Source code for botorch.optim.utils

#!/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.

Utilities for optimization.

from __future__ import annotations

import warnings
from collections import OrderedDict
from inspect import signature
from typing import Any, Callable, Dict, List, Optional, Tuple, Union

import numpy as np
import torch
from botorch.acquisition.acquisition import AcquisitionFunction
from botorch.exceptions.errors import BotorchError
from botorch.exceptions.warnings import BotorchWarning
from botorch.models.gpytorch import GPyTorchModel, ModelListGPyTorchModel
from botorch.optim.numpy_converter import set_params_with_array, TorchAttr
from gpytorch.mlls.exact_marginal_log_likelihood import ExactMarginalLogLikelihood
from gpytorch.mlls.marginal_log_likelihood import MarginalLogLikelihood
from gpytorch.mlls.sum_marginal_log_likelihood import SumMarginalLogLikelihood
from gpytorch.utils.errors import NanError, NotPSDError
from torch import Tensor

[docs]def sample_all_priors(model: GPyTorchModel) -> None: r"""Sample from hyperparameter priors (in-place). Args: model: A GPyTorchModel. """ for _, module, prior, closure, setting_closure in model.named_priors(): if setting_closure is None: raise RuntimeError( "Must provide inverse transform to be able to sample from prior." ) try: setting_closure(module, prior.sample(closure(module).shape)) except NotImplementedError: warnings.warn( f"`rsample` not implemented for {type(prior)}. Skipping.", BotorchWarning, )
[docs]def columnwise_clamp( X: Tensor, lower: Optional[Union[float, Tensor]] = None, upper: Optional[Union[float, Tensor]] = None, raise_on_violation: bool = False, ) -> Tensor: r"""Clamp values of a Tensor in column-wise fashion (with support for t-batches). This function is useful in conjunction with optimizers from the torch.optim package, which don't natively handle constraints. If you apply this after a gradient step you can be fancy and call it "projected gradient descent". This funtion is also useful for post-processing candidates generated by the scipy optimizer that satisfy bounds only up to numerical accuracy. Args: X: The `b x n x d` input tensor. If 2-dimensional, `b` is assumed to be 1. lower: The column-wise lower bounds. If scalar, apply bound to all columns. upper: The column-wise upper bounds. If scalar, apply bound to all columns. raise_on_violation: If `True`, raise an exception when the elments in `X` are out of the specified bounds (up to numerical accuracy). This is useful for post-processing candidates generated by optimizers that satisfy imposed bounds only up to numerical accuracy. Returns: The clamped tensor. """ min_bounds = _expand_bounds(lower, X) max_bounds = _expand_bounds(upper, X) if min_bounds is not None and max_bounds is not None: if torch.any(min_bounds > max_bounds): raise ValueError("Minimum values must be <= maximum values") Xout = X if min_bounds is not None: Xout = Xout.max(min_bounds) if max_bounds is not None: Xout = Xout.min(max_bounds) if raise_on_violation and not torch.allclose(Xout, X): raise BotorchError("Original value(s) are out of bounds.") return Xout
[docs]def fix_features( X: Tensor, fixed_features: Optional[Dict[int, Optional[float]]] = None ) -> Tensor: r"""Fix feature values in a Tensor. The fixed features will have zero gradient in downstream calculations. Args: X: input Tensor with shape `... x p`, where `p` is the number of features fixed_features: A dictionary with keys as column indices and values equal to what the feature should be set to in `X`. If the value is None, that column is just considered fixed. Keys should be in the range `[0, p - 1]`. Returns: The tensor X with fixed features. """ if fixed_features is None: return X else: return [ X[..., i].unsqueeze(-1) if i not in fixed_features else _fix_feature(X[..., i].unsqueeze(-1), fixed_features[i]) for i in range(X.shape[-1]) ], dim=-1, )
def _fix_feature(Z: Tensor, value: Optional[float]) -> Tensor: r"""Helper function returns a Tensor like `Z` filled with `value` if provided.""" if value is None: return Z.detach() return torch.full_like(Z, value) def _expand_bounds( bounds: Optional[Union[float, Tensor]], X: Tensor ) -> Optional[Tensor]: r"""Expands a tensor representing bounds. Expand the dimension of bounds if necessary such that the dimension of bounds is the same as the dimension of `X`. Args: bounds: a bound (either upper or lower) of each entry of `X`. If this is a single float, then all entries have the same bound. Different sizes of tensors can be used to specify custom bounds. E.g., a `d`-dim tensor can be used to specify bounds for each column (last dimension) of `X`, or a tensor with same shape as `X` can be used to specify a different bound for each entry of `X`. X: `... x d` tensor Returns: A tensor of bounds expanded to the size of `X` if bounds is not None, and None if bounds is None. """ if bounds is not None: if not torch.is_tensor(bounds): bounds = torch.tensor(bounds) try: ebounds = bounds.expand_as(X) except RuntimeError: raise RuntimeError("Bounds must be broadcastable to X!") return, device=X.device) else: return None def _get_extra_mll_args( mll: MarginalLogLikelihood, ) -> Union[List[Tensor], List[List[Tensor]]]: r"""Obtain extra arguments for MarginalLogLikelihood objects. Get extra arguments (beyond the model output and training targets) required for the particular type of MarginalLogLikelihood for a forward pass. Args: mll: The MarginalLogLikelihood module. Returns: Extra arguments for the MarginalLogLikelihood. Returns an empty list if the mll type is unknown. """ if isinstance(mll, ExactMarginalLogLikelihood): return list(mll.model.train_inputs) elif isinstance(mll, SumMarginalLogLikelihood): return [list(x) for x in mll.model.train_inputs] return [] def _filter_kwargs(function: Callable, **kwargs: Any) -> Any: r"""Filter out kwargs that are not applicable for a given function. Return a copy of given kwargs dict with only the required kwargs.""" return {k: v for k, v in kwargs.items() if k in signature(function).parameters} def _scipy_objective_and_grad( x: np.ndarray, mll: MarginalLogLikelihood, property_dict: Dict[str, TorchAttr] ) -> Tuple[float, np.ndarray]: r"""Get objective and gradient in format that scipy expects. Args: x: The (flattened) input parameters. mll: The MarginalLogLikelihood module to evaluate. property_dict: The property dictionary required to "unflatten" the input parameter vector, as generated by `module_to_array`. Returns: 2-element tuple containing - The objective value. - The gradient of the objective. """ mll = set_params_with_array(mll, x, property_dict) train_inputs, train_targets = mll.model.train_inputs, mll.model.train_targets mll.zero_grad() try: # catch linear algebra errors in gpytorch output = mll.model(*train_inputs) args = [output, train_targets] + _get_extra_mll_args(mll) loss = -mll(*args).sum() except RuntimeError as e: return _handle_numerical_errors(error=e, x=x) loss.backward() param_dict = OrderedDict(mll.named_parameters()) grad = [] for p_name in property_dict: t = param_dict[p_name].grad if t is None: # this deals with parameters that do not affect the loss grad.append(np.zeros(property_dict[p_name].shape.numel())) else: grad.append(t.detach().view(-1).cpu().double().clone().numpy()) mll.zero_grad() return loss.item(), np.concatenate(grad) def _handle_numerical_errors( error: RuntimeError, x: np.ndarray ) -> Tuple[float, np.ndarray]: if isinstance(error, NotPSDError): raise error error_message = error.args[0] if len(error.args) > 0 else "" if ( isinstance(error, NanError) or "singular" in error_message # old pytorch message or "input is not positive-definite" in error_message # since pytorch #63864 ): return float("nan"), np.full_like(x, "nan") raise error # pragma: nocover
[docs]def get_X_baseline(acq_function: AcquisitionFunction) -> Optional[Tensor]: r"""Extract X_baseline from an acquisition function. This tries to find the baseline set of points. First, this checks if the acquisition function has an `X_baseline` attribute. If it does not, then this method attempts to use the model's `train_inputs` as `X_baseline`. Args: acq_function: The acquisition function. Returns An optional `n x d`-dim tensor of baseline points. This is None if no baseline points are found. """ try: X = acq_function.X_baseline # if there are no baseline points, use training points if X.shape[0] == 0: raise BotorchError except (BotorchError, AttributeError): try: # for entropy MOO methods model = acq_function.mo_model except AttributeError: try: # some acquisition functions do not have a model attribute # e.g. FixedFeatureAcquisitionFunction model = acq_function.model except AttributeError: warnings.warn("Failed to extract X_baseline.", BotorchWarning) return try: # make sure input transforms are not applied model.train() if isinstance(model, ModelListGPyTorchModel): X = model.models[0].train_inputs[0] else: X = model.train_inputs[0] except (BotorchError, AttributeError): warnings.warn("Failed to extract X_baseline.", BotorchWarning) return # just use one batch while X.ndim > 2: X = X[0] return X