1 // This file is part of Eigen, a lightweight C++ template library
2 // for linear algebra.
3 //
4 // Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
5 //
6 // This Source Code Form is subject to the terms of the Mozilla
7 // Public License v. 2.0. If a copy of the MPL was not distributed
8 // with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
9 
10 #include "main.h"
11 
12 #include <Eigen/CXX11/Tensor>
13 
14 using Eigen::Tensor;
15 
16 
17 struct InsertZeros {
18   DSizes<DenseIndex, 2> dimensions(const Tensor<float, 2>& input) const {
19     DSizes<DenseIndex, 2> result;
20     result[0] = input.dimension(0) * 2;
21     result[1] = input.dimension(1) * 2;
22     return result;
23   }
24 
25   template <typename Output, typename Device>
26   void eval(const Tensor<float, 2>& input, Output& output, const Device& device) const
27   {
28     array<DenseIndex, 2> strides;
29     strides[0] = 2;
30     strides[1] = 2;
31     output.stride(strides).device(device) = input;
32 
33     Eigen::DSizes<DenseIndex, 2> offsets(1,1);
34     Eigen::DSizes<DenseIndex, 2> extents(output.dimension(0)-1, output.dimension(1)-1);
35     output.slice(offsets, extents).stride(strides).device(device) = input.constant(0.0f);
36   }
37 };
38 
39 static void test_custom_unary_op()
40 {
41   Tensor<float, 2> tensor(3,5);
42   tensor.setRandom();
43 
44   Tensor<float, 2> result = tensor.customOp(InsertZeros());
45   VERIFY_IS_EQUAL(result.dimension(0), 6);
46   VERIFY_IS_EQUAL(result.dimension(1), 10);
47 
48   for (int i = 0; i < 6; i+=2) {
49     for (int j = 0; j < 10; j+=2) {
50       VERIFY_IS_EQUAL(result(i, j), tensor(i/2, j/2));
51     }
52   }
53   for (int i = 1; i < 6; i+=2) {
54     for (int j = 1; j < 10; j+=2) {
55       VERIFY_IS_EQUAL(result(i, j), 0);
56     }
57   }
58 }
59 
60 
61 struct BatchMatMul {
62   DSizes<DenseIndex, 3> dimensions(const Tensor<float, 3>& input1, const Tensor<float, 3>& input2) const {
63     DSizes<DenseIndex, 3> result;
64     result[0] = input1.dimension(0);
65     result[1] = input2.dimension(1);
66     result[2] = input2.dimension(2);
67     return result;
68   }
69 
70   template <typename Output, typename Device>
71   void eval(const Tensor<float, 3>& input1, const Tensor<float, 3>& input2,
72             Output& output, const Device& device) const
73   {
74     typedef Tensor<float, 3>::DimensionPair DimPair;
75     array<DimPair, 1> dims;
76     dims[0] = DimPair(1, 0);
77     for (int i = 0; i < output.dimension(2); ++i) {
78       output.template chip<2>(i).device(device) = input1.chip<2>(i).contract(input2.chip<2>(i), dims);
79     }
80   }
81 };
82 
83 
84 static void test_custom_binary_op()
85 {
86   Tensor<float, 3> tensor1(2,3,5);
87   tensor1.setRandom();
88   Tensor<float, 3> tensor2(3,7,5);
89   tensor2.setRandom();
90 
91   Tensor<float, 3> result = tensor1.customOp(tensor2, BatchMatMul());
92   for (int i = 0; i < 5; ++i) {
93     typedef Tensor<float, 3>::DimensionPair DimPair;
94     array<DimPair, 1> dims;
95     dims[0] = DimPair(1, 0);
96     Tensor<float, 2> reference = tensor1.chip<2>(i).contract(tensor2.chip<2>(i), dims);
97     TensorRef<Tensor<float, 2> > val = result.chip<2>(i);
98     for (int j = 0; j < 2; ++j) {
99       for (int k = 0; k < 7; ++k) {
100         VERIFY_IS_APPROX(val(j, k), reference(j, k));
101       }
102     }
103   }
104 }
105 
106 
107 void test_cxx11_tensor_custom_op()
108 {
109   CALL_SUBTEST(test_custom_unary_op());
110   CALL_SUBTEST(test_custom_binary_op());
111 }
112