Enable OnnxTransformer to accept KeyDataViewTypes as if they were UInt32

src/Microsoft.ML.OnnxTransformer/OnnxTransform.cs

@@ -389,7 +389,14 @@ public Mapper(OnnxTransformer parent, DataViewSchema inputSchema) :
                         throw Host.Except($"Variable length input columns not supported");
 
                     if (type.GetItemType() != inputNodeInfo.DataViewType.GetItemType())
-                        throw Host.ExceptSchemaMismatch(nameof(inputSchema), "input", _parent.Inputs[i], inputNodeInfo.DataViewType.GetItemType().ToString(), type.ToString());
+                    {
+                        // If the ONNX model input node expects a type that mismatches with the type of the input IDataView column that is provided
+                        // then throw an exception.
+                        // This is done except in the case where the ONNX model input node expects a UInt32 but the input column is actually KeyDataViewType
+                        // This is done to support a corner case originated in NimbusML. For more info, see: https://github.com/microsoft/NimbusML/issues/426
+                        if (!(type.GetItemType() is KeyDataViewType && inputNodeInfo.DataViewType.GetItemType().RawType == typeof(UInt32)))
+                            throw Host.ExceptSchemaMismatch(nameof(inputSchema), "input", _parent.Inputs[i], inputNodeInfo.DataViewType.GetItemType().ToString(), type.ToString());
+                    }
 
                     // If the column is one dimension we make sure that the total size of the Onnx shape matches.
                     // Compute the total size of the known dimensions of the shape.

test/Microsoft.ML.Tests/OnnxConversionTest.cs

@@ -1450,6 +1450,74 @@ public void CopyColumnsOnnxTest()
             Done();
         }
 
+        [Fact]
+        public void UseKeyDataViewTypeAsUInt32InOnnxInput()
+        {
+            // In this test an onnx model which expect a uin32 input column is applied to a KeyDataViewType input column
+            // This, is done as needed by NimbusML. For more context see: https://github.com/microsoft/NimbusML/issues/426
+
+            // Step 1: Load the Iris Dataset and apply a Value To Key Mapping to it.
+            // Save the resulting dataview in .idv format eliminating all hidden columns
+            var mlContext = new MLContext();
+            var loader = mlContext.Data.CreateTextLoader(
+                columns: new[]
+                    {
+                        new TextLoader.Column("Label", DataKind.String, 0),
+                        new TextLoader.Column("SepalLength", DataKind.Single, 1),
+                        new TextLoader.Column("SepalWidth", DataKind.Single, 2),
+                        new TextLoader.Column("PetalLength", DataKind.Single, 3),
+                        new TextLoader.Column("PetalWidth", DataKind.Single, 4)
+                    },
+                hasHeader: false
+            );
+
+            string dataPath = GetDataPath("iris.txt");
+            var originalData = loader.Load(dataPath);
+            var pipeline1 = mlContext.Transforms.Conversion.MapValueToKey("Label");
+            var mappedData = pipeline1.Fit(originalData).Transform(originalData);
+            string mappedDataPath = @"C:\Users\anvelazq\Desktop\is22\data-new.idv";
+            using (FileStream stream = new FileStream(mappedDataPath, FileMode.Create))
+                mlContext.Data.SaveAsBinary(mappedData, stream, keepHidden: false);
+
+            // Step 2: Load back the saved .idv
+            // This IDataView will have a Label column of type KeyDataViewType
+            // It's necessary to do this, because if I were to use mappedData directly inside the next
+            // steps, then when saving the ONNX model, it would actually also save the ValueToKeyTransformer part
+            // and that wouldn't reproduce the scenario.
+            IDataView reloadedData = mlContext.Data.LoadFromBinary(mappedDataPath);
+
+            // Step 3: Create ONNX model which simply applies Identity to Label column
+            var pipeline2 = mlContext.Transforms.CopyColumns("Label", "Label");
+            var model = pipeline2.Fit(reloadedData);
+
+            var onnxModelPath = GetOutputPath("onnxmodel1-kdvt-as-uint32.onnx");
+            using (FileStream stream = new FileStream(onnxModelPath, FileMode.Create))
+                mlContext.Model.ConvertToOnnx(model, reloadedData, stream);
+
+            // Step 4: Get input and output names of model
+            var onnxProtoBufModel = mlContext.Model.ConvertToOnnxProtobuf(model, reloadedData);
+            string[] inputNames = onnxProtoBufModel.Graph.Input.Select(valueInfoProto => valueInfoProto.Name).ToArray();
+            string[] outputNames = onnxProtoBufModel.Graph.Output.Select(valueInfoProto => valueInfoProto.Name).ToArray();
+
+            // Step 5: Apply Onnx Model
+            var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(outputNames, inputNames, onnxModelPath);
+            var onnxResult = onnxEstimator.Fit(reloadedData).Transform(reloadedData);
+
+            // Step 6: Compare results to an onnx model created using the mappedData IDataView
+            // Notice that this ONNX model would actually include the steps to do the ValueToKeyTransformer mapping
+            // And because of this, it can only be applied to reloadedData dataview, despite mappedData was used to create the model.
+            // If it's tried to apply this model to mappedData or reloadedData, it will throw an exception, since the ONNX model
+            // will expect a Label input of type string (which only originalData provides).
+            string onnxModelPath2 = GetOutputPath("onnxmodel2-kdvt-as-uint32.onnx");
+            using (FileStream stream = new FileStream(onnxModelPath2, FileMode.Create))
+                mlContext.Model.ConvertToOnnx(model, mappedData, stream);
+            var onnxEstimator2 = mlContext.Transforms.ApplyOnnxModel(outputNames, inputNames, onnxModelPath2);
+            var onnxResult2 = onnxEstimator2.Fit(originalData).Transform(originalData);
+
+            foreach (var name in outputNames)
+                CompareResults(name, name, onnxResult, onnxResult2);
+        }
+
         [Fact]
         public void FeatureSelectionOnnxTest()
         {