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Use the new DeviceOrd in the linalg module. (#9527)

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This commit is contained in:
Jiaming Yuan
2023-08-29 13:37:29 +08:00
committed by GitHub
parent 942b957eef
commit ddf2e68821
43 changed files with 252 additions and 273 deletions
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46
tests/cpp/common/test_linalg.cc
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@@ -3,7 +3,7 @@
*/
#include <gtest/gtest.h>
#include <xgboost/context.h>
#include <xgboost/host_device_vector.h>
#include <xgboost/host_device_vector.h> // for HostDeviceVector
#include <xgboost/linalg.h>
#include <cstddef> // size_t
@@ -14,8 +14,8 @@
namespace xgboost::linalg {
namespace {
auto kCpuId = Context::kCpuId;
}
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);
@@ -23,7 +23,7 @@ auto MakeMatrixFromTest(HostDeviceVector<float> *storage, std::size_t n_rows, st
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)}, -1};
auto m = linalg::TensorView<float, 2>{h_storage, {n_rows, static_cast<size_t>(n_cols)}, CPU()};
return m;
}
@@ -31,7 +31,7 @@ TEST(Linalg, MatrixView) {
size_t kRows = 31, kCols = 77;
HostDeviceVector<float> storage;
auto m = MakeMatrixFromTest(&storage, kRows, kCols);
ASSERT_EQ(m.DeviceIdx(), kCpuId);
ASSERT_EQ(m.Device(), CPU());
ASSERT_EQ(m(0, 0), 0);
ASSERT_EQ(m(kRows - 1, kCols - 1), storage.Size() - 1);
}
@@ -76,7 +76,7 @@ TEST(Linalg, TensorView) {
{
// as vector
TensorView<double, 1> vec{data, {data.size()}, -1};
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);
@@ -87,7 +87,7 @@ TEST(Linalg, TensorView) {
{
// as matrix
TensorView<double, 2> mat(data, {6, 4}, -1);
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);
@@ -96,7 +96,7 @@ TEST(Linalg, TensorView) {
{
// assignment
TensorView<double, 3> t{data, {2, 3, 4}, 0};
TensorView<double, 3> t{data, {2, 3, 4}, CPU()};
double pi = 3.14159;
auto old = t(1, 2, 3);
t(1, 2, 3) = pi;
@@ -201,7 +201,7 @@ TEST(Linalg, TensorView) {
}
{
// f-contiguous
TensorView<double, 3> t{data, {4, 3, 2}, {1, 4, 12}, kCpuId};
TensorView<double, 3> t{data, {4, 3, 2}, {1, 4, 12}, CPU()};
ASSERT_TRUE(t.Contiguous());
ASSERT_TRUE(t.FContiguous());
ASSERT_FALSE(t.CContiguous());
@@ -210,11 +210,11 @@ TEST(Linalg, TensorView) {
TEST(Linalg, Tensor) {
{
Tensor<float, 3> t{{2, 3, 4}, kCpuId, Order::kC};
auto view = t.View(kCpuId);
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(kCpuId);
auto k_view = as_const.View(CPU());
size_t n = 2 * 3 * 4;
ASSERT_EQ(t.Size(), n);
@@ -229,7 +229,7 @@ TEST(Linalg, Tensor) {
}
{
// Reshape
Tensor<float, 3> t{{2, 3, 4}, kCpuId, Order::kC};
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);
@@ -247,7 +247,7 @@ TEST(Linalg, Tensor) {
TEST(Linalg, Empty) {
{
auto t = TensorView<double, 2>{{}, {0, 3}, kCpuId, Order::kC};
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);
@@ -256,9 +256,9 @@ TEST(Linalg, Empty) {
}
}
{
auto t = Tensor<double, 2>{{0, 3}, kCpuId, Order::kC};
auto t = Tensor<double, 2>{{0, 3}, CPU(), Order::kC};
ASSERT_EQ(t.Size(), 0);
auto view = t.View(kCpuId);
auto view = t.View(CPU());
for (int32_t i : {0, 1, 2}) {
auto s = view.Slice(All(), i);
@@ -270,7 +270,7 @@ TEST(Linalg, Empty) {
}
TEST(Linalg, ArrayInterface) {
auto cpu = kCpuId;
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);
@@ -315,16 +315,16 @@ TEST(Linalg, Popc) {
}
TEST(Linalg, Stack) {
Tensor<float, 3> l{{2, 3, 4}, kCpuId, Order::kC};
ElementWiseTransformHost(l.View(kCpuId), omp_get_max_threads(),
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}, kCpuId, Order::kC};
ElementWiseTransformHost(r_0.View(kCpuId), omp_get_max_threads(),
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}, kCpuId, Order::kC};
Tensor<float, 3> r_1{{0, 3, 4}, CPU(), Order::kC};
Stack(&l, r_1);
ASSERT_EQ(l.Shape(0), 4);
@@ -335,7 +335,7 @@ TEST(Linalg, Stack) {
TEST(Linalg, FOrder) {
std::size_t constexpr kRows = 16, kCols = 3;
std::vector<float> data(kRows * kCols);
MatrixView<float> mat{data, {kRows, kCols}, Context::kCpuId, Order::kF};
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) {