#!/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"""
References
.. [burt2020svgp]
David R. Burt and Carl Edward Rasmussen and Mark van der Wilk,
Convergence of Sparse Variational Inference in Gaussian Process Regression,
Journal of Machine Learning Research, 2020,
http://jmlr.org/papers/v21/19-1015.html.
.. [hensman2013svgp]
James Hensman and Nicolo Fusi and Neil D. Lawrence, Gaussian Processes
for Big Data, Proceedings of the 29th Conference on Uncertainty in
Artificial Intelligence, 2013, https://arxiv.org/abs/1309.6835.
.. [moss2023ipa]
Henry B. Moss and Sebastian W. Ober and Victor Picheny,
Inducing Point Allocation for Sparse Gaussian Processes
in High-Throughput Bayesian Optimization,Proceedings of
the 25th International Conference on Artificial Intelligence
and Statistics, 2023, https://arxiv.org/pdf/2301.10123.pdf.
"""
from __future__ import annotations
import copy
import warnings
from typing import Optional, Type, Union
import torch
from botorch.models.gpytorch import GPyTorchModel
from botorch.models.transforms.input import InputTransform
from botorch.models.transforms.outcome import OutcomeTransform
from botorch.models.utils import validate_input_scaling
from botorch.models.utils.inducing_point_allocators import (
GreedyVarianceReduction,
InducingPointAllocator,
)
from botorch.posteriors.gpytorch import GPyTorchPosterior
from gpytorch.constraints import GreaterThan
from gpytorch.distributions import MultivariateNormal
from gpytorch.kernels import Kernel, MaternKernel, ScaleKernel
from gpytorch.likelihoods import (
GaussianLikelihood,
Likelihood,
MultitaskGaussianLikelihood,
)
from gpytorch.means import ConstantMean, Mean
from gpytorch.models import ApproximateGP
from gpytorch.priors import GammaPrior
from gpytorch.utils.memoize import clear_cache_hook
from gpytorch.variational import (
_VariationalDistribution,
_VariationalStrategy,
CholeskyVariationalDistribution,
IndependentMultitaskVariationalStrategy,
VariationalStrategy,
)
from torch import Tensor
MIN_INFERRED_NOISE_LEVEL = 1e-4
[docs]class ApproximateGPyTorchModel(GPyTorchModel):
r"""
Botorch wrapper class for various (variational) approximate GP models in
GPyTorch.
This can either include stochastic variational GPs (SVGPs) or
variational implementations of weight space approximate GPs.
"""
def __init__(
self,
model: Optional[ApproximateGP] = None,
likelihood: Optional[Likelihood] = None,
num_outputs: int = 1,
*args,
**kwargs,
) -> None:
r"""
Args:
model: Instance of gpytorch.approximate GP models. If omitted,
constructs a `_SingleTaskVariationalGP`.
likelihood: Instance of a GPyTorch likelihood. If omitted, uses a
either a `GaussianLikelihood` (if `num_outputs=1`) or a
`MultitaskGaussianLikelihood`(if `num_outputs>1`).
num_outputs: Number of outputs expected for the GP model.
args: Optional positional arguments passed to the
`_SingleTaskVariationalGP` constructor if no model is provided.
kwargs: Optional keyword arguments passed to the
`_SingleTaskVariationalGP` constructor if no model is provided.
"""
super().__init__()
self.model = (
_SingleTaskVariationalGP(num_outputs=num_outputs, *args, **kwargs)
if model is None
else model
)
if likelihood is None:
if num_outputs == 1:
self.likelihood = GaussianLikelihood()
else:
self.likelihood = MultitaskGaussianLikelihood(num_tasks=num_outputs)
else:
self.likelihood = likelihood
self._desired_num_outputs = num_outputs
@property
def num_outputs(self):
return self._desired_num_outputs
[docs] def posterior(
self, X, output_indices=None, observation_noise=False, *args, **kwargs
) -> GPyTorchPosterior:
self.eval() # make sure model is in eval mode
# input transforms are applied at `posterior` in `eval` mode, and at
# `model.forward()` at the training time
X = self.transform_inputs(X)
# check for the multi-batch case for multi-outputs b/c this will throw
# warnings
X_ndim = X.ndim
if self.num_outputs > 1 and X_ndim > 2:
X = X.unsqueeze(-3).repeat(*[1] * (X_ndim - 2), self.num_outputs, 1, 1)
dist = self.model(X)
if observation_noise:
dist = self.likelihood(dist, *args, **kwargs)
posterior = GPyTorchPosterior(distribution=dist)
if hasattr(self, "outcome_transform"):
posterior = self.outcome_transform.untransform_posterior(posterior)
return posterior
[docs] def forward(self, X, *args, **kwargs) -> MultivariateNormal:
if self.training:
X = self.transform_inputs(X)
return self.model(X)
class _SingleTaskVariationalGP(ApproximateGP):
"""
Base class wrapper for a stochastic variational Gaussian Process (SVGP)
model [hensman2013svgp]_.
Uses by default pivoted Cholesky initialization for allocating inducing points,
however, custom inducing point allocators can be provided.
"""
def __init__(
self,
train_X: Tensor,
train_Y: Optional[Tensor] = None,
num_outputs: int = 1,
learn_inducing_points=True,
covar_module: Optional[Kernel] = None,
mean_module: Optional[Mean] = None,
variational_distribution: Optional[_VariationalDistribution] = None,
variational_strategy: Type[_VariationalStrategy] = VariationalStrategy,
inducing_points: Optional[Union[Tensor, int]] = None,
inducing_point_allocator: Optional[InducingPointAllocator] = None,
) -> None:
r"""
Args:
train_X: Training inputs (due to the ability of the SVGP to sub-sample
this does not have to be all of the training inputs).
train_Y: Training targets (optional).
num_outputs: Number of output responses per input.
covar_module: Kernel function. If omitted, uses a `MaternKernel`.
mean_module: Mean of GP model. If omitted, uses a `ConstantMean`.
variational_distribution: Type of variational distribution to use
(default: CholeskyVariationalDistribution), the properties of the
variational distribution will encourage scalability or ease of
optimization.
variational_strategy: Type of variational strategy to use (default:
VariationalStrategy). The default setting uses "whitening" of the
variational distribution to make training easier.
inducing_points: The number or specific locations of the inducing points.
inducing_point_allocator: The `InducingPointAllocator` used to
initialize the inducing point locations. If omitted,
uses `GreedyVarianceReduction`.
"""
# We use the model subclass wrapper to deal with input / outcome transforms.
# The number of outputs will be correct here due to the check in
# SingleTaskVariationalGP.
input_batch_shape = train_X.shape[:-2]
aug_batch_shape = copy.deepcopy(input_batch_shape)
if num_outputs > 1:
aug_batch_shape += torch.Size((num_outputs,))
self._aug_batch_shape = aug_batch_shape
if covar_module is None:
covar_module = ScaleKernel(
base_kernel=MaternKernel(
nu=2.5,
ard_num_dims=train_X.shape[-1],
batch_shape=self._aug_batch_shape,
lengthscale_prior=GammaPrior(3.0, 6.0),
),
batch_shape=self._aug_batch_shape,
outputscale_prior=GammaPrior(2.0, 0.15),
).to(train_X)
self._subset_batch_dict = {
"mean_module.constant": -2,
"covar_module.raw_outputscale": -1,
"covar_module.base_kernel.raw_lengthscale": -3,
}
if inducing_point_allocator is None:
inducing_point_allocator = GreedyVarianceReduction()
# initialize inducing points if they are not given
if not isinstance(inducing_points, Tensor):
if inducing_points is None:
# number of inducing points is 25% the number of data points
# as a heuristic
inducing_points = int(0.25 * train_X.shape[-2])
inducing_points = inducing_point_allocator.allocate_inducing_points(
inputs=train_X,
covar_module=covar_module,
num_inducing=inducing_points,
input_batch_shape=input_batch_shape,
)
if variational_distribution is None:
variational_distribution = CholeskyVariationalDistribution(
num_inducing_points=inducing_points.shape[-2],
batch_shape=self._aug_batch_shape,
)
variational_strategy_instance = variational_strategy(
self,
inducing_points=inducing_points,
variational_distribution=variational_distribution,
learn_inducing_locations=learn_inducing_points,
)
# wrap variational models in independent multi-task variational strategy
if num_outputs > 1:
variational_strategy_instance = IndependentMultitaskVariationalStrategy(
base_variational_strategy=variational_strategy_instance,
num_tasks=num_outputs,
task_dim=-1,
)
super().__init__(variational_strategy=variational_strategy_instance)
self.mean_module = (
ConstantMean(batch_shape=self._aug_batch_shape).to(train_X)
if mean_module is None
else mean_module
)
self.covar_module = covar_module
def forward(self, X) -> MultivariateNormal:
mean_x = self.mean_module(X)
covar_x = self.covar_module(X)
latent_dist = MultivariateNormal(mean_x, covar_x)
return latent_dist
[docs]class SingleTaskVariationalGP(ApproximateGPyTorchModel):
r"""A single-task variational GP model following [hensman2013svgp]_.
By default, the inducing points are initialized though the
`GreedyVarianceReduction` of [burt2020svgp]_, which is known to be
effective for building globally accurate models. However, custom
inducing point allocators designed for specific down-stream tasks can also be
provided (see [moss2023ipa]_ for details), e.g. `GreedyImprovementReduction`
when the goal is to build a model suitable for standard BO.
A single-task variational GP using relatively strong priors on the Kernel
hyperparameters, which work best when covariates are normalized to the unit
cube and outcomes are standardized (zero mean, unit variance).
This model works in batch mode (each batch having its own hyperparameters).
When the training observations include multiple outputs, this model will use
batching to model outputs independently. However, batches of multi-output models
are not supported at this time, if you need to use those, please use a
ModelListGP.
Use this model if you have a lot of data or if your responses are non-Gaussian.
To train this model, you should use gpytorch.mlls.VariationalELBO and not
the exact marginal log likelihood.
Example:
>>> import torch
>>> from botorch.models import SingleTaskVariationalGP
>>> from gpytorch.mlls import VariationalELBO
>>>
>>> train_X = torch.rand(20, 2)
>>> model = SingleTaskVariationalGP(train_X)
>>> mll = VariationalELBO(
>>> model.likelihood, model.model, num_data=train_X.shape[-2]
>>> )
"""
def __init__(
self,
train_X: Tensor,
train_Y: Optional[Tensor] = None,
likelihood: Optional[Likelihood] = None,
num_outputs: int = 1,
learn_inducing_points: bool = True,
covar_module: Optional[Kernel] = None,
mean_module: Optional[Mean] = None,
variational_distribution: Optional[_VariationalDistribution] = None,
variational_strategy: Type[_VariationalStrategy] = VariationalStrategy,
inducing_points: Optional[Union[Tensor, int]] = None,
outcome_transform: Optional[OutcomeTransform] = None,
input_transform: Optional[InputTransform] = None,
inducing_point_allocator: Optional[InducingPointAllocator] = None,
) -> None:
r"""
Args:
train_X: Training inputs (due to the ability of the SVGP to sub-sample
this does not have to be all of the training inputs).
train_Y: Training targets (optional).
likelihood: Instance of a GPyTorch likelihood. If omitted, uses a
either a `GaussianLikelihood` (if `num_outputs=1`) or a
`MultitaskGaussianLikelihood`(if `num_outputs>1`).
num_outputs: Number of output responses per input (default: 1).
covar_module: Kernel function. If omitted, uses a `MaternKernel`.
mean_module: Mean of GP model. If omitted, uses a `ConstantMean`.
variational_distribution: Type of variational distribution to use
(default: CholeskyVariationalDistribution), the properties of the
variational distribution will encourage scalability or ease of
optimization.
variational_strategy: Type of variational strategy to use (default:
VariationalStrategy). The default setting uses "whitening" of the
variational distribution to make training easier.
inducing_points: The number or specific locations of the inducing points.
inducing_point_allocator: The `InducingPointAllocator` used to
initialize the inducing point locations. If omitted,
uses `GreedyVarianceReduction`.
"""
with torch.no_grad():
transformed_X = self.transform_inputs(
X=train_X, input_transform=input_transform
)
if train_Y is not None:
if outcome_transform is not None:
train_Y, _ = outcome_transform(train_Y)
self._validate_tensor_args(X=transformed_X, Y=train_Y)
validate_input_scaling(train_X=transformed_X, train_Y=train_Y)
if train_Y.shape[-1] != num_outputs:
num_outputs = train_Y.shape[-1]
self._num_outputs = num_outputs
self._input_batch_shape = train_X.shape[:-2]
aug_batch_shape = copy.deepcopy(self._input_batch_shape)
if num_outputs > 1:
aug_batch_shape += torch.Size([num_outputs])
self._aug_batch_shape = aug_batch_shape
if likelihood is None:
if num_outputs == 1:
noise_prior = GammaPrior(1.1, 0.05)
noise_prior_mode = (noise_prior.concentration - 1) / noise_prior.rate
likelihood = GaussianLikelihood(
noise_prior=noise_prior,
batch_shape=self._aug_batch_shape,
noise_constraint=GreaterThan(
MIN_INFERRED_NOISE_LEVEL,
transform=None,
initial_value=noise_prior_mode,
),
)
else:
likelihood = MultitaskGaussianLikelihood(num_tasks=num_outputs)
else:
self._is_custom_likelihood = True
if learn_inducing_points and (inducing_point_allocator is not None):
warnings.warn(
"After all the effort of specifying an inducing point allocator, "
"you probably want to stop the inducing point locations "
"being further optimized during the model fit. If so "
"then set `learn_inducing_points` to False.",
UserWarning,
)
if inducing_point_allocator is None:
self._inducing_point_allocator = GreedyVarianceReduction()
else:
self._inducing_point_allocator = inducing_point_allocator
model = _SingleTaskVariationalGP(
train_X=transformed_X,
train_Y=train_Y,
num_outputs=num_outputs,
learn_inducing_points=learn_inducing_points,
covar_module=covar_module,
mean_module=mean_module,
variational_distribution=variational_distribution,
variational_strategy=variational_strategy,
inducing_points=inducing_points,
inducing_point_allocator=self._inducing_point_allocator,
)
super().__init__(model=model, likelihood=likelihood, num_outputs=num_outputs)
if outcome_transform is not None:
self.outcome_transform = outcome_transform
if input_transform is not None:
self.input_transform = input_transform
# for model fitting utilities
# TODO: make this a flag?
self.model.train_inputs = [transformed_X]
if train_Y is not None:
self.model.train_targets = train_Y.squeeze(-1)
self.to(train_X)
@property
def batch_shape(self) -> torch.Size:
r"""The batch shape of the model.
This is a batch shape from an I/O perspective. 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`.
"""
return self._input_batch_shape
[docs] def init_inducing_points(
self,
inputs: Tensor,
) -> Tensor:
r"""
Reinitialize the inducing point locations in-place with the current kernel
applied to `inputs` through the model's inducing point allocation strategy.
The variational distribution and variational strategy caches are reset.
Args:
inputs: (\*batch_shape, n, d)-dim input data tensor.
Returns:
(\*batch_shape, m, d)-dim tensor of selected inducing point locations.
"""
var_strat = self.model.variational_strategy
clear_cache_hook(var_strat)
if hasattr(var_strat, "base_variational_strategy"):
var_strat = var_strat.base_variational_strategy
clear_cache_hook(var_strat)
with torch.no_grad():
num_inducing = var_strat.inducing_points.size(-2)
inducing_points = self._inducing_point_allocator.allocate_inducing_points(
inputs=inputs,
covar_module=self.model.covar_module,
num_inducing=num_inducing,
input_batch_shape=self._input_batch_shape,
)
var_strat.inducing_points.copy_(inducing_points)
var_strat.variational_params_initialized.fill_(0)
return inducing_points
