Fix shape error in optimize_acqf_cyclic

botorch/generation/gen.py

@@ -56,7 +56,8 @@ def gen_candidates_scipy(
     using `scipy.optimize.minimize` via a numpy converter.
 
     Args:
-        initial_conditions: Starting points for optimization.
+        initial_conditions: Starting points for optimization, with shape
+            (b) x q x d.
         acquisition_function: Acquisition function to be used.
         lower_bounds: Minimum values for each column of initial_conditions.
         upper_bounds: Maximum values for each column of initial_conditions.
@@ -162,7 +163,7 @@ def gen_candidates_scipy(
         X=initial_conditions, lower_bounds=lower_bounds, upper_bounds=upper_bounds
     )
     constraints = make_scipy_linear_constraints(
-        shapeX=clamped_candidates.shape,
+        shapeX=shapeX,
         inequality_constraints=inequality_constraints,
         equality_constraints=equality_constraints,
     )

botorch/optim/optimize.py

@@ -110,7 +110,9 @@ def optimize_acqf(
     Returns:
         A two-element tuple containing
 
-        - a `(num_restarts) x q x d`-dim tensor of generated candidates.
+        - A tensor of generated candidates. The shape is
+            -- `q x d` if `return_best_only` is True (default)
+            -- `num_restarts x q x d` if `return_best_only` is False
         - a tensor of associated acquisition values. If `sequential=False`,
             this is a `(num_restarts)`-dim tensor of joint acquisition values
             (with explicit restart dimension if `return_best_only=False`). If
@@ -158,6 +160,19 @@ def optimize_acqf(
             "initial conditions for the case of nonlinear inequality constraints."
         )
 
+    d = bounds.shape[1]
+    if initial_conditions_provided:
+        if batch_initial_conditions.ndim not in (2, 3):
+            raise ValueError(
+                "batch_initial_conditions must be 2-dimensional or 3-dimensional. "
+                f"Its shape is {batch_initial_conditions.shape}."
+            )
+        if batch_initial_conditions.shape[-1] != d:
+            raise ValueError(
+                f"batch_initial_conditions.shape[-1] must be {d}. The "
+                f"shape is {batch_initial_conditions.shape}."
+            )
+
     # Sets initial condition generator ic_gen if initial conditions not provided
     if not initial_conditions_provided:
         ic_gen = kwargs.pop("ic_generator", None)
@@ -298,7 +313,7 @@ def _optimize_batch_candidates(
             logger.info(f"Generated candidate batch {i+1} of {len(batched_ics)}.")
 
         batch_candidates = torch.cat(batch_candidates_list)
-        batch_acq_values = torch.cat(batch_acq_values_list)
+        batch_acq_values = torch.stack(batch_acq_values_list).flatten()
         return batch_candidates, batch_acq_values, opt_warnings
 
     batch_candidates, batch_acq_values, ws = _optimize_batch_candidates(timeout_sec)

botorch/optim/parameter_constraints.py

@@ -73,7 +73,7 @@ def make_scipy_linear_constraints(
     r"""Generate scipy constraints from torch representation.
 
     Args:
-        shapeX: The shape of the torch.Tensor to optimize over (i.e. `b x q x d`)
+        shapeX: The shape of the torch.Tensor to optimize over (i.e. `(b) x q x d`)
         inequality constraints: A list of tuples (indices, coefficients, rhs),
             with each tuple encoding an inequality constraint of the form
             `\sum_i (X[indices[i]] * coefficients[i]) >= rhs`, where
@@ -219,10 +219,35 @@ def _make_linear_constraints(
             version of the input tensor `X`, returning a scalar.
         - "jac": A callable evaluating the constraint's Jacobian on `x`, a flattened
             version of the input tensor `X`, returning a numpy array.
+
+    >>> shapeX = torch.Size([3, 5, 4])
+    >>> constraints = _make_linear_constraints(
+    ...     indices=torch.tensor([1., 2.]),
+    ...     coefficients=torch.tensor([-0.5, 1.3]),
+    ...     rhs=0.49,
+    ...     shapeX=shapeX,
+    ...     eq=True
+    ... )
+    >>> len(constraints)
+    15
+    >>> constraints[0].keys()
+    dict_keys(['type', 'fun', 'jac'])
+    >>> x = np.arange(60).reshape(shapeX)
+    >>> constraints[0]["fun"](x)
+    1.61  # 1 * -0.5 + 2 * 1.3 - 0.49
+    >>> constraints[0]["jac"](x)
+    [0., -0.5, 1.3, 0., 0., ...]
+    >>> constraints[1]["fun"](x)  #
+    4.81
     """
-    if len(shapeX) != 3:
-        raise UnsupportedError("`shapeX` must be `b x q x d`")
+    if len(shapeX) not in (2, 3):
+        raise UnsupportedError(
+            f"`shapeX` must be `(b) x q x d` (at least two-dimensional). It is "
+            f"{shapeX}."
+        )
     q, d = shapeX[-2:]
+    if len(shapeX) == 2:
+        shapeX = torch.Size([1, q, d])
     n = shapeX.numel()
     constraints: List[ScipyConstraintDict] = []
     coeffs = _arrayify(coefficients)

test/optim/test_optimize.py

@@ -334,24 +334,64 @@ def test_optimize_acqf_sequential_notimplemented(self):
             )
 
     def test_optimize_acqf_runs_given_batch_initial_conditions(self):
-        num_restarts, raw_samples, dim = 1, 1, 1
+        num_restarts, raw_samples, dim = 1, 2, 3
 
         opt_x = 2 / np.pi
-        # start near one (of many) optima
-        initial_conditions = (opt_x * 1.01) * torch.ones(
-            (num_restarts, raw_samples, dim)
-        )
+        # -x[i] * 1 >= -opt_x * 1.01 => x[i] <= opt_x * 1.01
+        inequality_constraints = [
+            (torch.tensor([i]), -torch.tensor([1]), -opt_x * 1.01) for i in range(dim)
+        ] + [
+            # x[i] * 1 >= opt_x * .99
+            (torch.tensor([i]), torch.tensor([1]), opt_x * 0.99)
+            for i in range(dim)
+        ]
+        q = 1
+
+        ic_shapes = [(1, 2, dim), (2, 1, dim), (1, dim)]
+
         torch.manual_seed(0)
-        batch_candidates, acq_value_list = optimize_acqf(
-            acq_function=SinOneOverXAcqusitionFunction(),
-            bounds=torch.stack([-1 * torch.ones(dim), torch.ones(dim)]),
-            q=1,
-            num_restarts=num_restarts,
-            raw_samples=raw_samples,
-            batch_initial_conditions=initial_conditions,
-        )
-        self.assertAlmostEqual(batch_candidates.item(), opt_x, delta=1e-5)
-        self.assertAlmostEqual(acq_value_list.item(), 1)
+        for shape in ic_shapes:
+            with self.subTest(shape=shape):
+                # start near one (of many) optima
+                initial_conditions = (opt_x * 1.01) * torch.ones(shape)
+                batch_candidates, acq_value_list = optimize_acqf(
+                    acq_function=SinOneOverXAcqusitionFunction(),
+                    bounds=torch.stack([-1 * torch.ones(dim), torch.ones(dim)]),
+                    q=q,
+                    num_restarts=num_restarts,
+                    raw_samples=raw_samples,
+                    batch_initial_conditions=initial_conditions,
+                    inequality_constraints=inequality_constraints,
+                )
+                self.assertAllClose(
+                    batch_candidates,
+                    opt_x * torch.ones_like(batch_candidates),
+                    # must be at least 50% closer to the optimum than it started
+                    atol=0.004,
+                    rtol=0.005,
+                )
+                self.assertAlmostEqual(acq_value_list.item(), 1, places=3)
+
+    def test_optimize_acqf_wrong_ic_shape_inequality_constraints(self) -> None:
+        dim = 3
+        ic_shapes = [(1, 2, dim + 1), (1, 2, dim, 1), (1, dim + 1), (1, 1), (dim,)]
+
+        for shape in ic_shapes:
+            with self.subTest(shape=shape):
+                initial_conditions = torch.ones(shape)
+                expected_error = (
+                    rf"batch_initial_conditions.shape\[-1\] must be {dim}\."
+                    if len(shape) in (2, 3)
+                    else r"batch_initial_conditions must be 2\-dimensional or "
+                )
+                with self.assertRaisesRegex(ValueError, expected_error):
+                    optimize_acqf(
+                        acq_function=MockAcquisitionFunction(),
+                        bounds=torch.stack([-1 * torch.ones(dim), torch.ones(dim)]),
+                        q=4,
+                        batch_initial_conditions=initial_conditions,
+                        num_restarts=1,
+                    )
 
     def test_optimize_acqf_warns_on_opt_failure(self):
         """
@@ -808,15 +848,20 @@ def test_optimize_acqf_cyclic(self, mock_optimize_acqf):
         tkwargs = {"device": self.device}
         bounds = torch.stack([torch.zeros(3), 4 * torch.ones(3)])
         inequality_constraints = [
-            [torch.tensor([3]), torch.tensor([4]), torch.tensor(5)]
+            [torch.tensor([2], dtype=int), torch.tensor([4.0]), torch.tensor(5.0)]
         ]
         mock_acq_function = MockAcquisitionFunction()
         for q, dtype in itertools.product([1, 3], (torch.float, torch.double)):
-            inequality_constraints[0] = [
-                t.to(**tkwargs) for t in inequality_constraints[0]
+            tkwargs["dtype"] = dtype
+            inequality_constraints = [
+                (
+                    # indices can't be floats or doubles
+                    inequality_constraints[0][0],
+                    inequality_constraints[0][1].to(**tkwargs),
+                    inequality_constraints[0][2].to(**tkwargs),
+                )
             ]
             mock_optimize_acqf.reset_mock()
-            tkwargs["dtype"] = dtype
             bounds = bounds.to(**tkwargs)
             candidate_rvs = []
             acq_val_rvs = []
@@ -855,23 +900,23 @@ def test_optimize_acqf_cyclic(self, mock_optimize_acqf):
                     post_processing_func=rounding_func,
                     cyclic_options={"maxiter": num_cycles},
                 )
-                # check that X_pending is set correctly in cyclic optimization
-                if q > 1:
-                    x_pending_call_args_list = mock_set_X_pending.call_args_list
-                    idxr = torch.ones(q, dtype=torch.bool, device=self.device)
-                    for i in range(len(x_pending_call_args_list) - 1):
-                        idxr[i] = 0
-                        self.assertTrue(
-                            torch.equal(
-                                x_pending_call_args_list[i][0][0], orig_candidates[idxr]
-                            )
+            # check that X_pending is set correctly in cyclic optimization
+            if q > 1:
+                x_pending_call_args_list = mock_set_X_pending.call_args_list
+                idxr = torch.ones(q, dtype=torch.bool, device=self.device)
+                for i in range(len(x_pending_call_args_list) - 1):
+                    idxr[i] = 0
+                    self.assertTrue(
+                        torch.equal(
+                            x_pending_call_args_list[i][0][0], orig_candidates[idxr]
                         )
-                        idxr[i] = 1
-                        orig_candidates[i] = candidate_rvs[i + 1]
-                    # check reset to base_X_pendingg
-                    self.assertIsNone(x_pending_call_args_list[-1][0][0])
-                else:
-                    mock_set_X_pending.assert_not_called()
+                    )
+                    idxr[i] = 1
+                    orig_candidates[i] = candidate_rvs[i + 1]
+                # check reset to base_X_pendingg
+                self.assertIsNone(x_pending_call_args_list[-1][0][0])
+            else:
+                mock_set_X_pending.assert_not_called()
             # check final candidates
             expected_candidates = (
                 torch.cat(candidate_rvs[-q:], dim=0) if q > 1 else candidate_rvs[0]