199 lines
6.5 KiB
Plaintext
199 lines
6.5 KiB
Plaintext
/*!
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* Copyright 2019 XGBoost contributors
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*/
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#include <xgboost/base.h>
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#include <utility>
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#include "../helpers.h"
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#include "../histogram_helpers.h"
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#include "gtest/gtest.h"
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#include "../../../src/common/hist_util.h"
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#include "../../../src/data/ellpack_page.cuh"
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namespace xgboost {
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TEST(EllpackPage, EmptyDMatrix) {
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constexpr int kNRows = 0, kNCols = 0, kMaxBin = 256;
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constexpr float kSparsity = 0;
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auto dmat = RandomDataGenerator(kNRows, kNCols, kSparsity).GenerateDMatix();
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auto& page = *dmat->GetBatches<EllpackPage>({0, kMaxBin}).begin();
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auto impl = page.Impl();
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ASSERT_EQ(impl->row_stride, 0);
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ASSERT_EQ(impl->cuts_.TotalBins(), 0);
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ASSERT_EQ(impl->gidx_buffer.Size(), 4);
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}
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TEST(EllpackPage, BuildGidxDense) {
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int constexpr kNRows = 16, kNCols = 8;
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auto page = BuildEllpackPage(kNRows, kNCols);
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std::vector<common::CompressedByteT> h_gidx_buffer(page->gidx_buffer.HostVector());
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common::CompressedIterator<uint32_t> gidx(h_gidx_buffer.data(), page->NumSymbols());
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ASSERT_EQ(page->row_stride, kNCols);
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std::vector<uint32_t> solution = {
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0, 3, 8, 9, 14, 17, 20, 21,
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0, 4, 7, 10, 14, 16, 19, 22,
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1, 3, 7, 11, 14, 15, 19, 21,
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2, 3, 7, 9, 13, 16, 20, 22,
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2, 3, 6, 9, 12, 16, 20, 21,
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1, 5, 6, 10, 13, 16, 20, 21,
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2, 5, 8, 9, 13, 17, 19, 22,
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2, 4, 6, 10, 14, 17, 19, 21,
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2, 5, 7, 9, 13, 16, 19, 22,
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0, 3, 8, 10, 12, 16, 19, 22,
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1, 3, 7, 10, 13, 16, 19, 21,
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1, 3, 8, 10, 13, 17, 20, 22,
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2, 4, 6, 9, 14, 15, 19, 22,
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1, 4, 6, 9, 13, 16, 19, 21,
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2, 4, 8, 10, 14, 15, 19, 22,
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1, 4, 7, 10, 14, 16, 19, 21,
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};
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for (size_t i = 0; i < kNRows * kNCols; ++i) {
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ASSERT_EQ(solution[i], gidx[i]);
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}
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}
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TEST(EllpackPage, BuildGidxSparse) {
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int constexpr kNRows = 16, kNCols = 8;
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auto page = BuildEllpackPage(kNRows, kNCols, 0.9f);
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std::vector<common::CompressedByteT> h_gidx_buffer(page->gidx_buffer.HostVector());
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common::CompressedIterator<uint32_t> gidx(h_gidx_buffer.data(), 25);
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ASSERT_LE(page->row_stride, 3);
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// row_stride = 3, 16 rows, 48 entries for ELLPack
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std::vector<uint32_t> solution = {
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15, 24, 24, 0, 24, 24, 24, 24, 24, 24, 24, 24, 20, 24, 24, 24,
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24, 24, 24, 24, 24, 5, 24, 24, 0, 16, 24, 15, 24, 24, 24, 24,
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24, 7, 14, 16, 4, 24, 24, 24, 24, 24, 9, 24, 24, 1, 24, 24
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};
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for (size_t i = 0; i < kNRows * page->row_stride; ++i) {
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ASSERT_EQ(solution[i], gidx[i]);
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}
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}
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struct ReadRowFunction {
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EllpackDeviceAccessor matrix;
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int row;
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bst_float* row_data_d;
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ReadRowFunction(EllpackDeviceAccessor matrix, int row, bst_float* row_data_d)
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: matrix(std::move(matrix)), row(row), row_data_d(row_data_d) {}
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__device__ void operator()(size_t col) {
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auto value = matrix.GetFvalue(row, col);
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if (isnan(value)) {
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value = -1;
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}
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row_data_d[col] = value;
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}
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};
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TEST(EllpackPage, Copy) {
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constexpr size_t kRows = 1024;
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constexpr size_t kCols = 16;
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constexpr size_t kPageSize = 1024;
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// Create a DMatrix with multiple batches.
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dmlc::TemporaryDirectory tmpdir;
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std::unique_ptr<DMatrix>
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dmat(CreateSparsePageDMatrixWithRC(kRows, kCols, kPageSize, true, tmpdir));
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BatchParam param{0, 256, kPageSize};
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auto page = (*dmat->GetBatches<EllpackPage>(param).begin()).Impl();
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// Create an empty result page.
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EllpackPageImpl result(0, page->cuts_, page->is_dense, page->row_stride,
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kRows);
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// Copy batch pages into the result page.
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size_t offset = 0;
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for (auto& batch : dmat->GetBatches<EllpackPage>(param)) {
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size_t num_elements = result.Copy(0, batch.Impl(), offset);
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offset += num_elements;
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}
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size_t current_row = 0;
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thrust::device_vector<bst_float> row_d(kCols);
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thrust::device_vector<bst_float> row_result_d(kCols);
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std::vector<bst_float> row(kCols);
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std::vector<bst_float> row_result(kCols);
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for (auto& page : dmat->GetBatches<EllpackPage>(param)) {
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auto impl = page.Impl();
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EXPECT_EQ(impl->base_rowid, current_row);
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for (size_t i = 0; i < impl->Size(); i++) {
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dh::LaunchN(0, kCols, ReadRowFunction(impl->GetDeviceAccessor(0), current_row, row_d.data().get()));
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thrust::copy(row_d.begin(), row_d.end(), row.begin());
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dh::LaunchN(0, kCols, ReadRowFunction(result.GetDeviceAccessor(0), current_row, row_result_d.data().get()));
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thrust::copy(row_result_d.begin(), row_result_d.end(), row_result.begin());
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EXPECT_EQ(row, row_result);
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current_row++;
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}
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}
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}
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TEST(EllpackPage, Compact) {
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constexpr size_t kRows = 16;
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constexpr size_t kCols = 2;
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constexpr size_t kPageSize = 1;
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constexpr size_t kCompactedRows = 8;
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// Create a DMatrix with multiple batches.
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dmlc::TemporaryDirectory tmpdir;
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std::unique_ptr<DMatrix>
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dmat(CreateSparsePageDMatrixWithRC(kRows, kCols, kPageSize, true, tmpdir));
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BatchParam param{0, 256, kPageSize};
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auto page = (*dmat->GetBatches<EllpackPage>(param).begin()).Impl();
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// Create an empty result page.
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EllpackPageImpl result(0, page->cuts_, page->is_dense, page->row_stride,
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kCompactedRows);
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// Compact batch pages into the result page.
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std::vector<size_t> row_indexes_h {
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SIZE_MAX, 0, 1, 2, SIZE_MAX, 3, SIZE_MAX, 4, 5, SIZE_MAX, 6, SIZE_MAX, 7, SIZE_MAX, SIZE_MAX,
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SIZE_MAX};
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thrust::device_vector<size_t> row_indexes_d = row_indexes_h;
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common::Span<size_t> row_indexes_span(row_indexes_d.data().get(), kRows);
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for (auto& batch : dmat->GetBatches<EllpackPage>(param)) {
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result.Compact(0, batch.Impl(), row_indexes_span);
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}
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size_t current_row = 0;
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thrust::device_vector<bst_float> row_d(kCols);
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thrust::device_vector<bst_float> row_result_d(kCols);
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std::vector<bst_float> row(kCols);
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std::vector<bst_float> row_result(kCols);
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for (auto& page : dmat->GetBatches<EllpackPage>(param)) {
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auto impl = page.Impl();
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EXPECT_EQ(impl->base_rowid, current_row);
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for (size_t i = 0; i < impl->Size(); i++) {
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size_t compacted_row = row_indexes_h[current_row];
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if (compacted_row == SIZE_MAX) {
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current_row++;
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continue;
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}
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dh::LaunchN(0, kCols, ReadRowFunction(impl->GetDeviceAccessor(0), current_row, row_d.data().get()));
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dh::safe_cuda (cudaDeviceSynchronize());
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thrust::copy(row_d.begin(), row_d.end(), row.begin());
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dh::LaunchN(0, kCols,
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ReadRowFunction(result.GetDeviceAccessor(0), compacted_row, row_result_d.data().get()));
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thrust::copy(row_result_d.begin(), row_result_d.end(), row_result.begin());
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EXPECT_EQ(row, row_result);
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current_row++;
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}
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}
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}
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} // namespace xgboost
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