a5a58102e5
This PR replaces the original RABIT implementation with a new one, which has already been partially merged into XGBoost. The new one features: - Federated learning for both CPU and GPU. - NCCL. - More data types. - A unified interface for all the underlying implementations. - Improved timeout handling for both tracker and workers. - Exhausted tests with metrics (fixed a couple of bugs along the way). - A reusable tracker for Python and JVM packages.
58 lines
1.6 KiB
C++
58 lines
1.6 KiB
C++
/**
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* Copyright 2022-2024, XGBoost Contributors
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*/
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#include <gtest/gtest.h>
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#include <xgboost/linalg.h>
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#include "../../src/common/linalg_op.h"
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#include "../../src/tree/fit_stump.h"
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#include "../collective/test_worker.h" // for TestDistributedGlobal
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#include "../helpers.h"
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namespace xgboost::tree {
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namespace {
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void TestFitStump(Context const *ctx, DataSplitMode split = DataSplitMode::kRow) {
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std::size_t constexpr kRows = 16, kTargets = 2;
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linalg::Matrix<GradientPair> gpair;
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gpair.SetDevice(ctx->Device());
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gpair.Reshape(kRows, kTargets);
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auto h_gpair = gpair.HostView();
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for (std::size_t i = 0; i < kRows; ++i) {
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for (std::size_t t = 0; t < kTargets; ++t) {
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h_gpair(i, t) = GradientPair{static_cast<float>(i), 1};
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}
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}
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linalg::Vector<float> out;
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MetaInfo info;
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info.data_split_mode = split;
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FitStump(ctx, info, gpair, kTargets, &out);
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auto h_out = out.HostView();
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for (auto it = linalg::cbegin(h_out); it != linalg::cend(h_out); ++it) {
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// sum_hess == kRows
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auto n = static_cast<float>(kRows);
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auto sum_grad = n * (n - 1) / 2;
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ASSERT_EQ(static_cast<float>(-sum_grad / n), *it);
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}
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}
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} // anonymous namespace
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TEST(InitEstimation, FitStump) {
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Context ctx;
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TestFitStump(&ctx);
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}
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#if defined(XGBOOST_USE_CUDA)
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TEST(InitEstimation, GPUFitStump) {
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Context ctx;
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ctx.UpdateAllowUnknown(Args{{"device", "cuda"}});
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TestFitStump(&ctx);
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}
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#endif // defined(XGBOOST_USE_CUDA)
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TEST(InitEstimation, FitStumpColumnSplit) {
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Context ctx;
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auto constexpr kWorldSize{3};
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collective::TestDistributedGlobal(kWorldSize, [&] { TestFitStump(&ctx, DataSplitMode::kCol); });
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}
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} // namespace xgboost::tree
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