1 /* Copyright 2017 The TensorFlow Authors. All Rights Reserved. 2 3 Licensed under the Apache License, Version 2.0 (the "License"); 4 you may not use this file except in compliance with the License. 5 You may obtain a copy of the License at 6 7 http://www.apache.org/licenses/LICENSE-2.0 8 9 Unless required by applicable law or agreed to in writing, software 10 distributed under the License is distributed on an "AS IS" BASIS, 11 WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 See the License for the specific language governing permissions and 13 limitations under the License. 14 ==============================================================================*/ 15 16 #ifndef TENSORFLOW_COMPILER_XLA_ARRAY4D_H_ 17 #define TENSORFLOW_COMPILER_XLA_ARRAY4D_H_ 18 19 #include <algorithm> 20 #include <functional> 21 #include <initializer_list> 22 #include <iterator> 23 #include <memory> 24 #include <numeric> 25 #include <random> 26 #include <string> 27 #include <vector> 28 29 #include "absl/strings/str_cat.h" 30 #include "absl/types/span.h" 31 #include "tensorflow/compiler/xla/array.h" 32 #include "tensorflow/compiler/xla/array2d.h" 33 #include "tensorflow/compiler/xla/types.h" 34 #include "tensorflow/core/platform/logging.h" 35 #include "tensorflow/core/platform/macros.h" 36 #include "tensorflow/core/platform/types.h" 37 38 namespace xla { 39 40 // Simple 4D array structure, similar in form to Array2D, for use primarily in 41 // testing and describing to XLA APIs values in the 4D array structures used 42 // in convolutions. 43 // 44 // The data layout is, in order from major to minor: 45 // 46 // First dimension: plane, batch, n1 47 // Second dimension: depth, feature, z, n2 48 // Third dimension: height, y, n3 49 // Fourth dimension: width, x, n4 50 // 51 // These dimensions are referred to by various names, so that is why 52 // more than one name is given above. See operator() for the exact 53 // calculation of 1d indices from 4d indices. 54 template <typename T> 55 class Array4D : public Array<T> { 56 public: 57 // Creates a 4D array, uninitialized values. 58 Array4D(int64 planes, int64 depth, int64 height, int64 width) 59 : Array<T>(std::vector<int64>{planes, depth, height, width}) {} 60 61 // Creates a 4D array, initialized to value. 62 Array4D(int64 planes, int64 depth, int64 height, int64 width, T value) 63 : Array<T>(std::vector<int64>{planes, depth, height, width}, value) {} 64 65 // Creates a 4D array, filled with values. 66 // 67 // We need to set a default type for Container so that code like 68 // Array4D(1, 1, 1, 1, {1}) will work. The template cannot infer the 69 // initializer_list type in that case without this default. 70 template <typename Container = std::initializer_list<T>> 71 Array4D(int64 planes, int64 depth, int64 height, int64 width, 72 const Container& values) 73 : Array4D(planes, depth, height, width) { 74 this->SetValues(values); 75 } 76 77 // Construct an Array4D with the given nested initializer list. 78 Array4D(std::initializer_list<std::initializer_list< 79 std::initializer_list<std::initializer_list<T>>>> 80 values) 81 : Array<T>(values) {} 82 83 // Creates an array of a floating-point type (half, bfloat16, float, 84 // or double) from the given nested initializer list of float values. 85 template <typename T2, typename = typename std::enable_if< 86 (std::is_same<T, Eigen::half>::value || 87 std::is_same<T, bfloat16>::value || 88 std::is_same<T, float>::value || 89 std::is_same<T, double>::value) && 90 std::is_same<T2, float>::value>::type> 91 Array4D(std::initializer_list<std::initializer_list< 92 std::initializer_list<std::initializer_list<T2>>>> 93 values) 94 : Array<T>(values) {} 95 96 // Numerically-named aliases for the various dimensions. This matches the 97 // dimension names used in array3d. 98 int64 n4() const { return this->dim(3); } 99 int64 n3() const { return this->dim(2); } 100 int64 n2() const { return this->dim(1); } 101 int64 n1() const { return this->dim(0); } 102 103 int64 width() const { return this->dim(3); } 104 int64 height() const { return this->dim(2); } 105 int64 depth() const { return this->dim(1); } 106 int64 planes() const { return this->dim(0); } 107 108 // Fills all of the {p,z} with the array provided, which specifies {y,x}. 109 void FillWithYX(const Array2D<T>& value) { 110 CHECK_EQ(value.height(), height()); 111 CHECK_EQ(value.width(), width()); 112 for (int64 plane = 0; plane < planes(); ++plane) { 113 for (int64 depth = 0; depth < this->depth(); ++depth) { 114 for (int64 height = 0; height < this->height(); ++height) { 115 for (int64 width = 0; width < this->width(); ++width) { 116 (*this)(plane, depth, height, width) = value(height, width); 117 } 118 } 119 } 120 } 121 } 122 123 // Fills all of the {x,y} with the array provided, which specifies {p,z}. 124 void FillWithPZ(const Array2D<T>& value) { 125 CHECK_EQ(value.height(), planes()); 126 CHECK_EQ(value.width(), depth()); 127 for (int64 height = 0; height < this->height(); ++height) { 128 for (int64 width = 0; width < this->width(); ++width) { 129 for (int64 plane = 0; plane < planes(); ++plane) { 130 for (int64 depth = 0; depth < this->depth(); ++depth) { 131 (*this)(plane, depth, height, width) = value(plane, depth); 132 } 133 } 134 } 135 } 136 } 137 138 // Fills each of the minor-dim matrices with a number designating which minor 139 // dim matrix is enclosed by the shape. 140 void FillWithMinorDimNum() { 141 LOG(INFO) << "width: " << this->width(); 142 LOG(INFO) << "height: " << this->height(); 143 LOG(INFO) << "depth: " << this->depth(); 144 LOG(INFO) << "planes: " << this->planes(); 145 for (int64 height = 0; height < this->height(); ++height) { 146 for (int64 width = 0; width < this->width(); ++width) { 147 for (int64 plane = 0; plane < planes(); ++plane) { 148 for (int64 depth = 0; depth < this->depth(); ++depth) { 149 float this_val = plane * this->depth() + depth; 150 (*this)(plane, depth, height, width) = this_val; 151 } 152 } 153 } 154 } 155 } 156 }; 157 158 } // namespace xla 159 160 #endif // TENSORFLOW_COMPILER_XLA_ARRAY4D_H_ 161
