1a33b50a0d
- Remove unused parameters. There are still many warnings that are not yet addressed. Currently, the warnings in dmlc-core dominate the error log. - Remove `distributed` parameter from metric. - Fixes some warnings about signed comparison.
493 lines
17 KiB
Plaintext
493 lines
17 KiB
Plaintext
/*!
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* Copyright 2017-2022 XGBoost contributors
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*/
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#include <gtest/gtest.h>
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#include <thrust/device_vector.h>
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#include <thrust/host_vector.h>
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#include <dmlc/filesystem.h>
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#include <xgboost/base.h>
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#include <random>
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#include <string>
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#include <vector>
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#include "../helpers.h"
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#include "../histogram_helpers.h"
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#include "xgboost/generic_parameters.h"
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#include "xgboost/json.h"
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#include "../../../src/data/sparse_page_source.h"
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#include "../../../src/tree/updater_gpu_hist.cu"
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#include "../../../src/tree/updater_gpu_common.cuh"
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#include "../../../src/common/common.h"
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#include "../../../src/tree/constraints.cuh"
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namespace xgboost {
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namespace tree {
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TEST(GpuHist, DeviceHistogram) {
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// Ensures that node allocates correctly after reaching `kStopGrowingSize`.
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dh::safe_cuda(cudaSetDevice(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<GradientPairPrecise, kStopGrowing> histogram;
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histogram.Init(0, kNBins);
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for (int i = 0; i < kNNodes; ++i) {
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histogram.AllocateHistograms({i});
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}
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histogram.Reset();
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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({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({kNNodes});
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histogram.AllocateHistograms({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({kNNodes + 1}););
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}
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std::vector<GradientPairPrecise> GetHostHistGpair() {
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// 24 bins, 3 bins for each feature (column).
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std::vector<GradientPairPrecise> hist_gpair = {
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{0.8314f, 0.7147f}, {1.7989f, 3.7312f}, {3.3846f, 3.4598f},
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{2.9277f, 3.5886f}, {1.8429f, 2.4152f}, {1.2443f, 1.9019f},
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{1.6380f, 2.9174f}, {1.5657f, 2.5107f}, {2.8111f, 2.4776f},
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{2.1322f, 3.0651f}, {3.2927f, 3.8540f}, {0.5899f, 0.9866f},
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{1.5185f, 1.6263f}, {2.0686f, 3.1844f}, {2.4278f, 3.0950f},
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{1.5105f, 2.1403f}, {2.6922f, 4.2217f}, {1.8122f, 1.5437f},
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{0.0000f, 0.0000f}, {4.3245f, 5.7955f}, {1.6903f, 2.1103f},
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{2.4012f, 4.4754f}, {3.6136f, 3.4303f}, {0.0000f, 0.0000f}
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};
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return hist_gpair;
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}
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template <typename GradientSumT>
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void TestBuildHist(bool use_shared_memory_histograms) {
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int const kNRows = 16, kNCols = 8;
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TrainParam param;
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std::vector<std::pair<std::string, std::string>> args {
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{"max_depth", "6"},
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{"max_leaves", "0"},
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};
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param.Init(args);
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auto page = BuildEllpackPage(kNRows, kNCols);
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BatchParam batch_param{};
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Context ctx{CreateEmptyGenericParam(0)};
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GPUHistMakerDevice<GradientSumT> maker(&ctx, page.get(), {}, kNRows, param, kNCols, kNCols,
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batch_param);
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xgboost::SimpleLCG gen;
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xgboost::SimpleRealUniformDistribution<bst_float> dist(0.0f, 1.0f);
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HostDeviceVector<GradientPair> gpair(kNRows);
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for (auto &gp : gpair.HostVector()) {
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bst_float grad = dist(&gen);
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bst_float hess = dist(&gen);
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gp = GradientPair(grad, hess);
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}
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gpair.SetDevice(0);
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thrust::host_vector<common::CompressedByteT> h_gidx_buffer (page->gidx_buffer.HostVector());
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maker.row_partitioner.reset(new RowPartitioner(0, kNRows));
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maker.hist.AllocateHistograms({0});
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maker.gpair = gpair.DeviceSpan();
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maker.histogram_rounding = CreateRoundingFactor<GradientSumT>(maker.gpair);
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BuildGradientHistogram(
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page->GetDeviceAccessor(0), maker.feature_groups->DeviceAccessor(0),
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gpair.DeviceSpan(), maker.row_partitioner->GetRows(0),
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maker.hist.GetNodeHistogram(0), maker.histogram_rounding,
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!use_shared_memory_histograms);
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DeviceHistogramStorage<GradientSumT>& d_hist = maker.hist;
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auto node_histogram = d_hist.GetNodeHistogram(0);
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// d_hist.data stored in float, not gradient pair
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thrust::host_vector<GradientSumT> h_result (d_hist.Data().size() / 2);
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size_t data_size =
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sizeof(GradientSumT) /
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(sizeof(GradientSumT) / sizeof(typename GradientSumT::ValueT));
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data_size *= d_hist.Data().size();
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dh::safe_cuda(cudaMemcpy(h_result.data(), node_histogram.data(), data_size,
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cudaMemcpyDeviceToHost));
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std::vector<GradientPairPrecise> solution = GetHostHistGpair();
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std::cout << std::fixed;
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for (size_t i = 0; i < h_result.size(); ++i) {
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ASSERT_FALSE(std::isnan(h_result[i].GetGrad()));
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EXPECT_NEAR(h_result[i].GetGrad(), solution[i].GetGrad(), 0.01f);
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EXPECT_NEAR(h_result[i].GetHess(), solution[i].GetHess(), 0.01f);
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}
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}
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TEST(GpuHist, BuildHistGlobalMem) {
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TestBuildHist<GradientPairPrecise>(false);
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}
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TEST(GpuHist, BuildHistSharedMem) {
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TestBuildHist<GradientPairPrecise>(true);
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}
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HistogramCutsWrapper GetHostCutMatrix () {
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HistogramCutsWrapper cmat;
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cmat.SetPtrs({0, 3, 6, 9, 12, 15, 18, 21, 24});
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cmat.SetMins({0.1f, 0.2f, 0.3f, 0.1f, 0.2f, 0.3f, 0.2f, 0.2f});
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// 24 cut fields, 3 cut fields for each feature (column).
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// Each row of the cut represents the cuts for a data column.
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cmat.SetValues({0.30f, 0.67f, 1.64f,
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0.32f, 0.77f, 1.95f,
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0.29f, 0.70f, 1.80f,
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0.32f, 0.75f, 1.85f,
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0.18f, 0.59f, 1.69f,
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0.25f, 0.74f, 2.00f,
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0.26f, 0.74f, 1.98f,
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0.26f, 0.71f, 1.83f});
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return cmat;
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}
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// TODO(trivialfis): This test is over simplified.
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TEST(GpuHist, EvaluateRootSplit) {
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constexpr int kNRows = 16;
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constexpr int kNCols = 8;
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TrainParam param;
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std::vector<std::pair<std::string, std::string>> args{
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{"max_depth", "1"},
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{"max_leaves", "0"},
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// Disable all other parameters.
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{"colsample_bynode", "1"},
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{"colsample_bylevel", "1"},
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{"colsample_bytree", "1"},
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{"min_child_weight", "0.01"},
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{"reg_alpha", "0"},
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{"reg_lambda", "0"},
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{"max_delta_step", "0"}};
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param.Init(args);
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for (size_t i = 0; i < kNCols; ++i) {
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param.monotone_constraints.emplace_back(0);
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}
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int max_bins = 4;
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// Initialize GPUHistMakerDevice
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auto page = BuildEllpackPage(kNRows, kNCols);
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BatchParam batch_param{};
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Context ctx{CreateEmptyGenericParam(0)};
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GPUHistMakerDevice<GradientPairPrecise> maker(&ctx, page.get(), {}, kNRows, param, kNCols, kNCols,
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batch_param);
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// Initialize GPUHistMakerDevice::node_sum_gradients
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maker.node_sum_gradients = {};
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// Initialize GPUHistMakerDevice::cut
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auto cmat = GetHostCutMatrix();
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// Copy cut matrix to device.
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page->Cuts() = cmat;
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maker.monotone_constraints = param.monotone_constraints;
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// Initialize GPUHistMakerDevice::hist
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maker.hist.Init(0, (max_bins - 1) * kNCols);
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maker.hist.AllocateHistograms({0});
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// Each row of hist_gpair represents gpairs for one feature.
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// Each entry represents a bin.
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std::vector<GradientPairPrecise> hist_gpair = GetHostHistGpair();
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std::vector<bst_float> hist;
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for (auto pair : hist_gpair) {
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hist.push_back(pair.GetGrad());
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hist.push_back(pair.GetHess());
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}
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ASSERT_EQ(maker.hist.Data().size(), hist.size());
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thrust::copy(hist.begin(), hist.end(),
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maker.hist.Data().begin());
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std::vector<float> feature_weights;
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maker.column_sampler.Init(kNCols, feature_weights, param.colsample_bynode,
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param.colsample_bylevel, param.colsample_bytree);
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RegTree tree;
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MetaInfo info;
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info.num_row_ = kNRows;
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info.num_col_ = kNCols;
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DeviceSplitCandidate res =
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maker.EvaluateRootSplit({6.4f, 12.8f}, 0).split;
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ASSERT_EQ(res.findex, 7);
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ASSERT_NEAR(res.fvalue, 0.26, xgboost::kRtEps);
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}
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void TestHistogramIndexImpl() {
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// Test if the compressed histogram index matches when using a sparse
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// dmatrix with and without using external memory
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int constexpr kNRows = 1000, kNCols = 10;
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// Build 2 matrices and build a histogram maker with that
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GenericParameter generic_param(CreateEmptyGenericParam(0));
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tree::GPUHistMaker hist_maker{&generic_param,ObjInfo{ObjInfo::kRegression}},
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hist_maker_ext{&generic_param,ObjInfo{ObjInfo::kRegression}};
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std::unique_ptr<DMatrix> hist_maker_dmat(
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CreateSparsePageDMatrixWithRC(kNRows, kNCols, 0, true));
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dmlc::TemporaryDirectory tempdir;
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std::unique_ptr<DMatrix> hist_maker_ext_dmat(
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CreateSparsePageDMatrixWithRC(kNRows, kNCols, 128UL, true, tempdir));
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std::vector<std::pair<std::string, std::string>> training_params = {
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{"max_depth", "10"},
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{"max_leaves", "0"}
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};
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hist_maker.Configure(training_params);
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hist_maker.InitDataOnce(hist_maker_dmat.get());
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hist_maker_ext.Configure(training_params);
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hist_maker_ext.InitDataOnce(hist_maker_ext_dmat.get());
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// Extract the device maker from the histogram makers and from that its compressed
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// histogram index
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const auto &maker = hist_maker.maker;
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auto grad = GenerateRandomGradients(kNRows);
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grad.SetDevice(0);
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maker->Reset(&grad, hist_maker_dmat.get(), kNCols);
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std::vector<common::CompressedByteT> h_gidx_buffer(maker->page->gidx_buffer.HostVector());
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const auto &maker_ext = hist_maker_ext.maker;
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maker_ext->Reset(&grad, hist_maker_ext_dmat.get(), kNCols);
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std::vector<common::CompressedByteT> h_gidx_buffer_ext(maker_ext->page->gidx_buffer.HostVector());
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ASSERT_EQ(maker->page->Cuts().TotalBins(), maker_ext->page->Cuts().TotalBins());
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ASSERT_EQ(maker->page->gidx_buffer.Size(), maker_ext->page->gidx_buffer.Size());
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}
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TEST(GpuHist, TestHistogramIndex) {
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TestHistogramIndexImpl();
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}
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void UpdateTree(HostDeviceVector<GradientPair>* gpair, DMatrix* dmat,
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size_t gpu_page_size, RegTree* tree,
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HostDeviceVector<bst_float>* preds, float subsample = 1.0f,
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const std::string& sampling_method = "uniform",
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int max_bin = 2) {
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if (gpu_page_size > 0) {
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// Loop over the batches and count the records
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int64_t batch_count = 0;
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int64_t row_count = 0;
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for (const auto& batch : dmat->GetBatches<EllpackPage>({0, max_bin})) {
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EXPECT_LT(batch.Size(), dmat->Info().num_row_);
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batch_count++;
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row_count += batch.Size();
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}
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EXPECT_GE(batch_count, 2);
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EXPECT_EQ(row_count, dmat->Info().num_row_);
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}
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Args args{
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{"max_depth", "2"},
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{"max_bin", std::to_string(max_bin)},
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{"min_child_weight", "0.0"},
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{"reg_alpha", "0"},
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{"reg_lambda", "0"},
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{"subsample", std::to_string(subsample)},
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{"sampling_method", sampling_method},
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};
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GenericParameter generic_param(CreateEmptyGenericParam(0));
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tree::GPUHistMaker hist_maker{&generic_param,ObjInfo{ObjInfo::kRegression}};
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hist_maker.Configure(args);
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std::vector<HostDeviceVector<bst_node_t>> position(1);
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hist_maker.Update(gpair, dmat, common::Span<HostDeviceVector<bst_node_t>>{position}, {tree});
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auto cache = linalg::VectorView<float>{preds->DeviceSpan(), {preds->Size()}, 0};
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hist_maker.UpdatePredictionCache(dmat, cache);
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}
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TEST(GpuHist, UniformSampling) {
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constexpr size_t kRows = 4096;
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constexpr size_t kCols = 2;
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constexpr float kSubsample = 0.9999;
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common::GlobalRandom().seed(1994);
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// Create an in-memory DMatrix.
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std::unique_ptr<DMatrix> dmat(CreateSparsePageDMatrixWithRC(kRows, kCols, 0, true));
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auto gpair = GenerateRandomGradients(kRows);
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// Build a tree using the in-memory DMatrix.
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RegTree tree;
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HostDeviceVector<bst_float> preds(kRows, 0.0, 0);
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UpdateTree(&gpair, dmat.get(), 0, &tree, &preds, 1.0, "uniform", kRows);
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// Build another tree using sampling.
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RegTree tree_sampling;
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HostDeviceVector<bst_float> preds_sampling(kRows, 0.0, 0);
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UpdateTree(&gpair, dmat.get(), 0, &tree_sampling, &preds_sampling, kSubsample,
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"uniform", kRows);
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// Make sure the predictions are the same.
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auto preds_h = preds.ConstHostVector();
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auto preds_sampling_h = preds_sampling.ConstHostVector();
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for (size_t i = 0; i < kRows; i++) {
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EXPECT_NEAR(preds_h[i], preds_sampling_h[i], 1e-8);
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}
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}
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TEST(GpuHist, GradientBasedSampling) {
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constexpr size_t kRows = 4096;
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constexpr size_t kCols = 2;
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constexpr float kSubsample = 0.9999;
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common::GlobalRandom().seed(1994);
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// Create an in-memory DMatrix.
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std::unique_ptr<DMatrix> dmat(CreateSparsePageDMatrixWithRC(kRows, kCols, 0, true));
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auto gpair = GenerateRandomGradients(kRows);
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// Build a tree using the in-memory DMatrix.
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RegTree tree;
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HostDeviceVector<bst_float> preds(kRows, 0.0, 0);
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UpdateTree(&gpair, dmat.get(), 0, &tree, &preds, 1.0, "uniform", kRows);
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// Build another tree using sampling.
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RegTree tree_sampling;
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HostDeviceVector<bst_float> preds_sampling(kRows, 0.0, 0);
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UpdateTree(&gpair, dmat.get(), 0, &tree_sampling, &preds_sampling, kSubsample,
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"gradient_based", kRows);
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// Make sure the predictions are the same.
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auto preds_h = preds.ConstHostVector();
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auto preds_sampling_h = preds_sampling.ConstHostVector();
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for (size_t i = 0; i < kRows; i++) {
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EXPECT_NEAR(preds_h[i], preds_sampling_h[i], 1e-3);
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}
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}
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TEST(GpuHist, ExternalMemory) {
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constexpr size_t kRows = 4096;
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constexpr size_t kCols = 2;
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constexpr size_t kPageSize = 1024;
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dmlc::TemporaryDirectory tmpdir;
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// Create a DMatrix with multiple batches.
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std::unique_ptr<DMatrix> dmat_ext(
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CreateSparsePageDMatrix(kRows, kCols, kRows / kPageSize, tmpdir.path + "/cache"));
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// Create a single batch DMatrix.
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std::unique_ptr<DMatrix> dmat(CreateSparsePageDMatrix(kRows, kCols, 1, tmpdir.path + "/cache"));
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auto gpair = GenerateRandomGradients(kRows);
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// Build a tree using the in-memory DMatrix.
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RegTree tree;
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HostDeviceVector<bst_float> preds(kRows, 0.0, 0);
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UpdateTree(&gpair, dmat.get(), 0, &tree, &preds, 1.0, "uniform", kRows);
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// Build another tree using multiple ELLPACK pages.
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RegTree tree_ext;
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HostDeviceVector<bst_float> preds_ext(kRows, 0.0, 0);
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UpdateTree(&gpair, dmat_ext.get(), kPageSize, &tree_ext, &preds_ext, 1.0, "uniform", kRows);
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// Make sure the predictions are the same.
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auto preds_h = preds.ConstHostVector();
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auto preds_ext_h = preds_ext.ConstHostVector();
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for (size_t i = 0; i < kRows; i++) {
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EXPECT_NEAR(preds_h[i], preds_ext_h[i], 1e-6);
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}
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}
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TEST(GpuHist, ExternalMemoryWithSampling) {
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constexpr size_t kRows = 4096;
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constexpr size_t kCols = 2;
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constexpr size_t kPageSize = 1024;
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constexpr float kSubsample = 0.5;
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const std::string kSamplingMethod = "gradient_based";
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common::GlobalRandom().seed(0);
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dmlc::TemporaryDirectory tmpdir;
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// Create a single batch DMatrix.
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std::unique_ptr<DMatrix> dmat(CreateSparsePageDMatrix(kRows, kCols, 1, tmpdir.path + "/cache"));
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// Create a DMatrix with multiple batches.
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std::unique_ptr<DMatrix> dmat_ext(
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CreateSparsePageDMatrix(kRows, kCols, kRows / kPageSize, tmpdir.path + "/cache"));
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auto gpair = GenerateRandomGradients(kRows);
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// Build a tree using the in-memory DMatrix.
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auto rng = common::GlobalRandom();
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RegTree tree;
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HostDeviceVector<bst_float> preds(kRows, 0.0, 0);
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UpdateTree(&gpair, dmat.get(), 0, &tree, &preds, kSubsample, kSamplingMethod,
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kRows);
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// Build another tree using multiple ELLPACK pages.
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common::GlobalRandom() = rng;
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RegTree tree_ext;
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HostDeviceVector<bst_float> preds_ext(kRows, 0.0, 0);
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UpdateTree(&gpair, dmat_ext.get(), kPageSize, &tree_ext, &preds_ext,
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kSubsample, kSamplingMethod, kRows);
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|
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// Make sure the predictions are the same.
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auto preds_h = preds.ConstHostVector();
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auto preds_ext_h = preds_ext.ConstHostVector();
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for (size_t i = 0; i < kRows; i++) {
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ASSERT_NEAR(preds_h[i], preds_ext_h[i], 1e-3);
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}
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}
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|
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TEST(GpuHist, ConfigIO) {
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GenericParameter generic_param(CreateEmptyGenericParam(0));
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std::unique_ptr<TreeUpdater> updater{
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TreeUpdater::Create("grow_gpu_hist", &generic_param, ObjInfo{ObjInfo::kRegression})};
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updater->Configure(Args{});
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|
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Json j_updater { Object() };
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updater->SaveConfig(&j_updater);
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ASSERT_TRUE(IsA<Object>(j_updater["gpu_hist_train_param"]));
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ASSERT_TRUE(IsA<Object>(j_updater["train_param"]));
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updater->LoadConfig(j_updater);
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|
|
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Json j_updater_roundtrip { Object() };
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updater->SaveConfig(&j_updater_roundtrip);
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ASSERT_TRUE(IsA<Object>(j_updater_roundtrip["gpu_hist_train_param"]));
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ASSERT_TRUE(IsA<Object>(j_updater_roundtrip["train_param"]));
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|
|
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ASSERT_EQ(j_updater, j_updater_roundtrip);
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}
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|
|
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TEST(GpuHist, MaxDepth) {
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GenericParameter generic_param(CreateEmptyGenericParam(0));
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size_t constexpr kRows = 16;
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size_t constexpr kCols = 4;
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auto p_mat = RandomDataGenerator{kRows, kCols, 0}.GenerateDMatrix();
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|
|
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auto learner = std::unique_ptr<Learner>(Learner::Create({p_mat}));
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learner->SetParam("max_depth", "32");
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learner->Configure();
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|
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ASSERT_THROW({learner->UpdateOneIter(0, p_mat);}, dmlc::Error);
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}
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} // namespace tree
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} // namespace xgboost
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