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test_device_helpers.cu
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test_device_helpers.cu
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/*!
* Copyright 2017 XGBoost contributors
*/
#include <thrust/device_vector.h>
#include <xgboost/base.h>
#include "../../../src/common/device_helpers.cuh"
#include "../../../src/common/quantile.h"
#include "../helpers.h"
#include "gtest/gtest.h"
TEST(SumReduce, Test) {
thrust::device_vector<float> data(100, 1.0f);
auto sum = dh::SumReduction(data.data().get(), data.size());
ASSERT_NEAR(sum, 100.0f, 1e-5);
}
void TestAtomicSizeT() {
size_t constexpr kThreads = 235;
dh::device_vector<size_t> out(1, 0);
auto d_out = dh::ToSpan(out);
dh::LaunchN(0, kThreads, [=]__device__(size_t idx){
atomicAdd(&d_out[0], static_cast<size_t>(1));
});
ASSERT_EQ(out[0], kThreads);
}
TEST(AtomicAdd, SizeT) {
TestAtomicSizeT();
}
void TestSegmentID() {
std::vector<size_t> segments{0, 1, 3};
thrust::device_vector<size_t> d_segments(segments);
auto s_segments = dh::ToSpan(d_segments);
dh::LaunchN(0, 1, [=]__device__(size_t idx) {
auto id = dh::SegmentId(s_segments, 0);
SPAN_CHECK(id == 0);
id = dh::SegmentId(s_segments, 1);
SPAN_CHECK(id == 1);
id = dh::SegmentId(s_segments, 2);
SPAN_CHECK(id == 1);
});
}
TEST(SegmentID, Basic) {
TestSegmentID();
}
TEST(SegmentedUnique, Basic) {
std::vector<float> values{0.1f, 0.2f, 0.3f, 0.62448811531066895f, 0.62448811531066895f, 0.4f};
std::vector<size_t> segments{0, 3, 6};
thrust::device_vector<float> d_values(values);
thrust::device_vector<xgboost::bst_feature_t> d_segments{segments};
thrust::device_vector<xgboost::bst_feature_t> d_segs_out(d_segments.size());
thrust::device_vector<float> d_vals_out(d_values.size());
size_t n_uniques = dh::SegmentedUnique(
d_segments.data().get(), d_segments.data().get() + d_segments.size(),
d_values.data().get(), d_values.data().get() + d_values.size(),
d_segs_out.data().get(), d_vals_out.data().get(),
thrust::equal_to<float>{});
CHECK_EQ(n_uniques, 5);
std::vector<float> values_sol{0.1f, 0.2f, 0.3f, 0.62448811531066895f, 0.4f};
for (auto i = 0 ; i < values_sol.size(); i ++) {
ASSERT_EQ(d_vals_out[i], values_sol[i]);
}
std::vector<xgboost::bst_feature_t> segments_sol{0, 3, 5};
for (size_t i = 0; i < d_segments.size(); ++i) {
ASSERT_EQ(segments_sol[i], d_segs_out[i]);
}
d_segments[1] = 4;
d_segments[2] = 6;
n_uniques = dh::SegmentedUnique(
d_segments.data().get(), d_segments.data().get() + d_segments.size(),
d_values.data().get(), d_values.data().get() + d_values.size(),
d_segs_out.data().get(), d_vals_out.data().get(),
thrust::equal_to<float>{});
ASSERT_EQ(n_uniques, values.size());
for (auto i = 0 ; i < values.size(); i ++) {
ASSERT_EQ(d_vals_out[i], values[i]);
}
}
namespace {
using SketchEntry = xgboost::common::WQSummary<float, float>::Entry;
struct SketchUnique {
bool __device__ operator()(SketchEntry const& a, SketchEntry const& b) const {
return a.value - b.value == 0;
}
};
struct IsSorted {
bool __device__ operator()(SketchEntry const& a, SketchEntry const& b) const {
return a.value < b.value;
}
};
} // namespace
namespace xgboost {
namespace common {
void TestSegmentedUniqueRegression(std::vector<SketchEntry> values, size_t n_duplicated) {
std::vector<bst_feature_t> segments{0, static_cast<bst_feature_t>(values.size())};
thrust::device_vector<SketchEntry> d_values(values);
thrust::device_vector<bst_feature_t> d_segments(segments);
thrust::device_vector<bst_feature_t> d_segments_out(segments.size());
size_t n_uniques = dh::SegmentedUnique(
d_segments.data().get(), d_segments.data().get() + d_segments.size(), d_values.data().get(),
d_values.data().get() + d_values.size(), d_segments_out.data().get(), d_values.data().get(),
SketchUnique{});
ASSERT_EQ(n_uniques, values.size() - n_duplicated);
ASSERT_TRUE(thrust::is_sorted(thrust::device, d_values.begin(),
d_values.begin() + n_uniques, IsSorted{}));
ASSERT_EQ(segments.at(0), d_segments_out[0]);
ASSERT_EQ(segments.at(1), d_segments_out[1] + n_duplicated);
}
TEST(DeviceHelpers, Reduce) {
size_t kSize = std::numeric_limits<uint32_t>::max();
auto it = thrust::make_counting_iterator(0ul);
dh::XGBCachingDeviceAllocator<char> alloc;
auto batched = dh::Reduce(thrust::cuda::par(alloc), it, it + kSize, 0ul, thrust::maximum<size_t>{});
CHECK_EQ(batched, kSize - 1);
}
TEST(SegmentedUnique, Regression) {
{
std::vector<SketchEntry> values{{3149, 3150, 1, 0.62392902374267578},
{3151, 3152, 1, 0.62418866157531738},
{3152, 3153, 1, 0.62419462203979492},
{3153, 3154, 1, 0.62431186437606812},
{3154, 3155, 1, 0.6244881153106689453125},
{3155, 3156, 1, 0.6244881153106689453125},
{3155, 3156, 1, 0.6244881153106689453125},
{3155, 3156, 1, 0.6244881153106689453125},
{3157, 3158, 1, 0.62552797794342041},
{3158, 3159, 1, 0.6256556510925293},
{3159, 3160, 1, 0.62571090459823608},
{3160, 3161, 1, 0.62577134370803833}};
TestSegmentedUniqueRegression(values, 3);
}
{
std::vector<SketchEntry> values{{3149, 3150, 1, 0.62392902374267578},
{3151, 3152, 1, 0.62418866157531738},
{3152, 3153, 1, 0.62419462203979492},
{3153, 3154, 1, 0.62431186437606812},
{3154, 3155, 1, 0.6244881153106689453125},
{3157, 3158, 1, 0.62552797794342041},
{3158, 3159, 1, 0.6256556510925293},
{3159, 3160, 1, 0.62571090459823608},
{3160, 3161, 1, 0.62577134370803833}};
TestSegmentedUniqueRegression(values, 0);
}
{
std::vector<SketchEntry> values;
TestSegmentedUniqueRegression(values, 0);
}
}
} // namespace common
} // namespace xgboost