24241ed6e3
This helps reduce the memory copying needed for dense data. In addition, it helps reduce memory usage even if external memory is not used. - Decouple the number of symbols needed in the compressor with the number of features when the data is dense. - Remove the fetch call in the `at_end_` iteration. - Reduce synchronization and kernel launches by using the `uvector` and ctx.
73 lines
2.2 KiB
Plaintext
73 lines
2.2 KiB
Plaintext
/**
|
|
* Copyright 2019-2024, XGBoost contributors
|
|
*/
|
|
#include <gtest/gtest.h>
|
|
#include <xgboost/data.h>
|
|
#include "../../../src/data/adapter.h"
|
|
#include "../helpers.h"
|
|
#include <thrust/device_vector.h>
|
|
#include "../../../src/data/device_adapter.cuh"
|
|
#include "test_array_interface.h"
|
|
using namespace xgboost; // NOLINT
|
|
|
|
void TestCudfAdapter()
|
|
{
|
|
constexpr size_t kRowsA {16};
|
|
constexpr size_t kRowsB {16};
|
|
std::vector<Json> columns;
|
|
thrust::device_vector<double> d_data_0(kRowsA);
|
|
thrust::device_vector<uint32_t> d_data_1(kRowsB);
|
|
|
|
columns.emplace_back(GenerateDenseColumn<double>("<f8", kRowsA, &d_data_0));
|
|
columns.emplace_back(GenerateDenseColumn<uint32_t>("<u4", kRowsB, &d_data_1));
|
|
|
|
Json column_arr {columns};
|
|
|
|
std::string str;
|
|
Json::Dump(column_arr, &str);
|
|
|
|
data::CudfAdapter adapter(str);
|
|
|
|
adapter.Next();
|
|
auto & batch = adapter.Value();
|
|
EXPECT_EQ(batch.Size(), kRowsA + kRowsB);
|
|
|
|
EXPECT_NO_THROW({
|
|
dh::LaunchN(batch.Size(), [=] __device__(size_t idx) {
|
|
auto element = batch.GetElement(idx);
|
|
KERNEL_CHECK(element.row_idx == idx / 2);
|
|
if (idx % 2 == 0) {
|
|
KERNEL_CHECK(element.column_idx == 0);
|
|
KERNEL_CHECK(element.value == element.row_idx * 2.0f);
|
|
} else {
|
|
KERNEL_CHECK(element.column_idx == 1);
|
|
KERNEL_CHECK(element.value == element.row_idx * 2.0f);
|
|
}
|
|
});
|
|
dh::safe_cuda(cudaDeviceSynchronize());
|
|
});
|
|
}
|
|
|
|
TEST(DeviceAdapter, CudfAdapter) {
|
|
TestCudfAdapter();
|
|
}
|
|
|
|
namespace xgboost::data {
|
|
TEST(DeviceAdapter, GetRowCounts) {
|
|
auto ctx = MakeCUDACtx(0);
|
|
|
|
for (bst_feature_t n_features : {1, 2, 4, 64, 128, 256}) {
|
|
HostDeviceVector<float> storage;
|
|
auto str_arr = RandomDataGenerator{8192, n_features, 0.0}
|
|
.Device(ctx.Device())
|
|
.GenerateArrayInterface(&storage);
|
|
auto adapter = CupyAdapter{str_arr};
|
|
HostDeviceVector<bst_idx_t> offset(adapter.NumRows() + 1, 0);
|
|
offset.SetDevice(ctx.Device());
|
|
auto rstride = GetRowCounts(&ctx, adapter.Value(), offset.DeviceSpan(), ctx.Device(),
|
|
std::numeric_limits<float>::quiet_NaN());
|
|
ASSERT_EQ(rstride, n_features);
|
|
}
|
|
}
|
|
} // namespace xgboost::data
|