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Updated Tensorflow.Net to 0.70.2 with Tensorflow 2.7.0. #7472

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Updated Tensorflow.Net to 0.70.2 with Tensorflow 2.7.0. #7472

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82e3275
Updated Tensorflow.Net to 0.70.2 with Tensorflow 2.7.0.
RossHNPC May 23, 2025
16782f6
Common code to ArrayUtils, dotnet added to Sdk install to resolve CI …
RossHNPC May 26, 2025
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5 changes: 3 additions & 2 deletions eng/Versions.props
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Original file line number Diff line number Diff line change
Expand Up @@ -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>
Expand All @@ -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>
Expand Down
6 changes: 4 additions & 2 deletions global.json
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Expand Up @@ -4,11 +4,13 @@
"runtimes": {
"dotnet": [
"$(DotNetRuntime60Version)",
"$(DotNetRuntime80Version)"
"$(DotNetRuntime80Version)",
"$(DotNetRuntime90Version)"
],
"dotnet/x86": [
"$(DotNetRuntime60Version)",
"$(DotNetRuntime80Version)"
"$(DotNetRuntime80Version)",
"$(DotNetRuntime90Version)"
]
}
},
Expand Down
21 changes: 21 additions & 0 deletions src/Microsoft.ML.Core/Utilities/ArrayUtils.cs
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Expand Up @@ -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;
}
}
}
18 changes: 18 additions & 0 deletions src/Microsoft.ML.DataView/VectorType.cs
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Expand Up @@ -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>
Expand Down
4 changes: 2 additions & 2 deletions src/Microsoft.ML.TensorFlow/TensorTypeExtensions.cs
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Original file line number Diff line number Diff line change
Expand Up @@ -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
Expand All @@ -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
Expand Down
62 changes: 31 additions & 31 deletions src/Microsoft.ML.TensorFlow/TensorflowTransform.cs
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Original file line number Diff line number Diff line change
Expand Up @@ -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;
Expand Down Expand Up @@ -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;
Expand Down Expand Up @@ -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);
Expand All @@ -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++)
Expand Down Expand Up @@ -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;
Expand All @@ -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));
Expand All @@ -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)
Expand Down Expand Up @@ -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];
Expand Down Expand Up @@ -517,7 +517,7 @@ public void Dispose()

if (Session != null && Session != IntPtr.Zero)
{
Session.close(); // invoked Dispose()
Session.Dispose();
}
}
finally
Expand All @@ -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) :
Expand All @@ -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]))
Expand All @@ -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)
{
Expand All @@ -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}.");

Expand All @@ -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);
}
}

Expand Down Expand Up @@ -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);
Expand All @@ -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);

Expand Down Expand Up @@ -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;
Expand Down Expand Up @@ -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;
Expand Down
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