102 lines
3.9 KiB
Plaintext
102 lines
3.9 KiB
Plaintext
#include <gtest/gtest.h>
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#include <vector>
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#include "../../helpers.h"
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#include "../../../../src/tree/gpu_hist/row_partitioner.cuh"
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#include "../../../../src/tree/gpu_hist/histogram.cuh"
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namespace xgboost {
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namespace tree {
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template <typename Gradient>
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void TestDeterministicHistogram(bool is_dense, int shm_size) {
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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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BatchParam batch_param{0, static_cast<int32_t>(kBins), 0};
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for (auto const& batch : matrix->GetBatches<EllpackPage>(batch_param)) {
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auto* page = batch.Impl();
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tree::RowPartitioner row_partitioner(0, kRows);
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auto ridx = row_partitioner.GetRows(0);
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int num_bins = kBins * kCols;
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dh::device_vector<Gradient> 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(0);
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FeatureGroups feature_groups(page->Cuts(), page->is_dense, shm_size,
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sizeof(Gradient));
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auto rounding = CreateRoundingFactor<Gradient>(gpair.DeviceSpan());
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BuildGradientHistogram(page->GetDeviceAccessor(0),
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feature_groups.DeviceAccessor(0), gpair.DeviceSpan(),
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ridx, d_histogram, rounding);
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std::vector<Gradient> 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(Gradient),
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cudaMemcpyDeviceToHost));
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for (size_t i = 0; i < kRounds; ++i) {
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dh::device_vector<Gradient> new_histogram(num_bins);
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auto d_new_histogram = dh::ToSpan(new_histogram);
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auto rounding = CreateRoundingFactor<Gradient>(gpair.DeviceSpan());
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BuildGradientHistogram(page->GetDeviceAccessor(0),
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feature_groups.DeviceAccessor(0),
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gpair.DeviceSpan(), ridx, d_new_histogram,
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rounding);
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std::vector<Gradient> 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(Gradient),
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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].GetGrad(), histogram_h[j].GetGrad());
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ASSERT_EQ(new_histogram_h[j].GetHess(), histogram_h[j].GetHess());
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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(0);
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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<Gradient> baseline(num_bins);
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BuildGradientHistogram(page->GetDeviceAccessor(0),
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single_group.DeviceAccessor(0),
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gpair.DeviceSpan(), ridx, dh::ToSpan(baseline),
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rounding);
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std::vector<Gradient> 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(Gradient),
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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].GetGrad(), histogram_h[i].GetGrad(),
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baseline_h[i].GetGrad() * 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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TestDeterministicHistogram<GradientPair>(is_dense, shm_size);
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TestDeterministicHistogram<GradientPairPrecise>(is_dense, shm_size);
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}
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}
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}
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} // namespace tree
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} // namespace xgboost
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