msbuild sdk extras update

Author: michaelgsharp

csharp/src/Microsoft.ML.OnnxRuntime/FixedBufferOnnxValue.shared.cs

@@ -4,7 +4,7 @@
 using Microsoft.ML.OnnxRuntime.Tensors;
 using System;
 
-#if NET8_0
+#if NET8_0_OR_GREATER
 using DotnetTensors = System.Numerics.Tensors;
 using TensorPrimitives = System.Numerics.Tensors.TensorPrimitives;
 #endif
@@ -14,7 +14,7 @@ namespace Microsoft.ML.OnnxRuntime
     /// <summary>
     /// This is a legacy class that is kept for backward compatibility.
     /// Use OrtValue based API.
-    /// 
+    ///
     /// Represents an OrtValue with its underlying buffer pinned
     /// </summary>
     public class FixedBufferOnnxValue : IDisposable
@@ -44,7 +44,7 @@ public static FixedBufferOnnxValue CreateFromTensor<T>(Tensor<T> value)
             return new FixedBufferOnnxValue(ref ortValue, OnnxValueType.ONNX_TYPE_TENSOR, elementType);
         }
 
-#if NET8_0
+#if NET8_0_OR_GREATER
 #pragma warning disable SYSLIB5001 // System.Numerics.Tensors is only in preview so we can continue receiving API feedback
         /// <summary>
         /// Creates a <see cref="FixedBufferOnnxValue"/> object from the tensor and pins its underlying buffer.
@@ -83,7 +83,7 @@ public static FixedBufferOnnxValue CreateFromDotnetTensor<T>(DotnetTensors.Tenso
         /// Here is an example of using a 3rd party library class for processing float16/bfloat16.
         /// Currently, to pass tensor data and create a tensor one must copy data to Float16/BFloat16 structures
         /// so DenseTensor can recognize it.
-        /// 
+        ///
         /// If you are using a library that has a class Half and it is blittable, that is its managed in memory representation
         /// matches native one and its size is 16-bits, you can use the following conceptual example
         /// to feed/fetch data for inference using Half array. This allows you to avoid copying data from your Half[] to Float16[]
@@ -94,7 +94,7 @@ public static FixedBufferOnnxValue CreateFromDotnetTensor<T>(DotnetTensors.Tenso
         /// var input_shape = new long[] {input.Length};
         /// Half[] output = new Half[40]; // Whatever the expected len/shape is must match
         /// var output_shape = new long[] {output.Length};
-        /// 
+        ///
         /// var memInfo = OrtMemoryInfo.DefaultInstance; // CPU
         ///
         /// using(var fixedBufferInput = FixedBufferOnnxvalue.CreateFromMemory{Half}(memInfo,

csharp/src/Microsoft.ML.OnnxRuntime/ManagedProjections.shared.cs

@@ -9,7 +9,7 @@
 using System.Reflection;
 
 
-#if NET8_0
+#if NET8_0_OR_GREATER
 using DotnetTensors = System.Numerics.Tensors;
 using TensorPrimitives = System.Numerics.Tensors.TensorPrimitives;
 #endif
@@ -173,7 +173,7 @@ private static OrtValue CreateMapProjection(NamedOnnxValue node, NodeMetadata el
         /// <exception cref="OnnxRuntimeException"></exception>
         private static OrtValue CreateTensorProjection(NamedOnnxValue node, NodeMetadata elementMeta)
         {
-#if NET8_0
+#if NET8_0_OR_GREATER
 #pragma warning disable SYSLIB5001 // System.Numerics.Tensors is only in preview so we can continue receiving API feedback
             if (node.Value is not TensorBase && node.Value.GetType().GetGenericTypeDefinition() != typeof(DotnetTensors.Tensor<>))
             {
@@ -226,4 +226,3 @@ private static OrtValue CreateTensorProjection(NamedOnnxValue node, NodeMetadata
         }
     }
 }
-

csharp/src/Microsoft.ML.OnnxRuntime/Microsoft.ML.OnnxRuntime.csproj

@@ -1,4 +1,4 @@
-<Project Sdk="MSBuild.Sdk.Extras/3.0.22">
+<Project Sdk="MSBuild.Sdk.Extras/3.0.44">
   <PropertyGroup>
     <!--- packaging properties -->
     <OrtPackageId Condition="'$(OrtPackageId)' == ''">Microsoft.ML.OnnxRuntime</OrtPackageId>

csharp/src/Microsoft.ML.OnnxRuntime/NamedOnnxValue.shared.cs

@@ -8,7 +8,7 @@
 using System.Diagnostics;
 using System.Linq;
 
-#if NET8_0
+#if NET8_0_OR_GREATER
 using DotnetTensors = System.Numerics.Tensors;
 using TensorPrimitives = System.Numerics.Tensors.TensorPrimitives;
 #endif
@@ -35,37 +35,37 @@ internal MapHelper(TensorBase keys, TensorBase values)
     /// <summary>
     /// This is a legacy class that is kept for backward compatibility.
     /// Use OrtValue based API.
-    /// 
-    /// The class associates a name with an Object. 
+    ///
+    /// The class associates a name with an Object.
     /// The name of the class is a misnomer, it does not hold any Onnx values,
     /// just managed representation of them.
-    /// 
+    ///
     /// The class is currently used as both inputs and outputs. Because it is non-
     /// disposable, it can not hold on to any native objects.
-    /// 
+    ///
     /// When used as input, we temporarily create OrtValues that map managed inputs
     /// directly. Thus we are able to avoid copying of contiguous data.
-    /// 
+    ///
     /// For outputs, tensor buffers works the same as input, providing it matches
     /// the expected output shape. For other types (maps and sequences) we create a copy of the data.
     /// This is because, the class is not Disposable and it is a public interface, thus it can not own
     /// the underlying OrtValues that must be destroyed before Run() returns.
-    /// 
+    ///
     /// To avoid data copying on output, use DisposableNamedOnnxValue class that is returned from Run() methods.
     /// This provides access to the native memory tensors and avoids copying.
-    /// 
+    ///
     /// It is a recursive structure that may contain Tensors (base case)
     /// Other sequences and maps. Although the OnnxValueType is exposed,
     /// the caller is supposed to know the actual data type contained.
-    /// 
+    ///
     /// The convention is that for tensors, it would contain a DenseTensor{T} instance or
     /// anything derived from Tensor{T}.
-    /// 
+    ///
     /// For sequences, it would contain a IList{T} where T is an instance of NamedOnnxValue that
     /// would contain a tensor or another type.
-    /// 
+    ///
     /// For Maps, it would contain a IDictionary{K, V} where K,V are primitive types or strings.
-    /// 
+    ///
     /// </summary>
     public class NamedOnnxValue
     {
@@ -145,7 +145,7 @@ public static NamedOnnxValue CreateFromTensor<T>(string name, Tensor<T> value)
             return new NamedOnnxValue(name, value, OnnxValueType.ONNX_TYPE_TENSOR);
         }
 
-#if NET8_0
+#if NET8_0_OR_GREATER
 #pragma warning disable SYSLIB5001 // System.Numerics.Tensors is only in preview so we can continue receiving API feedback
         /// <summary>
         /// This is a factory method that instantiates NamedOnnxValue
@@ -219,7 +219,7 @@ public Tensor<T> AsTensor<T>()
         }
 
 
-#if NET8_0
+#if NET8_0_OR_GREATER
 #pragma warning disable SYSLIB5001 // System.Numerics.Tensors is only in preview so we can continue receiving API feedback
         /// <summary>
         /// Try-get value as a Tensor&lt;T&gt;.
@@ -303,7 +303,7 @@ internal virtual IntPtr OutputToOrtValueHandle(NodeMetadata metadata, out IDispo
                 }
             }
 
-            throw new OnnxRuntimeException(ErrorCode.NotImplemented, 
+            throw new OnnxRuntimeException(ErrorCode.NotImplemented,
                 $"Can not create output OrtValue for NamedOnnxValue '{metadata.OnnxValueType}' type." +
                 $" Only tensors can be pre-allocated for outputs " +
                 $" Use Run() overloads that return DisposableNamedOnnxValue to get access to all Onnx value types that may be returned as output.");

csharp/src/Microsoft.ML.OnnxRuntime/OrtValue.shared.cs

@@ -12,7 +12,7 @@
 using System.Runtime.InteropServices;
 using System.Text;
 
-#if NET8_0
+#if NET8_0_OR_GREATER
 using DotnetTensors = System.Numerics.Tensors;
 using TensorPrimitives = System.Numerics.Tensors.TensorPrimitives;
 #endif
@@ -213,7 +213,7 @@ public ReadOnlySpan<T> GetTensorDataAsSpan<T>() where T : unmanaged
             return MemoryMarshal.Cast<byte, T>(byteSpan);
         }
 
-#if NET8_0
+#if NET8_0_OR_GREATER
 #pragma warning disable SYSLIB5001 // System.Numerics.Tensors is only in preview so we can continue receiving API feedback
         /// <summary>
         /// Returns a ReadOnlyTensorSpan<typeparamref name="T"/> over tensor native buffer that
@@ -261,7 +261,7 @@ public Span<T> GetTensorMutableDataAsSpan<T>() where T : unmanaged
             return MemoryMarshal.Cast<byte, T>(byteSpan);
         }
 
-#if NET8_0
+#if NET8_0_OR_GREATER
 #pragma warning disable SYSLIB5001 // System.Numerics.Tensors is only in preview so we can continue receiving API feedback
         /// <summary>
         /// Returns a TensorSpan<typeparamref name="T"/> over tensor native buffer.
@@ -297,7 +297,7 @@ public Span<byte> GetTensorMutableRawData()
             return GetTensorBufferRawData(typeof(byte));
         }
 
-#if NET8_0
+#if NET8_0_OR_GREATER
 #pragma warning disable SYSLIB5001 // Type is for evaluation purposes only and is subject to change or removal in future updates. Suppress this diagnostic to proceed.
         /// <summary>
         /// Provides mutable raw native buffer access.
@@ -686,7 +686,7 @@ public static OrtValue CreateTensorValueFromMemory<T>(T[] data, long[] shape) wh
             return OrtValue.CreateTensorValueFromMemory(OrtMemoryInfo.DefaultInstance, new Memory<T>(data), shape);
         }
 
-#if NET8_0
+#if NET8_0_OR_GREATER
 #pragma warning disable SYSLIB5001 // System.Numerics.Tensors is only in preview so it can continue receiving API feedback
         /// <summary>
         /// This is a factory method creates a native Onnxruntime OrtValue containing a tensor.
@@ -709,7 +709,6 @@ public static OrtValue CreateTensorValueFromDotnetTensorObject<T>(DotnetTensors.
                 var field = tensor.GetType().GetFields(BindingFlags.Instance | BindingFlags.NonPublic).Where(x => x.Name == "_values").FirstOrDefault();
                 var backingData = (T[])field.GetValue(tensor);
                 GCHandle handle = GCHandle.Alloc(backingData, GCHandleType.Pinned);
-                //GCHandle handle = GCHandle.Alloc(tensor.GetPinnableReference(), GCHandleType.Pinned);
                 var memHandle = new MemoryHandle(Unsafe.AsPointer(ref tensor.GetPinnableReference()), handle);
 
                 try

csharp/test/Microsoft.ML.OnnxRuntime.Tests.NetCoreApp/InferenceTest.netcore.cs

@@ -333,8 +333,8 @@ private void ThrowWrongOutputDimensionDotnetTensors()
             var inputData = tuple.Item2;
             var inputTensor = tuple.Item3;
             var inputs = new List<NamedOnnxValue> { NamedOnnxValue.CreateFromTensor<float>("data_0", inputTensor) };
-            var outputTensor = new DenseTensor<float>((ReadOnlySpan<int>)new[] { 1, 1001, 1, 1 });
-            var outputs = new List<NamedOnnxValue> { NamedOnnxValue.CreateFromTensor("softmaxout_1", outputTensor) };
+            var outputTensor = DotnetTensors.Tensor.Create([ 1, 1001, 1, 1 ], [4]);
+            var outputs = new List<NamedOnnxValue> { NamedOnnxValue.CreateFromDotnetTensor("softmaxout_1", outputTensor) };
             var ex = Assert.Throws<OnnxRuntimeException>(() => session.Run(inputs, outputs));
             // TODO: check exception message
             // InferenceSession::ValidateOutputs() does not check dims so far. Currently this will finally trigger an error in Softmax.
@@ -349,8 +349,8 @@ private void ThrowNoOutputDotnetTensors()
             var inputData = tuple.Item2;
             var inputTensor = tuple.Item3;
             var inputs = new List<NamedOnnxValue> { NamedOnnxValue.CreateFromTensor<float>("data_0", inputTensor) };
-            var outputTensor = new DenseTensor<float>((ReadOnlySpan<int>)new[] { 1, 1000, 1, 1 });
-            var outputs = new List<NamedOnnxValue> { NamedOnnxValue.CreateFromTensor("softmaxout_1", outputTensor) };
+            var outputTensor = DotnetTensors.Tensor.Create([1, 1001, 1, 1], [4]);
+            var outputs = new List<NamedOnnxValue> { NamedOnnxValue.CreateFromDotnetTensor("softmaxout_1", outputTensor) };
             var ex = Assert.Throws<OnnxRuntimeException>(() => session.Run(inputs, new NamedOnnxValue[0]));
             Assert.Contains("[ErrorCode:InvalidArgument] At least one output should be requested.", ex.Message);
             session.Dispose();
@@ -364,7 +364,7 @@ private void ThrowInconsistentPinnedOutputsDotnetTensors()
             var inputData = tuple.Item2;
             var inputTensor = tuple.Item3;
             var inputs = new List<NamedOnnxValue> { NamedOnnxValue.CreateFromTensor<float>("data_0", inputTensor) };
-            var outputTensor = new DenseTensor<float>((ReadOnlySpan<int>)new[] { 1, 1000, 1, 1 });
+            var outputTensor = DotnetTensors.Tensor.Create([1, 1001, 1, 1], [4]);
 
             using (var outputs = new DisposableListTest<FixedBufferOnnxValue>())
             {

csharp/tools/linux_pack/LinuxPackNativeNuget.csproj

@@ -7,7 +7,7 @@
      If you need a more sophisticated package for testing, you can run the production packaging pipeline against your
      branch and download the resulting nuget package from the build artifacts.
  -->
-<Project Sdk="MSBuild.Sdk.Extras/3.0.22">
+<Project Sdk="MSBuild.Sdk.Extras/3.0.44">
   <PropertyGroup>
     <TargetFrameworks>netstandard2.0</TargetFrameworks>
     <NuspecFile>$(OnnxRuntimeBuildDirectory)/NativeNuget.nuspec</NuspecFile>