Added posterior_transform to posterior method in ApproximateGPyTorchM…

…odel (#2531)

Summary:
## Motivation

This PR fixes #2530. Adds a new posterior_transform parameter to the posterior method of ApproximateGPyTorchModel.

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

Yes

Pull Request resolved: #2531

Test Plan:
I was able to generate candidate points with ExpectedImprovement acquisition function with a SingleTaskVariationalGP that was trained on 2 output columns.

## Related PRs

NA

Reviewed By: mgarrard

Differential Revision: D62652630

Pulled By: Balandat

fbshipit-source-id: 6870c8a1f47454e70951e7e7eb420cd05a2fb246

botorch/models/approximate_gp.py

@@ -31,10 +31,10 @@
 
 import copy
 import warnings
-
 from typing import Optional, Union
 
 import torch
+from botorch.acquisition.objective import PosteriorTransform
 from botorch.exceptions.warnings import UserInputWarning
 from botorch.models.gpytorch import GPyTorchModel
 from botorch.models.transforms.input import InputTransform
@@ -146,8 +146,16 @@ def train(self, mode: bool = True) -> Self:
         return Module.train(self, mode=mode)
 
     def posterior(
-        self, X, output_indices=None, observation_noise=False, *args, **kwargs
+        self,
+        X,
+        output_indices: Optional[list[int]] = None,
+        observation_noise: bool = False,
+        posterior_transform: Optional[PosteriorTransform] = None,
     ) -> GPyTorchPosterior:
+        if output_indices is not None:
+            raise NotImplementedError(  # pragma: no cover
+                f"{self.__class__.__name__}.posterior does not support output indices."
+            )
         self.eval()  # make sure model is in eval mode
 
         # input transforms are applied at `posterior` in `eval` mode, and at
@@ -161,11 +169,13 @@ def posterior(
             X = X.unsqueeze(-3).repeat(*[1] * (X_ndim - 2), self.num_outputs, 1, 1)
         dist = self.model(X)
         if observation_noise:
-            dist = self.likelihood(dist, *args, **kwargs)
+            dist = self.likelihood(dist)
 
         posterior = GPyTorchPosterior(distribution=dist)
         if hasattr(self, "outcome_transform"):
             posterior = self.outcome_transform.untransform_posterior(posterior)
+        if posterior_transform is not None:
+            posterior = posterior_transform(posterior)
         return posterior
 
     def forward(self, X) -> MultivariateNormal:

test/models/test_approximate_gp.py

@@ -8,6 +8,7 @@
 import warnings
 
 import torch
+from botorch.acquisition.objective import ScalarizedPosteriorTransform
 from botorch.exceptions.warnings import UserInputWarning
 from botorch.fit import fit_gpytorch_mll
 from botorch.models.approximate_gp import (
@@ -103,6 +104,16 @@ def test_posterior(self):
                 # test batch_shape property
                 self.assertEqual(model.batch_shape, tx.shape[:-2])
 
+        # Test that checks if posterior_transform is correctly applied
+        [tx1, ty1, test1] = all_tests["non_batched_mo"]
+        model1 = SingleTaskVariationalGP(tx1, ty1, inducing_points=tx1)
+        posterior_transform = ScalarizedPosteriorTransform(
+            weights=torch.tensor([1.0, 1.0], device=self.device)
+        )
+        posterior1 = model1.posterior(test1, posterior_transform=posterior_transform)
+        self.assertIsInstance(posterior1, GPyTorchPosterior)
+        self.assertEqual(posterior1.mean.shape[1], 1)
+
     def test_variational_setUp(self):
         for dtype in [torch.float, torch.double]:
             train_X = torch.rand(10, 1, device=self.device, dtype=dtype)