pytorch native code cleanup (intel-analytics#1642)

* code clean

* remove load

* fix style

TorchCriterion.scala

@@ -52,13 +52,14 @@ class TorchCriterion private(private val lossHolder: TorchModelHolder)
     val (sto1, off1, shape1) = TorchCriterion.extract(inputTabel)
     val (sto2, off2, shape2) = TorchCriterion.extract(targetTable)
 
-    val result = PytorchModel.lossForwardNative(nativeRef, sto1, off1, shape1, sto2, off2, shape2)
+    val result = PytorchModelWrapper.lossForwardNative(nativeRef, sto1, off1,
+      shape1, sto2, off2, shape2)
     Tensor(result.getData, result.getShape).mean()
   }
 
   override def updateGradInput(input: Activity, target: Activity): Activity = {
 
-    val result = PytorchModel.lossBackwardNative(nativeRef)
+    val result = PytorchModelWrapper.lossBackwardNative(nativeRef)
     if (result.length == 1) {
       val resultTensor = Tensor(result(0).getData, result(0).getShape)
       if (gradInput == null) {

TorchNet.scala

@@ -91,7 +91,8 @@ class TorchNet private(private val modelHolder: TorchModelHolder)
       PytorchModel.updateWeightNative(this.nativeRef, weights.storage().array())
     }
 
-    val result = PytorchModel.modelForwardNative(nativeRef, this.isTraining(), sto1, off1, shape1)
+    val result = PytorchModelWrapper.modelForwardNative(nativeRef,
+      this.isTraining(), sto1, off1, shape1)
     if (result.length == 1) {
       val resultTensor = Tensor(result(0).getData, result(0).getShape)
       if (output == null) {
@@ -115,7 +116,7 @@ class TorchNet private(private val modelHolder: TorchModelHolder)
 
     val (sto1, off1, shape1) = TorchCriterion.extract(gradOutputTable)
 
-    val result = PytorchModel.modelBackwardNative(nativeRef, sto1, off1, shape1)
+    val result = PytorchModelWrapper.modelBackwardNative(nativeRef, sto1, off1, shape1)
     // update gradients
     gradients.resizeAs(weights)
     val g = PytorchModel.getGradientNative(this.nativeRef)
@@ -156,16 +157,11 @@ class TorchNet private(private val modelHolder: TorchModelHolder)
 }
 
 object TorchNet {
-
-  PytorchModel.isLoaded
-  loadPytorchNatives() // load once per JVM
-
   private val modelBytesRegistry = new RegistryMap[Array[Byte]]()
 
   @transient
   private lazy val inDriver = NetUtils.isDriver
 
-
   class TorchModelHolder(@transient var torchBytes: Array[Byte], private var id: String)
     extends SerializationHolder {
 
@@ -218,27 +214,6 @@ object TorchNet {
     new TorchNet(new TorchModelHolder(modelbytes, modelPath))
   }
 
-  // extract libs from zoo jar file
-  private def loadPytorchNatives(): Unit = {
-    loadNativelib("pytorch/libpytorch-engine.so")
-  }
-
-  private def loadNativelib(path: String): Unit = {
-    val inputStream = TorchNet.getClass.getResourceAsStream(s"/${path}")
-    val file = File.createTempFile("PytorchLoader", "tmp")
-    val src = Channels.newChannel(inputStream)
-    val dest = new FileOutputStream(file).getChannel
-    dest.transferFrom(src, 0, Long.MaxValue)
-    dest.close()
-    src.close()
-    val filePath = file.getAbsolutePath
-    try {
-      System.load(filePath)
-    } finally {
-      file.delete()
-    }
-  }
-
   private[net] def loadPytorchModel(bytes: Array[Byte]): Long = {
     var nativeRef = -1L
     try {