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Copy System.Numerics.Tensors sources from dotnet/corefx into onnxrunt…
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csharp/src/Microsoft.ML.OnnxRuntime/Tensors/ArrayTensorExtensions.cs
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|---|---|---|
| @@ -0,0 +1,66 @@ | ||
| // Copyright (c) Microsoft Corporation. All rights reserved. | ||
| // Licensed under the MIT License. | ||
|
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| // This file is copied and adapted from the following git repository - | ||
| // https://github.com/dotnet/corefx | ||
| // Commit ID: bdd0814360d4c3a58860919f292a306242f27da1 | ||
| // Path: /src/System.Numerics.Tensors/src/System/Numerics/Tensors/ArrayTensorExtensions.cs | ||
| // Original license statement below - | ||
|
|
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| // Licensed to the .NET Foundation under one or more agreements. | ||
| // The .NET Foundation licenses this file to you under the MIT license. | ||
| // See the LICENSE file in the project root for more information. | ||
| using System; | ||
|
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| namespace Microsoft.ML.OnnxRuntime.Tensors | ||
| { | ||
| public static class ArrayTensorExtensions | ||
| { | ||
| /// <summary> | ||
| /// Creates a copy of this single-dimensional array as a DenseTensor<T> | ||
| /// </summary> | ||
| /// <typeparam name="T">Type contained in the array to copy to the DenseTensor<T>.</typeparam> | ||
| /// <param name="array">The array to create a DenseTensor<T> from.</param> | ||
| /// <returns>A 1-dimensional DenseTensor<T> with the same length and content as <paramref name="array"/>.</returns> | ||
| public static DenseTensor<T> ToTensor<T>(this T[] array) | ||
| { | ||
| return new DenseTensor<T>(array); | ||
| } | ||
|
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| /// <summary> | ||
| /// Creates a copy of this two-dimensional array as a DenseTensor<T> | ||
| /// </summary> | ||
| /// <typeparam name="T">Type contained in the array to copy to the DenseTensor<T>.</typeparam> | ||
| /// <param name="array">The array to create a DenseTensor<T> from.</param> | ||
| /// <param name="reverseStride">False (default) to indicate that the first dimension is most major (farthest apart) and the last dimension is most minor (closest together): row-major. True to indicate that the last dimension is most major (farthest apart) and the first dimension is most minor (closest together): column-major.</param> | ||
| /// <returns>A 2-dimensional DenseTensor<T> with the same dimensions and content as <paramref name="array"/>.</returns> | ||
| public static DenseTensor<T> ToTensor<T>(this T[,] array, bool reverseStride = false) | ||
| { | ||
| return new DenseTensor<T>(array, reverseStride); | ||
| } | ||
|
|
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| /// <summary> | ||
| /// Creates a copy of this three-dimensional array as a DenseTensor<T> | ||
| /// </summary> | ||
| /// <typeparam name="T">Type contained in the array to copy to the DenseTensor<T>.</typeparam> | ||
| /// <param name="array">The array to create a DenseTensor<T> from.</param> | ||
| /// <param name="reverseStride">False (default) to indicate that the first dimension is most major (farthest apart) and the last dimension is most minor (closest together): akin to row-major in a rank-2 tensor. True to indicate that the last dimension is most major (farthest apart) and the first dimension is most minor (closest together): akin to column-major in a rank-2 tensor.</param> | ||
| /// <returns>A 3-dimensional DenseTensor<T> with the same dimensions and content as <paramref name="array"/>.</returns> | ||
| public static DenseTensor<T> ToTensor<T>(this T[,,] array, bool reverseStride = false) | ||
| { | ||
| return new DenseTensor<T>(array, reverseStride); | ||
| } | ||
|
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| /// <summary> | ||
| /// Creates a copy of this n-dimensional array as a DenseTensor<T> | ||
| /// </summary> | ||
| /// <typeparam name="T">Type contained in the array to copy to the DenseTensor<T>.</typeparam> | ||
| /// <param name="array">The array to create a DenseTensor<T> from.</param> | ||
| /// <param name="reverseStride">False (default) to indicate that the first dimension is most major (farthest apart) and the last dimension is most minor (closest together): akin to row-major in a rank-2 tensor. True to indicate that the last dimension is most major (farthest apart) and the first dimension is most minor (closest together): akin to column-major in a rank-2 tensor.</param> | ||
| /// <returns>A n-dimensional DenseTensor<T> with the same dimensions and content as <paramref name="array"/>.</returns> | ||
| public static DenseTensor<T> ToTensor<T>(this Array array, bool reverseStride = false) | ||
| { | ||
| return new DenseTensor<T>(array, reverseStride); | ||
| } | ||
| } | ||
| } |
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csharp/src/Microsoft.ML.OnnxRuntime/Tensors/ArrayUtilities.cs
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| @@ -0,0 +1,227 @@ | ||
| // Copyright (c) Microsoft Corporation. All rights reserved. | ||
| // Licensed under the MIT License. | ||
|
|
||
| // This file is copied and adapted from the following git repository - | ||
| // https://github.com/dotnet/corefx | ||
| // Commit ID: bdd0814360d4c3a58860919f292a306242f27da1 | ||
| // Path: /src/System.Numerics.Tensors/src/System/Numerics/Tensors/ArrayUtilities.cs | ||
| // Original license statement below - | ||
|
|
||
| // Licensed to the .NET Foundation under one or more agreements. | ||
| // The .NET Foundation licenses this file to you under the MIT license. | ||
| // See the LICENSE file in the project root for more information. | ||
|
|
||
| using System.Diagnostics; | ||
| using System; | ||
|
|
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| namespace Microsoft.ML.OnnxRuntime.Tensors | ||
| { | ||
| internal static class ArrayUtilities | ||
| { | ||
| public const int StackallocMax = 16; | ||
|
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| public static long GetProduct(ReadOnlySpan<int> dimensions, int startIndex = 0) | ||
| { | ||
| if (dimensions.Length == 0) | ||
| { | ||
| return 0; | ||
| } | ||
|
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| long product = 1; | ||
| for (int i = startIndex; i < dimensions.Length; i++) | ||
| { | ||
| if (dimensions[i] < 0) | ||
| { | ||
| throw new ArgumentOutOfRangeException($"{nameof(dimensions)}[{i}]"); | ||
| } | ||
|
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| // we use a long which should be much larger than is ever used here, | ||
| // but still force checked | ||
| checked | ||
| { | ||
| product *= dimensions[i]; | ||
| } | ||
| } | ||
|
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| return product; | ||
| } | ||
|
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| public static bool IsAscending(ReadOnlySpan<int> values) | ||
| { | ||
| for (int i = 1; i < values.Length; i++) | ||
| { | ||
| if (values[i] < values[i - 1]) | ||
| { | ||
| return false; | ||
| } | ||
| } | ||
|
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| return true; | ||
| } | ||
|
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| public static bool IsDescending(ReadOnlySpan<int> values) | ||
| { | ||
| for (int i = 1; i < values.Length; i++) | ||
| { | ||
| if (values[i] > values[i - 1]) | ||
| { | ||
| return false; | ||
| } | ||
| } | ||
|
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| return true; | ||
| } | ||
|
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| /// <summary> | ||
| /// Gets the set of strides that can be used to calculate the offset of n-dimensions in a 1-dimensional layout | ||
| /// </summary> | ||
| /// <param name="dimensions"></param> | ||
| /// <param name="reverseStride"></param> | ||
| /// <returns></returns> | ||
| public static int[] GetStrides(ReadOnlySpan<int> dimensions, bool reverseStride = false) | ||
| { | ||
| int[] strides = new int[dimensions.Length]; | ||
|
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| int stride = 1; | ||
| if (reverseStride) | ||
| { | ||
| for (int i = 0; i < strides.Length; i++) | ||
| { | ||
| strides[i] = stride; | ||
| stride *= dimensions[i]; | ||
| } | ||
| } | ||
| else | ||
| { | ||
| for (int i = strides.Length - 1; i >= 0; i--) | ||
| { | ||
| strides[i] = stride; | ||
| stride *= dimensions[i]; | ||
| } | ||
| } | ||
|
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| return strides; | ||
| } | ||
|
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| public static void SplitStrides(int[] strides, int[] splitAxes, int[] newStrides, int stridesOffset, int[] splitStrides, int splitStridesOffset) | ||
| { | ||
| int newStrideIndex = 0; | ||
| for (int i = 0; i < strides.Length; i++) | ||
| { | ||
| int stride = strides[i]; | ||
| bool isSplit = false; | ||
| for (int j = 0; j < splitAxes.Length; j++) | ||
| { | ||
| if (splitAxes[j] == i) | ||
| { | ||
| splitStrides[splitStridesOffset + j] = stride; | ||
| isSplit = true; | ||
| break; | ||
| } | ||
| } | ||
|
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| if (!isSplit) | ||
| { | ||
| newStrides[stridesOffset + newStrideIndex++] = stride; | ||
| } | ||
| } | ||
| } | ||
|
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| /// <summary> | ||
| /// Calculates the 1-d index for n-d indices in layout specified by strides. | ||
| /// </summary> | ||
| /// <param name="strides"></param> | ||
| /// <param name="indices"></param> | ||
| /// <param name="startFromDimension"></param> | ||
| /// <returns></returns> | ||
| public static int GetIndex(int[] strides, ReadOnlySpan<int> indices, int startFromDimension = 0) | ||
| { | ||
| Debug.Assert(strides.Length == indices.Length); | ||
|
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| int index = 0; | ||
| for (int i = startFromDimension; i < indices.Length; i++) | ||
| { | ||
| index += strides[i] * indices[i]; | ||
| } | ||
|
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| return index; | ||
| } | ||
|
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| /// <summary> | ||
| /// Calculates the n-d indices from the 1-d index in a layout specificed by strides | ||
| /// </summary> | ||
| /// <param name="strides"></param> | ||
| /// <param name="reverseStride"></param> | ||
| /// <param name="index"></param> | ||
| /// <param name="indices"></param> | ||
| /// <param name="startFromDimension"></param> | ||
| public static void GetIndices(ReadOnlySpan<int> strides, bool reverseStride, int index, int[] indices, int startFromDimension = 0) | ||
| { | ||
| Debug.Assert(reverseStride ? IsAscending(strides) : IsDescending(strides), "Index decomposition requires ordered strides"); | ||
| Debug.Assert(strides.Length == indices.Length); | ||
|
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| int remainder = index; | ||
| for (int i = startFromDimension; i < strides.Length; i++) | ||
| { | ||
| // reverse the index for reverseStride so that we divide by largest stride first | ||
| var nIndex = reverseStride ? strides.Length - 1 - i : i; | ||
|
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| var stride = strides[nIndex]; | ||
| indices[nIndex] = remainder / stride; | ||
| remainder %= stride; | ||
| } | ||
| } | ||
|
|
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| /// <summary> | ||
| /// Calculates the n-d indices from the 1-d index in a layout specificed by strides | ||
| /// </summary> | ||
| /// <param name="strides"></param> | ||
| /// <param name="reverseStride"></param> | ||
| /// <param name="index"></param> | ||
| /// <param name="indices"></param> | ||
| /// <param name="startFromDimension"></param> | ||
| public static void GetIndices(ReadOnlySpan<int> strides, bool reverseStride, int index, Span<int> indices, int startFromDimension = 0) | ||
| { | ||
| Debug.Assert(reverseStride ? IsAscending(strides) : IsDescending(strides), "Index decomposition requires ordered strides"); | ||
| Debug.Assert(strides.Length == indices.Length); | ||
|
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| int remainder = index; | ||
| for (int i = startFromDimension; i < strides.Length; i++) | ||
| { | ||
| // reverse the index for reverseStride so that we divide by largest stride first | ||
| var nIndex = reverseStride ? strides.Length - 1 - i : i; | ||
|
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| var stride = strides[nIndex]; | ||
| indices[nIndex] = remainder / stride; | ||
| remainder %= stride; | ||
| } | ||
| } | ||
|
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| /// <summary> | ||
| /// Takes an 1-d index over n-d sourceStrides and recalculates it assuming same n-d coordinates over a different n-d strides | ||
| /// </summary> | ||
| public static int TransformIndexByStrides(int index, int[] sourceStrides, bool sourceReverseStride, int[] transformStrides) | ||
| { | ||
| Debug.Assert(index >= 0); | ||
| Debug.Assert(sourceReverseStride ? IsAscending(sourceStrides) : IsDescending(sourceStrides), "Index decomposition requires ordered strides"); | ||
| Debug.Assert(sourceStrides.Length == transformStrides.Length); | ||
|
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| int transformIndex = 0; | ||
| int remainder = index; | ||
|
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| for (int i = 0; i < sourceStrides.Length; i++) | ||
| { | ||
| // reverse the index for reverseStride so that we divide by largest stride first | ||
| var nIndex = sourceReverseStride ? sourceStrides.Length - 1 - i: i; | ||
|
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| var sourceStride = sourceStrides[nIndex]; | ||
| var transformStride = transformStrides[nIndex]; | ||
|
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| transformIndex += transformStride * (remainder / sourceStride); | ||
| remainder %= sourceStride; | ||
| } | ||
|
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| return transformIndex; | ||
| } | ||
| } | ||
| } |
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