Infer: Don't minimise to Nothing if there's an upper bound

Author: dwijnand

compiler/src/dotty/tools/dotc/typer/Inferencing.scala

@@ -183,7 +183,7 @@ object Inferencing {
               // else hold off instantiating unbounded unconstrained variable
             else if direction != 0 then
               instantiate(tvar, fromBelow = direction < 0)
-            else if variance >= 0 && (force.ifBottom == IfBottom.ok || tvar.hasLowerBound) then
+            else if variance >= 0 && (force.ifBottom == IfBottom.ok && !tvar.hasUpperBound || tvar.hasLowerBound) then
               instantiate(tvar, fromBelow = true)
             else if variance >= 0 && force.ifBottom == IfBottom.fail then
               return false

compiler/test/dotty/tools/dotc/typer/InstantiateModel.scala

@@ -0,0 +1,57 @@
+package dotty.tools
+package dotc
+package typer
+
+// Modelling the decision in IsFullyDefined
+object InstantiateModel:
+  enum LB  { case NN; case LL; case L1                             }; import LB.*
+  enum UB  { case AA; case UU; case U1                             }; import UB.*
+  enum Var { case V; case NotV                                     }; import Var.*
+  enum MSe { case M; case NotM                                     }; import MSe.*
+  enum Bot { case Fail; case Ok; case Flip                         }; import Bot.*
+  enum Act { case Min; case Max; case ToMax; case Skip; case False }; import Act.*
+
+  // NN/AA = Nothing/Any
+  // LL/UU = the original bounds, on the type parameter
+  // L1/U1 = the constrained bounds, on the type variable
+  // V     = variance >= 0 ("non-contravariant")
+  // MSe   = minimisedSelected
+  // Bot   = IfBottom
+  // ToMax = delayed maximisation, via addition to toMaximize
+  // Skip  = minimisedSelected "hold off instantiating"
+  // False = return false
+
+  // there are 9 combinations:
+  // # | LB | UB | d | // d = direction
+  // --+----+----+---+
+  // 1 | L1 | AA | - |       L1 <: T
+  // 2 | L1 | UU | - |       L1 <: T       <: UU
+  // 3 | LL | U1 | + | LL <:       T <: U1
+  // 4 | NN | U1 | + |             T <: U1
+  // 5 | L1 | U1 | 0 |       L1 <: T <: U1
+  // 6 | LL | UU | 0 | LL <:       T       <: UU
+  // 7 | LL | AA | 0 | LL <:       T
+  // 8 | NN | UU | 0 |             T       <: UU
+  // 9 | NN | AA | 0 |             T
+
+  def decide(lb: LB, ub: UB, v: Var, bot: Bot, m: MSe): Act = (lb, ub) match
+    case (L1, AA) => Min
+    case (L1, UU) => Min
+    case (LL, U1) => Max
+    case (NN, U1) => Max
+
+    case (L1, U1) => if m==M || v==V then Min else ToMax
+    case (LL, UU) => if m==M || v==V then Min else ToMax
+    case (LL, AA) => if m==M || v==V then Min else ToMax
+
+    case (NN, UU) => bot match
+      case _    if m==M => Max
+    //case Ok   if v    => Min   // removed
+      case Fail if v==V => False
+      case _            => ToMax
+
+    case (NN, AA) => bot match
+      case _    if m==M => Skip
+      case Ok   if v==V => Min
+      case Fail if v==V => False
+      case _            => ToMax

tests/neg/i15525.scala

@@ -37,7 +37,7 @@ def element22(
       transmittable20.Type / transmittable21.Type
   } = ???
 
-def test22 =
+def test22 = // error
   Resolution(
     element22(
       Resolution(element0), Resolution(element0), // error // error

tests/pos/i14218.scala

@@ -0,0 +1,15 @@
+class Pet
+class Cat extends Pet
+
+class Z1[ S2 <: Pet](val fn: S2 => Unit) // the case: given an upper bound
+class Z2[ S1       ](val fn: S1 => Unit) // the counter-example: works without an upper bound
+class Z3[-S3 <: Pet](val fn: S3 => Unit)
+
+abstract class Test:
+  def test =
+    val r1 = new Z1((_: Pet) => ()); eat[Z1[Pet]](r1) // the case: using the parameter bound in situ infers Z[Nothing]
+    val r2 = new Z2((_: Pet) => ()); eat[Z2[Pet]](r2) // counter-example: infers as desired without an upper bound
+    val r3 = new Z3((_: Pet) => ()); eat[Z3[Pet]](r3) // workaround: declare it contravariant
+    val r4 = new Z1((_: Cat) => ()); eat[Z1[Cat]](r4) // counter-example: infers as desired with a subtype
+
+  def eat[T](x: T): Unit