Remove workarounds for cusolver loading issue

Issue: bytedeco/javacpp-presets#1376 Fixed with bytedeco/javacpp-presets#1360
sbrunk · Jul 26, 2023 · e2c2825 · e2c2825
1 parent 624bd0f
commit e2c2825
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Showing 14 changed files with 15 additions and 75 deletions.
diff --git a/build.sbt b/build.sbt
@@ -30,6 +30,7 @@ val openblasVersion = "0.3.23"
 val mklVersion = "2023.1"
 ThisBuild / scalaVersion := "3.3.0"
 ThisBuild / javaCppVersion := "1.5.10-SNAPSHOT"
+ThisBuild / resolvers ++= Resolver.sonatypeOssRepos("snapshots")
 
 ThisBuild / githubWorkflowJavaVersions := Seq(JavaSpec.temurin("11"))
 
@@ -40,8 +41,7 @@ ThisBuild / enableGPU := false
 lazy val commonSettings = Seq(
   Compile / doc / scalacOptions ++= Seq("-groups", "-snippet-compiler:compile"),
   javaCppVersion := (ThisBuild / javaCppVersion).value,
-  javaCppPlatform := Seq(),
-  resolvers ++= Resolver.sonatypeOssRepos("snapshots")
+  javaCppPlatform := Seq()
   // This is a hack to avoid depending on the native libs when publishing
   // but conveniently have them on the classpath during development.
   // There's probably a cleaner way to do this.
@@ -75,8 +75,7 @@ lazy val core = project
       (if (enableGPU.value) "pytorch-gpu" else "pytorch") -> pytorchVersion,
       "mkl" -> mklVersion,
       "openblas" -> openblasVersion
-      // TODO remove cuda (not cuda-redist) once https://github.com/bytedeco/javacpp-presets/issues/1376 is fixed
-    ) ++ (if (enableGPU.value) Seq("cuda-redist" -> cudaVersion, "cuda" -> cudaVersion) else Seq()),
+    ) ++ (if (enableGPU.value) Seq("cuda-redist" -> cudaVersion) else Seq()),
     javaCppPlatform := org.bytedeco.sbt.javacpp.Platform.current,
     fork := true,
     Test / fork := true,

diff --git a/core/src/main/scala/torch/Tensor.scala b/core/src/main/scala/torch/Tensor.scala
@@ -53,7 +53,6 @@ import spire.math.{Complex, UByte}
 import scala.reflect.Typeable
 import internal.NativeConverters
 import internal.NativeConverters.toArray
-import internal.LoadCusolver
 import Device.CPU
 import Layout.Strided
 import org.bytedeco.pytorch.ByteArrayRef
@@ -802,7 +801,6 @@ type IntTensor = UInt8Tensor | Int8Tensor | Int16Tensor | Int32Tensor | Int64Ten
 type ComplexTensor = Complex32Tensor | Complex64Tensor | Complex128Tensor
 
 object Tensor:
-  LoadCusolver // TODO workaround for https://github.com/bytedeco/javacpp-presets/issues/1376
 
   def apply[D <: DType](native: pytorch.Tensor): Tensor[D] = (native.scalar_type().intern() match
     case ScalarType.Byte          => new UInt8Tensor(native)

diff --git a/core/src/main/scala/torch/cuda/package.scala b/core/src/main/scala/torch/cuda/package.scala
@@ -17,13 +17,11 @@
 package torch
 
 import org.bytedeco.pytorch.global.torch as torchNative
-import torch.internal.LoadCusolver
 
 /** This package adds support for CUDA tensor types, that implement the same function as CPU
   * tensors, but they utilize GPUs for computation.
   */
 package object cuda {
-  LoadCusolver
 
   /** Returns a Boolean indicating if CUDA is currently available. */
   def isAvailable: Boolean = torchNative.cuda_is_available()

diff --git a/core/src/main/scala/torch/internal/LoadCusolver.scala b/core/src/main/scala/torch/internal/LoadCusolver.scala
diff --git a/core/src/main/scala/torch/internal/NativeConverters.scala b/core/src/main/scala/torch/internal/NativeConverters.scala
@@ -35,12 +35,9 @@ import org.bytedeco.pytorch.GenericDictIterator
 import spire.math.Complex
 import spire.math.UByte
 import scala.annotation.targetName
-import internal.LoadCusolver
 
 private[torch] object NativeConverters:
 
-  LoadCusolver // TODO workaround for https://github.com/bytedeco/javacpp-presets/issues/1376
-
   inline def convertToOptional[T, U <: T | Option[T], V >: Null](i: U, f: T => V): V = i match
     case i: Option[T] => i.map(f(_)).orNull
     case i: T         => f(i)

diff --git a/core/src/main/scala/torch/nn/functional/package.scala b/core/src/main/scala/torch/nn/functional/package.scala
@@ -18,7 +18,6 @@ package torch
 package nn
 
 import functional.*
-import torch.internal.LoadCusolver
 
 /** @groupname nn_conv Convolution functions
   * @groupname nn_pooling Pooling functions
@@ -38,6 +37,4 @@ package object functional
     with Linear
     with Loss
     with Pooling
-    with Sparse {
-  LoadCusolver
-}
+    with Sparse
diff --git a/core/src/main/scala/torch/nn/init.scala b/core/src/main/scala/torch/nn/init.scala
@@ -33,11 +33,9 @@ import org.bytedeco.pytorch.kSigmoid
 import org.bytedeco.pytorch.kReLU
 import org.bytedeco.pytorch.kLeakyReLU
 import org.bytedeco.pytorch.Scalar
-import torch.internal.LoadCusolver
 
 // TODO implement remaining init functions
 object init:
-  LoadCusolver
   def kaimingNormal_(
       t: Tensor[?],
       a: Double = 0,

diff --git a/core/src/main/scala/torch/nn/package.scala b/core/src/main/scala/torch/nn/package.scala
@@ -16,16 +16,13 @@
 
 package torch
 
-import torch.internal.LoadCusolver
-
 /** These are the basic building blocks for graphs.
   *
   * @groupname nn_conv Convolution Layers
   * @groupname nn_linear Linear Layers
   * @groupname nn_utilities Utilities
   */
 package object nn {
-  LoadCusolver
 
   export modules.Module
   export modules.Default

diff --git a/core/src/main/scala/torch/nn/utils.scala b/core/src/main/scala/torch/nn/utils.scala
@@ -19,10 +19,8 @@ package nn
 
 import org.bytedeco.pytorch.global.torch as torchNative
 import org.bytedeco.pytorch.TensorVector
-import torch.internal.LoadCusolver
 
 object utils:
-  LoadCusolver
   def clipGradNorm_(
       parameters: Seq[Tensor[?]],
       max_norm: Double,

diff --git a/core/src/main/scala/torch/ops/IndexingSlicingJoiningOps.scala b/core/src/main/scala/torch/ops/IndexingSlicingJoiningOps.scala
@@ -20,14 +20,18 @@ package ops
 import internal.NativeConverters.*
 
 import org.bytedeco.pytorch.global.torch as torchNative
-import org.bytedeco.pytorch.{TensorArrayRef, TensorVector}
+import org.bytedeco.pytorch.TensorArrayRef
+import org.bytedeco.pytorch.TensorVector
 
 /** Indexing, Slicing, Joining, Mutating Ops
   *
   * https://pytorch.org/docs/stable/torch.html#indexing-slicing-joining-mutating-ops
   */
 private[torch] trait IndexingSlicingJoiningOps {
 
+  private def toArrayRef(tensors: Seq[Tensor[?]]): TensorArrayRef =
+    new TensorArrayRef(new TensorVector(tensors.map(_.native)*))
+
   /** Returns a view of the tensor conjugated and with the last two dimensions transposed.
     *
     * `x.adjoint()` is equivalent to `x.transpose(-2, -1).conj()` for complex tensors and to

diff --git a/core/src/main/scala/torch/ops/package.scala b/core/src/main/scala/torch/ops/package.scala
@@ -23,10 +23,6 @@ import org.bytedeco.pytorch.{MemoryFormatOptional, TensorArrayRef, TensorVector}
 
 package object ops {
 
-  private[torch] def toArrayRef(tensors: Seq[Tensor[?]]): TensorArrayRef =
-    val vector = new TensorVector(tensors.map(_.native) *)
-    new TensorArrayRef(vector.front(), vector.size())
-
   private[torch] def xLike[D <: DType, D2 <: DType | Derive](
       input: Tensor[D],
       dtype: D2,

diff --git a/core/src/main/scala/torch/package.scala b/core/src/main/scala/torch/package.scala
@@ -14,8 +14,6 @@
  * limitations under the License.
  */
 
-import torch.internal.LoadCusolver
-
 import scala.util.Using
 
 /** The torch package contains data structures for multi-dimensional tensors and defines
@@ -37,7 +35,6 @@ package object torch
     with ops.PointwiseOps
     with ops.RandomSamplingOps
     with ops.ReductionOps {
-  LoadCusolver // TODO workaround for https://github.com/bytedeco/javacpp-presets/issues/1376
 
   /** Disable gradient calculation for [[op]].
     *

diff --git a/docs/installation.md b/docs/installation.md
@@ -25,6 +25,7 @@ libraryDependencies += Seq(
 ```scala
 //> using scala "3.3"
 //> using repository "sonatype-s01:snapshots"
+//> using repository "sonatype:snapshots"
 //> using lib "dev.storch::core:@VERSION@"
 ```
 
@@ -66,6 +67,7 @@ fork := true
 ```scala
 //> using scala "3.3"
 //> using repository "sonatype-s01:snapshots"
+//> using repository "sonatype:snapshots"
 //> using lib "dev.storch::core:@VERSION@"
 //> using lib "org.bytedeco:pytorch-platform:@PYTORCH_VERSION@-@JAVACPP_VERSION@"
 ```
@@ -101,6 +103,7 @@ fork := true
 ```scala
 //> using scala "3.3"
 //> using repository "sonatype-s01:snapshots"
+//> using repository "sonatype:snapshots"
 //> using lib "dev.storch::core:@VERSION@"
 //> using lib "org.bytedeco:openblas:@OPENBLAS_VERSION@-@JAVACPP_VERSION@,classifier=linux-x86_64"
 //> using lib "org.bytedeco:pytorch:@PYTORCH_VERSION@-@JAVACPP_VERSION@,classifier=linux-x86_64"
@@ -159,6 +162,7 @@ fork := true
 ```scala
 //> using scala "3.3"
 //> using repository "sonatype-s01:snapshots"
+//> using repository "sonatype:snapshots"
 //> using lib "dev.storch::core:@VERSION@"
 //> using lib "org.bytedeco:pytorch-platform-gpu:@PYTORCH_VERSION@-@JAVACPP_VERSION@"
 //> using lib "org.bytedeco:cuda-platform-redist:@CUDA_VERSION@-@JAVACPP_VERSION@"
@@ -193,18 +197,13 @@ fork := true
 ```
 
 @:choice(scala-cli)
-
-**Warning**: This is currently **not working** due to with scala-cli not resolving mixed dependencies with and without
-classifiers or  with multiple classifiers. Please use the platform variant above until it is solved.
-
 ```scala
 //> using scala "3.3"
 //> using repository "sonatype-s01:snapshots"
+//> using repository "sonatype:snapshots"
 //> using lib "dev.storch::core:@VERSION@"
 //> using lib "org.bytedeco:pytorch:@PYTORCH_VERSION@-@JAVACPP_VERSION@,classifier=linux-x86_64-gpu"
 //> using lib "org.bytedeco:openblas:@OPENBLAS_VERSION@-@JAVACPP_VERSION@,classifier=linux-x86_64"
-//> using lib "org.bytedeco:cuda:@CUDA_VERSION@-@JAVACPP_VERSION@"
-//> using lib "org.bytedeco:cuda:@CUDA_VERSION@-@JAVACPP_VERSION@,classifier=linux-x86_64"
 //> using lib "org.bytedeco:cuda:@CUDA_VERSION@-@JAVACPP_VERSION@,classifier=linux-x86_64-redist"
 ```
 
@@ -226,12 +225,7 @@ resolvers ++= Resolver.sonatypeOssRepos("snapshots")
 libraryDependencies += Seq(
   "dev.storch" %% "core" % "@VERSION@",
 )
-javaCppPresetLibs ++= Seq(
-  "pytorch-gpu" -> "@PYTORCH_VERSION@",
-  "openblas" -> "@OPENBLAS_VERSION@",
-  "cuda-redist" -> "@CUDA_VERSION@",
-  "cuda" -> "@CUDA_VERSION@"
-)
+javaCppPresetLibs ++= Seq("pytorch-gpu" -> "@PYTORCH_VERSION@", "openblas" -> "@OPENBLAS_VERSION@", "cuda-redist" -> "@CUDA_VERSION@")
 fork := true
 ```
 

diff --git a/vision/src/main/scala/torchvision/datasets/MNIST.scala b/vision/src/main/scala/torchvision/datasets/MNIST.scala
@@ -27,7 +27,6 @@ import java.util.zip.GZIPInputStream
 import scala.util.Try
 import scala.util.Success
 import scala.util.Failure
-import torch.internal.LoadCusolver
 
 trait MNISTBase(
     val mirrors: Seq[String],
@@ -37,7 +36,6 @@ trait MNISTBase(
     val train: Boolean,
     val download: Boolean
 ) extends TensorDataset[Float32, Int64] {
-  LoadCusolver
 
   private def downloadAndExtractArchive(url: URL, target: Path): Unit =
     println(s"downloading from $url")