Source code for gpytorch.variational.unwhitened_variational_strategy

#!/usr/bin/env python3

import math

import torch

from gpytorch.variational.cholesky_variational_distribution import CholeskyVariationalDistribution

from .. import settings
from ..distributions import MultivariateNormal
from ..lazy import (
from ..utils.broadcasting import _mul_broadcast_shape
from ..utils.cholesky import psd_safe_cholesky
from ..utils.errors import NotPSDError
from ..utils.memoize import add_to_cache, cached
from ._variational_strategy import _VariationalStrategy

[docs]class UnwhitenedVariationalStrategy(_VariationalStrategy): r""" Similar to :obj:`~gpytorch.variational.VariationalStrategy`, but does not perform the whitening operation. In almost all cases :obj:`~gpytorch.variational.VariationalStrategy` is preferable, with a few exceptions: - When the inducing points are exactly equal to the training points (i.e. :math:`\mathbf Z = \mathbf X`). Unwhitened models are faster in this case. - When the number of inducing points is very large (e.g. >2000). Unwhitened models can use CG for faster computation. :param ~gpytorch.models.ApproximateGP model: Model this strategy is applied to. Typically passed in when the VariationalStrategy is created in the __init__ method of the user defined model. :param torch.Tensor inducing_points: Tensor containing a set of inducing points to use for variational inference. :param ~gpytorch.variational.VariationalDistribution variational_distribution: A VariationalDistribution object that represents the form of the variational distribution :math:`q(\mathbf u)` :param bool learn_inducing_points: (optional, default True): Whether or not the inducing point locations :math:`\mathbf Z` should be learned (i.e. are they parameters of the model). """ has_fantasy_strategy = True @cached(name="cholesky_factor", ignore_args=True) def _cholesky_factor(self, induc_induc_covar): # Maybe used - if we're not using CG L = psd_safe_cholesky(delazify(induc_induc_covar)) return TriangularLazyTensor(L) @property @cached(name="prior_distribution_memo") def prior_distribution(self): out = self.model.forward(self.inducing_points) res = MultivariateNormal(out.mean, out.lazy_covariance_matrix.add_jitter()) return res @property @cached(name="pseudo_points_memo") def pseudo_points(self): # TODO: implement for other distributions # retrieve the variational mean, m and covariance matrix, S. if not isinstance(self._variational_distribution, CholeskyVariationalDistribution): raise NotImplementedError( "Only CholeskyVariationalDistribution has pseudo-point support currently, ", "but your _variational_distribution is a ", self._variational_distribution.__name__, ) # retrieve the variational mean, m and covariance matrix, S. var_cov_root = TriangularLazyTensor(self._variational_distribution.chol_variational_covar) var_cov = CholLazyTensor(var_cov_root) var_mean = self.variational_distribution.mean # .unsqueeze(-1) if var_mean.shape[-1] != 1: var_mean = var_mean.unsqueeze(-1) # R = K - S Kmm = self.model.covar_module(self.inducing_points) res = Kmm - var_cov cov_diff = res # D_a = (S^{-1} - K^{-1})^{-1} = S + S R^{-1} S # note that in the whitened case R = I - S, unwhitened R = K - S # we compute (R R^{T})^{-1} R^T S for stability reasons as R is probably not PSD. eval_lhs = var_cov.evaluate() eval_rhs = cov_diff.transpose(-1, -2).matmul(eval_lhs) inner_term = cov_diff.matmul(cov_diff.transpose(-1, -2)) # TODO: flag the jitter here inner_solve = inner_term.add_jitter(1e-3).inv_matmul(eval_rhs, eval_lhs.transpose(-1, -2)) inducing_covar = var_cov + inner_solve # mean term: D_a S^{-1} m # unwhitened: (S - S R^{-1} S) S^{-1} m = (I - S R^{-1}) m rhs = cov_diff.transpose(-1, -2).matmul(var_mean) inner_rhs_mean_solve = inner_term.add_jitter(1e-3).inv_matmul(rhs) pseudo_target_mean = var_mean + var_cov.matmul(inner_rhs_mean_solve) # ensure inducing covar is psd try: pseudo_target_covar = CholLazyTensor(inducing_covar.add_jitter(1e-3).cholesky()).evaluate() except NotPSDError: from gpytorch.lazy import DiagLazyTensor evals, evecs = inducing_covar.symeig(eigenvectors=True) pseudo_target_covar = evecs.matmul(DiagLazyTensor(evals + 1e-4)).matmul(evecs.transpose(-1, -2)).evaluate() return pseudo_target_covar, pseudo_target_mean def forward(self, x, inducing_points, inducing_values, variational_inducing_covar=None): # If our points equal the inducing points, we're done if torch.equal(x, inducing_points): if variational_inducing_covar is None: raise RuntimeError else: return MultivariateNormal(inducing_values, variational_inducing_covar) # Otherwise, we have to marginalize num_induc = inducing_points.size(-2) full_inputs =[inducing_points, x], dim=-2) full_output = self.model.forward(full_inputs) full_mean, full_covar = full_output.mean, full_output.lazy_covariance_matrix # Mean terms test_mean = full_mean[..., num_induc:] induc_mean = full_mean[..., :num_induc] mean_diff = (inducing_values - induc_mean).unsqueeze(-1) # Covariance terms induc_induc_covar = full_covar[..., :num_induc, :num_induc].add_jitter() induc_data_covar = full_covar[..., :num_induc, num_induc:].evaluate() data_data_covar = full_covar[..., num_induc:, num_induc:] # Compute Cholesky factorization of inducing covariance matrix if or (num_induc <= settings.max_cholesky_size.value()): induc_induc_covar = CholLazyTensor(self._cholesky_factor(induc_induc_covar)) # If we are making predictions and don't need variances, we can do things very quickly. if not and settings.skip_posterior_variances.on(): self._mean_cache = induc_induc_covar.inv_matmul(mean_diff).detach() predictive_mean = torch.add( test_mean, induc_data_covar.transpose(-2, -1).matmul(self._mean_cache).squeeze(-1) ) predictive_covar = ZeroLazyTensor(test_mean.size(-1), test_mean.size(-1)) return MultivariateNormal(predictive_mean, predictive_covar) # Expand everything to the right size shapes = [mean_diff.shape[:-1], induc_data_covar.shape[:-1], induc_induc_covar.shape[:-1]] if variational_inducing_covar is not None: root_variational_covar = variational_inducing_covar.root_decomposition().root.evaluate() shapes.append(root_variational_covar.shape[:-1]) shape = _mul_broadcast_shape(*shapes) mean_diff = mean_diff.expand(*shape, mean_diff.size(-1)) induc_data_covar = induc_data_covar.expand(*shape, induc_data_covar.size(-1)) induc_induc_covar = induc_induc_covar.expand(*shape, induc_induc_covar.size(-1)) if variational_inducing_covar is not None: root_variational_covar = root_variational_covar.expand(*shape, root_variational_covar.size(-1)) # Cache the kernel matrix with the cached CG calls if prior_dist = MultivariateNormal(induc_mean, induc_induc_covar) add_to_cache(self, "prior_distribution_memo", prior_dist) # Compute predictive mean if variational_inducing_covar is None: left_tensors = mean_diff else: left_tensors =[mean_diff, root_variational_covar], -1) inv_products = induc_induc_covar.inv_matmul(induc_data_covar, left_tensors.transpose(-1, -2)) predictive_mean = torch.add(test_mean, inv_products[..., 0, :]) # Compute covariance if interp_data_data_var, _ = induc_induc_covar.inv_quad_logdet( induc_data_covar, logdet=False, reduce_inv_quad=False ) data_covariance = DiagLazyTensor((data_data_covar.diag() - interp_data_data_var).clamp(0, math.inf)) else: neg_induc_data_data_covar = torch.matmul( induc_data_covar.transpose(-1, -2).mul(-1), induc_induc_covar.inv_matmul(induc_data_covar) ) data_covariance = data_data_covar + neg_induc_data_data_covar predictive_covar = PsdSumLazyTensor(RootLazyTensor(inv_products[..., 1:, :].transpose(-1, -2)), data_covariance) # Done! return MultivariateNormal(predictive_mean, predictive_covar)