acquisition function wrapper (#1532)

Summary:
Pull Request resolved: #1532

Add a wrapper for modifying inputs/outputs. This is useful for not only probabilistic reparameterization, but will also simplify other integrated AFs (e.g. MCMC) as well as fixed feature AFs and things like prior-guided AFs

Differential Revision: D41629186

fbshipit-source-id: c52722b2946207e219ad5f49e6fa314706cdd953

botorch/acquisition/fixed_feature.py

@@ -16,11 +16,11 @@
 
 import torch
 from botorch.acquisition.acquisition import AcquisitionFunction
+from botorch.acquisition.wrapper import AbstractAcquisitionFunctionWrapper
 from torch import Tensor
-from torch.nn import Module
 
 
-class FixedFeatureAcquisitionFunction(AcquisitionFunction):
+class FixedFeatureAcquisitionFunction(AbstractAcquisitionFunctionWrapper):
     """A wrapper around AquisitionFunctions to fix a subset of features.
 
     Example:
@@ -56,8 +56,7 @@ def __init__(
                 combination of `Tensor`s and numbers which can be broadcasted
                 to form a tensor with trailing dimension size of `d_f`.
         """
-        Module.__init__(self)
-        self.acq_func = acq_function
+        AbstractAcquisitionFunctionWrapper.__init__(self, acq_function=acq_function)
         dtype = torch.float
         device = torch.device("cpu")
         self.d = d
@@ -126,24 +125,13 @@ def forward(self, X: Tensor):
         X_full = self._construct_X_full(X)
         return self.acq_func(X_full)
 
-    @property
-    def X_pending(self):
-        r"""Return the `X_pending` of the base acquisition function."""
-        try:
-            return self.acq_func.X_pending
-        except (ValueError, AttributeError):
-            raise ValueError(
-                f"Base acquisition function {type(self.acq_func).__name__} "
-                "does not have an `X_pending` attribute."
-            )
-
-    @X_pending.setter
-    def X_pending(self, X_pending: Optional[Tensor]):
+    def set_X_pending(self, X_pending: Optional[Tensor]):
         r"""Sets the `X_pending` of the base acquisition function."""
         if X_pending is not None:
-            self.acq_func.X_pending = self._construct_X_full(X_pending)
+            full_X_pending = self._construct_X_full(X_pending)
         else:
-            self.acq_func.X_pending = X_pending
+            full_X_pending = None
+        self.acq_func.set_X_pending(full_X_pending)
 
     def _construct_X_full(self, X: Tensor) -> Tensor:
         r"""Constructs the full input for the base acquisition function.

botorch/acquisition/penalized.py

@@ -15,9 +15,8 @@
 
 import torch
 from botorch.acquisition.acquisition import AcquisitionFunction
-from botorch.acquisition.analytic import AnalyticAcquisitionFunction
 from botorch.acquisition.objective import GenericMCObjective
-from botorch.exceptions import UnsupportedError
+from botorch.acquisition.wrapper import AbstractAcquisitionFunctionWrapper
 from torch import Tensor
 
 
@@ -139,7 +138,7 @@ def forward(self, X: Tensor) -> Tensor:
         return regularization_term
 
 
-class PenalizedAcquisitionFunction(AcquisitionFunction):
+class PenalizedAcquisitionFunction(AbstractAcquisitionFunctionWrapper):
     r"""Single-outcome acquisition function regularized by the given penalty.
 
     The usage is similar to:
@@ -161,29 +160,16 @@ def __init__(
             penalty_func: The regularization function.
             regularization_parameter: Regularization parameter used in optimization.
         """
-        super().__init__(model=raw_acqf.model)
-        self.raw_acqf = raw_acqf
+        AcquisitionFunction.__init__(self, model=raw_acqf.model)
+        AbstractAcquisitionFunctionWrapper.__init__(self, acq_function=raw_acqf)
         self.penalty_func = penalty_func
         self.regularization_parameter = regularization_parameter
 
     def forward(self, X: Tensor) -> Tensor:
-        raw_value = self.raw_acqf(X=X)
+        raw_value = self.acq_func(X=X)
         penalty_term = self.penalty_func(X)
         return raw_value - self.regularization_parameter * penalty_term
 
-    @property
-    def X_pending(self) -> Optional[Tensor]:
-        return self.raw_acqf.X_pending
-
-    def set_X_pending(self, X_pending: Optional[Tensor] = None) -> None:
-        if not isinstance(self.raw_acqf, AnalyticAcquisitionFunction):
-            self.raw_acqf.set_X_pending(X_pending=X_pending)
-        else:
-            raise UnsupportedError(
-                "The raw acquisition function is Analytic and does not account "
-                "for X_pending yet."
-            )
-
 
 def group_lasso_regularizer(X: Tensor, groups: List[List[int]]) -> Tensor:
     r"""Computes the group lasso regularization function for the given point.

botorch/acquisition/proximal.py

@@ -15,6 +15,8 @@
 
 import torch
 from botorch.acquisition import AcquisitionFunction
+
+from botorch.acquisition.wrapper import AbstractAcquisitionFunctionWrapper
 from botorch.exceptions.errors import UnsupportedError
 from botorch.models import ModelListGP
 from botorch.models.gpytorch import BatchedMultiOutputGPyTorchModel
@@ -25,7 +27,7 @@
 from torch.nn import Module
 
 
-class ProximalAcquisitionFunction(AcquisitionFunction):
+class ProximalAcquisitionFunction(AbstractAcquisitionFunctionWrapper):
     """A wrapper around AcquisitionFunctions to add proximal weighting of the
     acquisition function. The acquisition function is
     weighted via a squared exponential centered at the last training point,
@@ -70,17 +72,14 @@ def __init__(
             beta: If not None, apply a softplus transform to the base acquisition
                 function, allows negative base acquisition function values.
         """
-        Module.__init__(self)
-
-        self.acq_func = acq_function
+        AbstractAcquisitionFunctionWrapper.__init__(self, acq_function=acq_function)
         model = self.acq_func.model
 
         if hasattr(acq_function, "X_pending"):
             if acq_function.X_pending is not None:
                 raise UnsupportedError(
                     "Proximal acquisition function requires `X_pending` to be None."
                 )
-            self.X_pending = acq_function.X_pending
 
         self.register_buffer("proximal_weights", proximal_weights)
         self.register_buffer(
@@ -91,6 +90,12 @@ def __init__(
 
         _validate_model(model, proximal_weights)
 
+    def set_X_pending(self, X_pending: Optional[Tensor]) -> None:
+        r"""Sets the `X_pending` of the base acquisition function."""
+        raise UnsupportedError(
+            "Proximal acquisition function does not support `X_pending`."
+        )
+
     @t_batch_mode_transform(expected_q=1, assert_output_shape=False)
     def forward(self, X: Tensor) -> Tensor:
         r"""Evaluate base acquisition function with proximal weighting.

botorch/acquisition/wrapper.py

@@ -0,0 +1,55 @@
+#!/usr/bin/env python3
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+r"""
+A wrapper classes around AcquisitionFunctions to modify inputs and outputs.
+"""
+
+from __future__ import annotations
+
+from abc import ABC, abstractmethod
+from typing import Optional
+
+from botorch.acquisition.acquisition import AcquisitionFunction
+from torch import Tensor
+from torch.nn import Module
+
+
+class AbstractAcquisitionFunctionWrapper(AcquisitionFunction, ABC):
+    r"""Abstract acquisition wrapper."""
+
+    def __init__(self, acq_function: AcquisitionFunction) -> None:
+        Module.__init__(self)
+        self.acq_func = acq_function
+
+    @property
+    def X_pending(self) -> Optional[Tensor]:
+        r"""Return the `X_pending` of the base acquisition function."""
+        try:
+            return self.acq_func.X_pending
+        except (ValueError, AttributeError):
+            raise ValueError(
+                f"Base acquisition function {type(self.acq_func).__name__} "
+                "does not have an `X_pending` attribute."
+            )
+
+    def set_X_pending(self, X_pending: Optional[Tensor]) -> None:
+        r"""Sets the `X_pending` of the base acquisition function."""
+        self.acq_func.set_X_pending(X_pending)
+
+    @abstractmethod
+    def forward(self, X: Tensor) -> Tensor:
+        r"""Evaluate the wrapped acquisition function on the candidate set X.
+
+        Args:
+            X: A `(b) x q x d`-dim Tensor of `(b)` t-batches with `q` `d`-dim
+                design points each.
+
+        Returns:
+            A `(b)`-dim Tensor of acquisition function values at the given
+            design points `X`.
+        """
+        pass  # pragma: no cover

sphinx/source/acquisition.rst

@@ -21,6 +21,11 @@ Analytic Acquisition Function API
 .. autoclass:: AnalyticAcquisitionFunction
     :members:
 
+Acquisition Function Wrapper API
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+.. automodule:: botorch.acquisition.wrapper
+    :members:
+
 Cached Cholesky Acquisition Function API
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 .. automodule:: botorch.acquisition.cached_cholesky
@@ -65,7 +70,7 @@ Multi-Objective Analytic Acquisition Functions
 .. automodule:: botorch.acquisition.multi_objective.analytic
     :members:
     :exclude-members: MultiObjectiveAnalyticAcquisitionFunction
-	
+
 Multi-Objective Joint Entropy Search Acquisition Functions
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 .. automodule:: botorch.acquisition.multi_objective.joint_entropy_search
@@ -86,7 +91,7 @@ Multi-Objective Multi-Fidelity Acquisition Functions
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 .. automodule:: botorch.acquisition.multi_objective.multi_fidelity
     :members:
-	
+
 Multi-Objective Predictive Entropy Search Acquisition Functions
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 .. automodule:: botorch.acquisition.multi_objective.predictive_entropy_search

test/acquisition/test_fixed_feature.py

@@ -87,7 +87,7 @@ def test_fixed_features(self):
             qEI_ff.set_X_pending(X_pending[..., :-1])
             self.assertAllClose(qEI.X_pending, X_pending)
             # test setting to None
-            qEI_ff.X_pending = None
+            qEI_ff.set_X_pending(None)
             self.assertIsNone(qEI_ff.X_pending)
 
         # test gradient

test/acquisition/test_proximal.py

@@ -209,9 +209,15 @@ def test_proximal(self):
 
             # test for x_pending points
             pending_acq = DummyAcquisitionFunction(model)
-            pending_acq.set_X_pending(torch.rand(3, 3, device=self.device, dtype=dtype))
+            X_pending = torch.rand(3, 3, device=self.device, dtype=dtype)
+            pending_acq.set_X_pending(X_pending)
             with self.assertRaises(UnsupportedError):
                 ProximalAcquisitionFunction(pending_acq, proximal_weights)
+            # test setting pending points
+            pending_acq.set_X_pending(None)
+            af = ProximalAcquisitionFunction(pending_acq, proximal_weights)
+            with self.assertRaises(UnsupportedError):
+                af.set_X_pending(X_pending)
 
             # test model with multi-batch training inputs
             train_X = torch.rand(5, 2, 3, device=self.device, dtype=dtype)

test/acquisition/test_wrapper.py

@@ -0,0 +1,52 @@
+#!/usr/bin/env python3
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torch
+from botorch.acquisition.analytic import ExpectedImprovement
+from botorch.acquisition.monte_carlo import qExpectedImprovement
+from botorch.acquisition.wrapper import AbstractAcquisitionFunctionWrapper
+from botorch.exceptions.errors import UnsupportedError
+from botorch.utils.testing import BotorchTestCase, MockModel, MockPosterior
+
+
+class DummyWrapper(AbstractAcquisitionFunctionWrapper):
+    def forward(self, X):
+        return self.acq_func(X)
+
+
+class TestAbstractAcquisitionFunctionWrapper(BotorchTestCase):
+    def test_abstract_acquisition_function_wrapper(self):
+        for dtype in (torch.float, torch.double):
+            mm = MockModel(
+                MockPosterior(
+                    mean=torch.rand(1, 1, dtype=dtype, device=self.device),
+                    variance=torch.ones(1, 1, dtype=dtype, device=self.device),
+                )
+            )
+            acq_func = ExpectedImprovement(model=mm, best_f=-1.0)
+            wrapped_af = DummyWrapper(acq_function=acq_func)
+            self.assertIs(wrapped_af.acq_func, acq_func)
+            # test forward
+            X = torch.rand(1, 1, dtype=dtype, device=self.device)
+            with torch.no_grad():
+                wrapped_val = wrapped_af(X)
+                af_val = acq_func(X)
+            self.assertEqual(wrapped_val.item(), af_val.item())
+
+            # test X_pending
+            with self.assertRaises(ValueError):
+                self.assertIsNone(wrapped_af.X_pending)
+            with self.assertRaises(UnsupportedError):
+                wrapped_af.set_X_pending(X)
+            acq_func = qExpectedImprovement(model=mm, best_f=-1.0)
+            wrapped_af = DummyWrapper(acq_function=acq_func)
+            self.assertIsNone(wrapped_af.X_pending)
+            wrapped_af.set_X_pending(X)
+            self.assertTrue(torch.equal(X, wrapped_af.X_pending))
+            self.assertTrue(torch.equal(X, acq_func.X_pending))
+            wrapped_af.set_X_pending(None)
+            self.assertIsNone(wrapped_af.X_pending)
+            self.assertIsNone(acq_func.X_pending)