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Fix compiler warnings. (#8022)

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- Remove/fix unused parameters
- Remove deprecated code in rabit.
- Update dmlc-core.
This commit is contained in:
Jiaming Yuan
2022-06-22 21:29:10 +08:00
committed by GitHub
parent e44a082620
commit 142a208a90
61 changed files with 230 additions and 579 deletions
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22
tests/cpp/common/test_hist_util.cc
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@@ -38,15 +38,16 @@ void ParallelGHistBuilderReset() {
target_hist[i] = collection[i];
}
common::BlockedSpace2d space(kNodes, [&](size_t node) { return kTasksPerNode; }, 1);
common::BlockedSpace2d space(
kNodes, [&](size_t /* node*/) { return kTasksPerNode; }, 1);
hist_builder.Reset(nthreads, kNodes, space, target_hist);
common::ParallelFor2d(space, nthreads, [&](size_t inode, common::Range1d r) {
common::ParallelFor2d(space, nthreads, [&](size_t inode, common::Range1d) {
const size_t tid = omp_get_thread_num();
GHistRow hist = hist_builder.GetInitializedHist(tid, inode);
// fill hist by some non-null values
for(size_t j = 0; j < kBins; ++j) {
for (size_t j = 0; j < kBins; ++j) {
hist[j].Add(kValue, kValue);
}
});
@@ -56,15 +57,16 @@ void ParallelGHistBuilderReset() {
for(size_t i = 0; i < target_hist.size(); ++i) {
target_hist[i] = collection[i];
}
common::BlockedSpace2d space2(kNodesExtended, [&](size_t node) { return kTasksPerNode; }, 1);
common::BlockedSpace2d space2(
kNodesExtended, [&](size_t /*node*/) { return kTasksPerNode; }, 1);
hist_builder.Reset(nthreads, kNodesExtended, space2, target_hist);
common::ParallelFor2d(space2, nthreads, [&](size_t inode, common::Range1d r) {
common::ParallelFor2d(space2, nthreads, [&](size_t inode, common::Range1d) {
const size_t tid = omp_get_thread_num();
GHistRow hist = hist_builder.GetInitializedHist(tid, inode);
// fill hist by some non-null values
for(size_t j = 0; j < kBins; ++j) {
for (size_t j = 0; j < kBins; ++j) {
ASSERT_EQ(0.0, hist[j].GetGrad());
ASSERT_EQ(0.0, hist[j].GetHess());
}
@@ -92,11 +94,12 @@ void ParallelGHistBuilderReduceHist(){
target_hist[i] = collection[i];
}
common::BlockedSpace2d space(kNodes, [&](size_t node) { return kTasksPerNode; }, 1);
common::BlockedSpace2d space(
kNodes, [&](size_t /*node*/) { return kTasksPerNode; }, 1);
hist_builder.Reset(nthreads, kNodes, space, target_hist);
// Simple analog of BuildHist function, works in parallel for both tree-nodes and data in node
common::ParallelFor2d(space, nthreads, [&](size_t inode, common::Range1d r) {
common::ParallelFor2d(space, nthreads, [&](size_t inode, common::Range1d) {
const size_t tid = omp_get_thread_num();
GHistRow hist = hist_builder.GetInitializedHist(tid, inode);
@@ -260,8 +263,7 @@ TEST(HistUtil, DenseCutsExternalMemory) {
for (auto num_rows : sizes) {
auto x = GenerateRandom(num_rows, num_columns);
dmlc::TemporaryDirectory tmpdir;
auto dmat =
GetExternalMemoryDMatrixFromData(x, num_rows, num_columns, 50, tmpdir);
auto dmat = GetExternalMemoryDMatrixFromData(x, num_rows, num_columns, tmpdir);
for (auto num_bins : bin_sizes) {
HistogramCuts cuts = SketchOnDMatrix(dmat.get(), num_bins, common::OmpGetNumThreads(0));
ValidateCuts(cuts, dmat.get(), num_bins);

22
tests/cpp/common/test_hist_util.cu
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@@ -252,8 +252,7 @@ TEST(HistUtil, DeviceSketchMultipleColumnsExternal) {
for (auto num_rows : sizes) {
auto x = GenerateRandom(num_rows, num_columns);
dmlc::TemporaryDirectory temp;
auto dmat =
GetExternalMemoryDMatrixFromData(x, num_rows, num_columns, 100, temp);
auto dmat = GetExternalMemoryDMatrixFromData(x, num_rows, num_columns, temp);
for (auto num_bins : bin_sizes) {
auto cuts = DeviceSketch(0, dmat.get(), num_bins);
ValidateCuts(cuts, dmat.get(), num_bins);
@@ -269,7 +268,7 @@ TEST(HistUtil, DeviceSketchExternalMemoryWithWeights) {
dmlc::TemporaryDirectory temp;
for (auto num_rows : sizes) {
auto x = GenerateRandom(num_rows, num_columns);
auto dmat = GetExternalMemoryDMatrixFromData(x, num_rows, num_columns, 100, temp);
auto dmat = GetExternalMemoryDMatrixFromData(x, num_rows, num_columns, temp);
dmat->Info().weights_.HostVector() = GenerateRandomWeights(num_rows);
for (auto num_bins : bin_sizes) {
auto cuts = DeviceSketch(0, dmat.get(), num_bins);
@@ -284,17 +283,15 @@ auto MakeUnweightedCutsForTest(Adapter adapter, int32_t num_bins, float missing,
HostDeviceVector<FeatureType> ft;
SketchContainer sketch_container(ft, num_bins, adapter.NumColumns(), adapter.NumRows(), 0);
MetaInfo info;
AdapterDeviceSketch(adapter.Value(), num_bins, info, std::numeric_limits<float>::quiet_NaN(),
&sketch_container);
AdapterDeviceSketch(adapter.Value(), num_bins, info, missing, &sketch_container, batch_size);
sketch_container.MakeCuts(&batched_cuts);
return batched_cuts;
}
template <typename Adapter>
void ValidateBatchedCuts(Adapter adapter, int num_bins, int num_columns, int num_rows,
DMatrix* dmat, size_t batch_size = 0) {
void ValidateBatchedCuts(Adapter adapter, int num_bins, DMatrix* dmat, size_t batch_size = 0) {
common::HistogramCuts batched_cuts = MakeUnweightedCutsForTest(
adapter, num_bins, std::numeric_limits<float>::quiet_NaN());
adapter, num_bins, std::numeric_limits<float>::quiet_NaN(), batch_size);
ValidateCuts(batched_cuts, dmat, num_bins);
}
@@ -448,8 +445,7 @@ TEST(HistUtil, AdapterDeviceSketchCategorical) {
auto dmat = GetDMatrixFromData(x, n, 1);
auto x_device = thrust::device_vector<float>(x);
auto adapter = AdapterFromData(x_device, n, 1);
ValidateBatchedCuts(adapter, num_bins, adapter.NumColumns(),
adapter.NumRows(), dmat.get());
ValidateBatchedCuts(adapter, num_bins, dmat.get());
TestCategoricalSketchAdapter(n, num_categories, num_bins, true);
TestCategoricalSketchAdapter(n, num_categories, num_bins, false);
}
@@ -466,7 +462,7 @@ TEST(HistUtil, AdapterDeviceSketchMultipleColumns) {
auto x_device = thrust::device_vector<float>(x);
for (auto num_bins : bin_sizes) {
auto adapter = AdapterFromData(x_device, num_rows, num_columns);
ValidateBatchedCuts(adapter, num_bins, num_columns, num_rows, dmat.get());
ValidateBatchedCuts(adapter, num_bins, dmat.get());
}
}
}
@@ -481,7 +477,7 @@ TEST(HistUtil, AdapterDeviceSketchBatches) {
auto dmat = GetDMatrixFromData(x, num_rows, num_columns);
auto x_device = thrust::device_vector<float>(x);
auto adapter = AdapterFromData(x_device, num_rows, num_columns);
ValidateBatchedCuts(adapter, num_bins, num_columns, num_rows, dmat.get(), batch_size);
ValidateBatchedCuts(adapter, num_bins, dmat.get(), batch_size);
}
}
@@ -504,7 +500,7 @@ TEST(HistUtil, SketchingEquivalent) {
EXPECT_EQ(dmat_cuts.Ptrs(), adapter_cuts.Ptrs());
EXPECT_EQ(dmat_cuts.MinValues(), adapter_cuts.MinValues());
ValidateBatchedCuts(adapter, num_bins, num_columns, num_rows, dmat.get());
ValidateBatchedCuts(adapter, num_bins, dmat.get());
}
}
}

11
tests/cpp/common/test_hist_util.h
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@@ -74,7 +74,7 @@ GetDMatrixFromData(const std::vector<float> &x, int num_rows, int num_columns) {
inline std::shared_ptr<DMatrix> GetExternalMemoryDMatrixFromData(
const std::vector<float>& x, int num_rows, int num_columns,
size_t page_size, const dmlc::TemporaryDirectory& tempdir) {
const dmlc::TemporaryDirectory& tempdir) {
// Create the svm file in a temp dir
const std::string tmp_file = tempdir.path + "/temp.libsvm";
std::ofstream fo(tmp_file.c_str());
@@ -92,10 +92,9 @@ inline std::shared_ptr<DMatrix> GetExternalMemoryDMatrixFromData(
}
// Test that elements are approximately equally distributed among bins
inline void TestBinDistribution(const HistogramCuts &cuts, int column_idx,
const std::vector<float> &sorted_column,
const std::vector<float> &sorted_weights,
int num_bins) {
inline void TestBinDistribution(const HistogramCuts& cuts, int column_idx,
const std::vector<float>& sorted_column,
const std::vector<float>& sorted_weights) {
std::map<int, int> bin_weights;
for (auto i = 0ull; i < sorted_column.size(); i++) {
auto bin_idx = cuts.SearchBin(sorted_column[i], column_idx);
@@ -175,7 +174,7 @@ inline void ValidateColumn(const HistogramCuts& cuts, int column_idx,
std::copy(cuts.Values().begin() + cuts.Ptrs()[column_idx],
cuts.Values().begin() + cuts.Ptrs()[column_idx + 1],
column_cuts.begin());
TestBinDistribution(cuts, column_idx, sorted_column, sorted_weights, num_bins);
TestBinDistribution(cuts, column_idx, sorted_column, sorted_weights);
TestRank(column_cuts, sorted_column, sorted_weights);
}
}

3
tests/cpp/common/test_quantile.cu
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@@ -79,6 +79,7 @@ TEST(GPUQuantile, Unique) {
// if with_error is true, the test tolerates floating point error
void TestQuantileElemRank(int32_t device, Span<SketchEntry const> in,
Span<bst_row_t const> d_columns_ptr, bool with_error = false) {
dh::safe_cuda(cudaSetDevice(device));
std::vector<SketchEntry> h_in(in.size());
dh::CopyDeviceSpanToVector(&h_in, in);
std::vector<bst_row_t> h_columns_ptr(d_columns_ptr.size());
@@ -478,7 +479,7 @@ TEST(GPUQuantile, SameOnAllWorkers) {
dh::CopyDeviceSpanToVector(&h_base_line, base_line);
size_t offset = 0;
for (size_t i = 0; i < world; ++i) {
for (decltype(world) i = 0; i < world; ++i) {
auto comp = dh::ToSpan(all_workers).subspan(offset, size_as_float);
std::vector<float> h_comp(comp.size());
dh::CopyDeviceSpanToVector(&h_comp, comp);

2
tests/cpp/common/test_span.h
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@@ -248,7 +248,7 @@ struct TestIterCompare {
XGBOOST_DEVICE void operator()() {
this->operator()(0);
}
XGBOOST_DEVICE void operator()(int _idx) {
XGBOOST_DEVICE void operator()(size_t) { // size_t for CUDA index
float arr[16];
InitializeRange(arr, arr + 16);
Span<float> s (arr);

5
tests/cpp/common/test_threading_utils.cc
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@@ -12,9 +12,8 @@ TEST(CreateBlockedSpace2d, Test) {
constexpr size_t kDim2 = 3;
constexpr size_t kGrainSize = 1;
BlockedSpace2d space(kDim1, [&](size_t i) {
return kDim2;
}, kGrainSize);
BlockedSpace2d space(
kDim1, [&](size_t) { return kDim2; }, kGrainSize);
ASSERT_EQ(kDim1 * kDim2, space.Size());

2
tests/cpp/helpers.cc
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@@ -651,7 +651,7 @@ RMMAllocatorPtr SetUpRMMResourceForCppTests(int argc, char** argv) {
#else // defined(XGBOOST_USE_RMM) && XGBOOST_USE_RMM == 1
class RMMAllocator {};
void DeleteRMMResource(RMMAllocator* r) {}
void DeleteRMMResource(RMMAllocator*) {}
RMMAllocatorPtr SetUpRMMResourceForCppTests(int, char**) { return {nullptr, DeleteRMMResource}; }
#endif // !defined(XGBOOST_USE_RMM) || XGBOOST_USE_RMM != 1

4
tests/cpp/predictor/test_gpu_predictor.cu
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@@ -56,7 +56,7 @@ TEST(GPUPredictor, Basic) {
std::vector<float>& gpu_out_predictions_h = gpu_out_predictions.predictions.HostVector();
std::vector<float>& cpu_out_predictions_h = cpu_out_predictions.predictions.HostVector();
float abs_tolerance = 0.001;
for (int j = 0; j < gpu_out_predictions.predictions.Size(); j++) {
for (size_t j = 0; j < gpu_out_predictions.predictions.Size(); j++) {
ASSERT_NEAR(gpu_out_predictions_h[j], cpu_out_predictions_h[j], abs_tolerance);
}
}
@@ -118,7 +118,7 @@ TEST(GPUPredictor, ExternalMemoryTest) {
gpu_predictor->PredictBatch(dmat.get(), &out_predictions, model, 0);
EXPECT_EQ(out_predictions.predictions.Size(), dmat->Info().num_row_ * n_classes);
const std::vector<float> &host_vector = out_predictions.predictions.ConstHostVector();
for (int i = 0; i < host_vector.size() / n_classes; i++) {
for (size_t i = 0; i < host_vector.size() / n_classes; i++) {
ASSERT_EQ(host_vector[i * n_classes], 2.0);
ASSERT_EQ(host_vector[i * n_classes + 1], 0.5);
ASSERT_EQ(host_vector[i * n_classes + 2], 0.5);

13
tests/cpp/predictor/test_predictor.cc
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@@ -45,7 +45,7 @@ void TestTrainingPrediction(size_t rows, size_t bins,
size_t constexpr kIters = 3;
std::unique_ptr<Learner> learner;
auto train = [&](std::string predictor, HostDeviceVector<float> *out) {
auto train = [&](std::string predictor) {
p_hist->Info().labels.Reshape(rows, 1);
auto &h_label = p_hist->Info().labels.Data()->HostVector();
@@ -59,6 +59,7 @@ void TestTrainingPrediction(size_t rows, size_t bins,
learner->SetParam("num_feature", std::to_string(kCols));
learner->SetParam("num_class", std::to_string(kClasses));
learner->SetParam("max_bin", std::to_string(bins));
learner->SetParam("predictor", predictor);
learner->Configure();
for (size_t i = 0; i < kIters; ++i) {
@@ -77,11 +78,11 @@ void TestTrainingPrediction(size_t rows, size_t bins,
}
};
HostDeviceVector<float> predictions_0;
train("cpu_predictor", &predictions_0);
HostDeviceVector<float> predictions_1;
train("gpu_predictor", &predictions_1);
if (tree_method == "gpu_hist") {
train("gpu_predictor");
} else {
train("cpu_predictor");
}
}
void TestInplacePrediction(std::shared_ptr<DMatrix> x, std::string predictor, bst_row_t rows,

1
tests/cpp/tree/gpu_hist/test_histogram.cu
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@@ -143,7 +143,6 @@ void TestGPUHistogramCategorical(size_t num_categories) {
std::vector<GradientPairPrecise> h_cat_hist(cat_hist.size());
thrust::copy(cat_hist.begin(), cat_hist.end(), h_cat_hist.begin());
auto cat_sum = std::accumulate(h_cat_hist.begin(), h_cat_hist.end(), GradientPairPrecise{});
std::vector<GradientPairPrecise> h_encode_hist(encode_hist.size());
thrust::copy(encode_hist.begin(), encode_hist.end(), h_encode_hist.begin());

2
tests/cpp/tree/gpu_hist/test_row_partitioner.cu
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@@ -119,7 +119,7 @@ void TestFinalise() {
rp.FinalisePosition(
&ctx, task, &position,
[=] __device__(RowPartitioner::RowIndexT ridx, int position) { return 7; },
[] XGBOOST_DEVICE(size_t idx) { return false; });
[] XGBOOST_DEVICE(size_t) { return false; });
auto position = rp.GetPositionHost();
for (auto p : position) {

3
tests/cpp/tree/hist/test_histogram.cc
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@@ -181,8 +181,7 @@ void TestSyncHist(bool is_distributed) {
starting_index, sync_count);
} else {
histogram.SyncHistogramLocal(&tree, nodes_for_explicit_hist_build_,
nodes_for_subtraction_trick_, starting_index,
sync_count);
nodes_for_subtraction_trick_);
}
using GHistRowT = common::GHistRow;