Source code for botorch.sampling.get_sampler

#!/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.


from typing import Any, Type, Union

import torch
from botorch.logging import logger
from botorch.posteriors.deterministic import DeterministicPosterior
from botorch.posteriors.gpytorch import GPyTorchPosterior
from botorch.posteriors.posterior import Posterior
from botorch.posteriors.posterior_list import PosteriorList
from botorch.posteriors.torch import TorchPosterior
from botorch.posteriors.transformed import TransformedPosterior
from botorch.sampling.base import MCSampler
from botorch.sampling.list_sampler import ListSampler
from botorch.sampling.normal import (
    IIDNormalSampler,
    NormalMCSampler,
    SobolQMCNormalSampler,
)
from botorch.sampling.stochastic_samplers import StochasticSampler
from botorch.utils.dispatcher import Dispatcher
from gpytorch.distributions import MultivariateNormal
from torch.distributions import Distribution
from torch.quasirandom import SobolEngine


def _posterior_to_distribution_encoder(
    posterior: Posterior,
) -> Union[Type[Distribution], Type[Posterior]]:
    r"""An encoder returning the type of the distribution for `TorchPosterior`
    and the type of the posterior for the rest.
    """
    if isinstance(posterior, TorchPosterior):
        return type(posterior.distribution)
    return type(posterior)


GetSampler = Dispatcher("get_sampler", encoder=_posterior_to_distribution_encoder)


[docs]def get_sampler(
    posterior: TorchPosterior, sample_shape: torch.Size, **kwargs: Any
) -> MCSampler:
    r"""Get the sampler for the given posterior.

    The sampler can be used as `sampler(posterior)` to produce samples
    suitable for use in acquisition function optimization via SAA.

    Args:
        posterior: A `Posterior` to get the sampler for.
        sample_shape: The sample shape of the samples produced by the
            given sampler. The full shape of the resulting samples is
            given by `posterior._extended_shape(sample_shape)`.
        kwargs: Optional kwargs, passed down to the samplers during construction.

    Returns:
        The `MCSampler` object for the given posterior.
    """
    kwargs["sample_shape"] = sample_shape
    return GetSampler(posterior, **kwargs)


@GetSampler.register(MultivariateNormal)
def _get_sampler_mvn(
    posterior: GPyTorchPosterior, sample_shape: torch.Size, **kwargs: Any
) -> NormalMCSampler:
    r"""The Sobol normal sampler for the `MultivariateNormal` posterior.

    If the output dim is too large, falls back to `IIDNormalSampler`.
    """
    sampler = SobolQMCNormalSampler(sample_shape=sample_shape, **kwargs)
    collapsed_shape = sampler._get_collapsed_shape(posterior=posterior)
    base_collapsed_shape = collapsed_shape[len(sample_shape) :]
    if base_collapsed_shape.numel() > SobolEngine.MAXDIM:
        logger.warning(
            f"Output dim {base_collapsed_shape.numel()} is too large for the "
            "Sobol engine. Using IIDNormalSampler instead."
        )
        sampler = IIDNormalSampler(sample_shape=sample_shape, **kwargs)
    return sampler


@GetSampler.register(TransformedPosterior)
def _get_sampler_derived(
    posterior: TransformedPosterior, sample_shape: torch.Size, **kwargs: Any
) -> MCSampler:
    r"""Get the sampler for the underlying posterior."""
    return get_sampler(
        posterior=posterior._posterior, sample_shape=sample_shape, **kwargs
    )


@GetSampler.register(PosteriorList)
def _get_sampler_list(
    posterior: PosteriorList, sample_shape: torch.Size, **kwargs: Any
) -> MCSampler:
    r"""Get the `ListSampler` with the appropriate list of samplers."""
    samplers = [
        get_sampler(posterior=p, sample_shape=sample_shape, **kwargs)
        for p in posterior.posteriors
    ]
    return ListSampler(*samplers)


@GetSampler.register(DeterministicPosterior)
def _get_sampler_deterministic(
    posterior: DeterministicPosterior, sample_shape: torch.Size, **kwargs: Any
) -> MCSampler:
    r"""Get the dummy `StochasticSampler` for the `DeterministicPosterior`."""
    return StochasticSampler(sample_shape=sample_shape, **kwargs)


@GetSampler.register(object)
def _not_found_error(
    posterior: Posterior, sample_shape: torch.Size, **kwargs: Any
) -> None:
    raise NotImplementedError(
        f"A registered `MCSampler` for posterior {posterior} is not found. You can "
        "implement and register one using `@GetSampler.register`."
    )