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This enables the use of `SingleTaskVariationalGP` with certain botorch features (e.g. with entropy-based acquistion functions as requested in meta-pytorch#1795). This is a bit of a band-aid, the proper thing to do here is to fix up the PR upstreaming this to gpytorch (cornellius-gp/gpytorch#2307) to enable support for `batch_shape` on all approximate gpytorch models, and then just call that on the `model` in `ApproximateGPyTorchModel`.
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Summary: This enables the use of `SingleTaskVariationalGP` with certain botorch features (e.g. with entropy-based acquistion functions as requested in #1795). This is a bit of a band-aid, the proper thing to do here is to fix up the PR upstreaming this to gpytorch (cornellius-gp/gpytorch#2307) to enable support for `batch_shape` on all approximate gpytorch models, and then just call that on the `model` in `ApproximateGPyTorchModel`. Pull Request resolved: #1799 Reviewed By: dme65 Differential Revision: D45107761 Pulled By: Balandat fbshipit-source-id: 44f1893d4ab915f744c17dfd5da4efd4923c66ae
Implements #2301. TODO: Verify compatibility with the botorch setup of other models
gpleiss
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| " \n", | ||
| " # Sparse Variational Formulation (inducing variables initialised as randn)\n", | ||
| " q_u = CholeskyVariationalDistribution(n_inducing, batch_shape=self.batch_shape) \n", | ||
| " q_u = CholeskyVariationalDistribution(n_inducing, batch_shape=torch.Size([data_dim])) \n", |
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Why aren't we using the self.batch_shape property here?
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Let's hold off. I have been running into some issues with this approach (caused by the fact that in botorch the batch shape is not necessarily the same as the gpytorch-internal batch shape. So right now this would cause a bunch of headaches and bugs on our end rather than only resolving some. We'll have to rethink if this is the right approach. |
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Implements #2301.