**tkwargs)
mask = torch.rand(X_cand2.shape, **tkwargs) < 0.5
X_cand2[mask] = target_point.repeat(len(X_cand2), 1).to(**tkwargs)[mask]
X_cand2 = X_cand2[
acq_function(X_cand2.unsqueeze(1)).topk(num_restarts // 5).indices
]
# (3) Perturbations of points on the Pareto frontier (done by TuRBO and Spearmint)
X_cand3 = X_pareto.clone()[torch.randint(high=len(X_pareto), size=(raw_samples,))]
mask = X_cand3 != target_point
X_cand3[mask] += 0.2 * torch.randn(*X_cand3.shape, **tkwargs)[mask]
X_cand3 = torch.clamp(X_cand3, min=0.0, max=1.0)
X_cand3 = X_cand3[
acq_function(X_cand3.unsqueeze(1)).topk(num_restarts // 5).indices
]
# (4) Apply a Bernoulli mask to points on the Pareto frontier
X_cand4 = X_pareto.clone()[torch.randint(high=len(X_pareto), size=(raw_samples,))]
mask = torch.rand(X_cand4.shape, **tkwargs) < 0.5
X_cand4[mask] = target_point.repeat(len(X_cand4), 1).to(**tkwargs)[mask].clone()
X_cand4 = X_cand4[
acq_function(X_cand4.unsqueeze(1)).topk(num_restarts // 5).indices
]
return torch.cat((X_cand1, X_cand2, X_cand3, X_cand4), dim=0).unsqueeze(1)
