Fixed condition_on_observations in fully Bayesian models (#2151)

Summary:
## Motivation

Conditioning on observations in fully bayesian models - enables fully Bayesian JES & KG(?).

### Have you read the [Contributing Guidelines on pull requests](https://github.com/pytorch/botorch/blob/main/CONTRIBUTING.md#pull-requests)?

Yes.

Pull Request resolved: #2151

Test Plan:
Tests are written to ensure functionality for inferred and fixed noise.  __note that the `_aug_batch_shape` attribute assignment was removed in `condition_on_observations`.__ In `FullyBayesianGPs`, this argument could not be assigned (hence the removal). I could not find the use for this argument, and all tests passed when removing it.

Other changes are commented throughout, and the changes were made so as to assure that FBGPs can have one set of training data throughout. Howver, conditioning on obervations adds a batch dim to the training data (which is necessary in GPyTorch [here](https://github.com/cornellius-gp/gpytorch/blob/58c033564d28a5537397bc464827783313534e56/gpytorch/models/exact_gp.py#L176)) to infer the correct batch dim.

Reviewed By: dme65

Differential Revision: D52256296

Pulled By: saitcakmak

fbshipit-source-id: e340897d76e02c32ef7a981bef8a77c49e030ad1

botorch/models/fully_bayesian.py

@@ -498,9 +498,8 @@ def forward(self, X: Tensor) -> MultivariateNormal:
         rest of this method will not run.
         """
         self._check_if_fitted()
-        x = X.unsqueeze(MCMC_DIM)
-        mean_x = self.mean_module(x)
-        covar_x = self.covar_module(x)
+        mean_x = self.mean_module(X)
+        covar_x = self.covar_module(X)
         return MultivariateNormal(mean_x, covar_x)
 
     # pyre-ignore[14]: Inconsistent override
@@ -534,11 +533,45 @@ def posterior(
         """
         self._check_if_fitted()
         posterior = super().posterior(
-            X=X,
+            X=X.unsqueeze(MCMC_DIM),
             output_indices=output_indices,
             observation_noise=observation_noise,
             posterior_transform=posterior_transform,
             **kwargs,
         )
         posterior = GaussianMixturePosterior(distribution=posterior.distribution)
         return posterior
+
+    def condition_on_observations(
+        self, X: Tensor, Y: Tensor, **kwargs: Any
+    ) -> BatchedMultiOutputGPyTorchModel:
+        """Conditions on additional observations for a Fully Bayesian model (either
+        identical across models or unique per-model).
+
+        Args:
+            X: A `batch_shape x num_samples x d`-dim Tensor, where `d` is
+                the dimension of the feature space and `batch_shape` is the number of
+                sampled models.
+            Y: A `batch_shape x num_samples x 1`-dim Tensor, where `d` is
+                the dimension of the feature space and `batch_shape` is the number of
+                sampled models.
+
+        Returns:
+            BatchedMultiOutputGPyTorchModel: A fully bayesian model conditioned on
+              given observations. The returned model has `batch_shape` copies of the
+              training data in case of identical observations (and `batch_shape`
+              training datasets otherwise).
+        """
+        if X.ndim == 2 and Y.ndim == 2:
+            # To avoid an error in GPyTorch when inferring the batch dimension, we add
+            # the explicit batch shape here. The result is that the conditioned model
+            # will have 'batch_shape' copies of the training data.
+            X = X.repeat(self.batch_shape + (1, 1))
+            Y = Y.repeat(self.batch_shape + (1, 1))
+
+        elif X.ndim < Y.ndim:
+            # We need to duplicate the training data to enable correct batch
+            # size inference in gpytorch.
+            X = X.repeat(*(Y.shape[:-2] + (1, 1)))
+
+        return super().condition_on_observations(X, Y, **kwargs)

botorch/models/gpytorch.py

@@ -226,7 +226,8 @@ def condition_on_observations(self, X: Tensor, Y: Tensor, **kwargs: Any) -> Mode
             >>> new_Y = torch.sin(new_X[:, 0]) + torch.cos(new_X[:, 1])
             >>> model = model.condition_on_observations(X=new_X, Y=new_Y)
         """
-        Yvar = kwargs.get("noise", None)
+        Yvar = kwargs.pop("noise", None)
+
         if hasattr(self, "outcome_transform"):
             # pass the transformed data to get_fantasy_model below
             # (unless we've already trasnformed if BatchedMultiOutputGPyTorchModel)
@@ -242,6 +243,7 @@ def condition_on_observations(self, X: Tensor, Y: Tensor, **kwargs: Any) -> Mode
                 kwargs.update({"noise": Yvar.squeeze(-1)})
         # get_fantasy_model will properly copy any existing outcome transforms
         # (since it deepcopies the original model)
+
         return self.get_fantasy_model(inputs=X, targets=Y, **kwargs)
 
 
@@ -492,7 +494,8 @@ def condition_on_observations(
         fantasy_model._input_batch_shape = fantasy_model.train_targets.shape[
             : (-1 if self._num_outputs == 1 else -2)
         ]
-        fantasy_model._aug_batch_shape = fantasy_model.train_targets.shape[:-1]
+        if not self._is_fully_bayesian:
+            fantasy_model._aug_batch_shape = fantasy_model.train_targets.shape[:-1]
         return fantasy_model
 
     def subset_output(self, idcs: List[int]) -> BatchedMultiOutputGPyTorchModel:

test/models/test_fully_bayesian.py

@@ -124,6 +124,18 @@ def _get_unnormalized_data(self, infer_noise: bool, **tkwargs):
             )
         return train_X, train_Y, train_Yvar, test_X
 
+    def _get_unnormalized_condition_data(
+        self, num_models: int, num_cond: int, infer_noise: bool, **tkwargs
+    ):
+        with torch.random.fork_rng():
+            torch.manual_seed(0)
+            cond_X = 5 + 5 * torch.rand(num_models, num_cond, 4, **tkwargs)
+            cond_Y = 10 + torch.sin(cond_X[..., :1])
+            cond_Yvar = (
+                None if infer_noise else 0.1 * torch.ones(cond_Y.shape, **tkwargs)
+            )
+        return cond_X, cond_Y, cond_Yvar
+
     def _get_mcmc_samples(
         self, num_samples: int, dim: int, infer_noise: bool, **tkwargs
     ):
@@ -656,6 +668,110 @@ def test_custom_pyro_model(self):
                     atol=5e-4,
                 )
 
+    def test_condition_on_observation(self):
+        # The following conditioned data shapes should work (output describes):
+        # training data shape after cond(batch shape in output is req. in gpytorch)
+        # X: num_models x n x d, Y: num_models x n x d --> num_models x n x d
+        # X: n x d, Y: n x d --> num_models x n x d
+        # X: n x d, Y: num_models x n x d --> num_models x n x d
+        num_models = 3
+        num_cond = 2
+        for infer_noise, dtype in itertools.product(
+            (True, False), (torch.float, torch.double)
+        ):
+            tkwargs = {"device": self.device, "dtype": dtype}
+            train_X, train_Y, train_Yvar, test_X = self._get_unnormalized_data(
+                infer_noise=infer_noise, **tkwargs
+            )
+            num_train, num_dims = train_X.shape
+            # condition on different observations per model to obtain num_models sets
+            # of training data
+            cond_X, cond_Y, cond_Yvar = self._get_unnormalized_condition_data(
+                num_models=num_models,
+                num_cond=num_cond,
+                infer_noise=infer_noise,
+                **tkwargs
+            )
+            model = SaasFullyBayesianSingleTaskGP(
+                train_X=train_X,
+                train_Y=train_Y,
+                train_Yvar=train_Yvar,
+            )
+            mcmc_samples = self._get_mcmc_samples(
+                num_samples=num_models,
+                dim=train_X.shape[-1],
+                infer_noise=infer_noise,
+                **tkwargs
+            )
+            model.load_mcmc_samples(mcmc_samples)
+
+            # need to forward pass before conditioning
+            model.posterior(train_X)
+            cond_model = model.condition_on_observations(
+                cond_X, cond_Y, noise=cond_Yvar
+            )
+            posterior = cond_model.posterior(test_X)
+            self.assertEqual(
+                posterior.mean.shape, torch.Size([num_models, len(test_X), 1])
+            )
+
+            # since the data is not equal for the conditioned points, a batch size
+            # is added to the training data
+            self.assertEqual(
+                cond_model.train_inputs[0].shape,
+                torch.Size([num_models, num_train + num_cond, num_dims]),
+            )
+
+            # the batch shape of the condition model is added during conditioning
+            self.assertEqual(cond_model.batch_shape, torch.Size([num_models]))
+
+            # condition on identical sets of data (i.e. one set) for all models
+            # i.e, with no batch shape. This infers the batch shape.
+            cond_X_nobatch, cond_Y_nobatch = cond_X[0], cond_Y[0]
+            model = SaasFullyBayesianSingleTaskGP(
+                train_X=train_X,
+                train_Y=train_Y,
+                train_Yvar=train_Yvar,
+            )
+            mcmc_samples = self._get_mcmc_samples(
+                num_samples=num_models,
+                dim=train_X.shape[-1],
+                infer_noise=infer_noise,
+                **tkwargs
+            )
+            model.load_mcmc_samples(mcmc_samples)
+
+            # conditioning without a batch size - the resulting conditioned model
+            # will still have a batch size
+            model.posterior(train_X)
+            cond_model = model.condition_on_observations(
+                cond_X_nobatch, cond_Y_nobatch, noise=cond_Yvar
+            )
+            self.assertEqual(
+                cond_model.train_inputs[0].shape,
+                torch.Size([num_models, num_train + num_cond, num_dims]),
+            )
+
+            # test repeated conditining
+            repeat_cond_X = cond_X + 5
+            repeat_cond_model = cond_model.condition_on_observations(
+                repeat_cond_X, cond_Y, noise=cond_Yvar
+            )
+            self.assertEqual(
+                repeat_cond_model.train_inputs[0].shape,
+                torch.Size([num_models, num_train + 2 * num_cond, num_dims]),
+            )
+
+            # test repeated conditioning without a batch size
+            repeat_cond_X_nobatch = cond_X_nobatch + 10
+            repeat_cond_model2 = repeat_cond_model.condition_on_observations(
+                repeat_cond_X_nobatch, cond_Y_nobatch, noise=cond_Yvar
+            )
+            self.assertEqual(
+                repeat_cond_model2.train_inputs[0].shape,
+                torch.Size([num_models, num_train + 3 * num_cond, num_dims]),
+            )
+
     def test_bisect(self):
         def f(x):
             return 1 + x