Fix undefined symbols, Migrate more ints -> sd::LongType

codegen/op-codegen/pom.xml

@@ -128,7 +128,7 @@
         <dependency>
             <groupId>com.fasterxml.jackson.module</groupId>
             <artifactId>jackson-module-kotlin</artifactId>
-            <version>2.9.9</version>
+            <version>${jackson.version}</version>
         </dependency>
 
         <dependency>

codegen/op-codegen/src/main/ops/org/nd4j/codegen/mixins/Mixins.kt

@@ -94,7 +94,7 @@ val scalar = Mixin("scalar"){
 val reduce = Mixin("reduce"){
     Input(DataType.NUMERIC, "in") { description = "Input variable" }
     Arg(DataType.BOOL,"keepDims"){ description = "Whether to keep the original  dimensions or produce a shrunk array with less dimensions"; defaultValue = false}
-    Arg(DataType.INT, "dimensions"){  count = AtLeast(0); isVargarg = true;  description = "Dimensions to reduce over. If dimensions are not specified, full array reduction is performed" }
+    Arg(DataType.LONG, "dimensions"){  count = AtLeast(0); isVargarg = true;  description = "Dimensions to reduce over. If dimensions are not specified, full array reduction is performed" }
     Output(DataType.NUMERIC, "output"){ description = "Reduced array of rank (input rank - num dimensions)" }
 }
 
@@ -148,7 +148,7 @@ val reduce3 = Mixin("reduce3"){
     Input(DataType.NUMERIC, "y") { description = "Input variable y" }
     Arg(DataType.BOOL,"keepDims",{description = "Whether to preserve original dimensions or not"; defaultValue = false})
     Arg(DataType.BOOL,"isComplex",{description = "Depending on the implementation, such as distance calculations, this can determine whether all distance calculations for all points should be done."; defaultValue = false})
-    val dims = Arg(DataType.INT, "dimensions"){ count = AtLeast(0);  isVargarg = true; description = "Dimensions to reduce over. If dimensions are not specified, full array reduction is performed" }
+    val dims = Arg(DataType.LONG, "dimensions"){ count = AtLeast(0);  isVargarg = true; description = "Dimensions to reduce over. If dimensions are not specified, full array reduction is performed" }
     Output(DataType.NUMERIC, "output"){ description = "Output variable" }
 }
 
@@ -169,7 +169,7 @@ val indexAccum = Mixin("indexAccum"){
     javaPackage = "org.nd4j.linalg.api.ops.impl.indexaccum"
     val input = Input(DataType.NUMERIC, "in") { description = "Input variable" }
     val keepDims = Arg(DataType.BOOL, "keepDims") { description = "If true: keep the dimensions that are reduced on (as length 1). False: remove the reduction dimensions"; defaultValue = false }
-    val dims = Arg(DataType.INT, "dimensions"){ count = AtLeast(1); isVargarg = true; description = "Dimensions to reduce over. If dimensions are not specified, full array reduction is performed" }
+    val dims = Arg(DataType.LONG, "dimensions"){ count = AtLeast(1); isVargarg = true; description = "Dimensions to reduce over. If dimensions are not specified, full array reduction is performed" }
     Output(DataType.NUMERIC, "output"){ description = "Reduced array of rank (input rank - num dimensions)" }
 
     Signature(input, dims)
@@ -194,7 +194,7 @@ val indexAccumCustom = Mixin("indexAccumCustom"){
     javaPackage = "org.nd4j.linalg.api.ops.impl.indexaccum.custom"
     val input = Input(DataType.NUMERIC, "in") { description = "Input variable" }
     val keepDims = Arg(DataType.BOOL, "keepDims") { description = "If true: keep the dimensions that are reduced on (as length 1). False: remove the reduction dimensions"; defaultValue = false }
-    val dims = Arg(DataType.INT, "dimensions"){ count = AtLeast(1); isVargarg = true; description = "Dimensions to reduce over. If dimensions are not specified, full array reduction is performed" }
+    val dims = Arg(DataType.LONG, "dimensions"){ count = AtLeast(1); isVargarg = true; description = "Dimensions to reduce over. If dimensions are not specified, full array reduction is performed" }
     Output(DataType.NUMERIC, "output"){ description = "Reduced array of rank (input rank - num dimensions)" }
 
     Signature(input, dims)

codegen/op-codegen/src/main/ops/org/nd4j/codegen/ops/Math.kt

@@ -226,11 +226,10 @@ fun Math() =  Namespace("Math") {
         javaPackage = "org.nd4j.linalg.api.ops.impl.transforms.clip"
         val x = Input(NUMERIC, "x") { description = "Input variable" }
         val clipValue = Arg(NUMERIC, "clipValue") { description = "Clipping value (maximum l2 norm)" }
-        Arg(INT, "dimensions"){ count = AtLeast(0); description = "Dimensions to reduce over. If dimensions are not specified, full array reduction is performed"}  //; defaultValue = intArrayOf(0) }   //TODO
+        Arg(LONG, "dimensions"){ count = AtLeast(0); description = "Dimensions to reduce over. If dimensions are not specified, full array reduction is performed"}  //; defaultValue = intArrayOf(0) }   //TODO
         Output(NUMERIC, "output"){ description = "Output variable" }
 
-//        AllParamSignature(withOutput = false)
-//        Signature(x, clipValue)
+
         Doc(Language.ANY, DocScope.ALL){
             """ 
                 Clipping by L2 norm, optionally along dimension(s)
@@ -264,7 +263,7 @@ fun Math() =  Namespace("Math") {
         javaOpClass = "ClipByAvgNorm"
         Input(NUMERIC, "x") { description = "Input variable" }
         Arg(NUMERIC, "clipValue") { description = "Value for clipping" }
-        Arg(INT, "dimensions"){ count = AtLeast(0); description = "Dimensions to reduce over"}
+        Arg(LONG, "dimensions"){ count = AtLeast(0); description = "Dimensions to reduce over"}
         Output(NUMERIC, "output"){ description = "Output variable" }
         Doc(Language.ANY, DocScope.ALL){
             """
@@ -557,7 +556,7 @@ fun Math() =  Namespace("Math") {
         Arg(INT, "rows") { description = "Number of rows" }
         Arg(INT, "cols") { description = "Number of columns" }
         Arg(DATA_TYPE, "dataType") { description = "Data type" } //TODO: Mapped DataType to INT.
-        Arg(DataType.INT, "dimensions"){ count = AtLeast(0)}
+        Arg(DataType.LONG, "dimensions"){ count = AtLeast(0)}
         Output(NUMERIC, "output"){ description = "Identity matrix" }
         Doc(Language.ANY, DocScope.ALL){
             """
@@ -819,7 +818,7 @@ fun Math() =  Namespace("Math") {
     Op("logSumExp") {
         javaPackage = "org.nd4j.linalg.api.ops.impl.reduce.custom"
         Input(NUMERIC, "input") { description = "Input variable" }
-        Arg(INT, "dimensions"){ count = AtLeast(0); description = "Optional dimensions to reduce along" }
+        Arg(LONG, "dimensions"){ count = AtLeast(0); description = "Optional dimensions to reduce along" }
         Output(NUMERIC, "output"){ description = "Output variable" }
         Doc(Language.ANY, DocScope.ALL){
             """
@@ -972,7 +971,7 @@ fun Math() =  Namespace("Math") {
     Op("moments") {
         javaPackage = "org.nd4j.linalg.api.ops.impl.reduce"
         Input(NUMERIC, "input") { description = "Input to calculate moments for" }
-        Arg(INT, "axes"){ count = AtLeast(0); description = "Dimensions to perform calculation over" }
+        Arg(LONG, "axes"){ count = AtLeast(0); description = "Dimensions to perform calculation over" }
         Arg(BOOL,"keepDims") { description = "Whether to keep dimensions during reduction or not. "}
         Output(NUMERIC, "output_mean"){ description = "Mean variable" }
         Output(NUMERIC, "output_variance"){ description = "Variance variable" }
@@ -1335,7 +1334,7 @@ fun Math() =  Namespace("Math") {
     Op("standardize") {
         javaPackage = "org.nd4j.linalg.api.ops.impl.transforms.custom"
         Input(NUMERIC, "x") { description = "Input variable" }
-        Arg(INT, "dimensions"){ count = AtLeast(1); description = "" } //TODO: Missing description for dimension.
+        Arg(LONG, "dimensions"){ count = AtLeast(1); description = "" } //TODO: Missing description for dimension.
         Output(NUMERIC, "output"){ description = "Output variable" }
         Doc(Language.ANY, DocScope.ALL){
             """

codegen/op-codegen/src/main/ops/org/nd4j/codegen/ops/NeuralNetwork.kt

@@ -428,7 +428,7 @@ fun NN() = Namespace("NN") {
         val g = Input(NUMERIC, "gain") { description = "Gain" }
         Input(NUMERIC, "bias") { description = "Bias"; defaultValue = null}
         val ch = Arg(BOOL, "channelsFirst") { description = "For 2D input - unused. True for NCHW (minibatch, channels, height, width), false for NHWC data" }
-        val dim = Arg(INT, "dimensions") { count = AtLeast(1); description = "Dimensions to perform layer norm over - dimension=1 for 2d/MLP data, dimension=1,2,3 for CNNs" }
+        val dim = Arg(LONG, "dimensions") { count = AtLeast(1); description = "Dimensions to perform layer norm over - dimension=1 for 2d/MLP data, dimension=1,2,3 for CNNs" }
 
         Output(NUMERIC, "output") { description = "Output variable" }