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Handle duplicated values in sketching. (#6178)

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* Accumulate weights in duplicated values.
* Fix device id in iterative dmatrix.
This commit is contained in:
Jiaming Yuan
2020-10-10 19:32:44 +08:00
committed by GitHub
parent ab5b35134f
commit 2241563f23
9 changed files with 250 additions and 54 deletions
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3
tests/cpp/common/test_hist_util.cu
View File

@@ -314,10 +314,9 @@ TEST(HistUtil, AdapterDeviceSketchMemory) {
ConsoleLogger::Configure({{"verbosity", "3"}});
auto cuts = MakeUnweightedCutsForTest(adapter, num_bins, std::numeric_limits<float>::quiet_NaN());
ConsoleLogger::Configure({{"verbosity", "0"}});
size_t bytes_constant = 1000;
size_t bytes_required = detail::RequiredMemory(
num_rows, num_columns, num_rows * num_columns, num_bins, false);
EXPECT_LE(dh::GlobalMemoryLogger().PeakMemory(), bytes_required + bytes_constant);
EXPECT_LE(dh::GlobalMemoryLogger().PeakMemory(), bytes_required * 1.05);
EXPECT_GE(dh::GlobalMemoryLogger().PeakMemory(), bytes_required * 0.95);
}

203
tests/cpp/common/test_quantile.cu
View File

@@ -45,12 +45,11 @@ void TestSketchUnique(float sparsity) {
detail::GetColumnSizesScan(0, kCols, n_cuts, batch_iter, is_valid, 0, end,
&cut_sizes_scan, &column_sizes_scan);
auto const& cut_sizes = cut_sizes_scan.HostVector();
ASSERT_LE(sketch.Data().size(), cut_sizes.back());
if (sparsity == 0) {
ASSERT_EQ(sketch.Data().size(), n_cuts * kCols);
} else {
ASSERT_EQ(sketch.Data().size(), cut_sizes.back());
}
std::vector<size_t> h_columns_ptr(sketch.ColumnsPtr().size());
dh::CopyDeviceSpanToVector(&h_columns_ptr, sketch.ColumnsPtr());
ASSERT_EQ(sketch.Data().size(), h_columns_ptr.back());
sketch.Unique();
ASSERT_TRUE(thrust::is_sorted(thrust::device, sketch.Data().data(),
@@ -67,32 +66,37 @@ 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::LaunchN(device, in.size(), [=]XGBOOST_DEVICE(size_t idx) {
auto column_id = dh::SegmentId(d_columns_ptr, idx);
auto in_column = in.subspan(d_columns_ptr[column_id],
d_columns_ptr[column_id + 1] -
d_columns_ptr[column_id]);
auto constexpr kEps = 1e-6f;
idx -= d_columns_ptr[column_id];
float prev_rmin = idx == 0 ? 0.0f : in_column[idx-1].rmin;
float prev_rmax = idx == 0 ? 0.0f : in_column[idx-1].rmax;
float rmin_next = in_column[idx].RMinNext();
std::vector<SketchEntry> h_in(in.size());
dh::CopyDeviceSpanToVector(&h_in, in);
std::vector<bst_row_t> h_columns_ptr(d_columns_ptr.size());
dh::CopyDeviceSpanToVector(&h_columns_ptr, d_columns_ptr);
if (with_error) {
SPAN_CHECK(in_column[idx].rmin + in_column[idx].rmin * kEps >= prev_rmin);
SPAN_CHECK(in_column[idx].rmax + in_column[idx].rmin * kEps >= prev_rmax);
SPAN_CHECK(in_column[idx].rmax + in_column[idx].rmin * kEps >= rmin_next);
} else {
SPAN_CHECK(in_column[idx].rmin >= prev_rmin);
SPAN_CHECK(in_column[idx].rmax >= prev_rmax);
SPAN_CHECK(in_column[idx].rmax >= rmin_next);
for (size_t i = 1; i < d_columns_ptr.size(); ++i) {
auto column_id = i - 1;
auto beg = h_columns_ptr[column_id];
auto end = h_columns_ptr[i];
auto in_column = Span<SketchEntry>{h_in}.subspan(beg, end - beg);
for (size_t idx = 1; idx < in_column.size(); ++idx) {
float prev_rmin = in_column[idx - 1].rmin;
float prev_rmax = in_column[idx - 1].rmax;
float rmin_next = in_column[idx].RMinNext();
if (with_error) {
ASSERT_GE(in_column[idx].rmin + in_column[idx].rmin * kRtEps,
prev_rmin);
ASSERT_GE(in_column[idx].rmax + in_column[idx].rmin * kRtEps,
prev_rmax);
ASSERT_GE(in_column[idx].rmax + in_column[idx].rmin * kRtEps,
rmin_next);
} else {
ASSERT_GE(in_column[idx].rmin, prev_rmin);
ASSERT_GE(in_column[idx].rmax, prev_rmax);
ASSERT_GE(in_column[idx].rmax, rmin_next);
}
}
});
// Force sync to terminate current test instead of a later one.
dh::DebugSyncDevice(__FILE__, __LINE__);
}
}
TEST(GPUQuantile, Prune) {
constexpr size_t kRows = 1000, kCols = 100;
RunWithSeedsAndBins(kRows, [=](int32_t seed, size_t n_bins, MetaInfo const& info) {
@@ -108,16 +112,12 @@ TEST(GPUQuantile, Prune) {
AdapterDeviceSketch(adapter.Value(), n_bins, info,
std::numeric_limits<float>::quiet_NaN(), &sketch);
auto n_cuts = detail::RequiredSampleCutsPerColumn(n_bins, kRows);
ASSERT_EQ(sketch.Data().size(), n_cuts * kCols);
// LE because kRows * kCols is pushed into sketch, after removing
// duplicated entries we might not have that much inputs for prune.
ASSERT_LE(sketch.Data().size(), n_cuts * kCols);
sketch.Prune(n_bins);
if (n_bins <= kRows) {
ASSERT_EQ(sketch.Data().size(), n_bins * kCols);
} else {
// LE because kRows * kCols is pushed into sketch, after removing
// duplicated entries we might not have that much inputs for prune.
ASSERT_LE(sketch.Data().size(), kRows * kCols);
}
ASSERT_LE(sketch.Data().size(), kRows * kCols);
// This is not necessarily true for all inputs without calling unique after
// prune.
ASSERT_TRUE(thrust::is_sorted(thrust::device, sketch.Data().data(),
@@ -278,6 +278,45 @@ TEST(GPUQuantile, MergeDuplicated) {
}
}
TEST(GPUQuantile, MultiMerge) {
constexpr size_t kRows = 20, kCols = 1;
int32_t world = 2;
RunWithSeedsAndBins(kRows, [=](int32_t seed, size_t n_bins,
MetaInfo const &info) {
// Set up single node version
HostDeviceVector<FeatureType> ft;
SketchContainer sketch_on_single_node(ft, n_bins, kCols, kRows, 0);
size_t intermediate_num_cuts = std::min(
kRows * world, static_cast<size_t>(n_bins * WQSketch::kFactor));
std::vector<SketchContainer> containers;
for (auto rank = 0; rank < world; ++rank) {
HostDeviceVector<float> storage;
std::string interface_str = RandomDataGenerator{kRows, kCols, 0}
.Device(0)
.Seed(rank + seed)
.GenerateArrayInterface(&storage);
data::CupyAdapter adapter(interface_str);
HostDeviceVector<FeatureType> ft;
containers.emplace_back(ft, n_bins, kCols, kRows, 0);
AdapterDeviceSketch(adapter.Value(), n_bins, info,
std::numeric_limits<float>::quiet_NaN(),
&containers.back());
}
for (auto &sketch : containers) {
sketch.Prune(intermediate_num_cuts);
sketch_on_single_node.Merge(sketch.ColumnsPtr(), sketch.Data());
sketch_on_single_node.FixError();
}
TestQuantileElemRank(0, sketch_on_single_node.Data(),
sketch_on_single_node.ColumnsPtr());
sketch_on_single_node.Unique();
TestQuantileElemRank(0, sketch_on_single_node.Data(),
sketch_on_single_node.ColumnsPtr());
});
}
TEST(GPUQuantile, AllReduceBasic) {
// This test is supposed to run by a python test that setups the environment.
std::string msg {"Skipping AllReduce test"};
@@ -442,5 +481,99 @@ TEST(GPUQuantile, SameOnAllWorkers) {
return;
#endif // !defined(__linux__) && defined(XGBOOST_USE_NCCL)
}
TEST(GPUQuantile, Push) {
size_t constexpr kRows = 100;
std::vector<float> data(kRows);
std::fill(data.begin(), data.begin() + (data.size() / 2), 0.3f);
std::fill(data.begin() + (data.size() / 2), data.end(), 0.5f);
int32_t n_bins = 128;
bst_feature_t constexpr kCols = 1;
std::vector<Entry> entries(kRows);
for (bst_feature_t i = 0; i < entries.size(); ++i) {
Entry e{i, data[i]};
entries[i] = e;
}
dh::device_vector<Entry> d_entries(entries);
dh::device_vector<size_t> columns_ptr(2);
columns_ptr[0] = 0;
columns_ptr[1] = kRows;
HostDeviceVector<FeatureType> ft;
SketchContainer sketch(ft, n_bins, kCols, kRows, 0);
sketch.Push(dh::ToSpan(d_entries), dh::ToSpan(columns_ptr), dh::ToSpan(columns_ptr), kRows, {});
auto sketch_data = sketch.Data();
thrust::host_vector<SketchEntry> h_sketch_data(sketch_data.size());
auto ptr = thrust::device_ptr<SketchEntry const>(sketch_data.data());
thrust::copy(ptr, ptr + sketch_data.size(), h_sketch_data.begin());
ASSERT_EQ(h_sketch_data.size(), 2);
auto v_0 = h_sketch_data[0];
ASSERT_EQ(v_0.rmin, 0);
ASSERT_EQ(v_0.wmin, kRows / 2.0f);
ASSERT_EQ(v_0.rmax, kRows / 2.0f);
auto v_1 = h_sketch_data[1];
ASSERT_EQ(v_1.rmin, kRows / 2.0f);
ASSERT_EQ(v_1.wmin, kRows / 2.0f);
ASSERT_EQ(v_1.rmax, static_cast<float>(kRows));
}
TEST(GPUQuantile, MultiColPush) {
size_t constexpr kRows = 100, kCols = 4;
std::vector<float> data(kRows * kCols);
std::fill(data.begin(), data.begin() + (data.size() / 2), 0.3f);
std::vector<Entry> entries(kRows * kCols);
for (bst_feature_t c = 0; c < kCols; ++c) {
for (size_t r = 0; r < kRows; ++r) {
float v = (r >= kRows / 2) ? 0.7 : 0.4;
auto e = Entry{c, v};
entries[c * kRows + r] = e;
}
}
int32_t n_bins = 16;
HostDeviceVector<FeatureType> ft;
SketchContainer sketch(ft, n_bins, kCols, kRows, 0);
dh::device_vector<Entry> d_entries {entries};
dh::device_vector<size_t> columns_ptr(kCols + 1, 0);
for (size_t i = 1; i < kCols + 1; ++i) {
columns_ptr[i] = kRows;
}
thrust::inclusive_scan(thrust::device, columns_ptr.begin(), columns_ptr.end(),
columns_ptr.begin());
dh::device_vector<size_t> cuts_ptr(columns_ptr);
sketch.Push(dh::ToSpan(d_entries), dh::ToSpan(columns_ptr),
dh::ToSpan(cuts_ptr), kRows * kCols, {});
auto sketch_data = sketch.Data();
ASSERT_EQ(sketch_data.size(), kCols * 2);
auto ptr = thrust::device_ptr<SketchEntry const>(sketch_data.data());
std::vector<SketchEntry> h_sketch_data(sketch_data.size());
thrust::copy(ptr, ptr + sketch_data.size(), h_sketch_data.begin());
for (size_t i = 0; i < kCols; ++i) {
auto v_0 = h_sketch_data[i * 2];
ASSERT_EQ(v_0.rmin, 0);
ASSERT_EQ(v_0.wmin, kRows / 2.0f);
ASSERT_EQ(v_0.rmax, kRows / 2.0f);
auto v_1 = h_sketch_data[i * 2 + 1];
ASSERT_EQ(v_1.rmin, kRows / 2.0f);
ASSERT_EQ(v_1.wmin, kRows / 2.0f);
ASSERT_EQ(v_1.rmax, static_cast<float>(kRows));
}
}
} // namespace common
} // namespace xgboost