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Partial rewrite EllpackPage (#5352)

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This commit is contained in:
Rory Mitchell
2020-03-11 10:15:53 +13:00
committed by GitHub
parent 7a99f8f27f
commit 3ad4333b0e
23 changed files with 496 additions and 733 deletions
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46
tests/cpp/data/test_ellpack_page.cu
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@@ -19,23 +19,19 @@ TEST(EllpackPage, EmptyDMatrix) {
auto dmat = *CreateDMatrix(kNRows, kNCols, kSparsity);
auto& page = *dmat->GetBatches<EllpackPage>({0, kMaxBin, kGpuBatchNRows}).begin();
auto impl = page.Impl();
ASSERT_EQ(impl->matrix.info.feature_segments.size(), 1);
ASSERT_EQ(impl->matrix.info.min_fvalue.size(), 0);
ASSERT_EQ(impl->matrix.info.gidx_fvalue_map.size(), 0);
ASSERT_EQ(impl->matrix.info.row_stride, 0);
ASSERT_EQ(impl->matrix.info.n_bins, 0);
ASSERT_EQ(impl->gidx_buffer.size(), 4);
ASSERT_EQ(impl->row_stride, 0);
ASSERT_EQ(impl->cuts_.TotalBins(), 0);
ASSERT_EQ(impl->gidx_buffer.Size(), 4);
}
TEST(EllpackPage, BuildGidxDense) {
int constexpr kNRows = 16, kNCols = 8;
auto page = BuildEllpackPage(kNRows, kNCols);
std::vector<common::CompressedByteT> h_gidx_buffer(page->gidx_buffer.size());
dh::CopyDeviceSpanToVector(&h_gidx_buffer, page->gidx_buffer);
common::CompressedIterator<uint32_t> gidx(h_gidx_buffer.data(), 25);
std::vector<common::CompressedByteT> h_gidx_buffer(page->gidx_buffer.HostVector());
common::CompressedIterator<uint32_t> gidx(h_gidx_buffer.data(), page->NumSymbols());
ASSERT_EQ(page->matrix.info.row_stride, kNCols);
ASSERT_EQ(page->row_stride, kNCols);
std::vector<uint32_t> solution = {
0, 3, 8, 9, 14, 17, 20, 21,
@@ -64,11 +60,10 @@ TEST(EllpackPage, BuildGidxSparse) {
int constexpr kNRows = 16, kNCols = 8;
auto page = BuildEllpackPage(kNRows, kNCols, 0.9f);
std::vector<common::CompressedByteT> h_gidx_buffer(page->gidx_buffer.size());
dh::CopyDeviceSpanToVector(&h_gidx_buffer, page->gidx_buffer);
std::vector<common::CompressedByteT> h_gidx_buffer(page->gidx_buffer.HostVector());
common::CompressedIterator<uint32_t> gidx(h_gidx_buffer.data(), 25);
ASSERT_LE(page->matrix.info.row_stride, 3);
ASSERT_LE(page->row_stride, 3);
// row_stride = 3, 16 rows, 48 entries for ELLPack
std::vector<uint32_t> solution = {
@@ -76,16 +71,16 @@ TEST(EllpackPage, BuildGidxSparse) {
24, 24, 24, 24, 24, 5, 24, 24, 0, 16, 24, 15, 24, 24, 24, 24,
24, 7, 14, 16, 4, 24, 24, 24, 24, 24, 9, 24, 24, 1, 24, 24
};
for (size_t i = 0; i < kNRows * page->matrix.info.row_stride; ++i) {
for (size_t i = 0; i < kNRows * page->row_stride; ++i) {
ASSERT_EQ(solution[i], gidx[i]);
}
}
struct ReadRowFunction {
EllpackMatrix matrix;
EllpackDeviceAccessor matrix;
int row;
bst_float* row_data_d;
ReadRowFunction(EllpackMatrix matrix, int row, bst_float* row_data_d)
ReadRowFunction(EllpackDeviceAccessor matrix, int row, bst_float* row_data_d)
: matrix(std::move(matrix)), row(row), row_data_d(row_data_d) {}
__device__ void operator()(size_t col) {
@@ -110,7 +105,8 @@ TEST(EllpackPage, Copy) {
auto page = (*dmat->GetBatches<EllpackPage>(param).begin()).Impl();
// Create an empty result page.
EllpackPageImpl result(0, page->matrix.info, kRows);
EllpackPageImpl result(0, page->cuts_, page->is_dense, page->row_stride,
kRows);
// Copy batch pages into the result page.
size_t offset = 0;
@@ -126,13 +122,13 @@ TEST(EllpackPage, Copy) {
std::vector<bst_float> row_result(kCols);
for (auto& page : dmat->GetBatches<EllpackPage>(param)) {
auto impl = page.Impl();
EXPECT_EQ(impl->matrix.base_rowid, current_row);
EXPECT_EQ(impl->base_rowid, current_row);
for (size_t i = 0; i < impl->Size(); i++) {
dh::LaunchN(0, kCols, ReadRowFunction(impl->matrix, current_row, row_d.data().get()));
dh::LaunchN(0, kCols, ReadRowFunction(impl->GetDeviceAccessor(0), current_row, row_d.data().get()));
thrust::copy(row_d.begin(), row_d.end(), row.begin());
dh::LaunchN(0, kCols, ReadRowFunction(result.matrix, current_row, row_result_d.data().get()));
dh::LaunchN(0, kCols, ReadRowFunction(result.GetDeviceAccessor(0), current_row, row_result_d.data().get()));
thrust::copy(row_result_d.begin(), row_result_d.end(), row_result.begin());
EXPECT_EQ(row, row_result);
@@ -155,7 +151,8 @@ TEST(EllpackPage, Compact) {
auto page = (*dmat->GetBatches<EllpackPage>(param).begin()).Impl();
// Create an empty result page.
EllpackPageImpl result(0, page->matrix.info, kCompactedRows);
EllpackPageImpl result(0, page->cuts_, page->is_dense, page->row_stride,
kCompactedRows);
// Compact batch pages into the result page.
std::vector<size_t> row_indexes_h {
@@ -174,7 +171,7 @@ TEST(EllpackPage, Compact) {
std::vector<bst_float> row_result(kCols);
for (auto& page : dmat->GetBatches<EllpackPage>(param)) {
auto impl = page.Impl();
EXPECT_EQ(impl->matrix.base_rowid, current_row);
EXPECT_EQ(impl->base_rowid, current_row);
for (size_t i = 0; i < impl->Size(); i++) {
size_t compacted_row = row_indexes_h[current_row];
@@ -183,11 +180,12 @@ TEST(EllpackPage, Compact) {
continue;
}
dh::LaunchN(0, kCols, ReadRowFunction(impl->matrix, current_row, row_d.data().get()));
dh::LaunchN(0, kCols, ReadRowFunction(impl->GetDeviceAccessor(0), current_row, row_d.data().get()));
dh::safe_cuda (cudaDeviceSynchronize());
thrust::copy(row_d.begin(), row_d.end(), row.begin());
dh::LaunchN(0, kCols,
ReadRowFunction(result.matrix, compacted_row, row_result_d.data().get()));
ReadRowFunction(result.GetDeviceAccessor(0), compacted_row, row_result_d.data().get()));
thrust::copy(row_result_d.begin(), row_result_d.end(), row_result.begin());
EXPECT_EQ(row, row_result);

45
tests/cpp/data/test_sparse_page_dmatrix.cu
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@@ -3,6 +3,7 @@
#include <dmlc/filesystem.h>
#include "../helpers.h"
#include "../../../src/common/compressed_iterator.h"
#include "../../../src/data/ellpack_page.cuh"
namespace xgboost {
@@ -58,31 +59,29 @@ TEST(SparsePageDMatrix, EllpackPageContent) {
BatchParam param{0, 2, 0, 0};
auto impl = (*dmat->GetBatches<EllpackPage>(param).begin()).Impl();
EXPECT_EQ(impl->matrix.base_rowid, 0);
EXPECT_EQ(impl->matrix.n_rows, kRows);
EXPECT_FALSE(impl->matrix.info.is_dense);
EXPECT_EQ(impl->matrix.info.row_stride, 2);
EXPECT_EQ(impl->matrix.info.n_bins, 4);
EXPECT_EQ(impl->base_rowid, 0);
EXPECT_EQ(impl->n_rows, kRows);
EXPECT_FALSE(impl->is_dense);
EXPECT_EQ(impl->row_stride, 2);
EXPECT_EQ(impl->cuts_.TotalBins(), 4);
auto impl_ext = (*dmat_ext->GetBatches<EllpackPage>(param).begin()).Impl();
EXPECT_EQ(impl_ext->matrix.base_rowid, 0);
EXPECT_EQ(impl_ext->matrix.n_rows, kRows);
EXPECT_FALSE(impl_ext->matrix.info.is_dense);
EXPECT_EQ(impl_ext->matrix.info.row_stride, 2);
EXPECT_EQ(impl_ext->matrix.info.n_bins, 4);
EXPECT_EQ(impl_ext->base_rowid, 0);
EXPECT_EQ(impl_ext->n_rows, kRows);
EXPECT_FALSE(impl_ext->is_dense);
EXPECT_EQ(impl_ext->row_stride, 2);
EXPECT_EQ(impl_ext->cuts_.TotalBins(), 4);
std::vector<common::CompressedByteT> buffer(impl->gidx_buffer.size());
std::vector<common::CompressedByteT> buffer_ext(impl_ext->gidx_buffer.size());
dh::CopyDeviceSpanToVector(&buffer, impl->gidx_buffer);
dh::CopyDeviceSpanToVector(&buffer_ext, impl_ext->gidx_buffer);
std::vector<common::CompressedByteT> buffer(impl->gidx_buffer.HostVector());
std::vector<common::CompressedByteT> buffer_ext(impl_ext->gidx_buffer.HostVector());
EXPECT_EQ(buffer, buffer_ext);
}
struct ReadRowFunction {
EllpackMatrix matrix;
EllpackDeviceAccessor matrix;
int row;
bst_float* row_data_d;
ReadRowFunction(EllpackMatrix matrix, int row, bst_float* row_data_d)
ReadRowFunction(EllpackDeviceAccessor matrix, int row, bst_float* row_data_d)
: matrix(std::move(matrix)), row(row), row_data_d(row_data_d) {}
__device__ void operator()(size_t col) {
@@ -110,8 +109,8 @@ TEST(SparsePageDMatrix, MultipleEllpackPageContent) {
BatchParam param{0, kMaxBins, 0, kPageSize};
auto impl = (*dmat->GetBatches<EllpackPage>(param).begin()).Impl();
EXPECT_EQ(impl->matrix.base_rowid, 0);
EXPECT_EQ(impl->matrix.n_rows, kRows);
EXPECT_EQ(impl->base_rowid, 0);
EXPECT_EQ(impl->n_rows, kRows);
size_t current_row = 0;
thrust::device_vector<bst_float> row_d(kCols);
@@ -120,13 +119,13 @@ TEST(SparsePageDMatrix, MultipleEllpackPageContent) {
std::vector<bst_float> row_ext(kCols);
for (auto& page : dmat_ext->GetBatches<EllpackPage>(param)) {
auto impl_ext = page.Impl();
EXPECT_EQ(impl_ext->matrix.base_rowid, current_row);
EXPECT_EQ(impl_ext->base_rowid, current_row);
for (size_t i = 0; i < impl_ext->Size(); i++) {
dh::LaunchN(0, kCols, ReadRowFunction(impl->matrix, current_row, row_d.data().get()));
dh::LaunchN(0, kCols, ReadRowFunction(impl->GetDeviceAccessor(0), current_row, row_d.data().get()));
thrust::copy(row_d.begin(), row_d.end(), row.begin());
dh::LaunchN(0, kCols, ReadRowFunction(impl_ext->matrix, current_row, row_ext_d.data().get()));
dh::LaunchN(0, kCols, ReadRowFunction(impl_ext->GetDeviceAccessor(0), current_row, row_ext_d.data().get()));
thrust::copy(row_ext_d.begin(), row_ext_d.end(), row_ext.begin());
EXPECT_EQ(row, row_ext);
@@ -155,8 +154,8 @@ TEST(SparsePageDMatrix, EllpackPageMultipleLoops) {
size_t current_row = 0;
for (auto& page : dmat_ext->GetBatches<EllpackPage>(param)) {
auto impl_ext = page.Impl();
EXPECT_EQ(impl_ext->matrix.base_rowid, current_row);
current_row += impl_ext->matrix.n_rows;
EXPECT_EQ(impl_ext->base_rowid, current_row);
current_row += impl_ext->n_rows;
}
}

12
tests/cpp/helpers.h
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@@ -244,13 +244,13 @@ class HistogramCutsWrapper : public common::HistogramCuts {
public:
using SuperT = common::HistogramCuts;
void SetValues(std::vector<float> cuts) {
SuperT::cut_values_ = std::move(cuts);
SuperT::cut_values_.HostVector() = std::move(cuts);
}
void SetPtrs(std::vector<uint32_t> ptrs) {
SuperT::cut_ptrs_ = std::move(ptrs);
SuperT::cut_ptrs_.HostVector() = std::move(ptrs);
}
void SetMins(std::vector<float> mins) {
SuperT::min_vals_ = std::move(mins);
SuperT::min_vals_.HostVector() = std::move(mins);
}
};
} // anonymous namespace
@@ -279,10 +279,8 @@ inline std::unique_ptr<EllpackPageImpl> BuildEllpackPage(
row_stride = std::max(row_stride, offset_vec[i] - offset_vec[i-1]);
}
auto page = std::unique_ptr<EllpackPageImpl>(new EllpackPageImpl(dmat->get(), {0, 256, 0}));
page->InitInfo(0, (*dmat)->IsDense(), row_stride, cmat);
page->InitCompressedData(0, n_rows);
page->CreateHistIndices(0, batch, RowStateOnDevice(batch.Size(), batch.Size()));
auto page = std::unique_ptr<EllpackPageImpl>(
new EllpackPageImpl(0, cmat, batch, (*dmat)->IsDense(), row_stride));
delete dmat;

25
tests/cpp/tree/gpu_hist/test_gradient_based_sampler.cu
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@@ -3,6 +3,7 @@
#include "../../../../src/data/ellpack_page.cuh"
#include "../../../../src/tree/gpu_hist/gradient_based_sampler.cuh"
#include "../../helpers.h"
#include "dmlc/filesystem.h"
namespace xgboost {
namespace tree {
@@ -29,7 +30,7 @@ void VerifySampling(size_t page_size,
BatchParam param{0, 256, 0, page_size};
auto page = (*dmat->GetBatches<EllpackPage>(param).begin()).Impl();
if (page_size != 0) {
EXPECT_NE(page->matrix.n_rows, kRows);
EXPECT_NE(page->n_rows, kRows);
}
GradientBasedSampler sampler(page, kRows, param, subsample, sampling_method);
@@ -37,11 +38,11 @@ void VerifySampling(size_t page_size,
if (fixed_size_sampling) {
EXPECT_EQ(sample.sample_rows, kRows);
EXPECT_EQ(sample.page->matrix.n_rows, kRows);
EXPECT_EQ(sample.page->n_rows, kRows);
EXPECT_EQ(sample.gpair.size(), kRows);
} else {
EXPECT_NEAR(sample.sample_rows, sample_rows, kRows * 0.016f);
EXPECT_NEAR(sample.page->matrix.n_rows, sample_rows, kRows * 0.016f);
EXPECT_NEAR(sample.sample_rows, sample_rows, kRows * 0.016);
EXPECT_NEAR(sample.page->n_rows, sample_rows, kRows * 0.016f);
EXPECT_NEAR(sample.gpair.size(), sample_rows, kRows * 0.016f);
}
@@ -83,7 +84,7 @@ TEST(GradientBasedSampler, NoSampling_ExternalMemory) {
BatchParam param{0, 256, 0, kPageSize};
auto page = (*dmat->GetBatches<EllpackPage>(param).begin()).Impl();
EXPECT_NE(page->matrix.n_rows, kRows);
EXPECT_NE(page->n_rows, kRows);
GradientBasedSampler sampler(page, kRows, param, kSubsample, TrainParam::kUniform);
auto sample = sampler.Sample(gpair.DeviceSpan(), dmat.get());
@@ -91,21 +92,19 @@ TEST(GradientBasedSampler, NoSampling_ExternalMemory) {
EXPECT_EQ(sample.sample_rows, kRows);
EXPECT_EQ(sample.gpair.size(), gpair.Size());
EXPECT_EQ(sample.gpair.data(), gpair.DevicePointer());
EXPECT_EQ(sampled_page->matrix.n_rows, kRows);
EXPECT_EQ(sampled_page->n_rows, kRows);
std::vector<common::CompressedByteT> buffer(sampled_page->gidx_buffer.size());
dh::CopyDeviceSpanToVector(&buffer, sampled_page->gidx_buffer);
std::vector<common::CompressedByteT> buffer(sampled_page->gidx_buffer.HostVector());
common::CompressedIterator<common::CompressedByteT>
ci(buffer.data(), sampled_page->matrix.info.NumSymbols());
ci(buffer.data(), sampled_page->NumSymbols());
size_t offset = 0;
for (auto& batch : dmat->GetBatches<EllpackPage>(param)) {
auto page = batch.Impl();
std::vector<common::CompressedByteT> page_buffer(page->gidx_buffer.size());
dh::CopyDeviceSpanToVector(&page_buffer, page->gidx_buffer);
std::vector<common::CompressedByteT> page_buffer(page->gidx_buffer.HostVector());
common::CompressedIterator<common::CompressedByteT>
page_ci(page_buffer.data(), page->matrix.info.NumSymbols());
size_t num_elements = page->matrix.n_rows * page->matrix.info.row_stride;
page_ci(page_buffer.data(), page->NumSymbols());
size_t num_elements = page->n_rows * page->row_stride;
for (size_t i = 0; i < num_elements; i++) {
EXPECT_EQ(ci[i + offset], page_ci[i]);
}

6
tests/cpp/tree/gpu_hist/test_histogram.cu
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@@ -27,7 +27,7 @@ void TestDeterminsticHistogram() {
gpair.SetDevice(0);
auto rounding = CreateRoundingFactor<Gradient>(gpair.DeviceSpan());
BuildGradientHistogram(page->matrix, gpair.DeviceSpan(), ridx,
BuildGradientHistogram(page->GetDeviceAccessor(0), gpair.DeviceSpan(), ridx,
d_histogram, rounding, true);
for (size_t i = 0; i < kRounds; ++i) {
@@ -35,7 +35,7 @@ void TestDeterminsticHistogram() {
auto d_histogram = dh::ToSpan(new_histogram);
auto rounding = CreateRoundingFactor<Gradient>(gpair.DeviceSpan());
BuildGradientHistogram(page->matrix, gpair.DeviceSpan(), ridx,
BuildGradientHistogram(page->GetDeviceAccessor(0), gpair.DeviceSpan(), ridx,
d_histogram, rounding, true);
for (size_t j = 0; j < new_histogram.size(); ++j) {
@@ -50,7 +50,7 @@ void TestDeterminsticHistogram() {
auto gpair = GenerateRandomGradients(kRows, kLower, kUpper);
gpair.SetDevice(0);
dh::device_vector<Gradient> baseline(kBins * kCols);
BuildGradientHistogram(page->matrix, gpair.DeviceSpan(), ridx,
BuildGradientHistogram(page->GetDeviceAccessor(0), gpair.DeviceSpan(), ridx,
dh::ToSpan(baseline), rounding, true);
for (size_t i = 0; i < baseline.size(); ++i) {
EXPECT_NEAR(((Gradient)baseline[i]).GetGrad(), ((Gradient)histogram[i]).GetGrad(),

29
tests/cpp/tree/test_gpu_hist.cu
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@@ -97,12 +97,8 @@ void TestBuildHist(bool use_shared_memory_histograms) {
}
gpair.SetDevice(0);
thrust::host_vector<common::CompressedByteT> h_gidx_buffer (page->gidx_buffer.size());
thrust::host_vector<common::CompressedByteT> h_gidx_buffer (page->gidx_buffer.HostVector());
common::CompressedByteT* d_gidx_buffer_ptr = page->gidx_buffer.data();
dh::safe_cuda(cudaMemcpy(h_gidx_buffer.data(), d_gidx_buffer_ptr,
sizeof(common::CompressedByteT) * page->gidx_buffer.size(),
cudaMemcpyDeviceToHost));
maker.row_partitioner.reset(new RowPartitioner(0, kNRows));
maker.hist.AllocateHistogram(0);
@@ -196,15 +192,10 @@ TEST(GpuHist, EvaluateSplits) {
auto cmat = GetHostCutMatrix();
// Copy cut matrix to device.
maker.ba.Allocate(0,
&(page->matrix.info.feature_segments), cmat.Ptrs().size(),
&(page->matrix.info.min_fvalue), cmat.MinValues().size(),
&(page->matrix.info.gidx_fvalue_map), 24,
&(maker.monotone_constraints), kNCols);
dh::CopyVectorToDeviceSpan(page->matrix.info.feature_segments, cmat.Ptrs());
dh::CopyVectorToDeviceSpan(page->matrix.info.gidx_fvalue_map, cmat.Values());
dh::CopyVectorToDeviceSpan(maker.monotone_constraints, param.monotone_constraints);
dh::CopyVectorToDeviceSpan(page->matrix.info.min_fvalue, cmat.MinValues());
page->cuts_ = cmat;
maker.ba.Allocate(0, &(maker.monotone_constraints), kNCols);
dh::CopyVectorToDeviceSpan(maker.monotone_constraints,
param.monotone_constraints);
// Initialize GPUHistMakerDevice::hist
maker.hist.Init(0, (max_bins - 1) * kNCols);
@@ -274,15 +265,13 @@ void TestHistogramIndexImpl() {
// Extract the device maker from the histogram makers and from that its compressed
// histogram index
const auto &maker = hist_maker.maker;
std::vector<common::CompressedByteT> h_gidx_buffer(maker->page->gidx_buffer.size());
dh::CopyDeviceSpanToVector(&h_gidx_buffer, maker->page->gidx_buffer);
std::vector<common::CompressedByteT> h_gidx_buffer(maker->page->gidx_buffer.HostVector());
const auto &maker_ext = hist_maker_ext.maker;
std::vector<common::CompressedByteT> h_gidx_buffer_ext(maker_ext->page->gidx_buffer.size());
dh::CopyDeviceSpanToVector(&h_gidx_buffer_ext, maker_ext->page->gidx_buffer);
std::vector<common::CompressedByteT> h_gidx_buffer_ext(maker_ext->page->gidx_buffer.HostVector());
ASSERT_EQ(maker->page->matrix.info.n_bins, maker_ext->page->matrix.info.n_bins);
ASSERT_EQ(maker->page->gidx_buffer.size(), maker_ext->page->gidx_buffer.size());
ASSERT_EQ(maker->page->cuts_.TotalBins(), maker_ext->page->cuts_.TotalBins());
ASSERT_EQ(maker->page->gidx_buffer.Size(), maker_ext->page->gidx_buffer.Size());
}