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Perform QR composition in exact prediction strategy on the CPU #1224

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Jul 24, 2020
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Perform QR composition in exact prediction strategy on the CPU #1224

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Perform QR composition in exact prediction strategy on the CPU
Balandat Jul 23, 2020
4c1e48d
Add size check for CPU dispatch
Balandat Jul 23, 2020
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24 changes: 20 additions & 4 deletions gpytorch/models/exact_prediction_strategies.py
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Original file line number Diff line number Diff line change
Expand Up @@ -204,25 +204,41 @@ def get_fantasy_strategy(self, inputs, targets, full_inputs, full_targets, full_
batch_shape = fant_train_covar.shape[:-2]

L_inverse = self.covar_cache
L = delazify(self.lik_train_train_covar.root_decomposition().root)
L = self.lik_train_train_covar.root_decomposition().root
m, n = L.shape[-2:]

lower_left = fant_train_covar.matmul(L_inverse)
schur = fant_fant_covar - lower_left.matmul(lower_left.transpose(-2, -1))
schur_root = psd_safe_cholesky(schur, jitter=settings.cholesky_jitter.value())

# Form new root Z = [L 0; lower_left schur_root]

# # TODO: Special case triangular case once #1102 goes in
# if isinstance(L, TriangularLazyTensor):
# # The whole thing is triangular, we can just do two triangular solves
# ...
# else:

L = delazify(L)
num_fant = schur_root.size(-2)
m, n = L.shape[-2:]
new_root = torch.zeros(*batch_shape, m + num_fant, n + num_fant, device=L.device, dtype=L.dtype)
new_root[..., :m, :n] = L
new_root[..., m:, :n] = lower_left
new_root[..., m:, n:] = schur_root

# Use pseudo-inverse of Z as new inv root
Q, R = torch.qr(new_root)

if new_root.shape[-1] <= 2048:
# Dispatch to CPU so long as pytorch/pytorch#22573 is not fixed
device = new_root.device
Q, R = torch.qr(new_root.cpu())
Q = Q.to(device)
R = R.to(device)
else:
Q, R = torch.qr(new_root)

Rdiag = torch.diagonal(R, dim1=-2, dim2=-1)
# if R is almost singular, add jitter (Rdiag is a view, so this works)
# if R is almost singular, add jitter
zeroish = Rdiag.abs() < 1e-6
if torch.any(zeroish):
# can't use in-place operation here b/c it would mess up backward pass
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