#!/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.
r"""
Abstract base module for all BoTorch models.
"""
from __future__ import annotations
import warnings
from abc import ABC, abstractmethod
from collections import defaultdict
from copy import deepcopy
from typing import Any, Dict, List, Optional, Callable
import numpy as np
import torch
from botorch import settings
from botorch.models.utils import fantasize as fantasize_flag
from botorch.posteriors import Posterior, PosteriorList
from botorch.sampling.samplers import MCSampler
from botorch.utils.containers import TrainingData
from torch import Tensor
from torch.nn import Module, ModuleList
[docs]class Model(Module, ABC):
r"""Abstract base class for BoTorch models.
Args:
_has_transformed_inputs: A boolean denoting whether `train_inputs` are currently
stored as transformed or not.
_original_train_inputs: A Tensor storing the original train inputs for use in
`_revert_to_original_inputs`. Note that this is necessary since
transform / untransform cycle introduces numerical errors which lead
to upstream errors during training.
"""
_has_transformed_inputs: bool = False
_original_train_inputs: Optional[Tensor] = None
[docs] @abstractmethod
def posterior(
self,
X: Tensor,
output_indices: Optional[List[int]] = None,
observation_noise: bool = False,
posterior_transform: Optional[Callable[[Posterior], Posterior]] = None,
**kwargs: Any,
) -> Posterior:
r"""Computes the posterior over model outputs at the provided points.
Note: The input transforms should be applied here using
`self.transform_inputs(X)` after the `self.eval()` call and before
any `model.forward` or `model.likelihood` calls.
Args:
X: A `b x q x d`-dim Tensor, where `d` is the dimension of the
feature space, `q` is the number of points considered jointly,
and `b` is the batch dimension.
output_indices: A list of indices, corresponding to the outputs over
which to compute the posterior (if the model is multi-output).
Can be used to speed up computation if only a subset of the
model's outputs are required for optimization. If omitted,
computes the posterior over all model outputs.
observation_noise: If True, add observation noise to the posterior.
posterior_transform: An optional PosteriorTransform.
Returns:
A `Posterior` object, representing a batch of `b` joint distributions
over `q` points and `m` outputs each.
"""
pass # pragma: no cover
@property
def batch_shape(self) -> torch.Size:
r"""The batch shape of the model.
This is a batch shape from an I/O perspective, independent of the internal
representation of the model (as e.g. in BatchedMultiOutputGPyTorchModel).
For a model with `m` outputs, a `test_batch_shape x q x d`-shaped input `X`
to the `posterior` method returns a Posterior object over an output of
shape `broadcast(test_batch_shape, model.batch_shape) x q x m`.
"""
cls_name = self.__class__.__name__
raise NotImplementedError(f"{cls_name} does not define batch_shape property")
@property
def num_outputs(self) -> int:
r"""The number of outputs of the model."""
cls_name = self.__class__.__name__
raise NotImplementedError(f"{cls_name} does not define num_outputs property")
[docs] def subset_output(self, idcs: List[int]) -> Model:
r"""Subset the model along the output dimension.
Args:
idcs: The output indices to subset the model to.
Returns:
A `Model` object of the same type and with the same parameters as
the current model, subset to the specified output indices.
"""
raise NotImplementedError
[docs] def condition_on_observations(self, X: Tensor, Y: Tensor, **kwargs: Any) -> Model:
r"""Condition the model on new observations.
Args:
X: A `batch_shape x n' x d`-dim Tensor, where `d` is the dimension of
the feature space, `n'` is the number of points per batch, and
`batch_shape` is the batch shape (must be compatible with the
batch shape of the model).
Y: A `batch_shape' x n' x m`-dim Tensor, where `m` is the number of
model outputs, `n'` is the number of points per batch, and
`batch_shape'` is the batch shape of the observations.
`batch_shape'` must be broadcastable to `batch_shape` using
standard broadcasting semantics. If `Y` has fewer batch dimensions
than `X`, it is assumed that the missing batch dimensions are
the same for all `Y`.
Returns:
A `Model` object of the same type, representing the original model
conditioned on the new observations `(X, Y)` (and possibly noise
observations passed in via kwargs).
"""
raise NotImplementedError(
f"`condition_on_observations` not implemented for {self.__class__.__name__}"
)
[docs] def fantasize(
self,
X: Tensor,
sampler: MCSampler,
observation_noise: bool = True,
**kwargs: Any,
) -> Model:
r"""Construct a fantasy model.
Constructs a fantasy model in the following fashion:
(1) compute the model posterior at `X` (including observation noise if
`observation_noise=True`).
(2) sample from this posterior (using `sampler`) to generate "fake"
observations.
(3) condition the model on the new fake observations.
Args:
X: A `batch_shape x n' x d`-dim Tensor, where `d` is the dimension of
the feature space, `n'` is the number of points per batch, and
`batch_shape` is the batch shape (must be compatible with the
batch shape of the model).
sampler: The sampler used for sampling from the posterior at `X`.
observation_noise: If True, include observation noise.
Returns:
The constructed fantasy model.
"""
propagate_grads = kwargs.pop("propagate_grads", False)
with fantasize_flag():
with settings.propagate_grads(propagate_grads):
post_X = self.posterior(X, observation_noise=observation_noise)
Y_fantasized = sampler(post_X) # num_fantasies x batch_shape x n' x m
return self.condition_on_observations(
X=self.transform_inputs(X), Y=Y_fantasized, **kwargs
)
def _set_transformed_inputs(self) -> None:
r"""Update training inputs with transformed inputs."""
if hasattr(self, "input_transform") and not self._has_transformed_inputs:
if hasattr(self, "train_inputs"):
self._original_train_inputs = self.train_inputs[0]
with torch.no_grad():
X_tf = self.input_transform.preprocess_transform(
self.train_inputs[0]
)
self.set_train_data(X_tf, strict=False)
self._has_transformed_inputs = True
else:
warnings.warn(
"Could not update `train_inputs` with transformed inputs "
f"since {self.__class__.__name__} does not have a `train_inputs` "
"attribute. Make sure that the `input_transform` is applied to "
"both the train inputs and test inputs.",
RuntimeWarning,
)
def _revert_to_original_inputs(self) -> None:
r"""Revert training inputs back to original."""
if hasattr(self, "input_transform") and self._has_transformed_inputs:
self.set_train_data(self._original_train_inputs, strict=False)
self._has_transformed_inputs = False
[docs] def eval(self) -> Model:
r"""Puts the model in `eval` mode and sets the transformed inputs."""
self._set_transformed_inputs()
return super().eval()
[docs] def train(self, mode: bool = True) -> Model:
r"""Puts the model in `train` mode and reverts to the original inputs.
Args:
mode: A boolean denoting whether to put in `train` or `eval` mode.
If `False`, model is put in `eval` mode.
"""
if mode:
self._revert_to_original_inputs()
else:
self._set_transformed_inputs()
return super().train(mode=mode)
[docs]class ModelList(Model):
r"""Container for a list of models."""
def __init__(self, *models: Model) -> None:
r"""A multi-output Model represented by a list of independent models.
Args:
*models: A variable number of models.
Example:
>>> m_1 = SingleTaskGP(train_X, train_Y
>>> m_2 = GenericDeterministicModel(lambda x: x.sum(dim=-1))
>>> m_12 = ModelList(m_1, m_2)
>>> m_12.predict(test_X)
"""
super().__init__()
self.models = ModuleList(models)
def _get_group_subset_indices(
self, idcs: Optional[List[int]]
) -> Dict[int, List[int]]:
r"""Convert global subset indices to indices for the individual models.
Args:
idcs: A list of inidices to which the `ModelList` model is to be
subset to.
Returns:
A dictionary mapping model indices to subset indices of the
respective model in the `ModelList`.
"""
if idcs is None:
return {i: None for i in range(len(self.models))}
output_sizes = [model.num_outputs for model in self.models]
cum_output_sizes = np.cumsum(output_sizes)
idcs = [idx % cum_output_sizes[-1] for idx in idcs]
group_indices: Dict[int, List[int]] = defaultdict(list)
for idx in idcs:
grp_idx = int(np.argwhere(idx < cum_output_sizes)[0])
sub_idx = idx - int(np.sum(output_sizes[:grp_idx]))
group_indices[grp_idx].append(sub_idx)
return group_indices
[docs] def posterior(
self,
X: Tensor,
output_indices: Optional[List[int]] = None,
observation_noise: bool = False,
**kwargs: Any,
) -> Posterior:
r"""Computes the posterior over model outputs at the provided points.
Note: The input transforms should be applied here using
`self.transform_inputs(X)` after the `self.eval()` call and before
any `model.forward` or `model.likelihood` calls.
Args:
X: A `b x q x d`-dim Tensor, where `d` is the dimension of the
feature space, `q` is the number of points considered jointly,
and `b` is the batch dimension.
output_indices: A list of indices, corresponding to the outputs over
which to compute the posterior (if the model is multi-output).
Can be used to speed up computation if only a subset of the
model's outputs are required for optimization. If omitted,
computes the posterior over all model outputs.
observation_noise: If True, add observation noise to the posterior.
Returns:
A `Posterior` object, representing a batch of `b` joint distributions
over `q` points and `m` outputs each.
"""
group_indices = self._get_group_subset_indices(idcs=output_indices)
posteriors = [
self.models[i].posterior(
X=X, output_indices=idcs, observation_noise=observation_noise
)
for i, idcs in group_indices.items()
]
return PosteriorList(*posteriors)
@property
def batch_shape(self) -> torch.Size:
r"""The batch shape of the model.
This is a batch shape from an I/O perspective, independent of the internal
representation of the model (as e.g. in BatchedMultiOutputGPyTorchModel).
For a model with `m` outputs, a `test_batch_shape x q x d`-shaped input `X`
to the `posterior` method returns a Posterior object over an output of
shape `broadcast(test_batch_shape, model.batch_shape) x q x m`.
"""
batch_shape = self.models[0].batch_shape
if all(batch_shape == m.batch_shape for m in self.models[1:]):
return batch_shape
# TODO: Allow broadcasting of model batch shapes
raise NotImplementedError(
f"`{self.__class__.__name__}.batch_shape` is only supported if all "
"constituent models have the same `batch_shape`."
)
@property
def num_outputs(self) -> int:
r"""The number of outputs of the model.
Equal to the sum of the number of outputs of the individual models
in the ModelList.
"""
return sum(model.num_outputs for model in self.models)
[docs] def subset_output(self, idcs: List[int]) -> Model:
r"""Subset the model along the output dimension.
Args:
idcs: The output indices to subset the model to. Relative to the
overall number of outputs of the model.
Returns:
A `Model` (either a `ModelList` or one of the submodels) with
the outputs subset to the indices in `idcs`.
Internally, this drops (if single-output) or subsets (if multi-output)
the constitutent models and returns them as a `ModelList`. If the
result is a single (possibly subset) model from the list, returns this
model (instead of forming a degenerate singe-model `ModelList`).
For instance, if `m = ModelList(m1, m2)` with `m1` a two-output model
and `m2` a single-output model, then `m.subset_output([1]) ` will return
the model `m1` subset to its second output.
"""
group_indices = self._get_group_subset_indices(idcs=idcs)
subset_models = [
deepcopy(self.models[grp_idx].subset_output(idcs=sub_idcs))
for grp_idx, sub_idcs in group_indices.items()
]
if len(subset_models) == 1:
return subset_models[0]
return self.__class__(*subset_models)
