Source code for botorch.optim.homotopy

# 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.

from __future__ import annotations

import math
from dataclasses import dataclass
from typing import Callable, List, Optional, Union

import torch
from torch import Tensor
from torch.nn import Parameter



[docs]
class FixedHomotopySchedule:
    """Homotopy schedule with a fixed list of values."""

    def __init__(self, values: List[float]) -> None:
        r"""Initialize FixedHomotopySchedule.

        Args:
            values: A list of values used in homotopy
        """
        self._values = values
        self.idx = 0

    @property
    def num_steps(self) -> int:
        return len(self._values)

    @property
    def value(self) -> float:
        return self._values[self.idx]

    @property
    def should_stop(self) -> bool:
        return self.idx == len(self._values)


[docs]
    def restart(self) -> None:
        self.idx = 0



[docs]
    def step(self) -> None:
        self.idx += 1





[docs]
class LinearHomotopySchedule(FixedHomotopySchedule):
    """Linear homotopy schedule."""

    def __init__(self, start: float, end: float, num_steps: int) -> None:
        r"""Initialize LinearHomotopySchedule.

        Args:
            start: start value of homotopy
            end: end value of homotopy
            num_steps: number of steps in the homotopy schedule.
        """
        super().__init__(
            values=torch.linspace(start, end, num_steps, dtype=torch.double).tolist()
        )




[docs]
class LogLinearHomotopySchedule(FixedHomotopySchedule):
    """Log-linear homotopy schedule."""

    def __init__(self, start: float, end: float, num_steps: int):
        r"""Initialize LogLinearHomotopySchedule.

        Args:
            start: start value of homotopy
            end: end value of homotopy
            num_steps: number of steps in the homotopy schedule.
        """
        super().__init__(
            values=torch.logspace(
                math.log10(start), math.log10(end), num_steps, dtype=torch.double
            ).tolist()
        )




[docs]
@dataclass
class HomotopyParameter:
    r"""Homotopy parameter.

    The parameter is expected to either be a torch parameter or a torch tensor which may
    correspond to a buffer of a module. The parameter has a corresponding schedule.
    """

    parameter: Union[Parameter, Tensor]
    schedule: FixedHomotopySchedule




[docs]
class Homotopy:
    """Generic homotopy class.

    This class is designed to be used in `optimize_acqf_homotopy`. Given a set of
    homotopy parameters and corresponding schedules we step through the homotopies
    until we have solved the final problem. We additionally support passing in a list
    of callbacks that will be executed each time `step`, `reset`, and `restart` are
    called.
    """

    def __init__(
        self,
        homotopy_parameters: List[HomotopyParameter],
        callbacks: Optional[List[Callable]] = None,
    ) -> None:
        r"""Initialize the homotopy.

        Args:
            homotopy_parameters: List of homotopy parameters
            callbacks: Optional list of callbacks that are executed each time
                `restart`, `reset`, or `step` are called. These may be used to, e.g.,
                reinitialize the acquisition function which is needed when using qNEHVI.
        """
        self._homotopy_parameters = homotopy_parameters
        self._callbacks = callbacks or []
        self._original_values = [
            hp.parameter.item() for hp in self._homotopy_parameters
        ]
        assert all(
            isinstance(hp.parameter, Parameter) or isinstance(hp.parameter, Tensor)
            for hp in self._homotopy_parameters
        )
        # Assume the same number of steps for now
        assert len({h.schedule.num_steps for h in self._homotopy_parameters}) == 1
        # Initialize the homotopy parameters
        self.restart()

    def _execute_callbacks(self) -> None:
        """Execute the callbacks."""
        for callback in self._callbacks:
            callback()

    @property
    def should_stop(self) -> bool:
        """Returns true if all schedules have reached the end."""
        return all(h.schedule.should_stop for h in self._homotopy_parameters)


[docs]
    def restart(self) -> None:
        """Restart the homotopy to use the initial value in the schedule."""
        for hp in self._homotopy_parameters:
            hp.schedule.restart()
            hp.parameter.data.fill_(hp.schedule.value)
        self._execute_callbacks()



[docs]
    def reset(self) -> None:
        """Reset the homotopy parameter to their original values."""
        for hp, val in zip(self._homotopy_parameters, self._original_values):
            hp.parameter.data.fill_(val)
        self._execute_callbacks()



[docs]
    def step(self) -> None:
        """Take a step according to the schedules."""
        for hp in self._homotopy_parameters:
            hp.schedule.step()
            if not hp.schedule.should_stop:
                hp.parameter.data.fill_(hp.schedule.value)
        self._execute_callbacks()