Add storage support for SobolQMCNormalSampler

ax/storage/botorch_modular_registry.py

@@ -75,6 +75,7 @@
     OutcomeTransform,
     Standardize,
 )
+from botorch.sampling.normal import SobolQMCNormalSampler
 
 # Miscellaneous BoTorch imports
 from gpytorch.constraints import Interval
@@ -168,6 +169,7 @@
     Interval: "Interval",
     GammaPrior: "GammaPrior",
     LogNormalPrior: "LogNormalPrior",
+    SobolQMCNormalSampler: "SobolQMCNormalSampler",
 }
 
 """
@@ -205,6 +207,7 @@
     LogNormalPrior: GPYTORCH_COMPONENT_REGISTRY,
     InputTransform: INPUT_TRANSFORM_REGISTRY,
     OutcomeTransform: OUTCOME_TRANSFORM_REGISTRY,
+    SobolQMCNormalSampler: GPYTORCH_COMPONENT_REGISTRY,
 }
 
 

ax/storage/json_store/encoders.py

@@ -63,6 +63,7 @@
 from ax.utils.common.typeutils import not_none
 from ax.utils.common.typeutils_torch import torch_type_to_str
 from botorch.models.transforms.input import ChainedInputTransform, InputTransform
+from botorch.sampling.base import MCSampler
 from botorch.utils.types import _DefaultType
 from torch import Tensor
 
@@ -593,6 +594,11 @@ def botorch_component_to_dict(input_obj: Any) -> Dict[str, Any]:
         state_dict = botorch_input_transform_to_init_args(input_transform=input_obj)
     else:
         state_dict = dict(input_obj.state_dict())
+    if isinstance(input_obj, MCSampler):
+        # The sampler args are not part of the state dict. Manually add them.
+        # Sample shape cannot be added to torch state dict since it is not a tensor.
+        state_dict["sample_shape"] = input_obj.sample_shape
+        state_dict["seed"] = input_obj.seed
     return {
         "__type": f"{class_type.__name__}",
         "index": class_type,

ax/storage/json_store/registry.py

@@ -167,6 +167,7 @@
 from botorch.acquisition.acquisition import AcquisitionFunction
 from botorch.models.model import Model
 from botorch.models.transforms.input import ChainedInputTransform, Normalize, Round
+from botorch.sampling.normal import SobolQMCNormalSampler
 from botorch.utils.types import DEFAULT
 from gpytorch.constraints import Interval
 from gpytorch.likelihoods.likelihood import Likelihood
@@ -252,6 +253,7 @@
     SearchSpace: search_space_to_dict,
     SingleDiagnosticBestModelSelector: best_model_selector_to_dict,
     HierarchicalSearchSpace: search_space_to_dict,
+    SobolQMCNormalSampler: botorch_component_to_dict,
     SumConstraint: sum_parameter_constraint_to_dict,
     Surrogate: surrogate_to_dict,
     BenchmarkMetric: metric_to_dict,
@@ -385,6 +387,7 @@
     "SurrogateMetric": BenchmarkMetric,  # backward-compatiblity
     # NOTE: SurrogateRunners -> SyntheticRunner on load due to complications
     "SurrogateRunner": SyntheticRunner,
+    "SobolQMCNormalSampler": SobolQMCNormalSampler,
     "SyntheticRunner": SyntheticRunner,
     "SurrogateSpec": SurrogateSpec,
     "Trial": Trial,

ax/storage/json_store/tests/test_json_store.py

@@ -130,6 +130,7 @@
     sobol_gpei_generation_node_gs,
 )
 from ax.utils.testing.utils import generic_equals
+from botorch.sampling.normal import SobolQMCNormalSampler
 
 
 # pyre-fixme[5]: Global expression must be annotated.
@@ -731,3 +732,20 @@ def test_generation_step_backwards_compatibility(self) -> None:
         generation_step = object_from_json(json)
         self.assertIsInstance(generation_step, GenerationStep)
         self.assertEqual(generation_step.model_kwargs, {"other_kwarg": 5})
+
+    def test_SobolQMCNormalSampler(self) -> None:
+        # This fails default equality checks, so testing it separately.
+        sampler = SobolQMCNormalSampler(sample_shape=torch.Size([2]))
+        sampler_json = object_to_json(
+            sampler,
+            encoder_registry=CORE_ENCODER_REGISTRY,
+            class_encoder_registry=CORE_CLASS_ENCODER_REGISTRY,
+        )
+        sampler_loaded = object_from_json(
+            sampler_json,
+            decoder_registry=CORE_DECODER_REGISTRY,
+            class_decoder_registry=CORE_CLASS_DECODER_REGISTRY,
+        )
+        self.assertIsInstance(sampler_loaded, SobolQMCNormalSampler)
+        self.assertEqual(sampler.sample_shape, sampler_loaded.sample_shape)
+        self.assertEqual(sampler.seed, sampler_loaded.seed)