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