// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com> // // This Source Code Form is subject to the terms of the Mozilla // Public License v. 2.0. If a copy of the MPL was not distributed // with this file, You can obtain one at http://mozilla.org/MPL/2.0/. #include "main.h" #include <limits> #include <numeric> #include <Eigen/CXX11/Tensor> using Eigen::Tensor; template <int DataLayout> static void test_trivial_reductions() { { Tensor<float, 0, DataLayout> tensor; tensor.setRandom(); array<ptrdiff_t, 0> reduction_axis; Tensor<float, 0, DataLayout> result = tensor.sum(reduction_axis); VERIFY_IS_EQUAL(result(), tensor()); } { Tensor<float, 1, DataLayout> tensor(7); tensor.setRandom(); array<ptrdiff_t, 0> reduction_axis; Tensor<float, 1, DataLayout> result = tensor.sum(reduction_axis); VERIFY_IS_EQUAL(result.dimension(0), 7); for (int i = 0; i < 7; ++i) { VERIFY_IS_EQUAL(result(i), tensor(i)); } } { Tensor<float, 2, DataLayout> tensor(2, 3); tensor.setRandom(); array<ptrdiff_t, 0> reduction_axis; Tensor<float, 2, DataLayout> result = tensor.sum(reduction_axis); VERIFY_IS_EQUAL(result.dimension(0), 2); VERIFY_IS_EQUAL(result.dimension(1), 3); for (int i = 0; i < 2; ++i) { for (int j = 0; j < 3; ++j) { VERIFY_IS_EQUAL(result(i, j), tensor(i, j)); } } } } template <int DataLayout> static void test_simple_reductions() { Tensor<float, 4, DataLayout> tensor(2, 3, 5, 7); tensor.setRandom(); array<ptrdiff_t, 2> reduction_axis2; reduction_axis2[0] = 1; reduction_axis2[1] = 3; Tensor<float, 2, DataLayout> result = tensor.sum(reduction_axis2); VERIFY_IS_EQUAL(result.dimension(0), 2); VERIFY_IS_EQUAL(result.dimension(1), 5); for (int i = 0; i < 2; ++i) { for (int j = 0; j < 5; ++j) { float sum = 0.0f; for (int k = 0; k < 3; ++k) { for (int l = 0; l < 7; ++l) { sum += tensor(i, k, j, l); } } VERIFY_IS_APPROX(result(i, j), sum); } } { Tensor<float, 0, DataLayout> sum1 = tensor.sum(); VERIFY_IS_EQUAL(sum1.rank(), 0); array<ptrdiff_t, 4> reduction_axis4; reduction_axis4[0] = 0; reduction_axis4[1] = 1; reduction_axis4[2] = 2; reduction_axis4[3] = 3; Tensor<float, 0, DataLayout> sum2 = tensor.sum(reduction_axis4); VERIFY_IS_EQUAL(sum2.rank(), 0); VERIFY_IS_APPROX(sum1(), sum2()); } reduction_axis2[0] = 0; reduction_axis2[1] = 2; result = tensor.prod(reduction_axis2); VERIFY_IS_EQUAL(result.dimension(0), 3); VERIFY_IS_EQUAL(result.dimension(1), 7); for (int i = 0; i < 3; ++i) { for (int j = 0; j < 7; ++j) { float prod = 1.0f; for (int k = 0; k < 2; ++k) { for (int l = 0; l < 5; ++l) { prod *= tensor(k, i, l, j); } } VERIFY_IS_APPROX(result(i, j), prod); } } { Tensor<float, 0, DataLayout> prod1 = tensor.prod(); VERIFY_IS_EQUAL(prod1.rank(), 0); array<ptrdiff_t, 4> reduction_axis4; reduction_axis4[0] = 0; reduction_axis4[1] = 1; reduction_axis4[2] = 2; reduction_axis4[3] = 3; Tensor<float, 0, DataLayout> prod2 = tensor.prod(reduction_axis4); VERIFY_IS_EQUAL(prod2.rank(), 0); VERIFY_IS_APPROX(prod1(), prod2()); } reduction_axis2[0] = 0; reduction_axis2[1] = 2; result = tensor.maximum(reduction_axis2); VERIFY_IS_EQUAL(result.dimension(0), 3); VERIFY_IS_EQUAL(result.dimension(1), 7); for (int i = 0; i < 3; ++i) { for (int j = 0; j < 7; ++j) { float max_val = std::numeric_limits<float>::lowest(); for (int k = 0; k < 2; ++k) { for (int l = 0; l < 5; ++l) { max_val = (std::max)(max_val, tensor(k, i, l, j)); } } VERIFY_IS_APPROX(result(i, j), max_val); } } { Tensor<float, 0, DataLayout> max1 = tensor.maximum(); VERIFY_IS_EQUAL(max1.rank(), 0); array<ptrdiff_t, 4> reduction_axis4; reduction_axis4[0] = 0; reduction_axis4[1] = 1; reduction_axis4[2] = 2; reduction_axis4[3] = 3; Tensor<float, 0, DataLayout> max2 = tensor.maximum(reduction_axis4); VERIFY_IS_EQUAL(max2.rank(), 0); VERIFY_IS_APPROX(max1(), max2()); } reduction_axis2[0] = 0; reduction_axis2[1] = 1; result = tensor.minimum(reduction_axis2); VERIFY_IS_EQUAL(result.dimension(0), 5); VERIFY_IS_EQUAL(result.dimension(1), 7); for (int i = 0; i < 5; ++i) { for (int j = 0; j < 7; ++j) { float min_val = (std::numeric_limits<float>::max)(); for (int k = 0; k < 2; ++k) { for (int l = 0; l < 3; ++l) { min_val = (std::min)(min_val, tensor(k, l, i, j)); } } VERIFY_IS_APPROX(result(i, j), min_val); } } { Tensor<float, 0, DataLayout> min1 = tensor.minimum(); VERIFY_IS_EQUAL(min1.rank(), 0); array<ptrdiff_t, 4> reduction_axis4; reduction_axis4[0] = 0; reduction_axis4[1] = 1; reduction_axis4[2] = 2; reduction_axis4[3] = 3; Tensor<float, 0, DataLayout> min2 = tensor.minimum(reduction_axis4); VERIFY_IS_EQUAL(min2.rank(), 0); VERIFY_IS_APPROX(min1(), min2()); } reduction_axis2[0] = 0; reduction_axis2[1] = 1; result = tensor.mean(reduction_axis2); VERIFY_IS_EQUAL(result.dimension(0), 5); VERIFY_IS_EQUAL(result.dimension(1), 7); for (int i = 0; i < 5; ++i) { for (int j = 0; j < 7; ++j) { float sum = 0.0f; int count = 0; for (int k = 0; k < 2; ++k) { for (int l = 0; l < 3; ++l) { sum += tensor(k, l, i, j); ++count; } } VERIFY_IS_APPROX(result(i, j), sum / count); } } { Tensor<float, 0, DataLayout> mean1 = tensor.mean(); VERIFY_IS_EQUAL(mean1.rank(), 0); array<ptrdiff_t, 4> reduction_axis4; reduction_axis4[0] = 0; reduction_axis4[1] = 1; reduction_axis4[2] = 2; reduction_axis4[3] = 3; Tensor<float, 0, DataLayout> mean2 = tensor.mean(reduction_axis4); VERIFY_IS_EQUAL(mean2.rank(), 0); VERIFY_IS_APPROX(mean1(), mean2()); } { Tensor<int, 1> ints(10); std::iota(ints.data(), ints.data() + ints.dimension(0), 0); TensorFixedSize<bool, Sizes<> > all; all = ints.all(); VERIFY(!all()); all = (ints >= ints.constant(0)).all(); VERIFY(all()); TensorFixedSize<bool, Sizes<> > any; any = (ints > ints.constant(10)).any(); VERIFY(!any()); any = (ints < ints.constant(1)).any(); VERIFY(any()); } } template <int DataLayout> static void test_reductions_in_expr() { Tensor<float, 4, DataLayout> tensor(2, 3, 5, 7); tensor.setRandom(); array<ptrdiff_t, 2> reduction_axis2; reduction_axis2[0] = 1; reduction_axis2[1] = 3; Tensor<float, 2, DataLayout> result(2, 5); result = result.constant(1.0f) - tensor.sum(reduction_axis2); VERIFY_IS_EQUAL(result.dimension(0), 2); VERIFY_IS_EQUAL(result.dimension(1), 5); for (int i = 0; i < 2; ++i) { for (int j = 0; j < 5; ++j) { float sum = 0.0f; for (int k = 0; k < 3; ++k) { for (int l = 0; l < 7; ++l) { sum += tensor(i, k, j, l); } } VERIFY_IS_APPROX(result(i, j), 1.0f - sum); } } } template <int DataLayout> static void test_full_reductions() { Tensor<float, 2, DataLayout> tensor(2, 3); tensor.setRandom(); array<ptrdiff_t, 2> reduction_axis; reduction_axis[0] = 0; reduction_axis[1] = 1; Tensor<float, 0, DataLayout> result = tensor.sum(reduction_axis); VERIFY_IS_EQUAL(result.rank(), 0); float sum = 0.0f; for (int i = 0; i < 2; ++i) { for (int j = 0; j < 3; ++j) { sum += tensor(i, j); } } VERIFY_IS_APPROX(result(0), sum); result = tensor.square().sum(reduction_axis).sqrt(); VERIFY_IS_EQUAL(result.rank(), 0); sum = 0.0f; for (int i = 0; i < 2; ++i) { for (int j = 0; j < 3; ++j) { sum += tensor(i, j) * tensor(i, j); } } VERIFY_IS_APPROX(result(), sqrtf(sum)); } struct UserReducer { static const bool PacketAccess = false; UserReducer(float offset) : offset_(offset) {} void reduce(const float val, float* accum) { *accum += val * val; } float initialize() const { return 0; } float finalize(const float accum) const { return 1.0f / (accum + offset_); } private: const float offset_; }; template <int DataLayout> static void test_user_defined_reductions() { Tensor<float, 2, DataLayout> tensor(5, 7); tensor.setRandom(); array<ptrdiff_t, 1> reduction_axis; reduction_axis[0] = 1; UserReducer reducer(10.0f); Tensor<float, 1, DataLayout> result = tensor.reduce(reduction_axis, reducer); VERIFY_IS_EQUAL(result.dimension(0), 5); for (int i = 0; i < 5; ++i) { float expected = 10.0f; for (int j = 0; j < 7; ++j) { expected += tensor(i, j) * tensor(i, j); } expected = 1.0f / expected; VERIFY_IS_APPROX(result(i), expected); } } template <int DataLayout> static void test_tensor_maps() { int inputs[2 * 3 * 5 * 7]; TensorMap<Tensor<int, 4, DataLayout> > tensor_map(inputs, 2, 3, 5, 7); TensorMap<Tensor<const int, 4, DataLayout> > tensor_map_const(inputs, 2, 3, 5, 7); const TensorMap<Tensor<const int, 4, DataLayout> > tensor_map_const_const( inputs, 2, 3, 5, 7); tensor_map.setRandom(); array<ptrdiff_t, 2> reduction_axis; reduction_axis[0] = 1; reduction_axis[1] = 3; Tensor<int, 2, DataLayout> result = tensor_map.sum(reduction_axis); Tensor<int, 2, DataLayout> result2 = tensor_map_const.sum(reduction_axis); Tensor<int, 2, DataLayout> result3 = tensor_map_const_const.sum(reduction_axis); for (int i = 0; i < 2; ++i) { for (int j = 0; j < 5; ++j) { int sum = 0; for (int k = 0; k < 3; ++k) { for (int l = 0; l < 7; ++l) { sum += tensor_map(i, k, j, l); } } VERIFY_IS_EQUAL(result(i, j), sum); VERIFY_IS_EQUAL(result2(i, j), sum); VERIFY_IS_EQUAL(result3(i, j), sum); } } } template <int DataLayout> static void test_static_dims() { Tensor<float, 4, DataLayout> in(72, 53, 97, 113); Tensor<float, 2, DataLayout> out(72, 97); in.setRandom(); #if !EIGEN_HAS_CONSTEXPR array<int, 2> reduction_axis; reduction_axis[0] = 1; reduction_axis[1] = 3; #else Eigen::IndexList<Eigen::type2index<1>, Eigen::type2index<3> > reduction_axis; #endif out = in.maximum(reduction_axis); for (int i = 0; i < 72; ++i) { for (int j = 0; j < 97; ++j) { float expected = -1e10f; for (int k = 0; k < 53; ++k) { for (int l = 0; l < 113; ++l) { expected = (std::max)(expected, in(i, k, j, l)); } } VERIFY_IS_APPROX(out(i, j), expected); } } } template <int DataLayout> static void test_innermost_last_dims() { Tensor<float, 4, DataLayout> in(72, 53, 97, 113); Tensor<float, 2, DataLayout> out(97, 113); in.setRandom(); // Reduce on the innermost dimensions. #if !EIGEN_HAS_CONSTEXPR array<int, 2> reduction_axis; reduction_axis[0] = 0; reduction_axis[1] = 1; #else // This triggers the use of packets for ColMajor. Eigen::IndexList<Eigen::type2index<0>, Eigen::type2index<1> > reduction_axis; #endif out = in.maximum(reduction_axis); for (int i = 0; i < 97; ++i) { for (int j = 0; j < 113; ++j) { float expected = -1e10f; for (int k = 0; k < 53; ++k) { for (int l = 0; l < 72; ++l) { expected = (std::max)(expected, in(l, k, i, j)); } } VERIFY_IS_APPROX(out(i, j), expected); } } } template <int DataLayout> static void test_innermost_first_dims() { Tensor<float, 4, DataLayout> in(72, 53, 97, 113); Tensor<float, 2, DataLayout> out(72, 53); in.setRandom(); // Reduce on the innermost dimensions. #if !EIGEN_HAS_CONSTEXPR array<int, 2> reduction_axis; reduction_axis[0] = 2; reduction_axis[1] = 3; #else // This triggers the use of packets for RowMajor. Eigen::IndexList<Eigen::type2index<2>, Eigen::type2index<3>> reduction_axis; #endif out = in.maximum(reduction_axis); for (int i = 0; i < 72; ++i) { for (int j = 0; j < 53; ++j) { float expected = -1e10f; for (int k = 0; k < 97; ++k) { for (int l = 0; l < 113; ++l) { expected = (std::max)(expected, in(i, j, k, l)); } } VERIFY_IS_APPROX(out(i, j), expected); } } } template <int DataLayout> static void test_reduce_middle_dims() { Tensor<float, 4, DataLayout> in(72, 53, 97, 113); Tensor<float, 2, DataLayout> out(72, 53); in.setRandom(); // Reduce on the innermost dimensions. #if !EIGEN_HAS_CONSTEXPR array<int, 2> reduction_axis; reduction_axis[0] = 1; reduction_axis[1] = 2; #else // This triggers the use of packets for RowMajor. Eigen::IndexList<Eigen::type2index<1>, Eigen::type2index<2>> reduction_axis; #endif out = in.maximum(reduction_axis); for (int i = 0; i < 72; ++i) { for (int j = 0; j < 113; ++j) { float expected = -1e10f; for (int k = 0; k < 53; ++k) { for (int l = 0; l < 97; ++l) { expected = (std::max)(expected, in(i, k, l, j)); } } VERIFY_IS_APPROX(out(i, j), expected); } } } void test_cxx11_tensor_reduction() { CALL_SUBTEST(test_trivial_reductions<ColMajor>()); CALL_SUBTEST(test_trivial_reductions<RowMajor>()); CALL_SUBTEST(test_simple_reductions<ColMajor>()); CALL_SUBTEST(test_simple_reductions<RowMajor>()); CALL_SUBTEST(test_reductions_in_expr<ColMajor>()); CALL_SUBTEST(test_reductions_in_expr<RowMajor>()); CALL_SUBTEST(test_full_reductions<ColMajor>()); CALL_SUBTEST(test_full_reductions<RowMajor>()); CALL_SUBTEST(test_user_defined_reductions<ColMajor>()); CALL_SUBTEST(test_user_defined_reductions<RowMajor>()); CALL_SUBTEST(test_tensor_maps<ColMajor>()); CALL_SUBTEST(test_tensor_maps<RowMajor>()); CALL_SUBTEST(test_static_dims<ColMajor>()); CALL_SUBTEST(test_static_dims<RowMajor>()); CALL_SUBTEST(test_innermost_last_dims<ColMajor>()); CALL_SUBTEST(test_innermost_last_dims<RowMajor>()); CALL_SUBTEST(test_innermost_first_dims<ColMajor>()); CALL_SUBTEST(test_innermost_first_dims<RowMajor>()); CALL_SUBTEST(test_reduce_middle_dims<ColMajor>()); CALL_SUBTEST(test_reduce_middle_dims<RowMajor>()); }