def test_construct_base_samples_from_posterior(self): # noqa: C901
for dtype in (torch.float, torch.double):
# single-output
mean = torch.zeros(2, device=self.device, dtype=dtype)
cov = torch.eye(2, device=self.device, dtype=dtype)
mvn = MultivariateNormal(mean=mean, covariance_matrix=cov)
posterior = GPyTorchPosterior(mvn=mvn)
for sample_shape, qmc, seed in itertools.product(
(torch.Size([5]), torch.Size([5, 3])), (False, True), (None, 1234)
):
expected_shape = sample_shape + torch.Size([2, 1])
samples = construct_base_samples_from_posterior(
posterior=posterior, sample_shape=sample_shape, qmc=qmc, seed=seed
)
self.assertEqual(samples.shape, expected_shape)
self.assertEqual(samples.device.type, self.device.type)
self.assertEqual(samples.dtype, dtype)
# single-output, batch mode
mean = torch.zeros(2, 2, device=self.device, dtype=dtype)
cov = torch.eye(2, device=self.device, dtype=dtype).expand(2, 2, 2)
mvn = MultivariateNormal(mean=mean, covariance_matrix=cov)
posterior = GPyTorchPosterior(mvn=mvn)
for sample_shape, qmc, seed, collapse_batch_dims in itertools.product(
(torch.Size([5]), torch.Size([5, 3])),
(False, True),
(None, 1234),
(False, True),
):
if collapse_batch_dims:
expected_shape = sample_shape + torch.Size([1, 2, 1])
else:
expected_shape = sample_shape + torch.Size([2, 2, 1])
samples = construct_base_samples_from_posterior(
posterior=posterior,
sample_shape=sample_shape,
qmc=qmc,
collapse_batch_dims=collapse_batch_dims,
seed=seed,
)
self.assertEqual(samples.shape, expected_shape)
self.assertEqual(samples.device.type, self.device.type)
self.assertEqual(samples.dtype, dtype)
# multi-output
mean = torch.zeros(2, 2, device=self.device, dtype=dtype)
cov = torch.eye(4, device=self.device, dtype=dtype)
mtmvn = MultitaskMultivariateNormal(mean=mean, covariance_matrix=cov)
posterior = GPyTorchPosterior(mvn=mtmvn)
for sample_shape, qmc, seed in itertools.product(
(torch.Size([5]), torch.Size([5, 3])), (False, True), (None, 1234)
):
expected_shape = sample_shape + torch.Size([2, 2])
samples = construct_base_samples_from_posterior(
posterior=posterior, sample_shape=sample_shape, qmc=qmc, seed=seed
)
self.assertEqual(samples.shape, expected_shape)
self.assertEqual(samples.device.type, self.device.type)
self.assertEqual(samples.dtype, dtype)
# multi-output, batch mode
mean = torch.zeros(2, 2, 2, device=self.device, dtype=dtype)
cov = torch.eye(4, device=self.device, dtype=dtype).expand(2, 4, 4)
mtmvn = MultitaskMultivariateNormal(mean=mean, covariance_matrix=cov)
posterior = GPyTorchPosterior(mvn=mtmvn)
for sample_shape, qmc, seed, collapse_batch_dims in itertools.product(
(torch.Size([5]), torch.Size([5, 3])),
(False, True),
(None, 1234),
(False, True),
):
if collapse_batch_dims:
expected_shape = sample_shape + torch.Size([1, 2, 2])
else:
expected_shape = sample_shape + torch.Size([2, 2, 2])
samples = construct_base_samples_from_posterior(
posterior=posterior,
sample_shape=sample_shape,
qmc=qmc,
collapse_batch_dims=collapse_batch_dims,
seed=seed,
)
self.assertEqual(samples.shape, expected_shape)
self.assertEqual(samples.device.type, self.device.type)
self.assertEqual(samples.dtype, dtype)
def test_construct_base_samples_from_posterior(self, cuda=False):
device = torch.device("cuda") if cuda else torch.device("cpu")
for dtype in (torch.float, torch.double):
# single-output
mean = torch.zeros(2, device=device, dtype=dtype)
cov = torch.eye(2, device=device, dtype=dtype)
mvn = MultivariateNormal(mean=mean, covariance_matrix=cov)
posterior = GPyTorchPosterior(mvn=mvn)
for sample_shape in (torch.Size([5]), torch.Size([5, 3])):
for qmc in (False, True):
for seed in (None, 1234):
expected_shape = sample_shape + torch.Size([2, 1])
samples = construct_base_samples_from_posterior(
posterior=posterior,
sample_shape=sample_shape,
qmc=qmc,
seed=seed,
)
self.assertEqual(samples.shape, expected_shape)
self.assertEqual(samples.device.type, device.type)
self.assertEqual(samples.dtype, dtype)
# single-output, batch mode
mean = torch.zeros(2, 2, device=device, dtype=dtype)
cov = torch.eye(2, device=device, dtype=dtype).expand(2, 2, 2)
mvn = MultivariateNormal(mean=mean, covariance_matrix=cov)
posterior = GPyTorchPosterior(mvn=mvn)
for sample_shape in (torch.Size([5]), torch.Size([5, 3])):
for qmc in (False, True):
for seed in (None, 1234):
for collapse_batch_dims in (False, True):
if collapse_batch_dims:
expected_shape = sample_shape + torch.Size([1, 2, 1])
else:
expected_shape = sample_shape + torch.Size([2, 2, 1])
samples = construct_base_samples_from_posterior(
posterior=posterior,
sample_shape=sample_shape,
qmc=qmc,
collapse_batch_dims=collapse_batch_dims,
seed=seed,
)
self.assertEqual(samples.shape, expected_shape)
self.assertEqual(samples.device.type, device.type)
self.assertEqual(samples.dtype, dtype)
# multi-output
mean = torch.zeros(2, 2, device=device, dtype=dtype)
cov = torch.eye(4, device=device, dtype=dtype)
mtmvn = MultitaskMultivariateNormal(mean=mean, covariance_matrix=cov)
posterior = GPyTorchPosterior(mvn=mtmvn)
for sample_shape in (torch.Size([5]), torch.Size([5, 3])):
for qmc in (False, True):
for seed in (None, 1234):
expected_shape = sample_shape + torch.Size([2, 2])
samples = construct_base_samples_from_posterior(
posterior=posterior,
sample_shape=sample_shape,
qmc=qmc,
seed=seed,
)
self.assertEqual(samples.shape, expected_shape)
self.assertEqual(samples.device.type, device.type)
self.assertEqual(samples.dtype, dtype)
# multi-output, batch mode
mean = torch.zeros(2, 2, 2, device=device, dtype=dtype)
cov = torch.eye(4, device=device, dtype=dtype).expand(2, 4, 4)
mtmvn = MultitaskMultivariateNormal(mean=mean, covariance_matrix=cov)
posterior = GPyTorchPosterior(mvn=mtmvn)
for sample_shape in (torch.Size([5]), torch.Size([5, 3])):
for qmc in (False, True):
for seed in (None, 1234):
for collapse_batch_dims in (False, True):
if collapse_batch_dims:
expected_shape = sample_shape + torch.Size([1, 2, 2])
else:
expected_shape = sample_shape + torch.Size([2, 2, 2])
samples = construct_base_samples_from_posterior(
posterior=posterior,
sample_shape=sample_shape,
qmc=qmc,
collapse_batch_dims=collapse_batch_dims,
seed=seed,
)
self.assertEqual(samples.shape, expected_shape)
self.assertEqual(samples.device.type, device.type)
self.assertEqual(samples.dtype, dtype)
