Added Onnx export functionality to PCATransformer

src/Microsoft.ML.OnnxConverter/OnnxUtils.cs

@@ -305,7 +305,7 @@ public static ModelProto MakeModel(List<NodeProto> nodes, string producerName, s
             model.IrVersion = (long)OnnxCSharpToProtoWrapper.Version.IrVersion;
             model.ModelVersion = modelVersion;
             model.OpsetImport.Add(new OperatorSetIdProto() { Domain = "ai.onnx.ml", Version = 1 });
-            model.OpsetImport.Add(new OperatorSetIdProto() { Domain = "", Version = 7 });
+            model.OpsetImport.Add(new OperatorSetIdProto() { Domain = "", Version = 9 });
             model.Graph = new GraphProto();
             var graph = model.Graph;
             graph.Node.Add(nodes);

src/Microsoft.ML.PCA/PcaTransformer.cs

@@ -11,6 +11,7 @@
 using Microsoft.ML.EntryPoints;
 using Microsoft.ML.Internal.CpuMath;
 using Microsoft.ML.Internal.Utilities;
+using Microsoft.ML.Model.OnnxConverter;
 using Microsoft.ML.Numeric;
 using Microsoft.ML.Runtime;
 using Microsoft.ML.Transforms;
@@ -511,7 +512,7 @@ internal static void ValidatePcaInput(IExceptionContext ectx, string name, DataV
                 throw ectx.ExceptSchemaMismatch(nameof(inputSchema), "input", name, "known-size vector of Single of two or more items", type.ToString());
         }
 
-        private sealed class Mapper : OneToOneMapperBase
+        private sealed class Mapper : OneToOneMapperBase, ISaveAsOnnx
         {
             public sealed class ColumnSchemaInfo
             {
@@ -596,6 +597,73 @@ private static void TransformFeatures(IExceptionContext ectx, in VBuffer<float>
 
                 dst = editor.Commit();
             }
+
+            public bool CanSaveOnnx(OnnxContext ctx) => true;
+
+            public void SaveAsOnnx(OnnxContext ctx)
+            {
+                Host.CheckValue(ctx, nameof(ctx));
+
+                for (int i = 0; i < _numColumns; i++)
+                {
+                    var colPair = _parent.ColumnPairs[i];
+                    var transformInfo = _parent._transformInfos[i];
+                    string inputColumnName = colPair.inputColumnName;
+                    string outputColumnName = colPair.outputColumnName;
+                    if (!ctx.ContainsColumn(inputColumnName))
+                    {
+                        ctx.RemoveColumn(colPair.outputColumnName, false);
+                        continue;
+                    }
+
+                    var dstVariableName = ctx.AddIntermediateVariable(transformInfo.OutputType, outputColumnName);
+                    SaveAsOnnxCore(ctx, i, ctx.GetVariableName(inputColumnName), dstVariableName);
+                }
+            }
+
+            private void SaveAsOnnxCore(OnnxContext ctx, int iinfo, string srcVariableName, string dstVariableName)
+            {
+                Host.CheckValue(ctx, nameof(ctx));
+
+                TransformInfo transformInfo = _parent._transformInfos[iinfo];
+                ColumnSchemaInfo schemaInfo = _parent._schemaInfos[iinfo];
+
+                float[] principalComponents = new float[transformInfo.Rank * transformInfo.Dimension];
+                for (int i = 0; i < transformInfo.Rank; i++)
+                {
+                    Array.Copy(transformInfo.Eigenvectors[i], 0, principalComponents, i * transformInfo.Dimension, transformInfo.Dimension);
+                }
+                long[] pcaDims = { transformInfo.Rank, transformInfo.Dimension };
+                var pcaMatrix = ctx.AddInitializer(principalComponents, pcaDims, "principalComponents");
+
+                float[] zeroMean = new float[transformInfo.Rank];
+                if (transformInfo.MeanProjected != null)
+                {
+                    Array.Copy(transformInfo.MeanProjected, zeroMean, transformInfo.Rank);
+                }
+
+                long[] meanDims = { transformInfo.Rank };
+                var zeroMeanNode = ctx.AddInitializer(zeroMean, meanDims, "meanVector");
+
+                // NB: Hack
+                // Currently ML.NET persists ONNX graphs in proto-buf 3 format but the Onnx runtime uses the proto-buf 2 format
+                // There is an incompatibility between the two where proto-buf 3 does not include variables whose values are zero
+                // In the Gemm node below, we want the srcVariableName matrix to be sent in without a transpose, so transA has to be zero
+                // Due to the incompatibility, we get an exception from the Onnx runtime
+                // To workaround this, we transpose the input data first with the Transpose operator and then use the Gemm operator with transA=1
+                // This should be removed once incompatibility is fixed.
+                string opType;
+                opType = "Transpose";
+                var transposeOutput = ctx.AddIntermediateVariable(schemaInfo.InputType, "TransposeOutput", true);
+                var transposeNode = ctx.CreateNode(opType, srcVariableName, transposeOutput, ctx.GetNodeName(opType), "");
+
+                opType = "Gemm";
+                var gemmNode = ctx.CreateNode(opType, new[] { transposeOutput, pcaMatrix, zeroMeanNode }, new[] { dstVariableName }, ctx.GetNodeName(opType), "");
+                gemmNode.AddAttribute("alpha", 1.0);
+                gemmNode.AddAttribute("beta", -1.0);
+                gemmNode.AddAttribute("transA", 1);
+                gemmNode.AddAttribute("transB", 1);
+            }
         }
 
         [TlcModule.EntryPoint(Name = "Transforms.PcaCalculator",

test/BaselineOutput/Common/Onnx/BinaryClassification/BreastCancer/ExcludeVariablesInOnnxConversion.txt

@@ -609,7 +609,7 @@
       "version": "1"
     },
     {
-      "version": "7"
+      "version": "9"
     }
   ]
 }

test/BaselineOutput/Common/Onnx/BinaryClassification/BreastCancer/LightGbmBinaryClassificationOnnxConversionTest.txt

@@ -526,7 +526,7 @@
       "version": "1"
     },
     {
-      "version": "7"
+      "version": "9"
     }
   ]
 }

test/BaselineOutput/Common/Onnx/BinaryClassification/BreastCancer/LogisticRegressionSaveModelToOnnxTest.txt

@@ -270,7 +270,7 @@
       "version": "1"
     },
     {
-      "version": "7"
+      "version": "9"
     }
   ]
 }

test/BaselineOutput/Common/Onnx/BinaryClassification/BreastCancer/ModelWithLessIO.txt

@@ -900,7 +900,7 @@
       "version": "1"
     },
     {
-      "version": "7"
+      "version": "9"
     }
   ]
 }

test/BaselineOutput/Common/Onnx/BinaryClassification/BreastCancer/OneHotBagPipeline.txt

@@ -674,7 +674,7 @@
       "version": "1"
     },
     {
-      "version": "7"
+      "version": "9"
     }
   ]
 }

test/BaselineOutput/Common/Onnx/Cluster/BreastCancer/Kmeans.txt

@@ -349,7 +349,7 @@
       "version": "1"
     },
     {
-      "version": "7"
+      "version": "9"
     }
   ]
 }

test/BaselineOutput/Common/Onnx/MultiClassClassification/BreastCancer/MultiClassificationLogisticRegressionSaveModelToOnnxTest.txt

@@ -426,7 +426,7 @@
       "version": "1"
     },
     {
-      "version": "7"
+      "version": "9"
     }
   ]
 }

test/BaselineOutput/Common/Onnx/Regression/Adult/SimplePipeline.txt

@@ -270,7 +270,7 @@
       "version": "1"
     },
     {
-      "version": "7"
+      "version": "9"
     }
   ]
 }

test/BaselineOutput/Common/Onnx/Transforms/Sentiment/SmallWordEmbed.txt

@@ -1116,7 +1116,7 @@
       "version": "1"
     },
     {
-      "version": "7"
+      "version": "9"
     }
   ]
 }

test/Microsoft.ML.TestFramework/BaseTestBaseline.cs

@@ -571,6 +571,9 @@ private bool MatchNumberWithTolerance(MatchCollection firstCollection, MatchColl
 
         public bool CompareNumbersWithTolerance(double expected, double actual, int? iterationOnCollection = null, int digitsOfPrecision = DigitsOfPrecision)
         {
+            if (double.IsNaN(expected) && double.IsNaN(actual))
+                return true;
+
             // this follows the IEEE recommendations for how to compare floating point numbers
             double allowedVariance = Math.Pow(10, -digitsOfPrecision);
             double delta = Round(expected, digitsOfPrecision) - Round(actual, digitsOfPrecision);

test/Microsoft.ML.Tests/OnnxConversionTest.cs

@@ -738,6 +738,46 @@ public void OnnxTypeConversionTest()
                     CompareResults(model.ColumnPairs[0].outputColumnName, outputNames[1], mlnetResult, onnxResult);
                 }
             }
+            Done();
+        }
+
+        [Fact]
+        public void PcaOnnxConversionTest()
+        {
+            var dataSource = GetDataPath(TestDatasets.generatedRegressionDataset.trainFilename);
+
+            var mlContext = new MLContext(seed: 1);
+            var dataView = mlContext.Data.LoadFromTextFile(dataSource, new[] {
+                new TextLoader.Column("label", DataKind.Single, 11),
+                new TextLoader.Column("features", DataKind.Single, 0, 10)
+            }, hasHeader: true, separatorChar: ';');
+
+            bool[] zeroMeans = { true, false };
+            foreach (var zeroMean in zeroMeans)
+            {
+                var pipeline = ML.Transforms.ProjectToPrincipalComponents("pca", "features", rank: 5, seed: 1, ensureZeroMean: zeroMean);
+                var model = pipeline.Fit(dataView);
+                var transformedData = model.Transform(dataView);
+                var onnxModel = mlContext.Model.ConvertToOnnxProtobuf(model, dataView);
+
+                var onnxFileName = "pca.onnx";
+                var onnxModelPath = GetOutputPath(onnxFileName);
+
+                SaveOnnxModel(onnxModel, onnxModelPath, null);
+
+                if (RuntimeInformation.IsOSPlatform(OSPlatform.Windows) && Environment.Is64BitProcess)
+                {
+                    // 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);
+                    CompareSelectedR4VectorColumns(model.ColumnPairs[0].outputColumnName, outputNames[2], transformedData, onnxResult);
+                }
+            }
+
+            Done();
         }
 
         private void CreateDummyExamplesToMakeComplierHappy()
@@ -845,7 +885,14 @@ private void CompareSelectedR4VectorColumns(string leftColumnName, string rightC
 
                     Assert.Equal(expected.Length, actual.Length);
                     for (int i = 0; i < expected.Length; ++i)
-                        Assert.Equal(expected.GetItemOrDefault(i), actual.GetItemOrDefault(i), precision);
+                    {
+                        // We are using float values. But the Assert.Equal function takes doubles.
+                        // And sometimes the converted doubles are different in their precision.
+                        // So make sure we compare floats
+                        float exp = expected.GetItemOrDefault(i);
+                        float act = actual.GetItemOrDefault(i);
+                        CompareNumbersWithTolerance(exp, act, null, precision);
+                    }
                 }
             }
         }