def test_psd_safe_cholesky_psd(self, cuda=False):
device = torch.device("cuda") if cuda else torch.device("cpu")
for dtype in (torch.float, torch.double):
for batch_mode in (False, True):
if batch_mode:
A = self._gen_test_psd().to(device=device, dtype=dtype)
else:
A = self._gen_test_psd()[0].to(device=device, dtype=dtype)
idx = torch.arange(A.shape[-1], device=A.device)
# default values
Aprime = A.clone()
Aprime[..., idx,
idx] += 1e-6 if A.dtype == torch.float32 else 1e-8
L_exp = torch.cholesky(Aprime)
with warnings.catch_warnings(record=True) as ws:
L_safe = psd_safe_cholesky(A)
self.assertEqual(len(ws), 1)
self.assertEqual(ws[-1].category, RuntimeWarning)
self.assertTrue(torch.allclose(L_exp, L_safe))
# user-defined value
Aprime = A.clone()
Aprime[..., idx, idx] += 1e-2
L_exp = torch.cholesky(Aprime)
with warnings.catch_warnings(record=True) as ws:
L_safe = psd_safe_cholesky(A, jitter=1e-2)
self.assertEqual(len(ws), 1)
self.assertEqual(ws[-1].category, RuntimeWarning)
self.assertTrue(torch.allclose(L_exp, L_safe))
def test_psd_safe_cholesky_psd(self, cuda=False):
device = torch.device("cuda") if cuda else torch.device("cpu")
for dtype in (torch.float, torch.double):
for batch_mode in (False, True):
if batch_mode:
A = self._gen_test_psd().to(device=device, dtype=dtype)
else:
A = self._gen_test_psd()[0].to(device=device, dtype=dtype)
idx = torch.arange(A.shape[-1], device=A.device)
# default values
Aprime = A.clone()
Aprime[..., idx, idx] += 1e-6 if A.dtype == torch.float32 else 1e-8
L_exp = torch.linalg.cholesky(Aprime)
with warnings.catch_warnings(record=True) as w:
# Makes sure warnings we catch don't cause `-w error` to fail
warnings.simplefilter("always", NumericalWarning)
L_safe = psd_safe_cholesky(A)
self.assertTrue(any(issubclass(w_.category, NumericalWarning) for w_ in w))
self.assertTrue(any("A not p.d., added jitter" in str(w_.message) for w_ in w))
self.assertTrue(torch.allclose(L_exp, L_safe))
# user-defined value
Aprime = A.clone()
Aprime[..., idx, idx] += 1e-2
L_exp = torch.linalg.cholesky(Aprime)
with warnings.catch_warnings(record=True) as w:
# Makes sure warnings we catch don't cause `-w error` to fail
warnings.simplefilter("always", NumericalWarning)
L_safe = psd_safe_cholesky(A, jitter=1e-2)
self.assertTrue(any(issubclass(w_.category, NumericalWarning) for w_ in w))
self.assertTrue(any("A not p.d., added jitter" in str(w_.message) for w_ in w))
self.assertTrue(torch.allclose(L_exp, L_safe))
def get_weights_posterior(X: Tensor, y: Tensor, sigma_sq: float) -> MultivariateNormal:
r"""Sample bayesian linear regression weights.
Args:
X: a `n x num_rff_features`-dim tensor of inputs
y: a `n`-dim tensor of outputs
sigma_sq: the noise variance
Returns:
The posterior distribution over the weights.
"""
with torch.no_grad():
A = X.T @ X + sigma_sq * torch.eye(X.shape[-1], dtype=X.dtype, device=X.device)
# mean is given by: m = S @ x.T @ y, where S = A_inv
# compute inverse of A using solves
# covariance is A_inv * sigma
L_A = psd_safe_cholesky(A)
# solve L_A @ u = I
Iw = torch.eye(L_A.shape[0], dtype=X.dtype, device=X.device)
u = torch.triangular_solve(Iw, L_A, upper=False).solution
# solve L_A^T @ S = u
A_inv = torch.triangular_solve(u, L_A.T).solution
m = A_inv @ X.T @ y
L = psd_safe_cholesky(A_inv * sigma_sq)
return MultivariateNormal(loc=m, scale_tril=L)
def cholesky_safe(K, jitter=1e-6, max_tries=100):
try:
lower_chol = psd_safe_cholesky(K, jitter=jitter, max_tries=1)
return lower_chol
except RuntimeError:
print("Not Choleskizable with jitter {}".format(jitter))
for i in range(max_tries):
inc_jitter = 5**i * jitter
print("Increasing jitter to {} and retrying.".format(inc_jitter))
try:
lower_chol = psd_safe_cholesky(K, jitter=inc_jitter, max_tries=1)
return lower_chol
except RuntimeError: continue
raise RuntimeError("Not Choleskizable at all.")
def sample(self, S, L, jitter=1e-5):
""" Sample the GRF at generalized location (S, L).
Parameters
----------
S: (M, d) Tensor
List of spatial locations.
L: (M) Tensor
List of response indices.
jitter: float
Jitter to add if covariance matrix is not diagonalisable.
Returns
-------
Z: (M) Tensor
The sampled value of Z_{s_i} component l_i.
"""
K = self.covariance.K(S, S, L, L)
# chol = torch.cholesky(K)
mu = self.mean(S, L)
# Sample M independent N(0, 1) RVs.
# TODO: Determine if this is better than doing Cholesky ourselves.
lower_chol = psd_safe_cholesky(K, jitter=jitter)
distr = MultivariateNormal(
loc=mu,
scale_tril=lower_chol)
sample = distr.sample()
#sample = mu + chol @ v
return sample.float()
def log_marginal(self, Y, gauss_mean, gauss_cov, **kwargs):
""" Computes the log marginal likelihood w.r.t the prior
log p(y|x) = -1/2 (Y-mu)' @ (K+sigma²I)^{-1} @ (Y-mu) - 1/2 \log |K+sigma^2I| - N/2 log(2pi)
Args:
`Y` (torch.tensor) :->: Observations Y with shape (Dy,MB)
`gauss_mean` (torch.tensor) :->: mean from p(f). Shape (Dy,MB)
`gauss_cov` (torch.tensor) :->: full covariance from p(f). Shape (Dy,MB,MB)
"""
N = Y.size(1)
Dy = self.out_dim
# compute mean and covariance from the marginal distribution p(y|x).
# This basically add the observation noise to the covariance
mx,Kxx = self.marginal_moments(gauss_mean,gauss_cov, diagonal = False)
# reshapes
mx = mx.view(Dy,N,1)
Y = Y.view(Dy,N,1)
# solve using cholesky
Y_mx = Y-mx
Lxx = psd_safe_cholesky(Kxx, upper = False, jitter = cg.global_jitter)
# Compute (Y-mu)' @ (K+sigma²I)^{-1} @ (Y-mu)
rhs = torch.cholesky_solve(Y_mx, Lxx, upper = False)
data_fit_term = torch.matmul(Y_mx.transpose(1,2),rhs)
complexity_term = 2*torch.log(torch.diagonal(Lxx, dim1 = 1, dim2 = 2)).sum(1)
cte = -N/2. * torch.log(2*cg.pi)
return -0.5*(data_fit_term + complexity_term ) + cte
def test_pivoted_cholesky(self, max_iter=3):
mat = self._create_mat().detach().requires_grad_(True)
mat.register_hook(_ensure_symmetric_grad)
mat_copy = mat.detach().clone().requires_grad_(True)
mat_copy.register_hook(_ensure_symmetric_grad)
# Forward (with function)
res, pivots = pivoted_cholesky(mat, rank=max_iter, return_pivots=True)
# Forward (manual pivoting, actual Cholesky)
inverse_pivots = inverse_permutation(pivots)
# Apply pivoting
pivoted_mat_copy = apply_permutation(mat_copy, pivots, pivots)
# Compute Cholesky
actual_pivoted = psd_safe_cholesky(pivoted_mat_copy)[..., :max_iter]
# Undo pivoting
actual = apply_permutation(actual_pivoted,
left_permutation=inverse_pivots)
self.assertAllClose(res, actual)
# Backward
grad_output = torch.randn_like(res)
res.backward(gradient=grad_output)
actual.backward(gradient=grad_output)
self.assertAllClose(mat.grad, mat_copy.grad)
def remove_inducing_points(self, idxs):
"""
Remove the inducing points corresponding to provided indices.
"""
mask = torch.ones(len(self.inducing_inputs), dtype=bool)
mask[idxs] = torch.tensor(False)
strat = self.variational_strategy
dist = strat._variational_distribution
# Point process probabilities
raw_p = self.variational_point_process.raw_probabilities[mask]
_reregister(self.variational_point_process, "raw_probabilities", raw_p)
# Inducing inputs
x_u = strat.inducing_points.data[mask]
_reregister(strat, "inducing_points", x_u)
# Inducing outputs
def _chol(L):
return psd_safe_cholesky(L @ L.T + I * 1e-8)
I = torch.eye(len(x_u), device=x_u.device)
m = dist.variational_mean
L = dist.chol_variational_covar
if m.dim() == 1:
m = m.data[mask]
elif m.dim() == 2:
m = m.data[:, mask]
else:
raise NotImplementedError
if L.dim() == 2:
S = (L @ L.T)[mask][:, mask]
L = psd_safe_cholesky(S + I * 1e-8)
elif L.dim() == 3:
S = (L @ L.transpose(1, 2))[:, mask][:, :, mask]
L = psd_safe_cholesky(S + I * 1e-8)
else:
raise NotImplementedError
_reregister(dist, "variational_mean", m)
_reregister(dist, "chol_variational_covar", L)
self.clear_caches()
def _update_covar(self) -> None:
r"""Update values derived from the data and hyperparameters
covar, covar_chol, and covar_inv will be of shape batch_shape x n x n
"""
self.covar = self._calc_covar(self.datapoints, self.datapoints)
self.covar_chol = psd_safe_cholesky(self.covar)
self.covar_inv = self._batch_chol_inv(self.covar_chol)
def test_psd_safe_cholesky_pd(self, cuda=False):
device = torch.device("cuda") if cuda else torch.device("cpu")
for dtype in (torch.float, torch.double):
for batch_mode in (False, True):
if batch_mode:
A = self._gen_test_psd().to(device=device, dtype=dtype)
D = torch.eye(2).type_as(A).unsqueeze(0).repeat(2, 1, 1)
else:
A = self._gen_test_psd()[0].to(device=device, dtype=dtype)
D = torch.eye(2).type_as(A)
A += D
# basic
L = torch.cholesky(A)
L_safe = psd_safe_cholesky(A)
self.assertTrue(torch.allclose(L, L_safe))
# upper
L = torch.cholesky(A, upper=True)
L_safe = psd_safe_cholesky(A, upper=True)
self.assertTrue(torch.allclose(L, L_safe))
# output tensors
L = torch.empty_like(A)
L_safe = torch.empty_like(A)
torch.cholesky(A, out=L)
psd_safe_cholesky(A, out=L_safe)
self.assertTrue(torch.allclose(L, L_safe))
# output tensors, upper
torch.cholesky(A, upper=True, out=L)
psd_safe_cholesky(A, upper=True, out=L_safe)
self.assertTrue(torch.allclose(L, L_safe))
# make sure jitter doesn't do anything if p.d.
L = torch.cholesky(A)
L_safe = psd_safe_cholesky(A, jitter=1e-2)
self.assertTrue(torch.allclose(L, L_safe))
def update_variational_distribution(self, x_new, y_new):
m_b, S_b = self._update_variational_moments(x_new, y_new)
q_mean = self.variational_strategy._variational_distribution.variational_mean
q_mean.data.copy_(m_b.squeeze(-1))
upper_new_covar = psd_safe_cholesky(S_b, jitter=self._jitter)
upper_q_covar = self.variational_strategy._variational_distribution.chol_variational_covar
upper_q_covar.copy_(upper_new_covar)
self.variational_strategy.variational_params_initialized.fill_(1)
def _update_covar(self, datapoints: Tensor) -> None:
r"""Update values derived from the data and hyperparameters
covar, covar_chol, and covar_inv will be of shape batch_shape x n x n
Args:
datapoints: (Transformed) datapoints for finding f_max
"""
self.covar = self._calc_covar(datapoints, datapoints)
self.covar_chol = psd_safe_cholesky(self.covar)
self.covar_inv = self._batch_chol_inv(self.covar_chol)
def sample(self, num_context, num_target):
r"""
Args:
num_context (int): Number of context points at the sample.
num_target (int): Number of target points at the sample.
Returns:
:class:`Tensor`.
Different between train mode and test mode:
*`train`: `num_context x x_dim`, `num_context x y_dim`, `num_total x x_dim`, `num_total x y_dim`
*`test`: `num_context x x_dim`, `num_context x y_dim`, `400 x x_dim`, `400 x y_dim`
"""
if self.train:
num_total = num_context + num_target
x_values = torch.empty(num_total,
self.x_dim).uniform_(*self.data_range)
else:
lower, upper = self.data_range
num_total = int((upper - lower) / 0.01 + 1)
x_values = torch.linspace(self.data_range[0], self.data_range[1],
num_total).unsqueeze(-1)
if self.random_params:
length_scale = torch.empty(self.y_dim, self.x_dim).uniform_(
0.1, self.length_scale) # [y, x]
output_scale = torch.empty(self.y_dim).uniform_(
0.1, self.output_scale) # [y]
else:
length_scale = torch.full((self.y_dim, self.x_dim),
self.length_scale)
output_scale = torch.full((self.y_dim, ), self.output_scale)
# [y_dim, num_total, num_total]
covariance = self.kernel(x_values, length_scale, output_scale)
cholesky = psd_safe_cholesky(covariance)
# [num_total, num_total] x [] = []
y_values = cholesky.matmul(torch.randn(self.y_dim, num_total,
1)).squeeze(2).transpose(0, 1)
if self.train:
context_x = x_values[:num_context, :]
context_y = y_values[:num_context, :]
else:
idx = torch.randperm(num_total)
context_x = torch.gather(x_values, 0,
idx[:num_context].unsqueeze(-1))
context_y = torch.gather(y_values, 0,
idx[:num_context].unsqueeze(-1))
return context_x, context_y, x_values, y_values
def _update_variational_moments(self, x, y):
C = self.current_C_matrix(x)
c = self.current_c_vec(x, y)
z_b = self.variational_strategy.inducing_points
Kbb = self.covar_module(z_b).evaluate()
L = psd_safe_cholesky(Kbb + C.evaluate(),
upper=False,
jitter=self._jitter)
m_b = Kbb @ torch.cholesky_solve(c, L, upper=False)
S_b = Kbb @ torch.cholesky_solve(Kbb, L, upper=False)
return m_b, S_b
def _update_inducing_points(gp, Z, m, S=None, L=None):
# Setting the inducing points for a given layer
assert (S is None) ^ (L is None)
strat = gp.variational_strategy
dist = strat._variational_distribution
strat.inducing_points.data = Z
dist.variational_mean.data = m
if S is not None:
L = psd_safe_cholesky(S)
dist.chol_variational_covar.data = L
gp.clear_caches()
def _inducing_inv_root(self):
if not self.training and hasattr(self, "_cached_kernel_inv_root"):
return self._cached_kernel_inv_root
else:
chol = psd_safe_cholesky(self._inducing_mat, upper=True)
eye = torch.eye(chol.size(-1),
device=chol.device,
dtype=chol.dtype)
inv_root = torch.triangular_solve(eye, chol)[0]
res = inv_root
if not self.training:
self._cached_kernel_inv_root = res
return res
def initialize_variational_dist(self):
"""Initialize variational distribution.
Describes what distribution to pass to the VariationalDistribution to initialize
with. Most commonly, this should be the prior distribution for the inducing
points, N(m_u, K_uu). However, if a subclass assumes a different
parameterization of the variational distribution, it may need to modify what the
prior is with respect to that reparameterization.
"""
prior_dist = self.prior_distribution
eval_prior_dist = torch.distributions.MultivariateNormal(
loc=prior_dist.mean,
scale_tril=psd_safe_cholesky(prior_dist.covariance_matrix),
)
self.variational_distribution.initialize_variational_distribution(
eval_prior_dist)
def update_distribution(self, model, X, Y, X_u, likelihood):
n_u = len(X_u)
v = likelihood.noise_covar.noise
b = 1 / v
full_inputs = torch.cat([X_u, X], dim=-2)
full_output = model.forward(full_inputs)
full_covar = full_output.lazy_covariance_matrix
K_MM = full_covar[..., :n_u, :n_u].add_jitter()
K_MN = full_covar[..., :n_u, n_u:].evaluate()
K_MM_inv = torch.inverse(K_MM.evaluate())
a = K_MM.inv_matmul(K_MN)
inner = a.matmul(a.T)
A = b * inner + K_MM_inv
S = torch.inverse(A)
m = b * S @ K_MM_inv @ K_MN @ Y
self.variational_mean = m.flatten()
self.chol_variational_covar = psd_safe_cholesky(S)
def sample(self, jitter=1e-5):
""" Sample the discretized GRF on the whole grid.
Returns
-------
Z: (M) Tensor
The sampled value of Z_{s_i} component l_i.
jitter: float
Jitter to add if covariance matrix is not diagonalisable.
"""
K = self.covariance_mat.list
mu = self.mean_vec.list
# Sample M independent N(0, 1) RVs.
# TODO: Determine if this is better than doing Cholesky ourselves.
lower_chol = psd_safe_cholesky(K, jitter=jitter)
distr = MultivariateNormal(loc=mu, scale_tril=lower_chol)
sample = distr.sample()
return GeneralizedVector.from_list(sample.float(), self.n_points,
self.n_out)
def current_C_matrix(self, x):
sigma2 = self.likelihood.noise
z_b = self.variational_strategy.inducing_points
Kbf = self.covar_module(z_b, x).evaluate()
C1 = Kbf @ Kbf.transpose(-1, -2) / sigma2
if self._old_C_matrix is None:
C2 = torch.zeros_like(C1)
else:
assert self._old_strat is not None
assert self._old_kernel is not None
z_a = self._old_strat.inducing_points.detach()
Kaa_old = self._old_kernel(z_a).add_jitter(self._jitter).detach()
C_old = self._old_C_matrix.detach()
Kab = self.covar_module(z_a, z_b).evaluate()
Kaa_old_inv_Kab = Kaa_old.inv_matmul(Kab)
C2 = Kaa_old_inv_Kab.transpose(-1,
-2) @ C_old.matmul(Kaa_old_inv_Kab)
C = C1 + C2
L = psd_safe_cholesky(C, upper=False, jitter=self._jitter)
L = lazy.TriangularLazyTensor(L, upper=False)
return lazy.CholLazyTensor(L, upper=False)
# Sample and plot
# ------------------------------------------------------
# Sample all components at all locations.
sample = my_discrete_grf.sample()
plot_grid_values(my_grid, sample)
# From now on, we will consider the drawn sample as ground truth.
# ---------------------------------------------------------------
ground_truth = sample
# Save for reproducibility.
np.save("ground_truth.npy", ground_truth.numpy())
# Use it to declare the data feed.
noise_std = torch.tensor([0.1, 0.1])
# Noise distribution
lower_chol = psd_safe_cholesky(torch.diag(noise_std**2))
noise_distr = MultivariateNormal(loc=torch.zeros(n_out), scale_tril=lower_chol)
def data_feed(node_ind):
noise_realization = noise_distr.sample()
return ground_truth[node_ind] + noise_realization
my_sensor = DiscreteSensor(my_discrete_grf)
# Excursion threshold.
lower = torch.tensor([2.3, 22.0]).float()
# Get the real excursion set and plot it.
excursion_ground_truth = (sample.isotopic > lower).float()
def __call__(self, x, y):
sigma2 = self.gp.likelihood.noise
z_b = self.gp.variational_strategy.inducing_points
Kff = self.gp.covar_module(x).evaluate()
Kbf = self.gp.covar_module(z_b, x).evaluate()
Kbb = self.gp.covar_module(z_b).add_jitter(self.gp._jitter)
Q1 = Kbf.transpose(-1, -2) @ Kbb.inv_matmul(Kbf)
Sigma1 = sigma2 * torch.eye(Q1.size(-1)).to(Q1.device)
# logp term
if self.gp._old_strat is None:
num_data = y.size(-2)
mean = torch.zeros(num_data).to(y.device)
covar = (Q1 + Sigma1) + self.gp._jitter * torch.eye(
Q1.size(-2)).to(Q1.device)
dist = distributions.MultivariateNormal(mean, covar)
logp_term = dist.log_prob(y.squeeze(-1)).sum() / y.size(-2)
else:
z_a = self.gp._old_strat.inducing_points.detach()
Kba = self.gp.covar_module(z_b, z_a).evaluate()
Kaa_old = self.gp._old_kernel(z_a).evaluate().detach()
Q2 = Kba.transpose(-1, -2) @ Kbb.inv_matmul(Kba)
zero_1 = torch.zeros(Q1.size(-2), Q2.size(-1)).to(Q1.device)
zero_2 = torch.zeros(Q2.size(-2), Q1.size(-1)).to(Q1.device)
Q = torch.cat([
torch.cat([Q1, zero_1], dim=-1),
torch.cat([zero_2, Q2], dim=-1)
],
dim=-2)
C_old = self.gp._old_C_matrix.detach()
Sigma2 = Kaa_old @ C_old.inv_matmul(Kaa_old)
Sigma2 = Sigma2 + self.gp._jitter * torch.eye(Sigma2.size(-2)).to(
Sigma2.device)
Sigma = torch.cat([
torch.cat([Sigma1, zero_1], dim=-1),
torch.cat([zero_2, Sigma2], dim=-1)
],
dim=-2)
y_hat = torch.cat([y, self.gp.pseudotargets])
mean = torch.zeros_like(y_hat.squeeze(-1))
covar = (Q + Sigma) + self.gp._jitter * torch.eye(Q.size(-2)).to(
Q.device)
dist = distributions.MultivariateNormal(mean, covar)
logp_term = dist.log_prob(y_hat.squeeze(-1)).sum() / y_hat.size(-2)
num_data = y_hat.size(-2)
# trace term
t1 = (Kff - Q1).diag().sum() / sigma2
t2 = 0
if self.gp._old_strat is not None:
LSigma2 = psd_safe_cholesky(Sigma2,
upper=False,
jitter=self.gp._jitter)
Kaa = self.gp.covar_module(z_a).evaluate().detach()
Sigma2_inv_Kaa = torch.cholesky_solve(Kaa, LSigma2, upper=False)
Sigma2_inv_Q2 = torch.cholesky_solve(Q2, LSigma2, upper=False)
t2 = Sigma2_inv_Kaa.diag().sum() - Sigma2_inv_Q2.diag().sum()
trace_term = -(t1 + t2) / 2 / num_data
if self._combine_terms:
return logp_term + trace_term
else:
return logp_term, trace_term, t1 / num_data, t2 / num_data
def _chol(L):
return psd_safe_cholesky(L @ L.T + I * 1e-8)
def _compute_information_gain(self, X: Tensor, mean_M: Tensor,
variance_M: Tensor,
covar_mM: Tensor) -> Tensor:
r"""Computes the information gain at the design points `X`.
Approximately computes the information gain at the design points `X`,
for both MES with noisy observations and multi-fidelity MES with noisy
observation and trace observations.
The implementation is inspired from the paper on multi-fidelity MES by
Takeno et. al. [Takeno2019mfmves]_. The notations in the comments in this
function follows the Appendix A in the paper.
Args:
X: A `batch_shape x 1 x d`-dim Tensor of `batch_shape` t-batches
with `1` `d`-dim design point each.
mean_M, variance_M: `batch_shape x num_fantasies`-dim Tensors of
`batch_shape` t-batches with `num_fantasies` fantasies.
`num_fantasies = 1` for non-fantasized models.
All are obtained without noise.
covar_mM: `batch_shape x num_fantasies x (1 + num_trace_observations)`
-dim Tensor. `num_fantasies = 1` for non-fantasized models.
All are obtained without noise.
Returns:
A `num_fantasies x batch_shape`-dim Tensor of information gains at the
given design points `X`.
"""
# compute the std_m, variance_m with noisy observation
posterior_m = self.model.posterior(X.unsqueeze(-3),
observation_noise=True)
mean_m = self.weight * posterior_m.mean.squeeze(-1)
# batch_shape x num_fantasies x (1 + num_trace_observations)
variance_m = posterior_m.mvn.covariance_matrix
# batch_shape x num_fantasies x (1 + num_trace_observations)^2
check_no_nans(variance_m)
# compute mean and std for fM|ym, x, Dt ~ N(u, s^2)
samples_m = self.weight * self.sampler(posterior_m).squeeze(-1)
# s_m x batch_shape x num_fantasies x (1 + num_trace_observations)
L = psd_safe_cholesky(variance_m)
temp_term = torch.cholesky_solve(covar_mM.unsqueeze(-1),
L).transpose(-2, -1)
# equivalent to torch.matmul(covar_mM.unsqueeze(-2), torch.inverse(variance_m))
# batch_shape x num_fantasies x 1 x (1 + num_trace_observations)
mean_pt1 = torch.matmul(temp_term, (samples_m - mean_m).unsqueeze(-1))
mean_new = mean_pt1.squeeze(-1).squeeze(-1) + mean_M
# s_m x batch_shape x num_fantasies
variance_pt1 = torch.matmul(temp_term, covar_mM.unsqueeze(-1))
variance_new = variance_M - variance_pt1.squeeze(-1).squeeze(-1)
# batch_shape x num_fantasies
stdv_new = variance_new.clamp_min(CLAMP_LB).sqrt()
# batch_shape x num_fantasies
# define normal distribution to compute cdf and pdf
normal = torch.distributions.Normal(
torch.zeros(1, device=X.device, dtype=X.dtype),
torch.ones(1, device=X.device, dtype=X.dtype),
)
# Compute p(fM <= f* | ym, x, Dt)
view_shape = ([self.num_mv_samples] + [1] * (len(X.shape) - 2) +
[self.num_fantasies]
) # s_M x batch_shape x num_fantasies
if self.X_pending is None:
view_shape[-1] = 1
max_vals = self.posterior_max_values.view(view_shape).unsqueeze(1)
# s_M x 1 x batch_shape x num_fantasies
normalized_mvs_new = (max_vals - mean_new) / stdv_new
# s_M x s_m x batch_shape x num_fantasies =
# s_M x 1 x batch_shape x num_fantasies - s_m x batch_shape x num_fantasies
cdf_mvs_new = normal.cdf(normalized_mvs_new).clamp_min(CLAMP_LB)
# Compute p(fM <= f* | x, Dt)
stdv_M = variance_M.sqrt()
normalized_mvs = (max_vals - mean_M) / stdv_M
# s_M x 1 x batch_shape x num_fantasies =
# s_M x 1 x 1 x num_fantasies - batch_shape x num_fantasies
cdf_mvs = normal.cdf(normalized_mvs).clamp_min(CLAMP_LB)
# s_M x 1 x batch_shape x num_fantasies
# Compute log(p(ym | x, Dt))
log_pdf_fm = posterior_m.mvn.log_prob(self.weight *
samples_m).unsqueeze(0)
# 1 x s_m x batch_shape x num_fantasies
# H0 = H(ym | x, Dt)
H0 = posterior_m.mvn.entropy() # batch_shape x num_fantasies
# regression adjusted H1 estimation, H1_hat = H1_bar - beta * (H0_bar - H0)
# H1 = E_{f*|x, Dt}[H(ym|f*, x, Dt)]
Z = cdf_mvs_new / cdf_mvs # s_M x s_m x batch_shape x num_fantasies
h1 = -Z * Z.log(
) - Z * log_pdf_fm # s_M x s_m x batch_shape x num_fantasies
check_no_nans(h1)
dim = [0, 1] # dimension of fm samples, fM samples
H1_bar = h1.mean(dim=dim)
h0 = -log_pdf_fm
H0_bar = h0.mean(dim=dim)
cov = ((h1 - H1_bar) * (h0 - H0_bar)).mean(dim=dim)
beta = cov / (h0.var(dim=dim) * h1.var(dim=dim)).sqrt()
H1_hat = H1_bar - beta * (H0_bar - H0)
ig = H0 - H1_hat # batch_shape x num_fantasies
ig = ig.permute(-1,
*range(ig.dim() - 1)) # num_fantasies x batch_shape
return ig
def update_variational_parameters(self,
new_x,
new_y,
new_inducing_points=None):
# if new_inducing_points = None
# this version of the variational update does NOT assume that the inducing points
# are moving around as we add new data. because we are using gradient based updates
# to optimize them, we do not compute any type of randomized updates to them as in
# bui et al.
if new_inducing_points is None:
new_inducing_points = self.variational_strategy.inducing_points.detach(
).clone()
self.set_streaming(True)
with torch.no_grad():
# self.register_streaming_loss(self.variational_strategy.variational_distribution)
self.register_streaming_loss()
if len(new_y.shape) == 1:
new_y = new_y.view(-1, 1)
S_a = self.variational_strategy.variational_distribution.lazy_covariance_matrix
K_aa_old = self.variational_strategy.prior_distribution.lazy_covariance_matrix
m_a = self.variational_strategy.variational_distribution.mean
D_a_inv = (S_a.evaluate().inverse() -
K_aa_old.evaluate().inverse())
# compute D S_a^{-1} m_a
pseudo_points = torch.solve(
S_a.inv_matmul(m_a).unsqueeze(-1), D_a_inv)[0]
# stack y and the pseudo points
hat_y = torch.cat((new_y.view(-1, 1), pseudo_points))
# we now create Sigma_\hat y = blockdiag(\sigma^2 I; D_a)
noise_diag = self.likelihood.noise * torch.eye(new_y.size(-2)).to(
new_y.device)
zero_part = torch.zeros(new_y.size(-2),
pseudo_points.size(0)).to(new_y.device)
tophalf = torch.cat((noise_diag, zero_part), -1)
bottomhalf = torch.cat((zero_part.t(), D_a_inv.inverse()), -1)
sigma_hat_y = torch.cat((tophalf, bottomhalf))
# stack the data to be able to compute covariances with it
# (x, a)
stacked_data = torch.cat(
(new_x, self.variational_strategy.inducing_points))
K_fb = self.covar_module(stacked_data, new_inducing_points)
K_bb = self.covar_module(new_inducing_points)
# C = K_{hat f b} K_{bb}^{-1} K_{b hat f} + Sigma_\hat y
pred_cov = K_fb @ (K_bb.inv_matmul(
K_fb.evaluate().t())) + sigma_hat_y
# the new mean is K_{hat f b} C^{-1} \hat y
new_mean = K_fb.t() @ torch.solve(
hat_y, pred_cov)[0].squeeze(-1).detach().contiguous()
# the new covariance is K_bb - K_{hat f b} C^{-1} K_{b hat f}
new_cov = K_bb - K_fb.t() @ torch.solve(K_fb.evaluate(),
pred_cov)[0]
new_variational_chol = psd_safe_cholesky(
new_cov.evaluate(),
jitter=cholesky_jitter.value()).detach().contiguous()
self.variational_strategy._variational_distribution.variational_mean.data.mul_(
0.).add_(new_mean)
self.variational_strategy._variational_distribution.chol_variational_covar.data.mul_(
0.).add_(new_variational_chol)
self.variational_strategy.inducing_points.data.mul_(0.).add_(
new_inducing_points.detach())
def test_psd_safe_cholesky_nan(self, cuda=False):
A = self._gen_test_psd().sqrt()
with self.assertRaises(NanError) as ctx:
psd_safe_cholesky(A)
self.assertTrue("NaN" in ctx.exception)
def _compute_information_gain(self, X: Tensor, mean_M: Tensor,
variance_M: Tensor,
covar_mM: Tensor) -> Tensor:
r"""Computes the information gain at the design points `X`.
Approximately computes the information gain at the design points `X`,
for both MES with noisy observations and multi-fidelity MES with noisy
observation and trace observations.
The implementation is inspired from the papers on multi-fidelity MES by
[Takeno2020mfmves]_. The notation in the comments in this function follows
the Appendix C of [Takeno2020mfmves]_.
`num_fantasies = 1` for non-fantasized models.
Args:
X: A `batch_shape x 1 x d`-dim Tensor of `batch_shape` t-batches
with `1` `d`-dim design point each.
mean_M: A `batch_shape x num_fantasies x (m)`-dim Tensor of means.
variance_M: A `batch_shape x num_fantasies x (m)`-dim Tensor of variances.
covar_mM: A
`batch_shape x num_fantasies x (m) x (1 + num_trace_observations)`-dim
Tensor of covariances.
Returns:
A `num_fantasies x batch_shape`-dim Tensor of information gains at the
given design points `X` (`num_fantasies=1` for non-fantasized models).
"""
# compute the std_m, variance_m with noisy observation
posterior_m = self.model.posterior(X.unsqueeze(-3),
observation_noise=True)
# batch_shape x num_fantasies x (m) x (1 + num_trace_observations)
mean_m = self.weight * posterior_m.mean.squeeze(-1)
# batch_shape x num_fantasies x (m) x (1 + num_trace_observations)
variance_m = posterior_m.mvn.covariance_matrix
check_no_nans(variance_m)
# compute mean and std for fM|ym, x, Dt ~ N(u, s^2)
samples_m = self.weight * self.sampler(posterior_m).squeeze(-1)
# s_m x batch_shape x num_fantasies x (m) (1 + num_trace_observations)
L = psd_safe_cholesky(variance_m)
temp_term = torch.cholesky_solve(covar_mM.unsqueeze(-1),
L).transpose(-2, -1)
# equivalent to torch.matmul(covar_mM.unsqueeze(-2), torch.inverse(variance_m))
# batch_shape x num_fantasies (m) x 1 x (1 + num_trace_observations)
mean_pt1 = torch.matmul(temp_term, (samples_m - mean_m).unsqueeze(-1))
mean_new = mean_pt1.squeeze(-1).squeeze(-1) + mean_M
# s_m x batch_shape x num_fantasies x (m)
variance_pt1 = torch.matmul(temp_term, covar_mM.unsqueeze(-1))
variance_new = variance_M - variance_pt1.squeeze(-1).squeeze(-1)
# batch_shape x num_fantasies x (m)
stdv_new = variance_new.clamp_min(CLAMP_LB).sqrt()
# batch_shape x num_fantasies x (m)
# define normal distribution to compute cdf and pdf
normal = torch.distributions.Normal(
torch.zeros(1, device=X.device, dtype=X.dtype),
torch.ones(1, device=X.device, dtype=X.dtype),
)
# Compute p(fM <= f* | ym, x, Dt)
view_shape = torch.Size([
self.posterior_max_values.shape[0],
# add 1s to broadcast across the batch_shape of X
*[1 for _ in range(X.ndim - self.posterior_max_values.ndim)],
*self.posterior_max_values.shape[1:],
]) # s_M x batch_shape x num_fantasies x (m)
max_vals = self.posterior_max_values.view(view_shape).unsqueeze(1)
# s_M x 1 x batch_shape x num_fantasies x (m)
normalized_mvs_new = (max_vals - mean_new) / stdv_new
# s_M x s_m x batch_shape x num_fantasies x (m) =
# s_M x 1 x batch_shape x num_fantasies x (m)
# - s_m x batch_shape x num_fantasies x (m)
cdf_mvs_new = normal.cdf(normalized_mvs_new).clamp_min(CLAMP_LB)
# Compute p(fM <= f* | x, Dt)
stdv_M = variance_M.sqrt()
normalized_mvs = (max_vals - mean_M) / stdv_M
# s_M x 1 x batch_shape x num_fantasies x (m) =
# s_M x 1 x 1 x num_fantasies x (m) - batch_shape x num_fantasies x (m)
cdf_mvs = normal.cdf(normalized_mvs).clamp_min(CLAMP_LB)
# s_M x 1 x batch_shape x num_fantasies x (m)
# Compute log(p(ym | x, Dt))
log_pdf_fm = posterior_m.mvn.log_prob(self.weight *
samples_m).unsqueeze(0)
# 1 x s_m x batch_shape x num_fantasies x (m)
# H0 = H(ym | x, Dt)
H0 = posterior_m.mvn.entropy() # batch_shape x num_fantasies x (m)
# regression adjusted H1 estimation, H1_hat = H1_bar - beta * (H0_bar - H0)
# H1 = E_{f*|x, Dt}[H(ym|f*, x, Dt)]
Z = cdf_mvs_new / cdf_mvs # s_M x s_m x batch_shape x num_fantasies x (m)
# s_M x s_m x batch_shape x num_fantasies x (m)
h1 = -Z * Z.log() - Z * log_pdf_fm
check_no_nans(h1)
dim = [0, 1] # dimension of fm samples, fM samples
H1_bar = h1.mean(dim=dim)
h0 = -log_pdf_fm
H0_bar = h0.mean(dim=dim)
cov = ((h1 - H1_bar) * (h0 - H0_bar)).mean(dim=dim)
beta = cov / (h0.var(dim=dim) * h1.var(dim=dim)).sqrt()
H1_hat = H1_bar - beta * (H0_bar - H0)
ig = H0 - H1_hat # batch_shape x num_fantasies x (m)
if self.posterior_max_values.ndim == 2:
permute_idcs = [-1, *range(ig.ndim - 1)]
else:
permute_idcs = [-2, *range(ig.ndim - 2), -1]
ig = ig.permute(*permute_idcs) # num_fantasies x batch_shape x (m)
return ig
def sample_cached_cholesky(
posterior: GPyTorchPosterior,
baseline_L: Tensor,
q: int,
base_samples: Tensor,
sample_shape: torch.Size,
max_tries: int = 6,
) -> Tensor:
r"""Get posterior samples at the `q` new points from the joint multi-output posterior.
Args:
posterior: The joint posterior is over (X_baseline, X).
baseline_L: The baseline lower triangular cholesky factor.
q: The number of new points in X.
base_samples: The base samples.
sample_shape: The sample shape.
max_tries: The number of tries for computing the Cholesky
decomposition with increasing jitter.
Returns:
A `sample_shape x batch_shape x q x m`-dim tensor of posterior
samples at the new points.
"""
# compute bottom left covariance block
if isinstance(posterior.mvn, MultitaskMultivariateNormal):
lazy_covar = extract_batch_covar(mt_mvn=posterior.mvn)
else:
lazy_covar = posterior.mvn.lazy_covariance_matrix
# Get the `q` new rows of the batched covariance matrix
bottom_rows = lazy_covar[..., -q:, :].evaluate()
# The covariance in block form is:
# [K(X_baseline, X_baseline), K(X_baseline, X)]
# [K(X, X_baseline), K(X, X)]
# bl := K(X, X_baseline)
# br := K(X, X)
# Get bottom right block of new covariance
bl, br = bottom_rows.split([bottom_rows.shape[-1] - q, q], dim=-1)
# Solve Ax = b
# where A = K(X_baseline, X_baseline) and b = K(X, X_baseline)^T
# and bl_chol := x^T
# bl_chol is the new `(batch_shape) x q x n`-dim bottom left block
# of the cholesky decomposition
bl_chol = torch.triangular_solve(bl.transpose(-2, -1),
baseline_L,
upper=False).solution.transpose(-2, -1)
# Compute the new bottom right block of the Cholesky
# decomposition via:
# Cholesky(K(X, X) - bl_chol @ bl_chol^T)
br_to_chol = br - bl_chol @ bl_chol.transpose(-2, -1)
# TODO: technically we should make sure that we add a
# consistent nugget to the cached covariance and the new block
br_chol = psd_safe_cholesky(br_to_chol, max_tries=max_tries)
# Create a `(batch_shape) x q x (n+q)`-dim tensor containing the
# `q` new bottom rows of the Cholesky decomposition
new_Lq = torch.cat([bl_chol, br_chol], dim=-1)
mean = posterior.mvn.mean
base_samples = _reshape_base_samples(
base_samples=base_samples,
sample_shape=sample_shape,
posterior=posterior,
)
if not isinstance(posterior.mvn, MultitaskMultivariateNormal):
# add output dim
mean = mean.unsqueeze(-1)
# add batch dim corresponding to output dim
new_Lq = new_Lq.unsqueeze(-3)
new_mean = mean[..., -q:, :]
res = (new_Lq.matmul(base_samples).add(
new_mean.transpose(-1, -2).unsqueeze(-1)).permute(
-1, *range(posterior.mvn.loc.dim() - 1), -2, -3).contiguous())
contains_nans = torch.isnan(res).any()
contains_infs = torch.isinf(res).any()
if contains_nans or contains_infs:
suffix_args = []
if contains_nans:
suffix_args.append("nans")
if contains_infs:
suffix_args.append("infs")
suffix = " and ".join(suffix_args)
raise NanError(f"Samples contain {suffix}.")
return res
