Make scalar → Tensor0 conversions work without an import, using Scala's into

.scalafmt.conf

@@ -1,4 +1,4 @@
-version = "3.9.8"
+version = "3.10.3"
 runner.dialect = scala3
 maxColumn = 999
 rewrite.scala3.convertToNewSyntax = true

AGENTS.md

@@ -266,9 +266,7 @@ val descendingSpaced = Tensor1(Axis[A]).linspace(Tensor0(1.0f), Tensor0(0.0f), 3
 val samples = Tensor1(Axis[B]).fromArray(Array(4.0f, 2.0f, 8.0f))
 val binEdges = Tensor1(Axis[A]).linspace(samples.min, samples.max, 4)
 
-// The typed factory fixes the value type; with dimwit.Conversions.given
-// plain literals are converted to Tensor0 of that type
-import dimwit.Conversions.given
+// The typed factory fixes the value type; plain literals are converted to Tensor0 of that type
 val halfSpaced = Tensor1(Axis[A], VType[Float16]).linspace(0.0f, 1.0f, 5)
 ```
 
@@ -474,10 +472,10 @@ val wrong = t.sum(Axis[C])
 // Conflicting definitions:
 // val t:
 //   dimwit.tensor.Tensor2[MdocApp0.this.A, MdocApp0.this.B,
-//     dimwit.tensor.DType.Float32] in class MdocApp0 at line 71 and
+//     dimwit.tensor.DType.Float32] in class MdocApp0 at line 70 and
 // val t:
 //   dimwit.tensor.Tensor2[MdocApp0.this.A, MdocApp0.this.B,
-//     dimwit.tensor.DType.Float32] in class MdocApp0 at line 117
+//     dimwit.tensor.DType.Float32] in class MdocApp0 at line 116
 //
 ```
 
@@ -519,10 +517,10 @@ val wrong = t + 5.0f  // Use +! instead
 // Conflicting definitions:
 // val t:
 //   dimwit.tensor.Tensor2[MdocApp0.this.A, MdocApp0.this.B,
-//     dimwit.tensor.DType.Float32] in class MdocApp0 at line 71 and
+//     dimwit.tensor.DType.Float32] in class MdocApp0 at line 70 and
 // val t:
 //   dimwit.tensor.Tensor2[MdocApp0.this.A, MdocApp0.this.B,
-//     dimwit.tensor.DType.Float32] in class MdocApp0 at line 126
+//     dimwit.tensor.DType.Float32] in class MdocApp0 at line 125
 //
 ```
 
@@ -612,19 +610,19 @@ val wrong = m1.dot(Axis[B])(m2)
 // Conflicting definitions:
 // val m1:
 //   dimwit.tensor.Tensor2[MdocApp1.this.A, MdocApp1.this.B,
-//     dimwit.tensor.DType.Float32] in class MdocApp1 at line 148 and
+//     dimwit.tensor.DType.Float32] in class MdocApp1 at line 147 and
 // val m1:
 //   dimwit.tensor.Tensor2[MdocApp1.this.A, MdocApp1.this.B,
-//     dimwit.tensor.DType.Float32] in class MdocApp1 at line 151
+//     dimwit.tensor.DType.Float32] in class MdocApp1 at line 150
 // 
 // error: 
 // Conflicting definitions:
 // val m2:
 //   dimwit.tensor.Tensor2[MdocApp1.this.B, MdocApp1.this.C,
-//     dimwit.tensor.DType.Float32] in class MdocApp1 at line 149 and
+//     dimwit.tensor.DType.Float32] in class MdocApp1 at line 148 and
 // val m2:
 //   dimwit.tensor.Tensor2[MdocApp1.this.C, MdocApp1.this.D,
-//     dimwit.tensor.DType.Float32] in class MdocApp1 at line 152
+//     dimwit.tensor.DType.Float32] in class MdocApp1 at line 151
 //
 ```
 
@@ -1007,7 +1005,6 @@ println(s"Block Hessian shapes: ${h_x1x1.shape}, ${h_x1x2.shape}, ${h_x2x1.shape
 
 ```scala
 import dimwit.*
-import dimwit.Conversions.given
 import dimwit.optimizer.{GradientDescent, GradientOptimizer}
 import dimwit.random.Random
 
@@ -1056,7 +1053,6 @@ val trained = optimizer.iterate(initModelParams)(gradFunc)
 
 ```scala
 import dimwit.optimizer.Lion
-import dimwit.Conversions.given // enables implicit conversion from Float to Tensor[V]
 
 // Lion optimizer with momentum
 val lionOptimizer = Lion(learningRate = 1e-3f, beta1 = 0.9f, beta2 = 0.99f, weightDecay = 0.0f)
@@ -1071,8 +1067,6 @@ val trainedLion = lionOptimizer.iterate(initModelParams)(gradFunc)
 ### Complete Training Example: Linear Regression
 
 ```scala
-import dimwit.Conversions.given // enables implicit conversion from Float to Tensor[V]
-
 // Define problem dimensions
 trait Sample derives Label
 trait InputDim derives Label

core/src/main/scala/dimwit/optimizer/GradientOptimizer.scala

@@ -1,7 +1,6 @@
 package dimwit.optimizer
 
 import dimwit.*
-import dimwit.Conversions.given
 import dimwit.autodiff.*
 import dimwit.autodiff.Grad
 import dimwit.tensortree.TreeOf

core/src/main/scala/dimwit/package.scala

@@ -101,9 +101,6 @@ package object dimwit:
   export dimwit.jax.Jit.{jit, jitDonating, jitDonatingUnsafe}
   export dimwit.jax.EagerCleanup.eagerCleanup
 
-  object Conversions:
-    export dimwit.tensor.Tensor0.{boolean2BooleanTensor, byte2IntegerTensor, short2IntegerTensor, int2IntegerTensor, long2IntegerTensor, float2FloatingTensor, int2FloatingTensor, double2FloatingTensor}
-
   // Export random object
   export dimwit.random.Random
   export dimwit.random.Random.Key

core/src/main/scala/dimwit/tensor/Tensor.scala

@@ -28,7 +28,7 @@ import DType.*
   * @param T The shape of the tensor, represented as a tuple of axis labels.
   * @param V The data type of the tensor elements.
   */
-class Tensor[T <: Tuple: Labels, V] private[dimwit] (
+into class Tensor[T <: Tuple: Labels, V] private[dimwit] (
     private[dimwit] val jaxValue: Jax.PyDynamic
 ):
 
@@ -201,6 +201,10 @@ object Tensor:
   /** Use the [[LikeFactory]] to create a tensor */
   def like[T <: Tuple: Labels, V](template: Tensor[T, V]): LikeFactory[T, V] = LikeFactory(template)
 
+  // The conversions from Scala scalars to a Tensor0, e.g. `t *! 2.0f`. Being members of the companion object, they
+  // are found without an import, and `into class Tensor` allows them without a feature warning.
+  export Tensor0Conversions.given
+
   // The functions on tensors, e.g. `Tensor.relu(t)` or `Tensor.sum(t, Axis[A])`.
   // Each one has an extension method with the same name, e.g. `t.relu` or `t.sum(Axis[A])`.
   export tensorops.ElementWiseOps.*
@@ -224,30 +228,6 @@ type Tensor4[L1, L2, L3, L4, V] = Tensor[(L1, L2, L3, L4), V]
   */
 object Tensor0:
 
-  given boolean2BooleanTensor[V: IsBoolean]: Conversion[Boolean, Tensor0[V]] with
-    def apply(value: Boolean): Tensor0[V] = Tensor0(VType[V])(value)
-
-  given byte2IntegerTensor[V: IsInteger]: Conversion[Byte, Tensor0[V]] with
-    def apply(value: Byte): Tensor0[V] = Tensor0(VType[V])(value)
-
-  given short2IntegerTensor[V: IsInteger]: Conversion[Short, Tensor0[V]] with
-    def apply(value: Short): Tensor0[V] = Tensor0(VType[V])(value)
-
-  given int2IntegerTensor[V: IsInteger]: Conversion[Int, Tensor0[V]] with
-    def apply(value: Int): Tensor0[V] = Tensor0(VType[V])(value)
-
-  given int2FloatingTensor[V: IsFloating]: Conversion[Int, Tensor0[V]] with
-    def apply(value: Int): Tensor0[V] = Tensor0(VType[V])(value.toFloat)
-
-  given long2IntegerTensor[V: IsInteger]: Conversion[Long, Tensor0[V]] with
-    def apply(value: Long): Tensor0[V] = Tensor0(VType[V])(value)
-
-  given float2FloatingTensor[V: IsFloating]: Conversion[Float, Tensor0[V]] with
-    def apply(value: Float): Tensor0[V] = Tensor0(VType[V])(value)
-
-  given double2FloatingTensor[V: IsFloating]: Conversion[Double, Tensor0[V]] with
-    def apply(value: Double): Tensor0[V] = Tensor0(VType[V])(value)
-
   object Value0Factory:
 
     def apply(value: Boolean): Tensor0[Bool] = Tensor0(VType[Bool])(value)

core/src/main/scala/dimwit/tensor/Tensor0Conversions.scala

@@ -0,0 +1,35 @@
+package dimwit.tensor
+
+import dimwit.tensor.ValueTypeClasses.IsBoolean
+import dimwit.tensor.ValueTypeClasses.IsFloating
+import dimwit.tensor.ValueTypeClasses.IsInteger
+
+/** Conversions from Scala scalars to a `Tensor0` of the tensor's value type, e.g. for `t *! 2.0f`.
+  *
+  * Users never import them: the `Tensor` companion object exports them, so the compiler finds them automatically.
+  */
+private[dimwit] object Tensor0Conversions:
+
+  given boolean2BooleanTensor[V: IsBoolean]: Conversion[Boolean, Tensor0[V]] with
+    def apply(value: Boolean): Tensor0[V] = Tensor0(VType[V])(value)
+
+  given byte2IntegerTensor[V: IsInteger]: Conversion[Byte, Tensor0[V]] with
+    def apply(value: Byte): Tensor0[V] = Tensor0(VType[V])(value)
+
+  given short2IntegerTensor[V: IsInteger]: Conversion[Short, Tensor0[V]] with
+    def apply(value: Short): Tensor0[V] = Tensor0(VType[V])(value)
+
+  given int2IntegerTensor[V: IsInteger]: Conversion[Int, Tensor0[V]] with
+    def apply(value: Int): Tensor0[V] = Tensor0(VType[V])(value)
+
+  given int2FloatingTensor[V: IsFloating]: Conversion[Int, Tensor0[V]] with
+    def apply(value: Int): Tensor0[V] = Tensor0(VType[V])(value.toFloat)
+
+  given long2IntegerTensor[V: IsInteger]: Conversion[Long, Tensor0[V]] with
+    def apply(value: Long): Tensor0[V] = Tensor0(VType[V])(value)
+
+  given float2FloatingTensor[V: IsFloating]: Conversion[Float, Tensor0[V]] with
+    def apply(value: Float): Tensor0[V] = Tensor0(VType[V])(value)
+
+  given double2FloatingTensor[V: IsFloating]: Conversion[Double, Tensor0[V]] with
+    def apply(value: Double): Tensor0[V] = Tensor0(VType[V])(value)

core/src/main/scala/dimwit/tensor/ValueExtensions.scala

@@ -7,8 +7,8 @@ import dimwit.tensor.tensorops.ElementWiseOps
 
 /** Operators with a Scala scalar on the left, e.g. `2.0f *! t` or `3 < t0`.
   *
-  * `t op scalar` works through the implicit conversions in [[dimwit.Conversions]], which convert the scalar to a
-  * `Tensor0` of the tensor's value type. `scalar op t` cannot use them, because Scala does not convert the receiver of
+  * `t op scalar` works through the implicit conversions exported on the [[Tensor]] companion object, which convert the
+  * scalar to a `Tensor0` of the tensor's value type. `scalar op t` cannot use them, because Scala does not convert the receiver of
   * a method call. These extension methods mirror every operator `t op scalar` by asking for the very same conversion,
   * so both orders compile for exactly the same scalar and value types, and in both the scalar takes the precision of
   * the tensor. Named methods like `elementEquals` or `and` are deliberately not mirrored: `2.0f.approxEquals(t)` reads

core/src/main/scala/dimwit/tensor/tensorops/StructuralOps.scala

@@ -141,8 +141,6 @@ private[dimwit] object StructuralOps:
     object AxisAbsent:
       given notContained[T <: Tuple, L](using NotGiven[Tuple.Contains[T, L] =:= true]): AxisAbsent[T, L] = new AxisAbsent[T, L] {}
 
-  import Util.*
-
   object TensorWhere:
 
     /** Returns a new tensor selecting elements from `ifTrue` where the condition is true

core/src/test/scala/dimwit/autodiff/AutodiffSuite.scala

@@ -1,7 +1,6 @@
 package dimwit.autodiff
 
 import dimwit.*
-import dimwit.Conversions.given
 
 /** A parameter tree, declared top level so its Mirror is available. */
 case class JacParams(w: Tensor1[A, Float32], b: Tensor1[B, Float32]) derives TensorTree

core/src/test/scala/dimwit/jax/JitSuite.scala

@@ -1,7 +1,6 @@
 package dimwit.jax
 
 import dimwit.*
-import dimwit.Conversions.given
 import me.shadaj.scalapy.py
 
 class JitSuite extends DimwitTest:

core/src/test/scala/dimwit/memory/DimWitMemorySuite.scala

@@ -1,7 +1,6 @@
 package dimwit.memory
 
 import dimwit.*
-import dimwit.Conversions.given
 import org.scalatest.DoNotDiscover
 import scala.compiletime.testing.typeCheckErrors
 import scala.compiletime.ops.double

core/src/test/scala/dimwit/optimizer/GradientOptimizerSuite.scala

@@ -1,7 +1,6 @@
 package dimwit.optimizer
 
 import dimwit.*
-import dimwit.Conversions.given
 
 class GradientOptimizerSuite extends DimwitTest:
 

core/src/test/scala/dimwit/python/PyWrapSuite.scala

@@ -1,7 +1,6 @@
 package dimwit.python
 
 import dimwit.*
-import dimwit.Conversions.given
 import dimwit.python.PyBridge
 import dimwit.jax.Jax
 import me.shadaj.scalapy.py

core/src/test/scala/dimwit/stats/DistributionSuite.scala

@@ -1,7 +1,6 @@
 package dimwit.stats
 
 import dimwit.*
-import dimwit.Conversions.given
 import dimwit.jax.Jax
 import dimwit.random.Random
 import dimwit.jax.Jax.scipy_stats as jstats

core/src/test/scala/dimwit/tensor/TensorCovarianceSuite.scala

@@ -1,7 +1,6 @@
 package dimwit.tensor
 
 import dimwit.*
-import dimwit.Conversions.given
 import scala.collection.View.Empty
 
 class TensorCovarianceSuite extends DimwitTest:

core/src/test/scala/dimwit/tensor/TensorCreationSuite.scala

@@ -193,7 +193,6 @@ class TensorCreationSuite extends DimwitTest:
         Tensor1(Axis[A]).linspace(Tensor0(0.0), Tensor0(1.0), 3).dtype shouldBe DType.Float64
 
     it("typed factory fixes the value type and accepts converted literals"):
-      import dimwit.Conversions.given
       Tensor1(Axis[A], VType[Float16]).linspace(0.0f, 1.0f, 3).dtype shouldBe DType.Float16
       Tensor1(Axis[A], VType[Float32]).linspace(0.0f, 1.0f, 3, endpoint = false) shouldEqual Tensor1(Axis[A]).fromArray(Array(0.0f, 1.0f / 3.0f, 2.0f / 3.0f))
 

core/src/test/scala/dimwit/tensor/TensorOpsBroadcastSuite.scala

@@ -1,7 +1,6 @@
 package dimwit.tensor
 
 import dimwit.*
-import dimwit.Conversions.given
 
 class TensorOpsBroadcastSuite extends DimwitTest:
 

core/src/test/scala/dimwit/tensor/TensorOpsElementwiseSuite.scala

@@ -1,7 +1,6 @@
 package dimwit.tensor
 
 import dimwit.*
-import dimwit.Conversions.given
 
 class TensorOpsElementwiseSuite extends DimwitTest:
 

core/src/test/scala/dimwit/tensor/TensorOpsFunctionalSuite.scala

@@ -1,7 +1,6 @@
 package dimwit.tensor
 
 import dimwit.*
-import dimwit.Conversions.given
 
 class TensorOpsFunctionalSuite extends DimwitTest:
 

core/src/test/scala/dimwit/tensor/TensorOpsReductionSuite.scala

@@ -1,7 +1,6 @@
 package dimwit.tensor
 
 import dimwit.*
-import dimwit.Conversions.given
 
 class TensorOpsReductionSuite extends DimwitTest:
 

core/src/test/scala/dimwit/tensortree/TreeOfSuite.scala

@@ -1,7 +1,6 @@
 package dimwit.tensortree
 
 import dimwit.*
-import dimwit.Conversions.given
 import dimwit.tensortree.TreeOf.*
 import dimwit.tensortree.TreeOf.given
 import dimwit.tensortree.TreeOf.ops.*

docs/quickstart.md

@@ -11,7 +11,6 @@ Before we start exploring the features of DimWit, let's look at a simple example
 ```scala
 // main imports for basic tensor operations and automatic differentiation
 import dimwit.*
-import dimwit.Conversions.given
 import dimwit.Autodiff.grad // TODO replace with cleaner import after PR is merged
 import dimwit.optimizer.GradientDescent // TODO replace with cleaner import after refactoring 
 
@@ -220,10 +219,10 @@ tensor1 + tensor3
 // Conflicting definitions:
 // val tensor1:
 //   dimwit.tensor.Tensor[(MdocApp1.this.A, MdocApp1.this.B),
-//     dimwit.tensor.DType.Float32] in class MdocApp1 at line 64 and
+//     dimwit.tensor.DType.Float32] in class MdocApp1 at line 63 and
 // val tensor1:
 //   dimwit.tensor.Tensor[(MdocApp1.this.A, MdocApp1.this.B),
-//     dimwit.tensor.DType.Float32] in class MdocApp1 at line 68
+//     dimwit.tensor.DType.Float32] in class MdocApp1 at line 67
 // 
 // val tensor1 = Tensor(Shape(Axis[A] -> 3, Axis[B] -> 2)).fill(1.0f)
 //     ^

examples/src/main/scala/dimwit/basic/KMeans.scala

@@ -1,6 +1,5 @@
 package dimwit.examples.basic.kmeans
 
-import dimwit.Conversions.given
 import dimwit.*
 import dimwit.random.Random
 import dimwit.stats.Normal

examples/src/main/scala/dimwit/basic/LogisticRegression.scala

@@ -1,6 +1,5 @@
 package dimwit.examples.basic
 
-import dimwit.Conversions.given
 import dimwit.*
 import dimwit.autodiff.*
 import dimwit.optimizer.GradientDescent

examples/src/main/scala/dimwit/complex/VariationalAutoencoder.scala

@@ -1,6 +1,5 @@
 package dimwit.examples.complex.vae
 
-import dimwit.Conversions.given
 import dimwit.*
 import dimwit.tensortree.TreeOf.*
 import dimwit.autodiff.*

examples/src/main/scala/dimwit/dataset/MNISTLoader.scala

@@ -1,6 +1,5 @@
 package dimwit.examples.dataset
 
-import dimwit.Conversions.given
 import dimwit.*
 import me.shadaj.scalapy.py