Updated Tensorflow.Net to 0.70.2 with Tensorflow 2.7.0.

eng/Versions.props

@@ -67,9 +67,9 @@
     <ParquetDotNetVersion>2.1.3</ParquetDotNetVersion>
     <PlotlyNETCSharpVersion>0.11.1</PlotlyNETCSharpVersion>
     <SharpZipLibVersion>1.4.2</SharpZipLibVersion>
-    <TensorflowDotNETVersion>0.20.1</TensorflowDotNETVersion>
+    <TensorflowDotNETVersion>0.70.2</TensorflowDotNETVersion>
     <TensorFlowMajorVersion>2</TensorFlowMajorVersion>
-    <TensorFlowVersion>2.3.1</TensorFlowVersion>
+    <TensorFlowVersion>2.7.0</TensorFlowVersion>
     <TorchSharpPyBridgeVersion>1.4.1</TorchSharpPyBridgeVersion>
     <AutoGenVersion>0.2.3</AutoGenVersion>
     <SemanticKernelVersion>1.48.0</SemanticKernelVersion>
@@ -90,6 +90,7 @@
     <BenchmarkDotNetVersion>0.13.12</BenchmarkDotNetVersion>
     <DotNetRuntime60Version>6.0.36</DotNetRuntime60Version>
     <DotNetRuntime80Version>8.0.16</DotNetRuntime80Version>
+	<DotNetRuntime90Version>9.0.5</DotNetRuntime90Version>
     <AwesomeAssertionsVersion>8.1.0</AwesomeAssertionsVersion>
     <MicrosoftCodeAnalysisTestingVersion>1.1.2</MicrosoftCodeAnalysisTestingVersion>
     <MicrosoftDotNetBuildTasksTemplatingVersion>9.0.0-beta.24212.4</MicrosoftDotNetBuildTasksTemplatingVersion>

global.json

@@ -4,11 +4,13 @@
     "runtimes": {
       "dotnet": [
         "$(DotNetRuntime60Version)",
-        "$(DotNetRuntime80Version)"
+        "$(DotNetRuntime80Version)",
+        "$(DotNetRuntime90Version)"
       ],
       "dotnet/x86": [
         "$(DotNetRuntime60Version)",
-        "$(DotNetRuntime80Version)"
+        "$(DotNetRuntime80Version)",
+		    "$(DotNetRuntime90Version)"
       ]
     }
   },

src/Microsoft.ML.Core/Utilities/ArrayUtils.cs

@@ -100,5 +100,26 @@ public static int EnsureSize<T>(ref T[] array, int min, int max, bool keepOld, o
             resized = true;
             return newSize;
         }
+
+        public static int[] CastLongArrayToIntArray(long[] source)
+        {
+            if (source == null)
+                throw new ArgumentNullException(nameof(source));
+
+            int[] result = new int[source.Length];
+
+            for (int i = 0; i < source.Length; i++)
+            {
+                long value = source[i];
+                if (value > int.MaxValue || value < int.MinValue)
+                {
+                    throw new OverflowException($"Value at index {i} ({value}) cannot be safely cast to int.");
+                }
+
+                result[i] = (int)value;
+            }
+
+            return result;
+        }
     }
 }

src/Microsoft.ML.DataView/VectorType.cs

@@ -67,6 +67,24 @@ public VectorDataViewType(PrimitiveDataViewType itemType, params int[] dimension
             Size = ComputeSize(Dimensions);
         }
 
+        /// <summary>
+        /// Constructs a potentially multi-dimensional vector type.
+        /// </summary>
+        /// <param name="itemType">The type of the items contained in the vector.</param>
+        /// <param name="dimensions">The dimensions. Note that, like <see cref="Dimensions"/>, must be non-empty, with all
+        /// non-negative values. Also, because <see cref="Size"/> is the product of <see cref="Dimensions"/>, the result of
+        /// multiplying all these values together must not overflow <see cref="int"/>.</param>
+        public VectorDataViewType(PrimitiveDataViewType itemType, params long[] dimensions)
+            : base(GetRawType(itemType))
+        {
+            Contracts.CheckParam(ArrayUtils.Size(dimensions) > 0, nameof(dimensions));
+            Contracts.CheckParam(dimensions.All(d => d >= 0), nameof(dimensions));
+
+            ItemType = itemType;
+            Dimensions = ArrayUtils.CastLongArrayToIntArray(dimensions).ToImmutableArray();
+            Size = ComputeSize(Dimensions);
+        }
+
         /// <summary>
         /// Constructs a potentially multi-dimensional vector type.
         /// </summary>

src/Microsoft.ML.TensorFlow/TensorTypeExtensions.cs

@@ -25,7 +25,7 @@ public static void ToScalar<T>(this Tensor tensor, ref T dst) where T : unmanage
                 return;
             }
 
-            if (typeof(T).as_dtype() != tensor.dtype)
+            if (typeof(T).as_tf_dtype() != tensor.dtype)
                 throw new NotSupportedException();
 
             unsafe
@@ -37,7 +37,7 @@ public static void ToScalar<T>(this Tensor tensor, ref T dst) where T : unmanage
 
         public static void CopyTo<T>(this Tensor tensor, Span<T> values) where T : unmanaged
         {
-            if (typeof(T).as_dtype() != tensor.dtype)
+            if (typeof(T).as_tf_dtype() != tensor.dtype)
                 throw new NotSupportedException();
 
             unsafe

src/Microsoft.ML.TensorFlow/TensorflowTransform.cs

@@ -16,8 +16,8 @@
 using Microsoft.ML.Runtime;
 using Microsoft.ML.TensorFlow;
 using Microsoft.ML.Transforms;
-using NumSharp;
 using Tensorflow;
+using Tensorflow.NumPy;
 using static Microsoft.ML.TensorFlow.TensorFlowUtils;
 using static Tensorflow.Binding;
 using Utils = Microsoft.ML.Internal.Utilities.Utils;
@@ -51,7 +51,7 @@ public sealed class TensorFlowTransformer : RowToRowTransformerBase, IDisposable
         internal readonly DataViewType[] OutputTypes;
         internal readonly TF_DataType[] TFOutputTypes;
         internal readonly TF_DataType[] TFInputTypes;
-        internal readonly TensorShape[] TFInputShapes;
+        internal readonly Shape[] TFInputShapes;
         internal readonly (Operation, int)[] TFInputOperations;
         internal readonly (Operation, int)[] TFOutputOperations;
         internal TF_Output[] TFInputNodes;
@@ -212,14 +212,14 @@ internal TensorFlowTransformer(IHostEnvironment env, TensorFlowEstimator.Options
             env.CheckValue(options, nameof(options));
         }
 
-        private static ITensorValueGetter CreateTensorValueGetter<T>(DataViewRow input, bool isVector, int colIndex, TensorShape tfShape)
+        private static ITensorValueGetter CreateTensorValueGetter<T>(DataViewRow input, bool isVector, int colIndex, Shape tfShape)
         {
             if (isVector)
                 return new TensorValueGetterVec<T>(input, colIndex, tfShape);
             return new TensorValueGetter<T>(input, colIndex, tfShape);
         }
 
-        private static ITensorValueGetter CreateTensorValueGetter(DataViewRow input, TF_DataType tfType, bool isVector, int colIndex, TensorShape tfShape)
+        private static ITensorValueGetter CreateTensorValueGetter(DataViewRow input, TF_DataType tfType, bool isVector, int colIndex, Shape tfShape)
         {
             var type = Tf2MlNetType(tfType);
             return Utils.MarshalInvoke(CreateTensorValueGetter<int>, type.RawType, input, isVector, colIndex, tfShape);
@@ -230,7 +230,7 @@ private static ITensorValueGetter[] GetTensorValueGetters(
             int[] inputColIndices,
             bool[] isInputVector,
             TF_DataType[] tfInputTypes,
-            TensorShape[] tfInputShapes)
+            Shape[] tfInputShapes)
         {
             var srcTensorGetters = new ITensorValueGetter[inputColIndices.Length];
             for (int i = 0; i < inputColIndices.Length; i++)
@@ -331,10 +331,10 @@ private static (Operation, int) GetOperationFromName(string operation, Session s
             return (session.graph.OperationByName(operation), 0);
         }
 
-        internal static (TF_DataType[] tfInputTypes, TensorShape[] tfInputShapes, (Operation, int)[]) GetInputInfo(IHost host, Session session, string[] inputs, int batchSize = 1)
+        internal static (TF_DataType[] tfInputTypes, Shape[] tfInputShapes, (Operation, int)[]) GetInputInfo(IHost host, Session session, string[] inputs, int batchSize = 1)
         {
             var tfInputTypes = new TF_DataType[inputs.Length];
-            var tfInputShapes = new TensorShape[inputs.Length];
+            var tfInputShapes = new Shape[inputs.Length];
             var tfInputOperations = new (Operation, int)[inputs.Length];
 
             int index = 0;
@@ -351,15 +351,15 @@ internal static (TF_DataType[] tfInputTypes, TensorShape[] tfInputShapes, (Opera
                     throw host.ExceptParam(nameof(session), $"Input type '{tfInputType}' of input column '{input}' is not supported in TensorFlow");
 
                 tfInputTypes[index] = tfInputType;
-                tfInputShapes[index] = ((Tensor)inputTensor).TensorShape;
+                tfInputShapes[index] = ((Tensor)inputTensor).shape;
                 tfInputOperations[index] = (inputTensor, inputTensorIndex);
                 index++;
             }
 
             return (tfInputTypes, tfInputShapes, tfInputOperations);
         }
 
-        internal static TensorShape GetTensorShape(TF_Output output, Graph graph, Status status = null)
+        internal static Shape GetTensorShape(TF_Output output, Graph graph, Status status = null)
         {
             if (graph == IntPtr.Zero)
                 throw new ObjectDisposedException(nameof(graph));
@@ -370,12 +370,12 @@ internal static TensorShape GetTensorShape(TF_Output output, Graph graph, Status
             cstatus.Check();
 
             if (n == -1)
-                return new TensorShape(new int[0]);
+                return new Shape(new int[0]);
 
             var dims = new long[n];
             c_api.TF_GraphGetTensorShape(graph, output, dims, dims.Length, cstatus.Handle);
             cstatus.Check();
-            return new TensorShape(dims.Select(x => (int)x).ToArray());
+            return new Shape(dims.Select(x => (int)x).ToArray());
         }
 
         internal static (TF_DataType[] tfOutputTypes, DataViewType[] outputTypes, (Operation, int)[]) GetOutputInfo(IHost host, Session session, string[] outputs, bool treatOutputAsBatched)
@@ -404,10 +404,10 @@ internal static (TF_DataType[] tfOutputTypes, DataViewType[] outputTypes, (Opera
                 // This is the work around in absence of reshape transformer.
                 var idims = shape.dims;
 
-                int[] dims = idims;
+                long[] dims = idims;
                 if (treatOutputAsBatched)
                 {
-                    dims = shape.ndim > 0 ? idims.Skip(idims[0] == -1 ? 1 : 0).ToArray() : new int[0];
+                    dims = shape.ndim > 0 ? idims.Skip(idims[0] == -1 ? 1 : 0).ToArray() : new long[0];
                 }
                 for (int j = 0; j < dims.Length; j++)
                     dims[j] = dims[j] == -1 ? 0 : dims[j];
@@ -517,7 +517,7 @@ public void Dispose()
 
                 if (Session != null && Session != IntPtr.Zero)
                 {
-                    Session.close(); // invoked Dispose()
+                    Session.Dispose();
                 }
             }
             finally
@@ -536,7 +536,7 @@ private sealed class Mapper : MapperBase
             private readonly TensorFlowTransformer _parent;
             private readonly int[] _inputColIndices;
             private readonly bool[] _isInputVector;
-            private readonly TensorShape[] _fullySpecifiedShapes;
+            private readonly Shape[] _fullySpecifiedShapes;
             private readonly ConcurrentBag<Runner> _runners;
 
             public Mapper(TensorFlowTransformer parent, DataViewSchema inputSchema) :
@@ -546,7 +546,7 @@ public Mapper(TensorFlowTransformer parent, DataViewSchema inputSchema) :
                 _parent = parent;
                 _inputColIndices = new int[_parent.Inputs.Length];
                 _isInputVector = new bool[_parent.Inputs.Length];
-                _fullySpecifiedShapes = new TensorShape[_parent.Inputs.Length];
+                _fullySpecifiedShapes = new Shape[_parent.Inputs.Length];
                 for (int i = 0; i < _parent.Inputs.Length; i++)
                 {
                     if (!inputSchema.TryGetColumnIndex(_parent.Inputs[i], out _inputColIndices[i]))
@@ -570,19 +570,19 @@ public Mapper(TensorFlowTransformer parent, DataViewSchema inputSchema) :
                         {
                             vecType = (VectorDataViewType)type;
                             var colTypeDims = vecType.Dimensions.Select(dim => (int)dim).ToArray();
-                            _fullySpecifiedShapes[i] = new TensorShape(colTypeDims);
+                            _fullySpecifiedShapes[i] = new Shape(colTypeDims);
                         }
                         else
                             // for primitive type use default TensorShape
-                            _fullySpecifiedShapes[i] = new TensorShape();
+                            _fullySpecifiedShapes[i] = new Shape(Array.Empty<long>());
                     }
                     else
                     {
                         vecType = (VectorDataViewType)type;
                         var colTypeDims = vecType.Dimensions.Select(dim => (int)dim).ToArray();
                         // If the column is one dimension we make sure that the total size of the TF shape matches.
                         // Compute the total size of the known dimensions of the shape.
-                        int valCount = 1;
+                        long valCount = 1;
                         int numOfUnkDim = 0;
                         foreach (var s in shape)
                         {
@@ -592,7 +592,7 @@ public Mapper(TensorFlowTransformer parent, DataViewSchema inputSchema) :
                                 numOfUnkDim++;
                         }
                         // The column length should be divisible by this, so that the other dimensions can be integral.
-                        int typeValueCount = type.GetValueCount();
+                        long typeValueCount = type.GetValueCount();
                         if (typeValueCount % valCount != 0)
                             throw Contracts.Except($"Input shape mismatch: Input '{_parent.Inputs[i]}' has shape {originalShape.ToString()}, but input data is of length {typeValueCount}.");
 
@@ -616,22 +616,22 @@ public Mapper(TensorFlowTransformer parent, DataViewSchema inputSchema) :
                             throw Contracts.Except($"Input shape mismatch: Input '{_parent.Inputs[i]}' has shape {originalShape.ToString()}, but input data is of length {typeValueCount}.");
 
                         // Fill in the unknown dimensions.
-                        var l = new int[originalShapeNdim];
+                        var l = new long[originalShapeNdim];
                         for (int ishape = 0; ishape < originalShapeNdim; ishape++)
-                            l[ishape] = originalShapeDims[ishape] == -1 ? (int)d : originalShapeDims[ishape];
-                        _fullySpecifiedShapes[i] = new TensorShape(l);
+                            l[ishape] = originalShapeDims[ishape] == -1 ? (long)d : originalShapeDims[ishape];
+                        _fullySpecifiedShapes[i] = new Shape(l);
                     }
 
                     if (_parent._addBatchDimensionInput)
                     {
                         // ndim of default TensorShape is -1, make originDim to 0 in this case.
                         // after addBatchDimension, input column will be changed: type -> type[]
                         var originDim = _fullySpecifiedShapes[i].ndim < 0 ? 0 : _fullySpecifiedShapes[i].ndim;
-                        var l = new int[originDim + 1];
+                        var l = new long[originDim + 1];
                         l[0] = 1;
                         for (int ishape = 1; ishape < l.Length; ishape++)
                             l[ishape] = _fullySpecifiedShapes[i].dims[ishape - 1];
-                        _fullySpecifiedShapes[i] = new TensorShape(l);
+                        _fullySpecifiedShapes[i] = new Shape(l);
                     }
                 }
 
@@ -720,7 +720,7 @@ private Delegate MakeGetter<T>(DataViewRow input, int iinfo, ITensorValueGetter[
                             UpdateCacheIfNeeded(input.Position, srcTensorGetters, activeOutputColNames, outputCache);
 
                             var tensor = outputCache.Outputs[_parent.Outputs[iinfo]];
-                            var tensorSize = tensor.TensorShape.dims.Where(x => x > 0).Aggregate((x, y) => x * y);
+                            var tensorSize = tensor.shape.dims.Where(x => x > 0).Aggregate((x, y) => x * y);
 
                             var editor = VBufferEditor.Create(ref dst, (int)tensorSize);
                             FetchStringData(tensor, editor.Values);
@@ -735,7 +735,7 @@ private Delegate MakeGetter<T>(DataViewRow input, int iinfo, ITensorValueGetter[
                             UpdateCacheIfNeeded(input.Position, srcTensorGetters, activeOutputColNames, outputCache);
 
                             var tensor = outputCache.Outputs[_parent.Outputs[iinfo]];
-                            var tensorSize = tensor.TensorShape.dims.Where(x => x > 0).Aggregate((x, y) => x * y);
+                            var tensorSize = tensor.shape.dims.Where(x => x > 0).Aggregate((x, y) => x * y);
 
                             var editor = VBufferEditor.Create(ref dst, (int)tensorSize);
 
@@ -821,10 +821,10 @@ private class TensorValueGetter<T> : ITensorValueGetter
         {
             private readonly ValueGetter<T> _srcgetter;
             private readonly T[] _bufferedData;
-            private readonly TensorShape _tfShape;
+            private readonly Shape _tfShape;
             private int _position;
 
-            public TensorValueGetter(DataViewRow input, int colIndex, TensorShape tfShape)
+            public TensorValueGetter(DataViewRow input, int colIndex, Shape tfShape)
             {
                 _srcgetter = input.GetGetter<T>(input.Schema[colIndex]);
                 _tfShape = tfShape;
@@ -864,15 +864,15 @@ public Tensor GetBufferedBatchTensor()
         private class TensorValueGetterVec<T> : ITensorValueGetter
         {
             private readonly ValueGetter<VBuffer<T>> _srcgetter;
-            private readonly TensorShape _tfShape;
+            private readonly Shape _tfShape;
             private VBuffer<T> _vBuffer;
             private T[] _denseData;
             private T[] _bufferedData;
             private int _position;
             private readonly long[] _dims;
             private readonly long _bufferedDataSize;
 
-            public TensorValueGetterVec(DataViewRow input, int colIndex, TensorShape tfShape)
+            public TensorValueGetterVec(DataViewRow input, int colIndex, Shape tfShape)
             {
                 _srcgetter = input.GetGetter<VBuffer<T>>(input.Schema[colIndex]);
                 _tfShape = tfShape;