Merge branch 'master' into parallel_runner

Author: isazi

doc/requirements.txt

@@ -82,7 +82,7 @@ tornado==6.5.1 ; python_version >= "3.9" and python_version < "3.15"
 traitlets==5.14.3 ; python_version >= "3.9" and python_version < "3.15"
 typing-extensions==4.12.2 ; python_version >= "3.9" and python_version < "3.15"
 tzdata==2025.1 ; python_version >= "3.9" and python_version < "3.15"
-urllib3==2.3.0 ; python_version >= "3.9" and python_version < "3.15"
+urllib3==2.5.0 ; python_version >= "3.9" and python_version < "3.15"
 wcwidth==0.2.13 ; python_version >= "3.9" and python_version < "3.15"
 webencodings==0.5.1 ; python_version >= "3.9" and python_version < "3.15"
 xmltodict==0.14.2 ; python_version >= "3.9" and python_version < "3.15"

kernel_tuner/strategies/common.py

@@ -3,6 +3,7 @@
 from time import perf_counter
 
 import numpy as np
+import numbers
 
 from kernel_tuner import util
 from kernel_tuner.searchspace import Searchspace
@@ -109,8 +110,15 @@ def __call__(self, x, check_restrictions=True):
             self.runner.last_strategy_start_time = perf_counter()
 
         # get numerical return value, taking optimization direction into account
-        return_value = result[self.tuning_options.objective] or sys.float_info.max
-        return_value = return_value if not self.tuning_options.objective_higher_is_better else -return_value
+        return_value = result[self.tuning_options.objective]
+
+        if isinstance(return_value, numbers.Number):
+            if self.tuning_options.objective_higher_is_better:
+                # flip the sign if higher means better
+                return_value = -return_value
+        else:
+            # this is not a valid configuration, just return max
+            return_value = sys.float_info.max
 
         return return_value
 

kernel_tuner/strategies/firefly_algorithm.py

@@ -94,7 +94,7 @@ def __init__(self, bounds):
         """Create Firefly at random position within bounds."""
         super().__init__(bounds)
         self.bounds = bounds
-        self.intensity = 1 / self.score
+        self.intensity = -self.score
 
     def distance_to(self, other):
         """Return Euclidian distance between self and other Firefly."""
@@ -103,10 +103,7 @@ def distance_to(self, other):
     def compute_intensity(self, fun):
         """Evaluate cost function and compute intensity at this position."""
         self.evaluate(fun)
-        if self.score == sys.float_info.max:
-            self.intensity = -sys.float_info.max
-        else:
-            self.intensity = 1 / self.score
+        self.intensity = -self.score
 
     def move_towards(self, other, beta, alpha):
         """Move firefly towards another given beta and alpha values."""

kernel_tuner/strategies/simulated_annealing.py

@@ -87,20 +87,24 @@ def tune(searchspace: Searchspace, runner, tuning_options):
 
 def acceptance_prob(old_cost, new_cost, T, tuning_options):
     """Annealing equation, with modifications to work towards a lower value."""
-    error_val = sys.float_info.max if not tuning_options.objective_higher_is_better else -sys.float_info.max
+    error_val = sys.float_info.max
+    res = 0.0
     # if start pos is not valid, always move
     if old_cost == error_val:
-        return 1.0
+        res = 1.0
     # if we have found a valid ps before, never move to nonvalid pos
-    if new_cost == error_val:
-        return 0.0
+    elif new_cost == error_val:
+        res = 0.0
     # always move if new cost is better
-    if new_cost < old_cost:
-        return 1.0
+    elif new_cost < old_cost:
+        res = 1.0
     # maybe move if old cost is better than new cost depending on T and random value
-    if tuning_options.objective_higher_is_better:
-        return np.exp(((new_cost-old_cost)/new_cost)/T)
-    return np.exp(((old_cost-new_cost)/old_cost)/T)
+    else:
+        if tuning_options.objective_higher_is_better:
+            res = np.exp(((new_cost-old_cost)/new_cost)/T)
+        else:
+            res = np.exp(((old_cost-new_cost)/old_cost)/T)
+    return res
 
 
 def neighbor(pos, searchspace: Searchspace):