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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
View File

@@ -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) {

11
tests/cpp/common/test_linalg.cu
View File

@@ -11,17 +11,18 @@
namespace xgboost::linalg {
namespace {
void TestElementWiseKernel() {
auto device = DeviceOrd::CUDA(0);
Tensor<float, 3> l{{2, 3, 4}, 0};
{
/**
* Non-contiguous
*/
// GPU view
auto t = l.View(0).Slice(linalg::All(), 1, linalg::All());
auto t = l.View(device).Slice(linalg::All(), 1, linalg::All());
ASSERT_FALSE(t.CContiguous());
ElementWiseTransformDevice(t, [] __device__(size_t i, float) { return i; });
// CPU view
t = l.View(Context::kCpuId).Slice(linalg::All(), 1, linalg::All());
t = l.View(DeviceOrd::CPU()).Slice(linalg::All(), 1, linalg::All());
size_t k = 0;
for (size_t i = 0; i < l.Shape(0); ++i) {
for (size_t j = 0; j < l.Shape(2); ++j) {
@@ -29,7 +30,7 @@ void TestElementWiseKernel() {
}
}
t = l.View(0).Slice(linalg::All(), 1, linalg::All());
t = l.View(device).Slice(linalg::All(), 1, linalg::All());
ElementWiseKernelDevice(t, [] XGBOOST_DEVICE(size_t i, float v) { SPAN_CHECK(v == i); });
}
@@ -37,11 +38,11 @@ void TestElementWiseKernel() {
/**
* Contiguous
*/
auto t = l.View(0);
auto t = l.View(device);
ElementWiseTransformDevice(t, [] XGBOOST_DEVICE(size_t i, float) { return i; });
ASSERT_TRUE(t.CContiguous());
// CPU view
t = l.View(Context::kCpuId);
t = l.View(DeviceOrd::CPU());
size_t ind = 0;
for (size_t i = 0; i < l.Shape(0); ++i) {

2
tests/cpp/common/test_ranking_utils.cu
View File

@@ -41,7 +41,7 @@ void TestCalcQueriesInvIDCG() {
p.UpdateAllowUnknown(Args{{"ndcg_exp_gain", "false"}});
cuda_impl::CalcQueriesInvIDCG(&ctx, linalg::MakeTensorView(&ctx, d_scores, d_scores.size()),
dh::ToSpan(group_ptr), inv_IDCG.View(ctx.gpu_id), p);
dh::ToSpan(group_ptr), inv_IDCG.View(ctx.Device()), p);
for (std::size_t i = 0; i < n_groups; ++i) {
double inv_idcg = inv_IDCG(i);
ASSERT_NEAR(inv_idcg, 0.00551782, kRtEps);

14
tests/cpp/common/test_stats.cu
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@@ -47,7 +47,7 @@ class StatsGPU : public ::testing::Test {
data.insert(data.cend(), seg.begin(), seg.end());
data.insert(data.cend(), seg.begin(), seg.end());
linalg::Tensor<float, 1> arr{data.cbegin(), data.cend(), {data.size()}, 0};
auto d_arr = arr.View(0);
auto d_arr = arr.View(DeviceOrd::CUDA(0));
auto key_it = dh::MakeTransformIterator<std::size_t>(
thrust::make_counting_iterator(0ul),
@@ -71,8 +71,8 @@ class StatsGPU : public ::testing::Test {
}
void Weighted() {
auto d_arr = arr_.View(0);
auto d_key = indptr_.View(0);
auto d_arr = arr_.View(DeviceOrd::CUDA(0));
auto d_key = indptr_.View(DeviceOrd::CUDA(0));
auto key_it = dh::MakeTransformIterator<std::size_t>(
thrust::make_counting_iterator(0ul),
@@ -81,7 +81,7 @@ class StatsGPU : public ::testing::Test {
dh::MakeTransformIterator<float>(thrust::make_counting_iterator(0ul),
[=] XGBOOST_DEVICE(std::size_t i) { return d_arr(i); });
linalg::Tensor<float, 1> weights{{10}, 0};
linalg::ElementWiseTransformDevice(weights.View(0),
linalg::ElementWiseTransformDevice(weights.View(DeviceOrd::CUDA(0)),
[=] XGBOOST_DEVICE(std::size_t, float) { return 1.0; });
auto w_it = weights.Data()->ConstDevicePointer();
for (auto const& pair : TestSet{{0.0f, 1.0f}, {0.5f, 3.0f}, {1.0f, 5.0f}}) {
@@ -102,7 +102,7 @@ class StatsGPU : public ::testing::Test {
data.insert(data.cend(), seg.begin(), seg.end());
data.insert(data.cend(), seg.begin(), seg.end());
linalg::Tensor<float, 1> arr{data.cbegin(), data.cend(), {data.size()}, 0};
auto d_arr = arr.View(0);
auto d_arr = arr.View(DeviceOrd::CUDA(0));
auto key_it = dh::MakeTransformIterator<std::size_t>(
thrust::make_counting_iterator(0ul),
@@ -125,8 +125,8 @@ class StatsGPU : public ::testing::Test {
}
void NonWeighted() {
auto d_arr = arr_.View(0);
auto d_key = indptr_.View(0);
auto d_arr = arr_.View(DeviceOrd::CUDA(0));
auto d_key = indptr_.View(DeviceOrd::CUDA(0));
auto key_it = dh::MakeTransformIterator<std::size_t>(
thrust::make_counting_iterator(0ul), [=] __device__(std::size_t i) { return d_key(i); });