Revert last three commits

hannah/conf/nas/aging_evolution_nas.yaml

@@ -31,7 +31,6 @@ n_jobs: 10
 presample: False
 total_candidates: 50
 num_selected_candidates: 20
-constrained_sampling_on_search: True
 bounds:
     val_error: 0.1
     # total_macs: 128000000

hannah/nas/constraints/random_walk.py

@@ -29,7 +29,6 @@
 from hannah.nas.parameters.parameters import Parameter
 from hannah.nas.parameters.parametrize import set_parametrization
 from hannah.nas.search.utils import np_to_primitive
-from hannah.nas.functional_operators.utils import get_active_parameters
 
 logger = logging.getLogger(__name__)
 
@@ -73,36 +72,36 @@ def hierarchical_parameter_dict(parameter, include_empty=False, flatten=False):
 }
 
 
-# def get_active_parameter(net):
-#     active_param_ids = []
-#     queue = [net]
-#     visited = [net.id]
-
-#     def extract_parameters(node):
-#         ids = []
-#         for k, p in node._PARAMETERS.items():
-#             if isinstance(p, Parameter):
-#                 ids.append(p.id)
-#         return ids
-
-#     while queue:
-#         current = queue.pop()
-#         if isinstance(current, ChoiceOp):
-#             # handle choices
-#             active_param_ids.append(current.switch.id)
-#             chosen_path = current.options[lazy(current.switch)]
-#             if chosen_path.id not in visited:
-#                 queue.append(chosen_path)
-#                 visited.append(chosen_path.id)
-#         else:
-#             # handle all other operators & tensors
-#             active_param_ids.extend(extract_parameters(current))
-#             for operand in current.operands:
-#                 if operand.id not in visited:
-#                     queue.append(operand)
-#                     visited.append(operand.id)
-
-#     return active_param_ids
+def get_active_parameter(net):
+    active_param_ids = []
+    queue = [net]
+    visited = [net.id]
+
+    def extract_parameters(node):
+        ids = []
+        for k, p in node._PARAMETERS.items():
+            if isinstance(p, Parameter):
+                ids.append(p.id)
+        return ids
+
+    while queue:
+        current = queue.pop()
+        if isinstance(current, ChoiceOp):
+            # handle choices
+            active_param_ids.append(current.switch.id)
+            chosen_path = current.options[lazy(current.switch)]
+            if chosen_path.id not in visited:
+                queue.append(chosen_path)
+                visited.append(chosen_path.id)
+        else:
+            # handle all other operators & tensors
+            active_param_ids.extend(extract_parameters(current))
+            for operand in current.operands:
+                if operand.id not in visited:
+                    queue.append(operand)
+                    visited.append(operand.id)
+
+    return active_param_ids
 
 
 class RandomWalkConstraintSolver:
@@ -194,7 +193,7 @@ def solve(self, module, parameters, fix_vars=[]):
             ct = 0
             while ct < self.max_iterations:
                 # active_params = get_active_parameter(params)
-                active_params = list(get_active_parameters(mod).keys())
+                active_params = get_active_parameter(mod)
 
                 param_keys = [p for p in all_param_keys if p in active_params]
                 current = con.lhs.evaluate()

hannah/nas/search/sampler/aging_evolution.py

@@ -30,9 +30,9 @@
 from hannah.nas.search.sampler.mutator import ParameterMutator
 from hannah.nas.search.utils import np_to_primitive
 
+from ...parametrization import SearchSpace
 from ...utils import is_pareto
 from .base_sampler import Sampler, SearchResult
-from hannah.nas.functional_operators.utils import get_active_parameters
 
 
 class FitnessFunction:
@@ -59,16 +59,14 @@ class AgingEvolutionSampler(Sampler):
     def __init__(
         self,
         parent_config,
-        search_space,
         parametrization: dict,
         population_size: int = 50,
         random_state = None,
         sample_size: int = 10,
-        mutation_rate: float = 0.01,
         eps: float = 0.1,
         output_folder=".",
     ):
-        super().__init__(parent_config, search_space=search_space, output_folder=output_folder)
+        super().__init__(parent_config, output_folder=output_folder)
         self.bounds = self.parent_config.nas.bounds
         self.parametrization = parametrization
 
@@ -81,7 +79,7 @@ def __init__(
         self.population_size = population_size
         self.sample_size = sample_size
         self.eps = eps
-        self.mutator = ParameterMutator(mutation_rate)
+        self.mutator = ParameterMutator(0.1)
 
         self.history = []
         self.population = []
@@ -120,11 +118,8 @@ def next_parameters(self):
 
             parent = sample[np.argmin(fitness)]
             parent_parametrization = set_parametrization(parent.parameters, self.parametrization)
-            parametrization = {key: param.current_value for key, param in parent_parametrization.items()}
-            active_parameters = get_active_parameters(self.search_space, parent_parametrization)
 
-            mutated_parameters, mutated_keys = self.mutator.mutate(active_parameters)
-            parametrization.update(mutated_parameters)
+            parametrization, mutated_keys = self.mutator.mutate(parent_parametrization)
 
         return parametrization, mutated_keys
 

hannah/nas/search/sampler/base_sampler.py

@@ -27,10 +27,8 @@ def costs(self):
 class Sampler(ABC):
     def __init__(self,
                  parent_config,
-                 search_space,
                  output_folder=".") -> None:
         self.history = []
-        self.search_space = search_space
         self.output_folder = Path(output_folder)
         self.parent_config = parent_config
 

hannah/nas/search/search.py

@@ -129,7 +129,6 @@ def before_search(self):
         parametrization = self.search_space.parametrization(flatten=True)
         self.sampler = instantiate(
             self.config.nas.sampler,
-            search_space=self.search_space,
             parametrization=parametrization,
             parent_config=self.config,
             _recursive_=False,
@@ -248,7 +247,7 @@ def sample_candidates(
         num_candidates=None,
         sort_key="val_error",
         presample=False,
-        constrain=True,
+        constrain=False,
     ):
         candidates = []
         skip_ct = 0