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Cleanup warnings. (#5247)

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From clang-tidy-9 and gcc-7: Invalid case style, narrowing definition, wrong
initialization order, unused variables.
This commit is contained in:
Jiaming Yuan
2020-01-31 14:52:15 +08:00
committed by GitHub
parent adc795929a
commit fe8d72b50b
8 changed files with 260 additions and 262 deletions
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4
tests/cpp/common/test_hist_util.cc
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@@ -46,7 +46,6 @@ TEST(ParallelGHistBuilder, Reset) {
hist_builder.Reset(nthreads, kNodes, space, target_hist);
common::ParallelFor2d(space, nthreads, [&](size_t inode, common::Range1d r) {
const size_t itask = r.begin();
const size_t tid = omp_get_thread_num();
GHistRow hist = hist_builder.GetInitializedHist(tid, inode);
@@ -65,7 +64,6 @@ TEST(ParallelGHistBuilder, Reset) {
hist_builder.Reset(nthreads, kNodesExtended, space2, target_hist);
common::ParallelFor2d(space2, nthreads, [&](size_t inode, common::Range1d r) {
const size_t itask = r.begin();
const size_t tid = omp_get_thread_num();
GHistRow hist = hist_builder.GetInitializedHist(tid, inode);
@@ -80,7 +78,6 @@ TEST(ParallelGHistBuilder, Reset) {
TEST(ParallelGHistBuilder, ReduceHist) {
constexpr size_t kBins = 10;
constexpr size_t kNodes = 5;
constexpr size_t kNodesExtended = 10;
constexpr size_t kTasksPerNode = 10;
constexpr double kValue = 1.0;
const size_t nthreads = GetNThreads();
@@ -104,7 +101,6 @@ TEST(ParallelGHistBuilder, ReduceHist) {
// 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) {
const size_t itask = r.begin();
const size_t tid = omp_get_thread_num();
GHistRow hist = hist_builder.GetInitializedHist(tid, inode);

165
tests/cpp/common/test_threading_utils.cc
View File

@@ -1,82 +1,83 @@
#include <gtest/gtest.h>
#include "../../../src/common/column_matrix.h"
#include "../../../src/common/threading_utils.h"
namespace xgboost {
namespace common {
TEST(CreateBlockedSpace2d, Test) {
constexpr size_t kDim1 = 5;
constexpr size_t kDim2 = 3;
constexpr size_t kGrainSize = 1;
BlockedSpace2d space(kDim1, [&](size_t i) {
return kDim2;
}, kGrainSize);
ASSERT_EQ(kDim1 * kDim2, space.Size());
for (auto i = 0; i < kDim1; i++) {
for (auto j = 0; j < kDim2; j++) {
ASSERT_EQ(space.GetFirstDimension(i*kDim2 + j), i);
ASSERT_EQ(j, space.GetRange(i*kDim2 + j).begin());
ASSERT_EQ(j + kGrainSize, space.GetRange(i*kDim2 + j).end());
}
}
}
TEST(ParallelFor2d, Test) {
constexpr size_t kDim1 = 100;
constexpr size_t kDim2 = 15;
constexpr size_t kGrainSize = 2;
// working space is matrix of size (kDim1 x kDim2)
std::vector<int> matrix(kDim1 * kDim2, 0);
BlockedSpace2d space(kDim1, [&](size_t i) {
return kDim2;
}, kGrainSize);
ParallelFor2d(space, 4, [&](size_t i, Range1d r) {
for (auto j = r.begin(); j < r.end(); ++j) {
matrix[i*kDim2 + j] += 1;
}
});
for (auto i = 0; i < kDim1 * kDim2; i++) {
ASSERT_EQ(matrix[i], 1);
}
}
TEST(ParallelFor2dNonUniform, Test) {
constexpr size_t kDim1 = 5;
constexpr size_t kGrainSize = 256;
// here are quite non-uniform distribution in space
// but ParallelFor2d should split them by blocks with max size = kGrainSize
// and process in balanced manner (optimal performance)
std::vector<size_t> dim2 { 1024, 500, 255, 5, 10000 };
BlockedSpace2d space(kDim1, [&](size_t i) {
return dim2[i];
}, kGrainSize);
std::vector<std::vector<int>> working_space(kDim1);
for (auto i = 0; i < kDim1; i++) {
working_space[i].resize(dim2[i], 0);
}
ParallelFor2d(space, 4, [&](size_t i, Range1d r) {
for (auto j = r.begin(); j < r.end(); ++j) {
working_space[i][j] += 1;
}
});
for (auto i = 0; i < kDim1; i++) {
for (auto j = 0; j < dim2[i]; j++) {
ASSERT_EQ(working_space[i][j], 1);
}
}
}
} // namespace common
} // namespace xgboost
#include <cstddef>
#include <gtest/gtest.h>
#include "../../../src/common/column_matrix.h"
#include "../../../src/common/threading_utils.h"
namespace xgboost {
namespace common {
TEST(CreateBlockedSpace2d, Test) {
constexpr size_t kDim1 = 5;
constexpr size_t kDim2 = 3;
constexpr size_t kGrainSize = 1;
BlockedSpace2d space(kDim1, [&](size_t i) {
return kDim2;
}, kGrainSize);
ASSERT_EQ(kDim1 * kDim2, space.Size());
for (size_t i = 0; i < kDim1; i++) {
for (size_t j = 0; j < kDim2; j++) {
ASSERT_EQ(space.GetFirstDimension(i*kDim2 + j), i);
ASSERT_EQ(j, space.GetRange(i*kDim2 + j).begin());
ASSERT_EQ(j + kGrainSize, space.GetRange(i*kDim2 + j).end());
}
}
}
TEST(ParallelFor2d, Test) {
constexpr size_t kDim1 = 100;
constexpr size_t kDim2 = 15;
constexpr size_t kGrainSize = 2;
// working space is matrix of size (kDim1 x kDim2)
std::vector<int> matrix(kDim1 * kDim2, 0);
BlockedSpace2d space(kDim1, [&](size_t i) {
return kDim2;
}, kGrainSize);
ParallelFor2d(space, 4, [&](size_t i, Range1d r) {
for (auto j = r.begin(); j < r.end(); ++j) {
matrix[i*kDim2 + j] += 1;
}
});
for (size_t i = 0; i < kDim1 * kDim2; i++) {
ASSERT_EQ(matrix[i], 1);
}
}
TEST(ParallelFor2dNonUniform, Test) {
constexpr size_t kDim1 = 5;
constexpr size_t kGrainSize = 256;
// here are quite non-uniform distribution in space
// but ParallelFor2d should split them by blocks with max size = kGrainSize
// and process in balanced manner (optimal performance)
std::vector<size_t> dim2 { 1024, 500, 255, 5, 10000 };
BlockedSpace2d space(kDim1, [&](size_t i) {
return dim2[i];
}, kGrainSize);
std::vector<std::vector<int>> working_space(kDim1);
for (size_t i = 0; i < kDim1; i++) {
working_space[i].resize(dim2[i], 0);
}
ParallelFor2d(space, 4, [&](size_t i, Range1d r) {
for (auto j = r.begin(); j < r.end(); ++j) {
working_space[i][j] += 1;
}
});
for (size_t i = 0; i < kDim1; i++) {
for (size_t j = 0; j < dim2[i]; j++) {
ASSERT_EQ(working_space[i][j], 1);
}
}
}
} // namespace common
} // namespace xgboost