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/**
* Copyright 2019-2024, XGBoost Contributors
*/
#include <gtest/gtest.h>
#include <thrust/device_vector.h>
#include <thrust/sort.h> // for sort
#include <thrust/unique.h> // for unique
#include <xgboost/base.h>
#include <xgboost/tree_model.h> // for RegTree
#include <cstddef> // for size_t
#include <cstdint> // for uint32_t
#include <iterator> // for distance
#include <vector> // for vector
#include "../../../../src/data/ellpack_page.cuh"
#include "../../../../src/tree/gpu_hist/expand_entry.cuh" // for GPUExpandEntry
#include "../../../../src/tree/gpu_hist/row_partitioner.cuh"
#include "../../../../src/tree/param.h" // for TrainParam
#include "../../collective/test_worker.h" // for TestDistributedGlobal
#include "../../helpers.h" // for RandomDataGenerator
namespace xgboost::tree {
void TestUpdatePositionBatch() {
const int kNumRows = 10;
auto ctx = MakeCUDACtx(0);
RowPartitioner rp;
rp.Reset(&ctx, kNumRows, 0);
auto rows = rp.GetRowsHost(0);
EXPECT_EQ(rows.size(), kNumRows);
for (auto i = 0ull; i < kNumRows; i++) {
EXPECT_EQ(rows[i], i);
}
std::vector<int> extra_data = {0};
// Send the first five training instances to the right node
// and the second 5 to the left node
rp.UpdatePositionBatch(
&ctx, {0}, {1}, {2}, extra_data,
[=] __device__(RowPartitioner::RowIndexT ridx, int, int) { return ridx > 4; });
rows = rp.GetRowsHost(1);
for (auto r : rows) {
EXPECT_GT(r, 4);
}
rows = rp.GetRowsHost(2);
for (auto r : rows) {
EXPECT_LT(r, 5);
}
// Split the left node again
rp.UpdatePositionBatch(
&ctx, {1}, {3}, {4}, extra_data,
[=] __device__(RowPartitioner::RowIndexT ridx, int, int) { return ridx < 7; });
EXPECT_EQ(rp.GetRows(3).size(), 2);
EXPECT_EQ(rp.GetRows(4).size(), 3);
}
TEST(RowPartitioner, Batch) { TestUpdatePositionBatch(); }
void TestSortPositionBatch(const std::vector<int>& ridx_in, const std::vector<Segment>& segments) {
auto ctx = MakeCUDACtx(0);
thrust::device_vector<cuda_impl::RowIndexT> ridx = ridx_in;
thrust::device_vector<cuda_impl::RowIndexT> ridx_tmp(ridx_in.size());
thrust::device_vector<cuda_impl::RowIndexT> counts(segments.size());
auto op = [=] __device__(auto ridx, int split_index, int data) {
return ridx % 2 == 0;
};
std::vector<int> op_data(segments.size());
std::vector<PerNodeData<int>> h_batch_info(segments.size());
dh::TemporaryArray<PerNodeData<int>> d_batch_info(segments.size());
std::size_t total_rows = 0;
for (size_t i = 0; i < segments.size(); i++) {
h_batch_info[i] = {segments.at(i), 0};
total_rows += segments.at(i).Size();
}
dh::safe_cuda(cudaMemcpyAsync(d_batch_info.data().get(), h_batch_info.data(),
h_batch_info.size() * sizeof(PerNodeData<int>), cudaMemcpyDefault,
nullptr));
dh::DeviceUVector<std::int8_t> tmp;
SortPositionBatch<decltype(op), int>(&ctx, dh::ToSpan(d_batch_info), dh::ToSpan(ridx),
dh::ToSpan(ridx_tmp), dh::ToSpan(counts), total_rows, op,
&tmp);
auto op_without_data = [=] __device__(auto ridx) {
return ridx % 2 == 0;
};
for (size_t i = 0; i < segments.size(); i++) {
auto begin = ridx.begin() + segments[i].begin;
auto end = ridx.begin() + segments[i].end;
bst_uint count = counts[i];
auto left_partition_count =
thrust::count_if(thrust::device, begin, begin + count, op_without_data);
EXPECT_EQ(left_partition_count, count);
auto right_partition_count =
thrust::count_if(thrust::device, begin + count, end, op_without_data);
EXPECT_EQ(right_partition_count, 0);
}
}
TEST(RowPartitioner, SortPositionBatch) {
TestSortPositionBatch({0, 1, 2, 3, 4, 5}, {{0, 3}, {3, 6}});
TestSortPositionBatch({0, 1, 2, 3, 4, 5}, {{0, 1}, {3, 6}});
TestSortPositionBatch({0, 1, 2, 3, 4, 5}, {{0, 6}});
TestSortPositionBatch({0, 1, 2, 3, 4, 5}, {{3, 6}, {0, 2}});
}
namespace {
void GetSplit(RegTree* tree, float split_value, std::vector<GPUExpandEntry>* candidates) {
CHECK(!tree->IsMultiTarget());
tree->ExpandNode(
/*nid=*/RegTree::kRoot, /*split_index=*/0, /*split_value=*/split_value,
/*default_left=*/true, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f,
/*left_sum=*/0.0f,
/*right_sum=*/0.0f);
candidates->front().nid = 0;
candidates->front().depth = 0;
candidates->front().split.fvalue = split_value;
candidates->front().split.findex = 0;
}
void TestExternalMemory() {
auto ctx = MakeCUDACtx(0);
bst_bin_t max_bin = 32;
auto p_fmat =
RandomDataGenerator{256, 16, 0.0f}.Batches(4).GenerateSparsePageDMatrix("temp", true);
std::vector<std::unique_ptr<RowPartitioner>> partitioners;
RegTree tree;
std::vector<GPUExpandEntry> candidates(1);
auto param = BatchParam{max_bin, TrainParam::DftSparseThreshold()};
float split_value{0.0f};
bst_feature_t const split_ind = 0;
dh::device_vector<bst_node_t> position(p_fmat->Info().num_row_, 0);
auto encode_op = [=] __device__(bst_idx_t, bst_node_t nidx) {
return nidx;
}; // NOLINT
for (auto const& page : p_fmat->GetBatches<EllpackPage>(&ctx, param)) {
if (partitioners.empty()) {
auto ptr = page.Impl()->Cuts().Ptrs()[split_ind + 1];
split_value = page.Impl()->Cuts().Values().at(ptr / 2);
GetSplit(&tree, split_value, &candidates);
}
partitioners.emplace_back(std::make_unique<RowPartitioner>());
partitioners.back()->Reset(&ctx, page.Size(), page.BaseRowId());
std::vector<RegTree::Node> splits{tree[0]};
auto acc = page.Impl()->GetDeviceAccessor(&ctx);
partitioners.back()->UpdatePositionBatch(
&ctx, {0}, {1}, {2}, splits,
[=] __device__(bst_idx_t ridx, std::int32_t nidx_in_batch, RegTree::Node const& node) {
auto fvalue = acc.GetFvalue(ridx, node.SplitIndex());
return fvalue <= node.SplitCond();
});
partitioners.back()->FinalisePosition(
&ctx, dh::ToSpan(position).subspan(page.BaseRowId(), page.Size()), page.BaseRowId(),
encode_op);
}
bst_idx_t n_left{0};
for (auto const& page : p_fmat->GetBatches<SparsePage>()) {
auto batch = page.GetView();
for (size_t i = 0; i < batch.Size(); ++i) {
if (batch[i][split_ind].fvalue < split_value) {
n_left++;
}
}
}
RegTree::Node node = tree[RegTree::kRoot];
auto n_left_pos =
thrust::count_if(position.cbegin(), position.cend(),
[=] XGBOOST_DEVICE(bst_node_t v) { return v == node.LeftChild(); });
ASSERT_EQ(n_left, n_left_pos);
thrust::sort(position.begin(), position.end());
auto end_it = thrust::unique(position.begin(), position.end());
ASSERT_EQ(std::distance(position.begin(), end_it), 2);
}
} // anonymous namespace
TEST(RowPartitioner, LeafPartitionExternalMemory) { TestExternalMemory(); }
namespace {
void TestEmptyNode(std::int32_t n_workers) {
collective::TestDistributedGlobal(n_workers, [] {
auto ctx = MakeCUDACtx(DistGpuIdx());
RowPartitioner partitioner;
bst_idx_t n_samples = (collective::GetRank() == 0) ? 0 : 1024;
bst_idx_t base_rowid = 0;
partitioner.Reset(&ctx, n_samples, base_rowid);
std::vector<RegTree::Node> splits(1);
partitioner.UpdatePositionBatch(
&ctx, {0}, {1}, {2}, splits,
[] XGBOOST_DEVICE(bst_idx_t ridx, std::int32_t /*nidx_in_batch*/, RegTree::Node) {
return ridx < 3;
});
ASSERT_EQ(partitioner.GetNumNodes(), 3);
if (collective::GetRank() == 0) {
for (std::size_t i = 0; i < 3; ++i) {
ASSERT_TRUE(partitioner.GetRows(i).empty());
}
}
ctx.CUDACtx()->Stream().Sync();
});
}
} // anonymous namespace
TEST(RowPartitioner, MGPUEmpty) {
std::int32_t n_workers = curt::AllVisibleGPUs();
TestEmptyNode(n_workers);
}
} // namespace xgboost::tree
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