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Use ellpack for prediction only when sparsepage doesn't exist. (#5504)

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This commit is contained in:
Jiaming Yuan
2020-04-10 12:15:46 +08:00
committed by GitHub
parent ad826e913f
commit 6671b42dd4
35 changed files with 166 additions and 116 deletions
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27
tests/cpp/predictor/test_gpu_predictor.cu
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@@ -31,7 +31,7 @@ TEST(GPUPredictor, Basic) {
for (size_t i = 1; i < 33; i *= 2) {
int n_row = i, n_col = i;
auto dmat = RandomDataGenerator(n_row, n_col, 0).GenerateDMatix();
auto dmat = RandomDataGenerator(n_row, n_col, 0).GenerateDMatrix();
LearnerModelParam param;
param.num_feature = n_col;
@@ -58,16 +58,33 @@ TEST(GPUPredictor, Basic) {
}
TEST(GPUPredictor, EllpackBasic) {
size_t constexpr kCols {8};
for (size_t bins = 2; bins < 258; bins += 16) {
size_t rows = bins * 16;
TestPredictionFromGradientIndex<EllpackPage>("gpu_predictor", rows, bins);
TestPredictionFromGradientIndex<EllpackPage>("gpu_predictor", bins, bins);
auto p_m = RandomDataGenerator{rows, kCols, 0.0}
.Bins(bins)
.Device(0)
.GenerateDeviceDMatrix(true);
TestPredictionFromGradientIndex<EllpackPage>("gpu_predictor", rows, kCols, p_m);
TestPredictionFromGradientIndex<EllpackPage>("gpu_predictor", bins, kCols, p_m);
}
}
TEST(GPUPredictor, EllpackTraining) {
size_t constexpr kRows { 128 };
TestTrainingPrediction(kRows, "gpu_hist");
size_t constexpr kRows { 128 }, kCols { 16 }, kBins { 64 };
auto p_ellpack = RandomDataGenerator{kRows, kCols, 0.0}
.Bins(kBins)
.Device(0)
.GenerateDeviceDMatrix(true);
std::vector<HostDeviceVector<float>> storage(kCols);
auto columnar = RandomDataGenerator{kRows, kCols, 0.0}
.Device(0)
.GenerateColumnarArrayInterface(&storage);
auto adapter = data::CudfAdapter(columnar);
std::shared_ptr<DMatrix> p_full {
DMatrix::Create(&adapter, std::numeric_limits<float>::quiet_NaN(), 1)
};
TestTrainingPrediction(kRows, "gpu_hist", p_full, p_ellpack);
}
TEST(GPUPredictor, ExternalMemoryTest) {