fixes ort memory leak

src/Microsoft.ML.OnnxTransformer/OnnxTransform.cs

@@ -507,14 +507,32 @@ protected override Delegate MakeGetter(DataViewRow input, int iinfo, Func<int, b
                 }
             }
 
-            private class OnnxRuntimeOutputCacher
+            private class OnnxRuntimeOutputCacher : IDisposable
             {
                 public long Position;
-                public Dictionary<string, NamedOnnxValue> Outputs;
+                public Dictionary<string, DisposableNamedOnnxValue> Outputs;
                 public OnnxRuntimeOutputCacher()
                 {
                     Position = -1;
-                    Outputs = new Dictionary<string, NamedOnnxValue>();
+                    Outputs = new Dictionary<string, DisposableNamedOnnxValue>();
+                }
+
+                private bool _isDisposed;
+                public void Dispose()
+                {
+                    if (_isDisposed)
+                        return;
+                    foreach (var onnxValue in Outputs.Values)
+                        onnxValue.Dispose();
+                    _isDisposed = true;
+                }
+
+                ~OnnxRuntimeOutputCacher()
+                {
+                    if (_isDisposed)
+                        return;
+                    foreach (var onnxValue in Outputs.Values)
+                        onnxValue.Dispose();
                 }
             }
 
@@ -534,6 +552,10 @@ private void UpdateCacheIfNeeded(long position, INamedOnnxValueGetter[] srcNamed
 
                     foreach (var outputNameOnnxValue in outputNamedOnnxValues)
                     {
+                        if(outputCache.Outputs.TryGetValue(outputNameOnnxValue.Name, out DisposableNamedOnnxValue value))
+                        {
+                            value.Dispose();
+                        }
                         outputCache.Outputs[outputNameOnnxValue.Name] = outputNameOnnxValue;
                     }
                     outputCache.Position = position;

src/Microsoft.ML.OnnxTransformer/OnnxUtils.cs

@@ -350,7 +350,7 @@ public static OnnxModel CreateFromBytes(byte[] modelBytes, int? gpuDeviceId = nu
         /// </summary>
         /// <param name="inputNamedOnnxValues">The NamedOnnxValues to score.</param>
         /// <returns>Resulting output NamedOnnxValues list.</returns>
-        public IReadOnlyCollection<NamedOnnxValue> Run(List<NamedOnnxValue> inputNamedOnnxValues)
+        public IDisposableReadOnlyCollection<DisposableNamedOnnxValue> Run(List<NamedOnnxValue> inputNamedOnnxValues)
         {
             return _session.Run(inputNamedOnnxValues);
         }

test/Microsoft.ML.Tests/OnnxConversionTest.cs

@@ -91,7 +91,7 @@ public void SimpleEndToEndOnnxConversionTest()
 
             // Step 3: Check ONNX model's text format. This test will be not necessary if Step 2 can run on Linux and
             // Mac to support cross-platform tests.
-            
+
             CheckEquality(subDir, onnxTextName, digitsOfPrecision: 3);
 
             Done();
@@ -139,7 +139,7 @@ private class BreastCancerBinaryClassification
         [Fact]
         public void KmeansOnnxConversionTest()
         {
-            // Create a new context for ML.NET operations. It can be used for exception tracking and logging, 
+            // Create a new context for ML.NET operations. It can be used for exception tracking and logging,
             // as a catalog of available operations and as the source of randomness.
             var mlContext = new MLContext(seed: 1);
 
@@ -384,7 +384,7 @@ public void TextNormalizingOnnxConversionTest()
                 new TextNormalizingEstimator(mlContext, keepDiacritics: true, caseMode: TextNormalizingEstimator.CaseMode.Upper, columns: new[] { ("UpperText", "text") })).Append(
                 new TextNormalizingEstimator(mlContext, keepDiacritics: true, caseMode: TextNormalizingEstimator.CaseMode.None, columns: new[] { ("OriginalText", "text") }));
             var onnxFileName = $"TextNormalizing.onnx";
-            
+
             TestPipeline(pipeline, dataView, onnxFileName, new ColumnComparison[] { new ColumnComparison("NormText"), new ColumnComparison("UpperText"), new ColumnComparison("OriginalText") });
 
             Done();
@@ -1154,7 +1154,7 @@ public void IndicateMissingValuesOnnxConversionTest()
 
             // IsNaN outputs a binary tensor. Support for this has been added in the latest version
             // of Onnxruntime, but that hasn't been released yet.
-            // So we need to convert its type to Int32 until then. 
+            // So we need to convert its type to Int32 until then.
             // ConvertType part of the pipeline can be removed once we pick up a new release of the Onnx runtime
 
             var pipeline = mlContext.Transforms.IndicateMissingValues(new[] { new InputOutputColumnPair("MissingIndicator", "Features"), })
@@ -1806,7 +1806,7 @@ public void NonDefaultColNamesMultiClassificationOnnxConversionTest()
             }
             Done();
         }
-        
+
         [Fact]
         public void OneHotHashEncodingOnnxConversionWithCustomOpSetVersionTest()
         {
@@ -2029,7 +2029,7 @@ private void TestPipeline<TLastTransformer, TRow>(EstimatorChain<TLastTransforme
         private void TestPipeline<TLastTransformer>(EstimatorChain<TLastTransformer> pipeline, IDataView dataView, string onnxFileName, ColumnComparison[] columnsToCompare, string onnxTxtName = null, string onnxTxtSubDir = null)
             where TLastTransformer : class, ITransformer
         {
-            var model = pipeline.Fit(dataView);
+            using var model = pipeline.Fit(dataView);
             var transformedData = model.Transform(dataView);
             var onnxModel = ML.Model.ConvertToOnnxProtobuf(model, dataView);