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/**
* Copyright 2022-2024, XGBoost Contributors
*/
#include <gtest/gtest.h>
#include <xgboost/context.h>
#include <xgboost/linalg.h> // Tensor,Vector
#include <algorithm> // for min
#include <thread> // for thread
#include "../../../src/common/linalg_op.h" // for begin, end
#include "../../../src/common/stats.h"
#include "../../../src/common/transform_iterator.h" // common::MakeIndexTransformIter
#include "../collective/test_worker.h"
#include "../helpers.h"
namespace xgboost::common {
TEST(Stats, Quantile) {
Context ctx;
{
linalg::Tensor<float, 1> arr({20.f, 0.f, 15.f, 50.f, 40.f, 0.f, 35.f}, {7}, DeviceOrd::CPU());
std::vector<size_t> index{0, 2, 3, 4, 6};
auto h_arr = arr.HostView();
auto beg = MakeIndexTransformIter([&](size_t i) { return h_arr(index[i]); });
auto end = beg + index.size();
auto q = Quantile(&ctx, 0.40f, beg, end);
ASSERT_EQ(q, 26.0);
q = Quantile(&ctx, 0.20f, beg, end);
ASSERT_EQ(q, 16.0);
q = Quantile(&ctx, 0.10f, beg, end);
ASSERT_EQ(q, 15.0);
}
{
std::vector<float> vec{1., 2., 3., 4., 5.};
auto beg = MakeIndexTransformIter([&](size_t i) { return vec[i]; });
auto end = beg + vec.size();
auto q = Quantile(&ctx, 0.5f, beg, end);
ASSERT_EQ(q, 3.);
}
}
TEST(Stats, WeightedQuantile) {
Context ctx;
linalg::Tensor<float, 1> arr({1.f, 2.f, 3.f, 4.f, 5.f}, {5}, DeviceOrd::CPU());
linalg::Tensor<float, 1> weight({1.f, 1.f, 1.f, 1.f, 1.f}, {5}, DeviceOrd::CPU());
auto h_arr = arr.HostView();
auto h_weight = weight.HostView();
auto beg = MakeIndexTransformIter([&](size_t i) { return h_arr(i); });
auto end = beg + arr.Size();
auto w = MakeIndexTransformIter([&](size_t i) { return h_weight(i); });
auto q = WeightedQuantile(&ctx, 0.50f, beg, end, w);
ASSERT_EQ(q, 3);
q = WeightedQuantile(&ctx, 0.0, beg, end, w);
ASSERT_EQ(q, 1);
q = WeightedQuantile(&ctx, 1.0, beg, end, w);
ASSERT_EQ(q, 5);
}
TEST(Stats, Median) {
Context ctx;
{
linalg::Tensor<float, 2> values{{.0f, .0f, 1.f, 2.f}, {4}, DeviceOrd::CPU()};
HostDeviceVector<float> weights;
linalg::Tensor<float, 1> out;
Median(&ctx, values, weights, &out);
auto m = out(0);
ASSERT_EQ(m, .5f);
#if defined(XGBOOST_USE_CUDA)
ctx = ctx.MakeCUDA(0);
ASSERT_FALSE(ctx.IsCPU());
Median(&ctx, values, weights, &out);
m = out(0);
ASSERT_EQ(m, .5f);
#endif // defined(XGBOOST_USE_CUDA)
}
{
ctx = ctx.MakeCPU();
// 4x2 matrix
linalg::Tensor<float, 2> values{{0.f, 0.f, 0.f, 0.f, 1.f, 1.f, 2.f, 2.f}, {4, 2}, ctx.Device()};
HostDeviceVector<float> weights;
linalg::Tensor<float, 1> out;
Median(&ctx, values, weights, &out);
ASSERT_EQ(out(0), .5f);
ASSERT_EQ(out(1), .5f);
#if defined(XGBOOST_USE_CUDA)
ctx = ctx.MakeCUDA(0);
Median(&ctx, values, weights, &out);
ASSERT_EQ(out(0), .5f);
ASSERT_EQ(out(1), .5f);
#endif // defined(XGBOOST_USE_CUDA)
}
}
namespace {
void TestMean(Context const* ctx) {
std::size_t n{128};
linalg::Vector<float> data({n}, ctx->Device());
auto h_v = data.HostView().Values();
std::iota(h_v.begin(), h_v.end(), .0f);
auto nf = static_cast<float>(n);
float mean = nf * (nf - 1) / 2 / n;
linalg::Vector<float> res{{1}, ctx->Device()};
Mean(ctx, data, &res);
auto h_res = res.HostView();
ASSERT_EQ(h_res.Size(), 1);
ASSERT_EQ(mean, h_res(0));
}
} // anonymous namespace
TEST(Stats, Mean) {
Context ctx;
TestMean(&ctx);
}
#if defined(XGBOOST_USE_CUDA)
TEST(Stats, GpuMean) {
auto ctx = MakeCUDACtx(0);
TestMean(&ctx);
}
#endif // defined(XGBOOST_USE_CUDA)
namespace {
void TestSampleMean(Context const* ctx) {
std::size_t m{32}, n{16};
linalg::Matrix<float> data({m, n}, ctx->Device());
auto h_data = data.HostView();
std::iota(linalg::begin(h_data), linalg::end(h_data), .0f);
linalg::Vector<float> mean;
SampleMean(ctx, false, data, &mean);
ASSERT_FLOAT_EQ(mean(0), 248.0f);
for (std::size_t i = 1; i < mean.Size(); ++i) {
ASSERT_EQ(mean(i), mean(i - 1) + 1.0f);
}
auto device = ctx->Device();
std::int32_t n_workers =
device.IsCPU() ? std::min(4u, std::thread::hardware_concurrency()) : curt::AllVisibleGPUs();
#if !defined(XGBOOST_USE_NCCL)
if (device.IsCUDA()) {
return;
}
#endif // !defined(XGBOOST_USE_NCCL)
collective::TestDistributedGlobal(n_workers, [m, n, device, n_workers] {
auto rank = collective::GetRank();
Context ctx = device.IsCUDA() ? MakeCUDACtx(DistGpuIdx()) : Context{};
collective::GetWorkerLocalThreads(collective::GetWorldSize(), &ctx);
linalg::Matrix<float> data({m, n}, ctx.Device());
auto h_data = data.HostView();
for (std::size_t i = 0; i < m; ++i) {
for (std::size_t j = 0; j < n; ++j) {
h_data(i, j) = i + (m * rank) + j;
}
}
linalg::Vector<float> mean;
SampleMean(&ctx, false, data, &mean);
ASSERT_EQ(mean.Size(), n);
double total = n_workers * m;
for (std::size_t i = 0; i < n; ++i) {
ASSERT_EQ(mean(i), (i + total - 1.0 + i) * total / 2.0 / total);
}
});
}
void TestWeightedSampleMean(Context const* ctx) {
std::size_t m{32}, n{16};
{
auto data = linalg::Constant(ctx, 1.0f, m, n);
HostDeviceVector<float> w{m, 0.0f, ctx->Device()};
auto h_w = w.HostSpan();
std::iota(h_w.data(), h_w.data() + h_w.size(), 1.0f);
linalg::Vector<float> mean;
WeightedSampleMean(ctx, false, data, w, &mean);
for (auto v : mean.HostView()) {
ASSERT_FLOAT_EQ(v, 1.0f);
}
}
{
linalg::Matrix<float> data({m, n}, ctx->Device());
auto h_data = data.HostView();
std::iota(linalg::begin(h_data), linalg::end(h_data), .0f);
HostDeviceVector<float> w{m, 1.0f, ctx->Device()};
linalg::Vector<float> mean;
WeightedSampleMean(ctx, false, data, w, &mean);
ASSERT_FLOAT_EQ(mean(0), 248.0f);
for (std::size_t i = 1; i < mean.Size(); ++i) {
ASSERT_EQ(mean(i), mean(i - 1) + 1.0f);
}
}
auto device = ctx->Device();
std::int32_t n_workers =
device.IsCPU() ? std::min(4u, std::thread::hardware_concurrency()) : curt::AllVisibleGPUs();
#if !defined(XGBOOST_USE_NCCL)
if (device.IsCUDA()) {
return;
}
#endif // !defined(XGBOOST_USE_NCCL)
collective::TestDistributedGlobal(n_workers, [m, n, device, n_workers] {
auto rank = collective::GetRank();
Context ctx = device.IsCUDA() ? MakeCUDACtx(DistGpuIdx()) : Context{};
collective::GetWorkerLocalThreads(collective::GetWorldSize(), &ctx);
linalg::Matrix<float> data({m, n}, ctx.Device());
auto h_data = data.HostView();
for (std::size_t i = 0; i < m; ++i) {
for (std::size_t j = 0; j < n; ++j) {
h_data(i, j) = i + (m * rank) + j;
}
}
HostDeviceVector<float> w{m, 1.0f, ctx.Device()};
linalg::Vector<float> mean;
WeightedSampleMean(&ctx, false, data, w, &mean);
ASSERT_EQ(mean.Size(), n);
double total = n_workers * m;
for (std::size_t i = 0; i < n; ++i) {
ASSERT_EQ(mean(i), (i + total - 1.0 + i) * total / 2.0 / total);
}
});
}
} // namespace
TEST(Stats, SampleMean) {
Context ctx;
TestSampleMean(&ctx);
}
TEST(Stats, WeightedSampleMean) {
Context ctx;
TestWeightedSampleMean(&ctx);
}
#if defined(XGBOOST_USE_CUDA)
TEST(Stats, GpuSampleMean) {
auto ctx = MakeCUDACtx(0);
TestSampleMean(&ctx);
}
TEST(Stats, GpuWeightedSampleMean) {
auto ctx = MakeCUDACtx(0);
TestWeightedSampleMean(&ctx);
}
#endif // defined(XGBOOST_USE_CUDA)
} // namespace xgboost::common
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