#!/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.
import dataclasses
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from ax.core.search_space import SearchSpaceDigest
from ax.models.torch.botorch import BotorchModel, get_rounding_func
from ax.models.torch.botorch_defaults import recommend_best_out_of_sample_point
from ax.models.torch.utils import (
_get_X_pending_and_observed,
_to_inequality_constraints,
get_botorch_objective_and_transform,
get_out_of_sample_best_point_acqf,
subset_model,
)
from ax.models.torch_base import TorchGenResults, TorchOptConfig
from ax.utils.common.typeutils import not_none
from botorch.acquisition.acquisition import AcquisitionFunction
from botorch.acquisition.cost_aware import InverseCostWeightedUtility
from botorch.acquisition.knowledge_gradient import (
qKnowledgeGradient,
qMultiFidelityKnowledgeGradient,
)
from botorch.acquisition.objective import MCAcquisitionObjective, PosteriorTransform
from botorch.acquisition.utils import (
expand_trace_observations,
project_to_target_fidelity,
)
from botorch.exceptions.errors import UnsupportedError
from botorch.models.cost import AffineFidelityCostModel
from botorch.models.model import Model
from botorch.optim.initializers import gen_one_shot_kg_initial_conditions
from botorch.optim.optimize import optimize_acqf
from botorch.sampling.normal import IIDNormalSampler, SobolQMCNormalSampler
from torch import Tensor
[docs]class KnowledgeGradient(BotorchModel):
r"""The Knowledge Gradient with one shot optimization.
Args:
cost_intercept: The cost intercept for the affine cost of the form
`cost_intercept + n`, where `n` is the number of generated points.
Only used for multi-fidelity optimzation (i.e., if fidelity_features
are present).
linear_truncated: If `False`, use an alternate downsampling + exponential
decay Kernel instead of the default `LinearTruncatedFidelityKernel`
(only relevant for multi-fidelity optimization).
kwargs: Model-specific kwargs.
"""
def __init__(
self,
cost_intercept: float = 1.0,
linear_truncated: bool = True,
use_input_warping: bool = False,
**kwargs: Any,
) -> None:
super().__init__(
best_point_recommender=recommend_best_out_of_sample_point,
linear_truncated=linear_truncated,
use_input_warping=use_input_warping,
**kwargs,
)
self.cost_intercept = cost_intercept
[docs] def gen(
self,
n: int,
search_space_digest: SearchSpaceDigest,
torch_opt_config: TorchOptConfig,
) -> TorchGenResults:
r"""Generate new candidates.
Args:
n: Number of candidates to generate.
search_space_digest: A SearchSpaceDigest object containing metadata
about the search space (e.g. bounds, parameter types).
torch_opt_config: A TorchOptConfig object containing optimization
arguments (e.g., objective weights, constraints).
Returns:
A TorchGenResults container, containing
- (n x d) tensor of generated points.
- n-tensor of weights for each point.
- Dictionary of model-specific metadata for the given
generation candidates.
"""
options = torch_opt_config.model_gen_options or {}
acf_options = options.get("acquisition_function_kwargs", {})
optimizer_options = options.get("optimizer_kwargs", {})
X_pending, X_observed = _get_X_pending_and_observed(
Xs=self.Xs,
objective_weights=torch_opt_config.objective_weights,
bounds=search_space_digest.bounds,
pending_observations=torch_opt_config.pending_observations,
outcome_constraints=torch_opt_config.outcome_constraints,
linear_constraints=torch_opt_config.linear_constraints,
fixed_features=torch_opt_config.fixed_features,
)
# subset model only to the outcomes we need for the optimization
model = not_none(self.model)
if options.get("subset_model", True):
subset_model_results = subset_model(
model=model,
objective_weights=torch_opt_config.objective_weights,
outcome_constraints=torch_opt_config.outcome_constraints,
)
model = subset_model_results.model
objective_weights = subset_model_results.objective_weights
outcome_constraints = subset_model_results.outcome_constraints
else:
objective_weights = torch_opt_config.objective_weights
outcome_constraints = torch_opt_config.outcome_constraints
objective, posterior_transform = get_botorch_objective_and_transform(
botorch_acqf_class=qKnowledgeGradient,
model=model,
objective_weights=objective_weights,
outcome_constraints=outcome_constraints,
X_observed=X_observed,
)
inequality_constraints = _to_inequality_constraints(
torch_opt_config.linear_constraints
)
# TODO: update optimizers to handle inequality_constraints
if inequality_constraints is not None:
raise UnsupportedError(
"Inequality constraints are not yet supported for KnowledgeGradient!"
)
# extract a few options
n_fantasies = acf_options.get("num_fantasies", 64)
qmc = acf_options.get("qmc", True)
seed_inner = acf_options.get("seed_inner", None)
num_restarts = optimizer_options.get("num_restarts", 40)
raw_samples = optimizer_options.get("raw_samples", 1024)
# get current value
current_value = self._get_current_value(
model=model,
search_space_digest=search_space_digest,
torch_opt_config=dataclasses.replace(
torch_opt_config,
objective_weights=objective_weights,
outcome_constraints=outcome_constraints,
),
X_observed=not_none(X_observed),
seed_inner=seed_inner,
qmc=qmc,
)
bounds_ = torch.tensor(
search_space_digest.bounds, dtype=self.dtype, device=self.device
)
bounds_ = bounds_.transpose(0, 1)
target_fidelities = {
k: v
for k, v in search_space_digest.target_values.items()
if k in search_space_digest.fidelity_features
}
# get acquisition function
acq_function = _instantiate_KG(
model=model,
objective=objective,
posterior_transform=posterior_transform,
qmc=qmc,
n_fantasies=n_fantasies,
num_trace_observations=options.get("num_trace_observations", 0),
mc_samples=acf_options.get("mc_samples", 256),
seed_inner=seed_inner,
seed_outer=acf_options.get("seed_outer", None),
X_pending=X_pending,
target_fidelities=target_fidelities,
fidelity_weights=options.get("fidelity_weights"),
current_value=current_value,
cost_intercept=self.cost_intercept,
)
# optimize and get new points
new_x = _optimize_and_get_candidates(
acq_function=acq_function,
bounds_=bounds_,
n=n,
num_restarts=num_restarts,
raw_samples=raw_samples,
optimizer_options=optimizer_options,
rounding_func=torch_opt_config.rounding_func,
inequality_constraints=inequality_constraints,
fixed_features=torch_opt_config.fixed_features,
)
return TorchGenResults(points=new_x, weights=torch.ones(n, dtype=self.dtype))
def _get_best_point_acqf(
self,
X_observed: Tensor,
objective_weights: Tensor,
mc_samples: int = 512,
fixed_features: Optional[Dict[int, float]] = None,
target_fidelities: Optional[Dict[int, float]] = None,
outcome_constraints: Optional[Tuple[Tensor, Tensor]] = None,
seed_inner: Optional[int] = None,
qmc: bool = True,
**kwargs: Any,
) -> Tuple[AcquisitionFunction, Optional[List[int]]]:
return get_out_of_sample_best_point_acqf(
model=not_none(self.model),
Xs=self.Xs,
objective_weights=objective_weights,
outcome_constraints=outcome_constraints,
X_observed=not_none(X_observed),
seed_inner=seed_inner,
fixed_features=fixed_features,
fidelity_features=self.fidelity_features,
target_fidelities=target_fidelities,
qmc=qmc,
)
def _get_current_value(
self,
model: Model,
search_space_digest: SearchSpaceDigest,
torch_opt_config: TorchOptConfig,
X_observed: Tensor,
seed_inner: Optional[int],
qmc: bool,
) -> Tensor:
r"""Computes the value of the current best point. This is the current_value
passed to KG.
NOTE: The current value is computed as the current value of the 'best point
acquisition function' (typically `PosteriorMean` or `qSimpleRegret`), not of
the Knowledge Gradient acquisition function.
"""
target_fidelities = {
k: v
for k, v in search_space_digest.target_values.items()
if k in search_space_digest.fidelity_features
}
best_point_acqf, non_fixed_idcs = get_out_of_sample_best_point_acqf(
model=model,
Xs=self.Xs,
objective_weights=torch_opt_config.objective_weights,
outcome_constraints=torch_opt_config.outcome_constraints,
X_observed=X_observed,
seed_inner=seed_inner,
fixed_features=torch_opt_config.fixed_features,
fidelity_features=self.fidelity_features,
target_fidelities=target_fidelities,
qmc=qmc,
)
# solution from previous iteration
recommended_point = self.best_point(
search_space_digest=search_space_digest,
torch_opt_config=torch_opt_config,
)
# pyre-fixme[16]: `Optional` has no attribute `detach`.
recommended_point = recommended_point.detach().unsqueeze(0)
# ensure correct device (`best_point` always returns a CPU tensor)
recommended_point = recommended_point.to(device=self.device)
# Extract acquisition value (TODO: Make this less painful and repetitive)
if non_fixed_idcs is not None:
recommended_point = recommended_point[..., non_fixed_idcs]
current_value = best_point_acqf(recommended_point).max()
return current_value
def _instantiate_KG(
model: Model,
objective: Optional[MCAcquisitionObjective] = None,
posterior_transform: Optional[PosteriorTransform] = None,
qmc: bool = True,
n_fantasies: int = 64,
mc_samples: int = 256,
num_trace_observations: int = 0,
seed_inner: Optional[int] = None,
seed_outer: Optional[int] = None,
X_pending: Optional[Tensor] = None,
current_value: Optional[Tensor] = None,
target_fidelities: Optional[Dict[int, float]] = None,
fidelity_weights: Optional[Dict[int, float]] = None,
cost_intercept: float = 1.0,
) -> qKnowledgeGradient:
r"""Instantiate either a `qKnowledgeGradient` or `qMultiFidelityKnowledgeGradient`
acquisition function depending on whether `target_fidelities` is defined.
"""
sampler_cls = SobolQMCNormalSampler if qmc else IIDNormalSampler
fantasy_sampler = sampler_cls(
sample_shape=torch.Size([n_fantasies]), seed=seed_outer
)
if isinstance(objective, MCAcquisitionObjective):
inner_sampler = sampler_cls(
sample_shape=torch.Size([mc_samples]), seed=seed_inner
)
else:
inner_sampler = None
if target_fidelities:
if fidelity_weights is None:
fidelity_weights = {f: 1.0 for f in target_fidelities}
if not set(target_fidelities) == set(fidelity_weights):
raise RuntimeError(
"Must provide the same indices for target_fidelities "
f"({set(target_fidelities)}) and fidelity_weights "
f" ({set(fidelity_weights)})."
)
cost_model = AffineFidelityCostModel(
fidelity_weights=fidelity_weights, fixed_cost=cost_intercept
)
cost_aware_utility = InverseCostWeightedUtility(cost_model=cost_model)
def project(X: Tensor) -> Tensor:
return project_to_target_fidelity(X=X, target_fidelities=target_fidelities)
def expand(X: Tensor) -> Tensor:
return expand_trace_observations(
X=X,
fidelity_dims=sorted(target_fidelities), # pyre-ignore: [6]
num_trace_obs=num_trace_observations,
)
return qMultiFidelityKnowledgeGradient(
model=model,
num_fantasies=n_fantasies,
sampler=fantasy_sampler,
objective=objective,
posterior_transform=posterior_transform,
inner_sampler=inner_sampler,
X_pending=X_pending,
current_value=current_value,
cost_aware_utility=cost_aware_utility,
project=project,
expand=expand,
)
return qKnowledgeGradient(
model=model,
num_fantasies=n_fantasies,
sampler=fantasy_sampler,
objective=objective,
posterior_transform=posterior_transform,
inner_sampler=inner_sampler,
X_pending=X_pending,
current_value=current_value,
)
def _optimize_and_get_candidates(
acq_function: qKnowledgeGradient,
bounds_: Tensor,
n: int,
num_restarts: int,
raw_samples: int,
# pyre-fixme[24]: Generic type `dict` expects 2 type parameters, use
# `typing.Dict` to avoid runtime subscripting errors.
optimizer_options: Dict,
rounding_func: Optional[Callable[[Tensor], Tensor]],
inequality_constraints: Optional[List[Tuple[Tensor, Tensor, float]]],
fixed_features: Optional[Dict[int, float]],
) -> Tensor:
r"""Generates initial conditions for optimization, optimize the acquisition
function, and return the candidates.
"""
batch_initial_conditions = gen_one_shot_kg_initial_conditions(
acq_function=acq_function,
bounds=bounds_,
q=n,
num_restarts=num_restarts,
raw_samples=raw_samples,
options={
"frac_random": optimizer_options.get("frac_random", 0.1),
"num_inner_restarts": num_restarts,
"raw_inner_samples": raw_samples,
},
)
botorch_rounding_func = get_rounding_func(rounding_func)
opt_options: Dict[str, Union[bool, float, int, str]] = {
"batch_limit": 8,
"maxiter": 200,
"method": "L-BFGS-B",
"nonnegative": False,
}
opt_options.update(optimizer_options.get("options", {}))
candidates, _ = optimize_acqf(
acq_function=acq_function,
bounds=bounds_,
q=n,
inequality_constraints=inequality_constraints,
fixed_features=fixed_features,
post_processing_func=botorch_rounding_func,
num_restarts=num_restarts,
raw_samples=raw_samples,
options=opt_options,
batch_initial_conditions=batch_initial_conditions,
)
new_x = candidates.detach().cpu()
return new_x
