Added onnx export support for CopyColumns (#4486)

Author: harishsk

src/Microsoft.ML.Data/Transforms/ColumnCopying.cs

@@ -197,7 +197,7 @@ private sealed class Mapper : OneToOneMapperBase, ISaveAsOnnx
             private readonly DataViewSchema _schema;
             private readonly (string outputColumnName, string inputColumnName)[] _columns;
 
-            public bool CanSaveOnnx(OnnxContext ctx) => ctx.GetOnnxVersion() == OnnxVersion.Experimental;
+            public bool CanSaveOnnx(OnnxContext ctx) => true;
 
             internal Mapper(ColumnCopyingTransformer parent, DataViewSchema inputSchema, (string outputColumnName, string inputColumnName)[] columns)
                 : base(parent.Host.Register(nameof(Mapper)), parent, inputSchema)
@@ -233,15 +233,16 @@ protected override DataViewSchema.DetachedColumn[] GetOutputColumnsCore()
 
             public void SaveAsOnnx(OnnxContext ctx)
             {
-                var opType = "CSharp";
+                var opType = "Identity";
 
                 foreach (var column in _columns)
                 {
                     var srcVariableName = ctx.GetVariableName(column.inputColumnName);
+                    if (!ctx.ContainsColumn(srcVariableName))
+                        continue;
                     _schema.TryGetColumnIndex(column.inputColumnName, out int colIndex);
                     var dstVariableName = ctx.AddIntermediateVariable(_schema[colIndex].Type, column.outputColumnName);
-                    var node = ctx.CreateNode(opType, srcVariableName, dstVariableName, ctx.GetNodeName(opType));
-                    node.AddAttribute("type", LoaderSignature);
+                    var node = ctx.CreateNode(opType, srcVariableName, dstVariableName, ctx.GetNodeName(opType), "");
                 }
             }
         }

test/Microsoft.ML.Tests/OnnxConversionTest.cs

@@ -1187,6 +1187,39 @@ void MulticlassTrainersOnnxConversionTest()
             Done();
         }
 
+        [Fact]
+        void CopyColumnsOnnxTest()
+        {
+            var mlContext = new MLContext(seed: 1);
+
+            var trainDataPath = GetDataPath(TestDatasets.generatedRegressionDataset.trainFilename);
+            var dataView = mlContext.Data.LoadFromTextFile<AdultData>(trainDataPath,
+                separatorChar: ';',
+                hasHeader: true);
+
+            var pipeline = mlContext.Transforms.CopyColumns("Target1", "Target");
+            var model = pipeline.Fit(dataView);
+            var transformedData = model.Transform(dataView);
+            var onnxModel = mlContext.Model.ConvertToOnnxProtobuf(model, dataView);
+
+            var onnxFileName = "copycolumns.onnx";
+            var onnxModelPath = GetOutputPath(onnxFileName);
+
+            SaveOnnxModel(onnxModel, onnxModelPath, null);
+
+            if (IsOnnxRuntimeSupported())
+            {
+                // Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
+                string[] inputNames = onnxModel.Graph.Input.Select(valueInfoProto => valueInfoProto.Name).ToArray();
+                string[] outputNames = onnxModel.Graph.Output.Select(valueInfoProto => valueInfoProto.Name).ToArray();
+                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(outputNames, inputNames, onnxModelPath);
+                var onnxTransformer = onnxEstimator.Fit(dataView);
+                var onnxResult = onnxTransformer.Transform(dataView);
+                CompareSelectedR4ScalarColumns(model.ColumnPairs[0].outputColumnName, outputNames[2], transformedData, onnxResult);
+            }
+            Done();
+        }
+
         private void CreateDummyExamplesToMakeComplierHappy()
         {
             var dummyExample = new BreastCancerFeatureVector() { Features = null };