pass gen_candidates callable in optimize_acqf

Summary: see title. This will support using stochastic optimization

Differential Revision: D41629164

fbshipit-source-id: a047ee6407de568715ff8650d23d7fdefa363a4d

Author: sdaulton

botorch/generation/gen.py

@@ -37,6 +37,8 @@
 
 logger = _get_logger()
 
+TGenCandidates = Callable[[Tensor, AcquisitionFunction, Any], Tuple[Tensor, Tensor]]
+
 
 def gen_candidates_scipy(
     initial_conditions: Tensor,
@@ -49,6 +51,7 @@ def gen_candidates_scipy(
     options: Optional[Dict[str, Any]] = None,
     fixed_features: Optional[Dict[int, Optional[float]]] = None,
     timeout_sec: Optional[float] = None,
+    **kwargs,
 ) -> Tuple[Tensor, Tensor]:
     r"""Generate a set of candidates using `scipy.optimize.minimize`.
 
@@ -281,6 +284,7 @@ def gen_candidates_torch(
     callback: Optional[Callable[[int, Tensor, Tensor], NoReturn]] = None,
     fixed_features: Optional[Dict[int, Optional[float]]] = None,
     timeout_sec: Optional[float] = None,
+    **kwargs,
 ) -> Tuple[Tensor, Tensor]:
     r"""Generate a set of candidates using a `torch.optim` optimizer.
 

botorch/optim/optimize.py

@@ -23,7 +23,7 @@
 from botorch.acquisition.knowledge_gradient import qKnowledgeGradient
 from botorch.exceptions import InputDataError, UnsupportedError
 from botorch.exceptions.warnings import OptimizationWarning
-from botorch.generation.gen import gen_candidates_scipy
+from botorch.generation.gen import gen_candidates_scipy, TGenCandidates
 from botorch.logging import logger
 from botorch.optim.initializers import (
     gen_batch_initial_conditions,
@@ -64,6 +64,7 @@ def optimize_acqf(
     post_processing_func: Optional[Callable[[Tensor], Tensor]] = None,
     batch_initial_conditions: Optional[Tensor] = None,
     return_best_only: bool = True,
+    gen_candidates: TGenCandidates = gen_candidates_scipy,
     sequential: bool = False,
     **kwargs: Any,
 ) -> Tuple[Tensor, Tensor]:
@@ -103,6 +104,8 @@ def optimize_acqf(
             this if you do not want to use default initialization strategy.
         return_best_only: If False, outputs the solutions corresponding to all
             random restart initializations of the optimization.
+        gen_candidates: A callable for generating candidates given initial
+            conditions. Default: `gen_candidates_scipy`
         sequential: If False, uses joint optimization, otherwise uses sequential
             optimization.
         kwargs: Additonal keyword arguments.
@@ -273,7 +276,7 @@ def _optimize_batch_candidates(
         if timeout_sec is not None:
             timeout_sec = (timeout_sec - start_time) / len(batched_ics)
 
-        scipy_kws = {
+        gen_kws = {
             "acquisition_function": acq_function,
             "lower_bounds": None if bounds[0].isinf().all() else bounds[0],
             "upper_bounds": None if bounds[1].isinf().all() else bounds[1],
@@ -289,8 +292,8 @@ def _optimize_batch_candidates(
             # optimize using random restart optimization
             with warnings.catch_warnings(record=True) as ws:
                 warnings.simplefilter("always", category=OptimizationWarning)
-                batch_candidates_curr, batch_acq_values_curr = gen_candidates_scipy(
-                    initial_conditions=batched_ics_, **scipy_kws
+                batch_candidates_curr, batch_acq_values_curr = gen_candidates(
+                    initial_conditions=batched_ics_, **gen_kwargs
                 )
             opt_warnings += ws
             batch_candidates_list.append(batch_candidates_curr)