Remove high-priority, soft weight constraint from distributions

src/vsc/model/constraint_dist_scope_model.py

@@ -3,9 +3,11 @@
 
 @author: mballance
 '''
+from typing import List, Tuple
 from vsc.model.constraint_inline_scope_model import ConstraintInlineScopeModel
 from vsc.model.constraint_dist_model import ConstraintDistModel
 from vsc.model.constraint_soft_model import ConstraintSoftModel
+from vsc.model.rand_state import RandState
 
 class ConstraintDistScopeModel(ConstraintInlineScopeModel):
     """Holds implementation data about dist constraint"""
@@ -16,10 +18,36 @@ def __init__(self, dist_c, constraints=None):
         self.dist_c : ConstraintDistModel = dist_c
 
         self.dist_soft_c : ConstraintSoftModel = None
-
-        # Indicates the current-target range. This is
-        # updated during the weight-selection process
+
+        # List of (weight, index) tuples
+        self.weight_list : List[Tuple[int, int]] = []
+        self.total_weight = 0
+
+        # Indicates the current-target range. This is used to
+        # by solvegroup_swizzler_range.
         self.target_range = 0
+
+    def next_target_range(self, randstate : RandState) -> int:
+        """Select the next target range from the weight list"""
+
+        seed_v = randstate.rng.randint(1, self.total_weight)
+
+        # Find the first range
+        i = 0
+        while i < len(self.weight_list):
+            seed_v -= self.weight_list[i][0]
+
+            if seed_v <= 0:
+                break
+
+            i += 1
+
+        if i >= len(self.weight_list):
+            i = len(self.weight_list)-1
+
+        self.target_range = self.weight_list[i][1]
+
+        return self.target_range
 
     def set_dist_soft_c(self, c : ConstraintSoftModel):
         self.addConstraint(c)

src/vsc/model/rand_set.py

@@ -21,11 +21,13 @@
 # @author: ballance
 
 from builtins import set
-from typing import Set, List
+from typing import Set, List, Dict
+from vsc.model.constraint_dist_scope_model import ConstraintDistScopeModel
 
 from vsc.model.constraint_model import ConstraintModel
 from vsc.model.field_model import FieldModel
 from vsc.model.constraint_soft_model import ConstraintSoftModel
+from vsc.model.field_scalar_model import FieldScalarModel
 from vsc.visitors.model_pretty_printer import ModelPrettyPrinter
 
 
@@ -43,7 +45,7 @@ def __init__(self, order=-1):
         self.soft_constraint_s : Set[ConstraintModel] = set()
         self.soft_constraint_l : List[ConstraintModel] = []
         self.soft_priority = 0
-        self.dist_field_m = {}
+        self.dist_field_m : Dict[FieldScalarModel, List[ConstraintDistScopeModel]] = {}
 
         # List of fields in each ordered set
         # Only non-none if order constraints impact this randset

src/vsc/model/rand_state.py

@@ -29,7 +29,7 @@ def rand_s(self):
 
         return val
 
-    def randint(self, low, high):
+    def randint(self, low, high) -> int:
         low = int(low)
         high = int(high)
 

src/vsc/model/solvegroup_swizzler_partsel.py

@@ -10,21 +10,24 @@
 from vsc.model.expr_literal_model import ExprLiteralModel
 from vsc.model.expr_model import ExprModel
 from vsc.model.expr_partselect_model import ExprPartselectModel
+from vsc.model.field_model import FieldModel
 from vsc.model.field_scalar_model import FieldScalarModel
+from vsc.model.rand_set import RandSet
+from vsc.model.rand_state import RandState
 from vsc.model.variable_bound_model import VariableBoundModel
 
 
 class SolveGroupSwizzlerPartsel(object):
 
     def __init__(self, randstate, solve_info, debug=0):
         self.debug = debug
-        self.randstate = randstate
+        self.randstate : RandState = randstate
         self.solve_info = solve_info
 
     def swizzle(self, 
-                btor, 
-                rs, 
-                bound_m):
+                btor,
+                rs : RandSet,
+                bound_m : VariableBoundModel):
         if self.debug > 0:
             print("--> swizzle_randvars")
 
@@ -51,7 +54,7 @@ def swizzle(self,
         if self.debug > 0:
             print("<-- swizzle_randvars")    
 
-    def swizzle_field_l(self, field_l, rs, bound_m, btor):
+    def swizzle_field_l(self, field_l, rs : RandSet, bound_m, btor):
         e = None
         if len(field_l) > 0:
             # Make a copy of the field list so we don't
@@ -96,7 +99,10 @@ def swizzle_field_l(self, field_l, rs, bound_m, btor):
         else:
             return False            
 
-    def swizzle_field(self, f, rs, bound_m) -> ExprModel:
+    def swizzle_field(self,
+                      f : FieldScalarModel,
+                      rs : RandSet,
+                      bound_m : VariableBoundModel)->ExprModel:
         ret = None
 
         if self.debug > 0:
@@ -106,14 +112,15 @@ def swizzle_field(self, f, rs, bound_m) -> ExprModel:
             if self.debug > 0:
                 print("Note: field %s is in dist map" % f.name)
                 for d in rs.dist_field_m[f]:
-                    print("  Target interval %d" % d.target_range)
+                    print("  Weight list %s" % d.weight_list)
             if len(rs.dist_field_m[f]) > 1:
                 target_d = self.randstate.randint(0, len(rs.dist_field_m[f])-1)
                 dist_scope_c = rs.dist_field_m[f][target_d]
             else:
                 dist_scope_c = rs.dist_field_m[f][0]
 
-            target_w = dist_scope_c.dist_c.weights[dist_scope_c.target_range]
+            target_range = dist_scope_c.next_target_range(self.randstate)
+            target_w = dist_scope_c.dist_c.weights[target_range]
             if target_w.rng_rhs is not None:
                 # Dual-bound range
                 val_l = target_w.rng_lhs.val()
@@ -129,6 +136,8 @@ def swizzle_field(self, f, rs, bound_m) -> ExprModel:
             else:
                 # Single value
                 val = target_w.rng_lhs.val()
+                if self.debug > 0:
+                    print("Select dist-weight value %d" % (int(val)))
                 ret = [ExprBinModel(
                     ExprFieldRefModel(f),
                     BinExprType.Eq,

src/vsc/model/solvegroup_swizzler_range.py

@@ -11,6 +11,7 @@
 from vsc.model.expr_literal_model import ExprLiteralModel
 from vsc.model.expr_model import ExprModel
 from vsc.model.field_scalar_model import FieldScalarModel
+from vsc.model.rand_set import RandSet
 from vsc.model.variable_bound_model import VariableBoundModel
 
 
@@ -103,7 +104,10 @@ def swizzle_field_l(self, field_l, rs, bound_m, btor):
         else:
             return False            
 
-    def swizzle_field(self, f, rs, bound_m) -> ExprModel:
+    def swizzle_field(self,
+                      f : FieldScalarModel, 
+                      rs : RandSet, 
+                      bound_m : VariableBoundModel)->ExprModel:
         ret = None
 
         if self.debug > 0:

src/vsc/visitors/dist_constraint_builder.py

@@ -96,56 +96,21 @@ def visit_constraint_dist(self, c):
 
         # Form a list of non-zero weighted tuples of weight/range
         # Sort in ascending order
-        weight_l = []                
-        total_w = 0
+        weight_list = []
+        total_weight = 0
         for i,w in enumerate(c.weights):
             weight = int(w.weight.val())
-            total_w += weight
+            total_weight += weight
             if weight > 0:
-                weight_l.append((weight, i))
-        weight_l.sort(key=lambda w:w[0])
-
-        seed_v = self.rng.randint(1, total_w)
-
-        # Find the first range 
-        i = 0
-        while i < len(weight_l):
-            seed_v -= weight_l[i][0]
-
-            if seed_v <= 0:
-                break
-
-            i += 1
+                weight_list.append((weight, i))
+        weight_list.sort(key=lambda w:w[0])
 
-        if i >= len(weight_l):
-            i = len(weight_l)-1
+        scope.weight_list = weight_list
+        scope.total_weight = total_weight
+
+        # Call next_target_range for solvegroup_swizzler_range to use
+        _ = scope.next_target_range(self.rng)
 
-        scope.target_range = weight_l[i][1]            
-        target_w = c.weights[weight_l[i][1]]
-        dist_soft_c = None
-        if target_w.rng_rhs is not None:
-            dist_soft_c = ConstraintSoftModel(
-                ExprBinModel(
-                    ExprBinModel(
-                        c.lhs,
-                        BinExprType.Ge,
-                        target_w.rng_lhs),
-                    BinExprType.And,
-                    ExprBinModel(
-                        c.lhs,
-                        BinExprType.Le,
-                        target_w.rng_rhs)))
-        else:
-            dist_soft_c = ConstraintSoftModel(
-                ExprBinModel(
-                    c.lhs,
-                    BinExprType.Eq,
-                    target_w.rng_lhs))
-        # Give dist constraints a high priority to allow
-        # them to override all user-defined soft constraints
-        dist_soft_c.priority = 1000000
-        scope.set_dist_soft_c(dist_soft_c)
-
         self.override_constraint(scope)
 
 

ve/unit/test_constraint_soft.py

@@ -70,7 +70,7 @@ def dist_a(self):
                 it.a == 1 #B
 
     def test_soft_dist_priority(self):
-        """Ensures that dist constraints take priority over soft constraints"""
+        """Ensures that soft constraints take priority over dist constraints"""
 
         @vsc.randobj
         class my_item(object):
@@ -90,18 +90,18 @@ def dist_a(self):
                     vsc.weight(1,  10),
                     vsc.weight(2,  10),
                     vsc.weight(4,  10),
-                    vsc.weight(8, 10)])
+                    vsc.weight(8,  10)])
 
         hist = [0]*9
         item = my_item()
         for i in range(100):
             item.randomize()
             hist[item.a] += 1
 
-        self.assertGreater(hist[0], 0) 
-        self.assertGreater(hist[1], 0) 
-        self.assertGreater(hist[2], 0) 
-        self.assertGreater(hist[4], 0) 
+        self.assertEqual(hist[0], 0)
+        self.assertEqual(hist[1], 0)
+        self.assertEqual(hist[2], 0)
+        self.assertEqual(hist[4], 0)
         self.assertGreater(hist[8], 0) 
 
     def test_compound_array(self):