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xgboost/tests/cpp/common/test_linalg.cc

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/**
* Copyright 2021-2023 by XGBoost Contributors
*/
#include <gtest/gtest.h>
#include <xgboost/context.h>
#include <xgboost/host_device_vector.h> // for HostDeviceVector
#include <xgboost/linalg.h>
#include <cstddef> // size_t
#include <numeric> // iota
#include <vector>
#include "../../../src/common/linalg_op.h"
namespace xgboost::linalg {
namespace {
DeviceOrd CPU() { return DeviceOrd::CPU(); }
} // namespace
auto MakeMatrixFromTest(HostDeviceVector<float> *storage, std::size_t n_rows, std::size_t n_cols) {
storage->Resize(n_rows * n_cols);
auto &h_storage = storage->HostVector();
std::iota(h_storage.begin(), h_storage.end(), 0);
auto m = linalg::TensorView<float, 2>{h_storage, {n_rows, static_cast<size_t>(n_cols)}, CPU()};
return m;
}
TEST(Linalg, MatrixView) {
size_t kRows = 31, kCols = 77;
HostDeviceVector<float> storage;
auto m = MakeMatrixFromTest(&storage, kRows, kCols);
ASSERT_EQ(m.Device(), CPU());
ASSERT_EQ(m(0, 0), 0);
ASSERT_EQ(m(kRows - 1, kCols - 1), storage.Size() - 1);
}
TEST(Linalg, VectorView) {
size_t kRows = 31, kCols = 77;
HostDeviceVector<float> storage;
auto m = MakeMatrixFromTest(&storage, kRows, kCols);
auto v = m.Slice(linalg::All(), 3);
for (size_t i = 0; i < v.Size(); ++i) {
ASSERT_EQ(v(i), m(i, 3));
}
ASSERT_EQ(v(0), 3);
}
TEST(Linalg, TensorView) {
Context ctx;
std::vector<double> data(2 * 3 * 4, 0);
std::iota(data.begin(), data.end(), 0);
auto t = MakeTensorView(&ctx, data, 2, 3, 4);
ASSERT_EQ(t.Shape()[0], 2);
ASSERT_EQ(t.Shape()[1], 3);
ASSERT_EQ(t.Shape()[2], 4);
float v = t(0, 1, 2);
ASSERT_EQ(v, 6);
auto s = t.Slice(1, All(), All());
ASSERT_EQ(s.Shape().size(), 2);
ASSERT_EQ(s.Shape()[0], 3);
ASSERT_EQ(s.Shape()[1], 4);
std::vector<std::vector<double>> sol{
{12.0, 13.0, 14.0, 15.0}, {16.0, 17.0, 18.0, 19.0}, {20.0, 21.0, 22.0, 23.0}};
for (size_t i = 0; i < s.Shape()[0]; ++i) {
for (size_t j = 0; j < s.Shape()[1]; ++j) {
ASSERT_EQ(s(i, j), sol[i][j]);
}
}
{
// as vector
TensorView<double, 1> vec{data, {data.size()}, CPU()};
ASSERT_EQ(vec.Size(), data.size());
ASSERT_EQ(vec.Shape(0), data.size());
ASSERT_EQ(vec.Shape().size(), 1);
for (size_t i = 0; i < data.size(); ++i) {
ASSERT_EQ(vec(i), data[i]);
}
}
{
// as matrix
TensorView<double, 2> mat(data, {6, 4}, CPU());
auto s = mat.Slice(2, All());
ASSERT_EQ(s.Shape().size(), 1);
s = mat.Slice(All(), 1);
ASSERT_EQ(s.Shape().size(), 1);
}
{
// assignment
TensorView<double, 3> t{data, {2, 3, 4}, CPU()};
double pi = 3.14159;
auto old = t(1, 2, 3);
t(1, 2, 3) = pi;
ASSERT_EQ(t(1, 2, 3), pi);
t(1, 2, 3) = old;
ASSERT_EQ(t(1, 2, 3), old);
}
{
// Don't assign the initial dimension, tensor should be able to deduce the correct dim
// for Slice.
auto t = MakeTensorView(&ctx, data, 2, 3, 4);
auto s = t.Slice(1, 2, All());
static_assert(decltype(s)::kDimension == 1);
}
{
auto t = MakeTensorView(&ctx, data, 2, 3, 4);
auto s = t.Slice(1, linalg::All(), 1);
ASSERT_EQ(s(0), 13);
ASSERT_EQ(s(1), 17);
ASSERT_EQ(s(2), 21);
}
{
// range slice
auto t = MakeTensorView(&ctx, data, 2, 3, 4);
auto s = t.Slice(linalg::All(), linalg::Range(1, 3), 2);
static_assert(decltype(s)::kDimension == 2);
std::vector<double> sol{6, 10, 18, 22};
auto k = 0;
for (size_t i = 0; i < s.Shape(0); ++i) {
for (size_t j = 0; j < s.Shape(1); ++j) {
ASSERT_EQ(s(i, j), sol.at(k));
k++;
}
}
ASSERT_FALSE(s.CContiguous());
}
{
// range slice
auto t = MakeTensorView(&ctx, data, 2, 3, 4);
auto s = t.Slice(1, linalg::Range(1, 3), linalg::Range(1, 3));
static_assert(decltype(s)::kDimension == 2);
std::vector<double> sol{17, 18, 21, 22};
auto k = 0;
for (size_t i = 0; i < s.Shape(0); ++i) {
for (size_t j = 0; j < s.Shape(1); ++j) {
ASSERT_EQ(s(i, j), sol.at(k));
k++;
}
}
ASSERT_FALSE(s.CContiguous());
}
{
// same as no slice.
auto t = MakeTensorView(&ctx, data, 2, 3, 4);
auto s = t.Slice(linalg::All(), linalg::Range(0, 3), linalg::Range(0, 4));
static_assert(decltype(s)::kDimension == 3);
auto all = t.Slice(linalg::All(), linalg::All(), linalg::All());
for (size_t i = 0; i < s.Shape(0); ++i) {
for (size_t j = 0; j < s.Shape(1); ++j) {
for (size_t k = 0; k < s.Shape(2); ++k) {
ASSERT_EQ(s(i, j, k), all(i, j, k));
}
}
}
ASSERT_TRUE(s.CContiguous());
ASSERT_TRUE(all.CContiguous());
}
{
// copy and move constructor.
auto t = MakeTensorView(&ctx, data, 2, 3, 4);
auto from_copy = t;
auto from_move = std::move(t);
for (size_t i = 0; i < t.Shape().size(); ++i) {
ASSERT_EQ(from_copy.Shape(i), from_move.Shape(i));
ASSERT_EQ(from_copy.Stride(i), from_copy.Stride(i));
}
}
{
// multiple slices
auto t = MakeTensorView(&ctx, data, 2, 3, 4);
auto s_0 = t.Slice(linalg::All(), linalg::Range(0, 2), linalg::Range(1, 4));
ASSERT_FALSE(s_0.CContiguous());
auto s_1 = s_0.Slice(1, 1, linalg::Range(0, 2));
ASSERT_EQ(s_1.Size(), 2);
ASSERT_TRUE(s_1.CContiguous());
ASSERT_TRUE(s_1.Contiguous());
ASSERT_EQ(s_1(0), 17);
ASSERT_EQ(s_1(1), 18);
auto s_2 = s_0.Slice(1, linalg::All(), linalg::Range(0, 2));
std::vector<double> sol{13, 14, 17, 18};
auto k = 0;
for (size_t i = 0; i < s_2.Shape(0); i++) {
for (size_t j = 0; j < s_2.Shape(1); ++j) {
ASSERT_EQ(s_2(i, j), sol[k]);
k++;
}
}
}
{
// f-contiguous
TensorView<double, 3> t{data, {4, 3, 2}, {1, 4, 12}, CPU()};
ASSERT_TRUE(t.Contiguous());
ASSERT_TRUE(t.FContiguous());
ASSERT_FALSE(t.CContiguous());
}
}
TEST(Linalg, Tensor) {
{
Tensor<float, 3> t{{2, 3, 4}, CPU(), Order::kC};
auto view = t.View(CPU());
auto const &as_const = t;
auto k_view = as_const.View(CPU());
size_t n = 2 * 3 * 4;
ASSERT_EQ(t.Size(), n);
ASSERT_TRUE(
std::equal(k_view.Values().cbegin(), k_view.Values().cend(), view.Values().cbegin()));
Tensor<float, 3> t_0{std::move(t)};
ASSERT_EQ(t_0.Size(), n);
ASSERT_EQ(t_0.Shape(0), 2);
ASSERT_EQ(t_0.Shape(1), 3);
ASSERT_EQ(t_0.Shape(2), 4);
}
{
// Reshape
Tensor<float, 3> t{{2, 3, 4}, CPU(), Order::kC};
t.Reshape(4, 3, 2);
ASSERT_EQ(t.Size(), 24);
ASSERT_EQ(t.Shape(2), 2);
t.Reshape(1);
ASSERT_EQ(t.Size(), 1);
t.Reshape(0, 0, 0);
ASSERT_EQ(t.Size(), 0);
t.Reshape(0, 3, 0);
ASSERT_EQ(t.Size(), 0);
ASSERT_EQ(t.Shape(1), 3);
t.Reshape(3, 3, 3);
ASSERT_EQ(t.Size(), 27);
}
}
TEST(Linalg, Empty) {
{
auto t = TensorView<double, 2>{{}, {0, 3}, CPU(), Order::kC};
for (int32_t i : {0, 1, 2}) {
auto s = t.Slice(All(), i);
ASSERT_EQ(s.Size(), 0);
ASSERT_EQ(s.Shape().size(), 1);
ASSERT_EQ(s.Shape(0), 0);
}
}
{
auto t = Tensor<double, 2>{{0, 3}, CPU(), Order::kC};
ASSERT_EQ(t.Size(), 0);
auto view = t.View(CPU());
for (int32_t i : {0, 1, 2}) {
auto s = view.Slice(All(), i);
ASSERT_EQ(s.Size(), 0);
ASSERT_EQ(s.Shape().size(), 1);
ASSERT_EQ(s.Shape(0), 0);
}
}
}
TEST(Linalg, ArrayInterface) {
auto cpu = CPU();
auto t = Tensor<double, 2>{{3, 3}, cpu, Order::kC};
auto v = t.View(cpu);
std::iota(v.Values().begin(), v.Values().end(), 0);
auto arr = Json::Load(StringView{ArrayInterfaceStr(v)});
ASSERT_EQ(get<Integer>(arr["shape"][0]), 3);
ASSERT_EQ(get<Integer>(arr["strides"][0]), 3 * sizeof(double));
ASSERT_FALSE(get<Boolean>(arr["data"][1]));
ASSERT_EQ(reinterpret_cast<double *>(get<Integer>(arr["data"][0])), v.Values().data());
TensorView<double const, 2> as_const = v;
auto const_arr = ArrayInterface(as_const);
ASSERT_TRUE(get<Boolean>(const_arr["data"][1]));
}
TEST(Linalg, Popc) {
{
uint32_t v{0};
ASSERT_EQ(detail::NativePopc(v), 0);
ASSERT_EQ(detail::Popc(v), 0);
v = 1;
ASSERT_EQ(detail::NativePopc(v), 1);
ASSERT_EQ(detail::Popc(v), 1);
v = 0xffffffff;
ASSERT_EQ(detail::NativePopc(v), 32);
ASSERT_EQ(detail::Popc(v), 32);
}
{
uint64_t v{0};
ASSERT_EQ(detail::NativePopc(v), 0);
ASSERT_EQ(detail::Popc(v), 0);
v = 1;
ASSERT_EQ(detail::NativePopc(v), 1);
ASSERT_EQ(detail::Popc(v), 1);
v = 0xffffffff;
ASSERT_EQ(detail::NativePopc(v), 32);
ASSERT_EQ(detail::Popc(v), 32);
v = 0xffffffffffffffff;
ASSERT_EQ(detail::NativePopc(v), 64);
ASSERT_EQ(detail::Popc(v), 64);
}
}
TEST(Linalg, Stack) {
Tensor<float, 3> l{{2, 3, 4}, CPU(), Order::kC};
ElementWiseTransformHost(l.View(CPU()), omp_get_max_threads(),
[=](size_t i, float) { return i; });
Tensor<float, 3> r_0{{2, 3, 4}, CPU(), Order::kC};
ElementWiseTransformHost(r_0.View(CPU()), omp_get_max_threads(),
[=](size_t i, float) { return i; });
Stack(&l, r_0);
Tensor<float, 3> r_1{{0, 3, 4}, CPU(), Order::kC};
Stack(&l, r_1);
ASSERT_EQ(l.Shape(0), 4);
Stack(&r_1, l);
ASSERT_EQ(r_1.Shape(0), l.Shape(0));
}
TEST(Linalg, FOrder) {
std::size_t constexpr kRows = 16, kCols = 3;
std::vector<float> data(kRows * kCols);
MatrixView<float> mat{data, {kRows, kCols}, CPU(), Order::kF};
float k{0};
for (std::size_t i = 0; i < kRows; ++i) {
for (std::size_t j = 0; j < kCols; ++j) {
mat(i, j) = k;
k++;
}
}
auto column = mat.Slice(linalg::All(), 1);
ASSERT_TRUE(column.FContiguous());
ASSERT_EQ(column.Stride(0), 1);
ASSERT_TRUE(column.CContiguous());
k = 1;
for (auto it = linalg::cbegin(column); it != linalg::cend(column); ++it) {
ASSERT_EQ(*it, k);
k += kCols;
}
k = 1;
auto ptr = column.Values().data();
for (auto it = ptr; it != ptr + kRows; ++it) {
ASSERT_EQ(*it, k);
k += kCols;
}
}
} // namespace xgboost::linalg
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