408 lines
16 KiB
Plaintext
408 lines
16 KiB
Plaintext
/**
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* Copyright 2020-2024, XGBoost Contributors
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*/
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#include <gtest/gtest.h>
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#include <xgboost/context.h> // for Context
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#include <memory> // for unique_ptr
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#include <vector> // for vector
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#include "../../../../src/tree/gpu_hist/histogram.cuh"
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#include "../../../../src/tree/gpu_hist/row_partitioner.cuh" // for RowPartitioner
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#include "../../../../src/tree/param.h" // for TrainParam
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#include "../../categorical_helpers.h" // for OneHotEncodeFeature
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#include "../../helpers.h"
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namespace xgboost::tree {
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TEST(Histogram, DeviceHistogramStorage) {
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// Ensures that node allocates correctly after reaching `kStopGrowingSize`.
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auto ctx = MakeCUDACtx(0);
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constexpr size_t kNBins = 128;
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constexpr int kNNodes = 4;
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constexpr size_t kStopGrowing = kNNodes * kNBins * 2u;
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DeviceHistogramStorage<kStopGrowing> histogram;
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histogram.Init(FstCU(), kNBins);
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for (int i = 0; i < kNNodes; ++i) {
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histogram.AllocateHistograms(&ctx, {i});
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}
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histogram.Reset(&ctx);
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ASSERT_EQ(histogram.Data().size(), kStopGrowing);
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// Use allocated memory but do not erase nidx_map.
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for (int i = 0; i < kNNodes; ++i) {
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histogram.AllocateHistograms(&ctx, {i});
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}
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for (int i = 0; i < kNNodes; ++i) {
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ASSERT_TRUE(histogram.HistogramExists(i));
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}
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// Add two new nodes
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histogram.AllocateHistograms(&ctx, {kNNodes});
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histogram.AllocateHistograms(&ctx, {kNNodes + 1});
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// Old cached nodes should still exist
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for (int i = 0; i < kNNodes; ++i) {
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ASSERT_TRUE(histogram.HistogramExists(i));
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}
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// Should be deleted
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ASSERT_FALSE(histogram.HistogramExists(kNNodes));
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// Most recent node should exist
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ASSERT_TRUE(histogram.HistogramExists(kNNodes + 1));
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// Add same node again - should fail
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EXPECT_ANY_THROW(histogram.AllocateHistograms(&ctx, {kNNodes + 1}););
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}
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void TestDeterministicHistogram(bool is_dense, int shm_size, bool force_global) {
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Context ctx = MakeCUDACtx(0);
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size_t constexpr kBins = 256, kCols = 120, kRows = 16384, kRounds = 16;
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float constexpr kLower = -1e-2, kUpper = 1e2;
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float sparsity = is_dense ? 0.0f : 0.5f;
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auto matrix = RandomDataGenerator(kRows, kCols, sparsity).GenerateDMatrix();
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auto batch_param = BatchParam{kBins, tree::TrainParam::DftSparseThreshold()};
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for (auto const& batch : matrix->GetBatches<EllpackPage>(&ctx, batch_param)) {
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auto* page = batch.Impl();
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tree::RowPartitioner row_partitioner{&ctx, kRows, page->base_rowid};
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auto ridx = row_partitioner.GetRows(0);
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bst_bin_t num_bins = kBins * kCols;
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dh::device_vector<GradientPairInt64> histogram(num_bins);
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auto d_histogram = dh::ToSpan(histogram);
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auto gpair = GenerateRandomGradients(kRows, kLower, kUpper);
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gpair.SetDevice(ctx.Device());
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FeatureGroups feature_groups(page->Cuts(), page->is_dense, shm_size, sizeof(GradientPairInt64));
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auto quantiser = GradientQuantiser(&ctx, gpair.DeviceSpan(), MetaInfo());
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DeviceHistogramBuilder builder;
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builder.Reset(&ctx, feature_groups.DeviceAccessor(ctx.Device()), force_global);
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builder.BuildHistogram(ctx.CUDACtx(), page->GetDeviceAccessor(ctx.Device()),
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feature_groups.DeviceAccessor(ctx.Device()), gpair.DeviceSpan(), ridx,
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d_histogram, quantiser);
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std::vector<GradientPairInt64> histogram_h(num_bins);
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dh::safe_cuda(cudaMemcpy(histogram_h.data(), d_histogram.data(),
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num_bins * sizeof(GradientPairInt64), cudaMemcpyDeviceToHost));
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for (std::size_t i = 0; i < kRounds; ++i) {
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dh::device_vector<GradientPairInt64> new_histogram(num_bins);
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auto d_new_histogram = dh::ToSpan(new_histogram);
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auto quantiser = GradientQuantiser(&ctx, gpair.DeviceSpan(), MetaInfo());
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DeviceHistogramBuilder builder;
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builder.Reset(&ctx, feature_groups.DeviceAccessor(ctx.Device()), force_global);
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builder.BuildHistogram(ctx.CUDACtx(), page->GetDeviceAccessor(ctx.Device()),
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feature_groups.DeviceAccessor(ctx.Device()), gpair.DeviceSpan(), ridx,
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d_new_histogram, quantiser);
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std::vector<GradientPairInt64> new_histogram_h(num_bins);
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dh::safe_cuda(cudaMemcpy(new_histogram_h.data(), d_new_histogram.data(),
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num_bins * sizeof(GradientPairInt64),
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cudaMemcpyDeviceToHost));
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for (size_t j = 0; j < new_histogram_h.size(); ++j) {
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ASSERT_EQ(new_histogram_h[j].GetQuantisedGrad(), histogram_h[j].GetQuantisedGrad());
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ASSERT_EQ(new_histogram_h[j].GetQuantisedHess(), histogram_h[j].GetQuantisedHess());
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}
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}
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{
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auto gpair = GenerateRandomGradients(kRows, kLower, kUpper);
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gpair.SetDevice(ctx.Device());
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// Use a single feature group to compute the baseline.
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FeatureGroups single_group(page->Cuts());
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dh::device_vector<GradientPairInt64> baseline(num_bins);
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DeviceHistogramBuilder builder;
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builder.Reset(&ctx, single_group.DeviceAccessor(ctx.Device()), force_global);
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builder.BuildHistogram(ctx.CUDACtx(), page->GetDeviceAccessor(ctx.Device()),
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single_group.DeviceAccessor(ctx.Device()), gpair.DeviceSpan(), ridx,
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dh::ToSpan(baseline), quantiser);
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std::vector<GradientPairInt64> baseline_h(num_bins);
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dh::safe_cuda(cudaMemcpy(baseline_h.data(), baseline.data().get(),
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num_bins * sizeof(GradientPairInt64),
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cudaMemcpyDeviceToHost));
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for (size_t i = 0; i < baseline.size(); ++i) {
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EXPECT_NEAR(baseline_h[i].GetQuantisedGrad(), histogram_h[i].GetQuantisedGrad(),
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baseline_h[i].GetQuantisedGrad() * 1e-3);
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}
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}
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}
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}
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TEST(Histogram, GPUDeterministic) {
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std::vector<bool> is_dense_array{false, true};
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std::vector<int> shm_sizes{48 * 1024, 64 * 1024, 160 * 1024};
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for (bool is_dense : is_dense_array) {
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for (int shm_size : shm_sizes) {
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for (bool force_global : {true, false}) {
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TestDeterministicHistogram(is_dense, shm_size, force_global);
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}
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}
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}
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}
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void ValidateCategoricalHistogram(size_t n_categories, common::Span<GradientPairInt64> onehot,
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common::Span<GradientPairInt64> cat) {
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auto cat_sum = std::accumulate(cat.cbegin(), cat.cend(), GradientPairInt64{});
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for (size_t c = 0; c < n_categories; ++c) {
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auto zero = onehot[c * 2];
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auto one = onehot[c * 2 + 1];
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auto chosen = cat[c];
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auto not_chosen = cat_sum - chosen;
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ASSERT_EQ(zero, not_chosen);
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ASSERT_EQ(one, chosen);
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}
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}
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// Test 1 vs rest categorical histogram is equivalent to one hot encoded data.
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void TestGPUHistogramCategorical(size_t num_categories) {
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auto ctx = MakeCUDACtx(0);
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size_t constexpr kRows = 340;
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size_t constexpr kBins = 256;
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auto x = GenerateRandomCategoricalSingleColumn(kRows, num_categories);
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auto cat_m = GetDMatrixFromData(x, kRows, 1);
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cat_m->Info().feature_types.HostVector().push_back(FeatureType::kCategorical);
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auto batch_param = BatchParam{kBins, tree::TrainParam::DftSparseThreshold()};
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tree::RowPartitioner row_partitioner{&ctx, kRows, 0};
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auto ridx = row_partitioner.GetRows(0);
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dh::device_vector<GradientPairInt64> cat_hist(num_categories);
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auto gpair = GenerateRandomGradients(kRows, 0, 2);
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gpair.SetDevice(DeviceOrd::CUDA(0));
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auto quantiser = GradientQuantiser(&ctx, gpair.DeviceSpan(), MetaInfo());
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/**
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* Generate hist with cat data.
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*/
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for (auto const &batch : cat_m->GetBatches<EllpackPage>(&ctx, batch_param)) {
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auto* page = batch.Impl();
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FeatureGroups single_group(page->Cuts());
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DeviceHistogramBuilder builder;
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builder.Reset(&ctx, single_group.DeviceAccessor(ctx.Device()), false);
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builder.BuildHistogram(ctx.CUDACtx(), page->GetDeviceAccessor(ctx.Device()),
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single_group.DeviceAccessor(ctx.Device()), gpair.DeviceSpan(), ridx,
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dh::ToSpan(cat_hist), quantiser);
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}
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/**
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* Generate hist with one hot encoded data.
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*/
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auto x_encoded = OneHotEncodeFeature(x, num_categories);
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auto encode_m = GetDMatrixFromData(x_encoded, kRows, num_categories);
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dh::device_vector<GradientPairInt64> encode_hist(2 * num_categories);
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for (auto const &batch : encode_m->GetBatches<EllpackPage>(&ctx, batch_param)) {
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auto* page = batch.Impl();
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FeatureGroups single_group(page->Cuts());
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DeviceHistogramBuilder builder;
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builder.Reset(&ctx, single_group.DeviceAccessor(ctx.Device()), false);
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builder.BuildHistogram(ctx.CUDACtx(), page->GetDeviceAccessor(ctx.Device()),
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single_group.DeviceAccessor(ctx.Device()), gpair.DeviceSpan(), ridx,
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dh::ToSpan(encode_hist), quantiser);
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}
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std::vector<GradientPairInt64> h_cat_hist(cat_hist.size());
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thrust::copy(cat_hist.begin(), cat_hist.end(), h_cat_hist.begin());
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std::vector<GradientPairInt64> h_encode_hist(encode_hist.size());
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thrust::copy(encode_hist.begin(), encode_hist.end(), h_encode_hist.begin());
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ValidateCategoricalHistogram(num_categories,
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common::Span<GradientPairInt64>{h_encode_hist},
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common::Span<GradientPairInt64>{h_cat_hist});
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}
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TEST(Histogram, GPUHistCategorical) {
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for (size_t num_categories = 2; num_categories < 8; ++num_categories) {
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TestGPUHistogramCategorical(num_categories);
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}
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}
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namespace {
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// Atomic add as type cast for test.
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XGBOOST_DEV_INLINE int64_t atomicAdd(int64_t *dst, int64_t src) { // NOLINT
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uint64_t* u_dst = reinterpret_cast<uint64_t*>(dst);
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uint64_t u_src = *reinterpret_cast<uint64_t*>(&src);
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uint64_t ret = ::atomicAdd(u_dst, u_src);
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return *reinterpret_cast<int64_t*>(&ret);
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}
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}
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void TestAtomicAdd() {
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size_t n_elements = 1024;
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dh::device_vector<int64_t> result_a(1, 0);
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auto d_result_a = result_a.data().get();
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dh::device_vector<int64_t> result_b(1, 0);
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auto d_result_b = result_b.data().get();
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/**
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* Test for simple inputs
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*/
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std::vector<int64_t> h_inputs(n_elements);
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for (size_t i = 0; i < h_inputs.size(); ++i) {
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h_inputs[i] = (i % 2 == 0) ? i : -i;
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}
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dh::device_vector<int64_t> inputs(h_inputs);
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auto d_inputs = inputs.data().get();
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dh::LaunchN(n_elements, [=] __device__(size_t i) {
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AtomicAdd64As32(d_result_a, d_inputs[i]);
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atomicAdd(d_result_b, d_inputs[i]);
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});
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ASSERT_EQ(result_a[0], result_b[0]);
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/**
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* Test for positive values that don't fit into 32 bit integer.
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*/
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thrust::fill(inputs.begin(), inputs.end(),
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(std::numeric_limits<uint32_t>::max() / 2));
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thrust::fill(result_a.begin(), result_a.end(), 0);
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thrust::fill(result_b.begin(), result_b.end(), 0);
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dh::LaunchN(n_elements, [=] __device__(size_t i) {
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AtomicAdd64As32(d_result_a, d_inputs[i]);
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atomicAdd(d_result_b, d_inputs[i]);
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});
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ASSERT_EQ(result_a[0], result_b[0]);
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ASSERT_GT(result_a[0], std::numeric_limits<uint32_t>::max());
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CHECK_EQ(thrust::reduce(inputs.begin(), inputs.end(), int64_t(0)), result_a[0]);
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/**
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* Test for negative values that don't fit into 32 bit integer.
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*/
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thrust::fill(inputs.begin(), inputs.end(),
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(std::numeric_limits<int32_t>::min() / 2));
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thrust::fill(result_a.begin(), result_a.end(), 0);
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thrust::fill(result_b.begin(), result_b.end(), 0);
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dh::LaunchN(n_elements, [=] __device__(size_t i) {
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AtomicAdd64As32(d_result_a, d_inputs[i]);
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atomicAdd(d_result_b, d_inputs[i]);
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});
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ASSERT_EQ(result_a[0], result_b[0]);
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ASSERT_LT(result_a[0], std::numeric_limits<int32_t>::min());
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CHECK_EQ(thrust::reduce(inputs.begin(), inputs.end(), int64_t(0)), result_a[0]);
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}
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TEST(Histogram, AtomicAddInt64) {
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TestAtomicAdd();
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}
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TEST(Histogram, Quantiser) {
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auto ctx = MakeCUDACtx(0);
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std::size_t n_samples{16};
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HostDeviceVector<GradientPair> gpair(n_samples, GradientPair{1.0, 1.0});
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gpair.SetDevice(ctx.Device());
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auto quantiser = GradientQuantiser(&ctx, gpair.DeviceSpan(), MetaInfo());
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for (auto v : gpair.ConstHostVector()) {
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auto gh = quantiser.ToFloatingPoint(quantiser.ToFixedPoint(v));
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ASSERT_EQ(gh.GetGrad(), 1.0);
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ASSERT_EQ(gh.GetHess(), 1.0);
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}
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}
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namespace {
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class HistogramExternalMemoryTest : public ::testing::TestWithParam<std::tuple<float, bool>> {
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public:
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void Run(float sparsity, bool force_global) {
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bst_idx_t n_samples{512}, n_features{12}, n_batches{3};
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std::vector<std::unique_ptr<RowPartitioner>> partitioners;
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auto p_fmat = RandomDataGenerator{n_samples, n_features, sparsity}
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.Batches(n_batches)
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.GenerateSparsePageDMatrix("cache", true);
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bst_bin_t n_bins = 16;
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BatchParam p{n_bins, TrainParam::DftSparseThreshold()};
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auto ctx = MakeCUDACtx(0);
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std::unique_ptr<FeatureGroups> fg;
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dh::device_vector<GradientPairInt64> single_hist;
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dh::device_vector<GradientPairInt64> multi_hist;
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auto gpair = GenerateRandomGradients(n_samples);
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gpair.SetDevice(ctx.Device());
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auto quantiser = GradientQuantiser{&ctx, gpair.ConstDeviceSpan(), p_fmat->Info()};
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std::shared_ptr<common::HistogramCuts> cuts;
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{
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/**
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* Multi page.
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*/
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std::int32_t k{0};
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for (auto const& page : p_fmat->GetBatches<EllpackPage>(&ctx, p)) {
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auto impl = page.Impl();
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if (k == 0) {
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// Initialization
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auto d_matrix = impl->GetDeviceAccessor(ctx.Device());
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fg = std::make_unique<FeatureGroups>(impl->Cuts());
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auto init = GradientPairInt64{0, 0};
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multi_hist = decltype(multi_hist)(impl->Cuts().TotalBins(), init);
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single_hist = decltype(single_hist)(impl->Cuts().TotalBins(), init);
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cuts = std::make_shared<common::HistogramCuts>(impl->Cuts());
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}
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partitioners.emplace_back(
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std::make_unique<RowPartitioner>(&ctx, impl->Size(), impl->base_rowid));
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auto ridx = partitioners.at(k)->GetRows(0);
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auto d_histogram = dh::ToSpan(multi_hist);
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DeviceHistogramBuilder builder;
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builder.Reset(&ctx, fg->DeviceAccessor(ctx.Device()), force_global);
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builder.BuildHistogram(ctx.CUDACtx(), impl->GetDeviceAccessor(ctx.Device()),
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fg->DeviceAccessor(ctx.Device()), gpair.ConstDeviceSpan(), ridx,
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d_histogram, quantiser);
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++k;
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}
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ASSERT_EQ(k, n_batches);
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}
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{
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/**
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* Single page.
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*/
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RowPartitioner partitioner{&ctx, p_fmat->Info().num_row_, 0};
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SparsePage concat;
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std::vector<float> hess(p_fmat->Info().num_row_, 1.0f);
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for (auto const& page : p_fmat->GetBatches<SparsePage>()) {
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concat.Push(page);
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}
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EllpackPageImpl page{&ctx, cuts, concat, p_fmat->IsDense(), p_fmat->Info().num_col_, {}};
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auto ridx = partitioner.GetRows(0);
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auto d_histogram = dh::ToSpan(single_hist);
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DeviceHistogramBuilder builder;
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builder.Reset(&ctx, fg->DeviceAccessor(ctx.Device()), force_global);
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builder.BuildHistogram(ctx.CUDACtx(), page.GetDeviceAccessor(ctx.Device()),
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fg->DeviceAccessor(ctx.Device()), gpair.ConstDeviceSpan(), ridx,
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d_histogram, quantiser);
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}
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std::vector<GradientPairInt64> h_single(single_hist.size());
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thrust::copy(single_hist.begin(), single_hist.end(), h_single.begin());
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std::vector<GradientPairInt64> h_multi(multi_hist.size());
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thrust::copy(multi_hist.begin(), multi_hist.end(), h_multi.begin());
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for (std::size_t i = 0; i < single_hist.size(); ++i) {
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ASSERT_EQ(h_single[i].GetQuantisedGrad(), h_multi[i].GetQuantisedGrad());
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ASSERT_EQ(h_single[i].GetQuantisedHess(), h_multi[i].GetQuantisedHess());
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}
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}
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};
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} // namespace
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TEST_P(HistogramExternalMemoryTest, ExternalMemory) {
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std::apply(&HistogramExternalMemoryTest::Run, std::tuple_cat(std::make_tuple(this), GetParam()));
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}
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INSTANTIATE_TEST_SUITE_P(Histogram, HistogramExternalMemoryTest, ::testing::ValuesIn([]() {
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std::vector<std::tuple<float, bool>> params;
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for (auto global : {true, false}) {
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for (auto sparsity : {0.0f, 0.2f, 0.8f}) {
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params.emplace_back(sparsity, global);
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}
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}
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return params;
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}()));
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} // namespace xgboost::tree
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